2,425 results
Search Results
2. Galois Connections for Patterns: An Algebra of Labelled Graphs
- Author
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Cohen, David A., Cooper, Martin C., Jeavons, Peter G., Živný, Stanislav, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cochez, Michael, editor, Croitoru, Madalina, editor, Marquis, Pierre, editor, and Rudolph, Sebastian, editor
- Published
- 2021
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3. Machine Breakdown Recovery in Production Scheduling with Simple Temporal Constraints
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Barták, Roman, Vlk, Marek, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Duval, Béatrice, editor, van den Herik, Jaap, editor, Loiseau, Stephane, editor, and Filipe, Joaquim, editor
- Published
- 2015
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4. Research Paper on Implementation of OCL Constraints in JAVA.
- Author
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Gupta, Shivani and Rattan, Dhavleesh
- Subjects
UNIFIED modeling language ,SOFTWARE engineering ,CONSTRAINT satisfaction ,COMPUTER software development ,OBJECT-oriented methods (Computer science) - Abstract
This paper provides an introduction to the UML in the field of software engineering. The first part gives information about the UML class diagrams. Second part of this paper provides an introduction to the formal specification of UML diagrams using OCL. It also provides a detail about why formalization of UML diagrams is important. It also gives introduction about how constraints are applied to the UML diagrams using OCL. It provides knowledge about different kinds of constraints which any applied on UML class diagram. Last part of this paper shows the source code implementation in java of Bank account example of OCL constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2017
5. Enhancing Retail Operations: Integrating Artificial Intelligence into the Theory of Constraints Thinking Process to Solve Shelf Issue.
- Author
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Aljaž, Tomaž
- Subjects
CONSTRAINT satisfaction ,CHATGPT ,ARTIFICIAL intelligence ,INVENTORY costs ,CUSTOMER satisfaction ,IDENTIFICATION - Abstract
Copyright of Electrotechnical Review / Elektrotehniski Vestnik is the property of Electrotechnical Society of Slovenia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
6. INTEGRATING VIRTUAL REALITY WITH INTERNET OF THINGS: ARCHITECTURES, APPLICATIONS AND CHALLENGES.
- Author
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ESWARAN, USHAA, ESWARAN, VISHAL, and ESWARAN, VIVEK
- Subjects
VIRTUAL reality ,INTERNET of things ,ARCHITECTURE ,SIMULATION methods & models ,CONSTRAINT satisfaction - Abstract
The integration of Virtual Reality (VR) environments with Internet of Things (IoT) infrastructure can enable more intuitive and immersive interactions. However, realizing the potential of this convergence requires overcoming technical constraints and implementation challenges. This paper reviews the motivations, architectures, applications, and open issues associated with combining VR and IoT. This study also aims to provide a comprehensive analysis of architectures, use cases and technical challenges involved in integrating VR and IoT to guide further research and real-world deployment. Use cases in manufacturing, energy, retail, entertainment and other sectors highlight the benefits like remote monitoring, training and collaboration unlocked by interfacing VR's realistic 3D visualizations with real-time IoT sensor data. This paper also discusses VR simulation of IoT systems for testing, limitations in interoperability, and security considerations. The outlook for VR-IoT solutions is promising, with 5G and edge computing advancements expected to address current bottlenecks to adoption. Human-centric design approaches focused on high-value use cases can enable impactful deployments across domains. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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7. A new knowledge-guided multi-objective optimisation for the multi-AGV dispatching problem in dynamic production environments.
- Author
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Liu, Lei, Qu, Ting, Thürer, Matthias, Ma, Lin, Zhang, Zhongfei, and Yuan, Mingze
- Subjects
PARTICLE swarm optimization ,DISTRIBUTION (Probability theory) ,EVOLUTIONARY algorithms ,AUTOMATED guided vehicle systems ,SATISFACTION ,CONSTRAINT satisfaction - Abstract
The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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8. A cerebellar operant conditioning-inspired constraint satisfaction approach for product design concept generation.
- Author
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Li, Mingdong, Lou, Shanhe, Gao, Yicong, Zheng, Hao, Hu, Bingtao, and Tan, Jianrong
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OPERANT conditioning ,CONSTRAINT satisfaction ,PRODUCT design ,CONCEPTUAL design ,NEW product development ,PROPOSITION (Logic) - Abstract
Conceptual design is a pivotal stage of new product development. The function-behaviour-structure framework is adopted in this stage to help designers search design space and generate conceptual solutions iteratively. Computer-aided methods developed within this framework will yield significant insight into facilitating the cognitive activities of designers. In order to solve the mapping process from behaviours to structures which is a typical constraint satisfaction problem, a cerebellar operant conditioning-inspired constraint satisfaction approach is proposed in this paper. The design constraints-driven operant conditioning and its regulation mechanism by the cerebellum are analysed for the first time. Proposition logic is applied to transfer the constraint satisfaction problem into a propositional satisfiability problem while an undirected graph is utilised to model design space. Inspired by the modularised cerebellar structure, a modularised constraint satisfaction neural network is constructed to determine the satisfiability of design problems. Conceptual solutions can be generated by clustering the embedding of nodes in this network. The proposed approach imitates the design constraint-driven operant conditioning to narrow down design space without assigning specific values to design components. It reduces design iterations and avoids combinatorial explosions during conceptual design. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. Perceptual Modes of Presentation as Object Files.
- Author
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Siegel, Gabriel
- Subjects
CONSTRAINT satisfaction - Abstract
Some have defended a Fregean view of perceptual content. On this view, the constituents of perceptual contents are Fregean modes of presentation (MOPs). In this paper, I propose that perceptual MOPs are best understood in terms of object files. Object files are episodic representations that store perceptual information about objects. This information is updated when sensory conditions change. On the proposed view, when a subject perceptually represents some object a under two distinct MOPs, then the subject initiates two object files that both refer to a. My defense of this view appeals to its satisfaction of four constraints that I argue theories of perceptual MOPs should satisfy. Furthermore, I show that some existent accounts of perceptual MOPs fail to satisfy them. The defended constraints also indicate what is unique about perceptual, as opposed to linguistic or cognitive, MOPs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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10. Spacecraft Close Proximity to Noncooperative Target Based on Pseudospectral Convex Method.
- Author
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Wang, Qian, Li, Shunli, Zhang, Yanquan, and Cheng, Min
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SPACE vehicles ,ROTATIONAL motion ,TRANSLATIONAL motion ,NONLINEAR programming ,CONSTRAINT satisfaction ,PROPORTIONAL navigation ,ARTIFICIAL satellite attitude control systems - Abstract
This paper proposes a trajectory-optimization problem for spacecraft close proximity to a noncooperative target, aiming at the generation of a six-degree-of-freedom (DOF) trajectory with the fuel-optimal objective value and considering multiple constraints on the control magnitude, line-of-sight, and glide-slope. The line-of-sight and glide-slope constraints are coupled between translational and rotational motions. The dual quaternion is an effective method for establishing the translationally and rotationally coupled model, because it can represent the translation and rotation in an integrated manner. Therefore, in this study, the trajectory-optimization problem of spacecraft close proximity coupled with position and attitude is established using dual quaternions. Next, the close-proximity trajectory-optimization problem is converted into a nonlinear programming problem, which can be solved efficiently using well-developed algorithms such as convex optimization. However, the zero-order hold used in the discrete method of convex optimization is an equidistant dispersion, which cannot guarantee the satisfaction of constraints between discrete points. Therefore the pseudospectral convex method is proposed using nonequidistant collocation points to mitigate the problem of constraint violation between discrete points and improve the accuracy and computational efficiency of the algorithm. The proposed algorithm can be applied to tasks such as rendezvous and docking with noncooperative targets and close proximity. Finally, the effectiveness of the proposed method was validated via numerical simulation, and the results were compared with those of the existing approach, GPOPS. The results indicate that the proposed algorithm is superior to GPOPS in computational efficiency and objective values. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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11. High‐level decision‐making for autonomous overtaking: An MPC‐based switching control approach.
- Author
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Wang, Xue‐Fang, Chen, Wen‐Hua, Jiang, Jingjing, and Yan, Yunda
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OVERTAKING ,DECISION making ,CONSTRAINED optimization ,CONSTRAINT satisfaction ,AUTONOMOUS vehicles ,PREDICTION models - Abstract
The key motivation of this paper lies in the development of a high‐level decision‐making framework for autonomous overtaking maneuvers on two‐lane country roads with dynamic oncoming traffic. To generate an optimal and safe decision sequence for such scenario, an innovative high‐level decision‐making framework that combines model predictive control (MPC) and switching control methodologies is introduced. Specifically, the autonomous vehicle is abstracted and modelled as a switched system. This abstraction allows vehicle to operate in different modes corresponding to different high‐level decisions. It establishes a crucial connection between high‐level decision‐making and low‐level behaviour of the autonomous vehicle. Furthermore, barrier functions and predictive models that account for the relationship between the autonomous vehicle and oncoming traffic are incorporated. This technique enables us to guarantee the satisfaction of constraints, while also assessing performance within a prediction horizon. By repeatedly solving the online constrained optimization problems, we not only generate an optimal decision sequence for overtaking safely and efficiently but also enhance the adaptability and robustness. This adaptability allows the system to respond effectively to potential changes and unexpected events. Finally, the performance of the proposed MPC framework is demonstrated via simulations of four driving scenarios, which shows that it can handle multiple behaviours. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
12. Observer-based hybrid control for global attitude tracking on SO(3) with input quantisation.
- Author
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Hashemi, Seyed Hamed, Pariz, Naser, and Hosseini Sani, Seyed Kamal
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LINEAR matrix inequalities ,POTENTIAL functions ,BODY image ,ANGULAR velocity ,CONSTRAINT satisfaction - Abstract
This paper studies the global attitude stabilization of a rigid body, a task that is subjected to topological obstacles. As a result of these obstructions, continuous feedbacks cannot globally stabilise the rigid body attitude. Therefore, this paper presents an observer-based hybrid controller to overcome these restrictions. Consequently, a new kind of synergistic potential function is designed which induces a gradient vector field to globally stabilise a given set. Moreover, the gradient of the proposed potential functions is utilised to derive a hybrid observer. Furthermore, this paper considers two types of constraints: angular velocity constraint and torque constraint. Afterward, these constraints are formulated in terms of the Linear Matrix Inequalities (LMI) optimisation problem to perform constraints satisfaction. Besides, this paper introduces a novel hybrid quantiser to deal with the problem of the low-price wireless network. Finally, a comparative study in simulations is provided to assess the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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13. Cost-Effective and Low Power IoT-Based Paper Supply Monitoring System: An Application Modeling Approach.
- Author
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Senadeera, S. D. Arunya P., Kyi, Su, Sirisung, Thanapol, Pongsupan, Watsamon, Taparugssanagorn, Attaphongse, Dailey, Matthew N., and Wai, Tun Aung
- Subjects
BATTERY storage plants ,POWER resources ,CONSTRAINT satisfaction ,INTERNET of things ,FAMILY-owned business enterprises ,WIRELESS Internet - Abstract
IoT designers face the dual complexity of obtaining good application-level performance and user satisfaction under constraints on computing and power resources. We introduce a new IoT device for paper roll supply management in bathrooms and kitchens, both for homes and businesses, that is extremely cost effective and battery power-efficient. The device can be installed on practically any paper roll dispenser and makes use of existing Wi-Fi infrastructure. Despite Wi-Fi's reputation as "unsupportive for power saving," we introduce and experimentally validate a methodology for using Wi-Fi networks with low power utilization, resulting in a system that provides very good management of paper supplies while only requiring battery charging once every 3–4 months. The new device has the potential to provide more households and businesses with real-time, data-driven automated supply chains. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
14. A HEURISTIC-ACTION-INVOLVED SAFE LANE-CHANGE OF AUTONOMOUS VEHICLES WITH MULTIPLE CONSTRAINTS ON ROAD CENTERLINE AND SPEED UNDER HIGHWAY ENVIRONMENT.
- Author
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Jun CHEN, Fazhan TAO, Zhumu FU, Haochen SUN, and Nan WANG
- Subjects
HEURISTIC ,AUTONOMOUS vehicles ,DEEP reinforcement learning ,SIMULATION methods & models ,CONSTRAINT satisfaction - Abstract
Lane-change (LC) is one of the most important topics in autonomous vehicles (AVs) on highways. To enhance the implementation of effective LC in AVs, this paper proposes a framework based on deep reinforcement learning, which takes into account heuristic actions and multiple constraints related to the centerline of the road and speed, to improve the overall performance of LC in AVs. Firstly, the influence of unreasonable vehicle actions on the algorithm training process is studied. To improve the rationality of the to-be-trained actions, a novel reasonable action screening mechanism is proposed. Secondly, to keep the vehicle on the centerline of the lane and avoid the collision with other vehicles, a method is designed to calculate the center position of the vehicle. Thirdly, a segmented speed reward mechanism is proposed to constrain vehicle speed. Subsequently, a dynamic reward function is established to train the control algorithm. Lastly, the proposed strategy is evaluated in two simulation scenarios of highways. The simulation results show that the proposed method can increase the number of reasonable actions by more than 30% and improve the success rate of obstacle avoidance with the increase of over 52% in both static and dynamic scenarios compared with the benchmark algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
15. A Hybrid Genetic Algorithm for Ground Station Scheduling Problems.
- Author
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Xu, Longzeng, Yu, Changhong, Wu, Bin, and Gao, Ming
- Subjects
EARTH stations ,TABU search algorithm ,CONSTRAINT satisfaction ,DATA transmission systems ,SCHEDULING ,GENETIC algorithms - Abstract
In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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16. A novel tri-stage with reward-switching mechanism for constrained multiobjective optimization problems.
- Author
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Qu, Jiqing, Li, Xuefeng, and Xiao, Hui
- Subjects
CONSTRAINED optimization ,EVOLUTIONARY algorithms ,CONSTRAINT satisfaction ,RELAXATION techniques ,SOURCE code - Abstract
The effective exploitation of infeasible solutions plays a crucial role in addressing constrained multiobjective optimization problems (CMOPs). However, existing constrained multiobjective optimization evolutionary algorithms (CMOEAs) encounter challenges in effectively balancing objective optimization and constraint satisfaction, particularly when tackling problems with complex infeasible regions. Subsequent to the prior exploration, this paper proposes a novel tri-stage with reward-switching mechanism framework (TSRSM), including the push, pull, and repush stages. Each stage consists of two coevolutionary populations, namely Pop 1 and Pop 2 . Throughout the three stages, Pop 1 is tasked with converging to the constrained Pareto front (CPF). However, Pop 2 is assigned with distinct tasks: (i) converging to the unconstrained Pareto front (UPF) in the push stage; (ii) utilizing constraint relaxation technique to discover the CPF in the pull stage; and (iii) revisiting the search for the UPF through knowledge transfer in the repush stage. Additionally, a novel reward-switching mechanism (RSM) is employed to transition between different stages, considering the extent of changes in the convergence and diversity of populations. Finally, the experimental results on three benchmark test sets and 30 real-world CMOPs demonstrate that TSRSM achieves competitive performance when compared with nine state-of-the-art CMOEAs. The source code is available at https://github.com/Qu-jq/TSRSM. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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17. Instant distributed MPC with reference governor.
- Author
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Figura, Martin, Su, Lanlan, Inoue, Masaki, and Gupta, Vijay
- Subjects
CLOSED loop systems ,GOVERNORS ,ADAPTIVE control systems ,PREDICTION models ,CONSTRAINT satisfaction ,SYSTEM dynamics - Abstract
Model predictive control is a popular choice for systems that must satisfy prescribed constraints on states and control inputs. Although much progress has been made in distributed model predictive control, existing algorithms tend to be computationally expensive. This limits their use in systems with fast dynamics. In this paper, we propose a new distributed model predictive control algorithm that we term as instant distributed model predictive control (iDMPC). The proposed algorithm employs a realisation of the primal-dual algorithm in the controller dynamics for fast computation. We show that the closed-loop system trajectories with the proposed iDMPC algorithm converge asymptotically to a desired reference. To ensure the satisfaction of the state constraints during the transient, we also include an explicit reference governor in the feedback loop. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
18. Distributed switched model-based predictive control for distributed large-scale systems with switched topology.
- Author
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Alinia Ahandani, Morteza, Kharrati, Hamed, Hashemzadeh, Farzad, and Baradarannia, Mahdi
- Subjects
CLOSED loop systems ,TOPOLOGY ,INVARIANT sets ,CONSTRAINT satisfaction ,TIME-varying networks ,ELECTRIC network topology ,TIME-frequency analysis ,ADAPTIVE control systems ,ADAPTIVE fuzzy control - Abstract
Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
19. Near‐optimal control of a class of output‐constrained systems using recurrent neural network: A control‐barrier function approach.
- Author
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Rad‐Moghadam, Surena and Farrokhi, Mohammad
- Subjects
CLOSED loop systems ,RECURRENT neural networks ,CONSTRAINT satisfaction ,STABILITY theory ,LYAPUNOV stability ,NONLINEAR systems ,CONSTRAINED optimization - Abstract
This paper proposes a near‐optimal controller design for the constrained nonlinear affine systems based on a Recurrent Neural Network (RNN) and Extended State Observers (ESOs). For this purpose, after defining a finite‐horizon integral‐type performance index, the prediction over the horizon is performed using the Taylor expansion that converts the primary problem into a finite‐dimensional optimization. In comparison with other controllers of the similar structure, the proposed method is capable of dealing with output constraints by employing the Control Barrier Function (CBF). The class of the output and input constraints are of the box‐type. Moreover, whereas several safe control approaches are proposed regardless of the performance of the closed‐loop system, this paper aims at achieving a near‐optimal performance as far as the constraints permit. As a result, a constrained optimization problem is achieved, where the online solution is obtained using a rapidly convergent RNN. Stability and the ease of implementation are some of the advantages of this network making the algorithm more reliable. Moreover, integrated stability analysis of the closed‐loop system that includes the dynamic RNN reveals that the closed‐loop system is stable in the sense of the Lyapunov stability theory. The effectiveness of the proposed control method in terms of the tracking performance and constraint satisfaction is illustrated through a simulating example of two‐inverted pendulums system. The results indicated advantages of the proposed method as compared with the recently published methods in well‐known literature. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
20. Maximal Queen Placements for the Mod 2 n-Queens Game.
- Author
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Qiao, Cindy
- Subjects
CHESSBOARDS ,GRAPH theory ,PROBLEM solving ,CONSTRAINT satisfaction ,GAME theory - Abstract
The century old problem of configuring n queens on a chessboard so that none of them attack one another, known as the "n-queens problem", has been studied intensively by researchers, along with many variants. To this day, the problem stands as a prominent example for backtracking search methods. Its demonstration of constraint satisfaction, as well as systematic and heuristic search methods, highlights its utility in fields such as artificial intelligence (AI) and program development. This paper focuses on a contemporary variant, the "mod 2 n-queens problem", recently proposed by Brown and Ladha. This paper uses graph theory to solve some of the open problems Brown and Ladha posed. [ABSTRACT FROM AUTHOR]
- Published
- 2021
21. WHAT MAKES AI DIFFERENT? EXPLORING AFFORDANCES AND CONSTRAINTS - THE CASE OF AUDITING.
- Author
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Jiaqi Yang, Marrone, Mauricio, and Amrollahi, Alireza
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AUDITING ,INFORMATION technology ,HUMAN-artificial intelligence interaction ,CONSTRAINT satisfaction ,GROUNDED theory - Abstract
This study aims to gain a comprehensive understanding of the differences between classic IT and AI artefacts. To achieve this objective, the study employs a grounded theory literature review approach and analyses 81 papers related to the application of classic IT and AI artefacts in the auditing industry. Drawing on the Technology Affordances and Constraints Theory, we examine the actions that can be potentially enabled or restricted by using classic IT and AI artefacts. This analysis allows us to conceptualise and compare the affordances and constraints associated with these two types of artefacts. The study addresses the need for more research on AI from both social and technical perspectives. Our findings may facilitate practitioners in improving their business processes and promoting effective collaboration between humans and AI. [ABSTRACT FROM AUTHOR]
- Published
- 2023
22. AUTONOMOUS APPLICATIONS IN TRANSPORTATION.
- Author
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SAINES, MADDIE
- Subjects
GLOBAL Positioning System ,DRIVERLESS cars ,ORBIT determination ,MULTISENSOR data fusion ,ANTENNAS (Electronics) ,CONSTRAINT satisfaction - Abstract
The article provides information on four papers presented at the ION GNSS+ conference, focusing on autonomous applications in transportation. The papers address topics such as integrity monitoring of autonomous navigation, estimation and reference systems in automation, evaluating GNSS performance for autonomous vehicles, and solving the localization problem in autonomous driving.
- Published
- 2023
23. Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing.
- Author
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Jiang, Weijin, Chen, Junpeng, Liu, Xiaoliang, Liu, Yuehua, and Lv, Sijian
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QUALITY of service ,GREEDY algorithms ,HEURISTIC algorithms ,SMART devices ,DATA quality ,CONSTRAINT satisfaction ,CROWDS - Abstract
With the rapid popularization and application of smart sensing devices, mobile crowd sensing (MCS) has made rapid development. MCS mobilizes personnel with various sensing devices to collect data. Task distribution as the key point and difficulty in the field of MCS has attracted wide attention from scholars. However, the current research on participant selection methods whose main goal is data quality is not deep enough. Different from most of these previous studies, this paper studies the participant selection scheme on the multitask condition in MCS. According to the tasks completed by the participants in the past, the accumulated reputation and willingness of participants are used to construct a quality of service model (QoS). On the basis of maximizing QoS, two heuristic greedy algorithms are used to solve participation; two options are proposed: task-centric and user-centric. The distance constraint factor, integrity constraint factor, and reputation constraint factor are introduced into our algorithms. The purpose is to select the most suitable set of participants on the premise of ensuring the QoS, as far as possible to improve the platform's final revenue and the benefits of participants. We used a real data set and generated a simulation data set to evaluate the feasibility and effectiveness of the two algorithms. Detailedly compared our algorithms with the existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, we established a step data pricing model to quantitatively compare the quality of data uploaded by participants. Experimental results show that two algorithms proposed in this paper have achieved better results in task quality than existing algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
24. Output feedback stochastic MPC for tracking control of quadrotors with disturbances.
- Author
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Xue, Ruochen, Dai, Li, Wang, Peizhan, Sun, Zhongqi, and Xia, Yuanqing
- Subjects
CONSTRAINT satisfaction ,ROBUST control ,STOCHASTIC models ,MATHEMATICAL models ,PROBLEM solving - Abstract
In this paper, the trajectory tracking problem of controlling a constrained quadrotor with unmeasurable system states in an environment with stochastic wind‐gust disturbance is considered. The mathematical model of the quadrotor is divided into the translational system and the rotational system, while only the measurement output of the quadrotor can be accessed. A new output‐based control method is developed for solving this problem. In the translational control system, an output feedback stochastic model predictive control (MPC) algorithm is proposed to generate the optimal control sequence with less conservativeness, by taking into account the information on the distribution of the disturbances and the uncertainty resulting from the attitude tracking error. The closed‐loop probabilistic constraints satisfaction, the recursive feasibility and the stability of the algorithm are further proved. In the rotational system, the active disturbance rejection control (ADRC) method to estimate and compensate for external disturbances is leveraged and robust control for attitude tracking is accomplished. The convergence of the disturbance estimator and the stability proof are provided. Finally, the robustness and effectiveness of the proposed control strategy are verified by an illustrative example. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Performance-based active controller design for nonlinear structures using modified black hole optimization.
- Author
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Yaghoobi, Saber, Fadali, M Sami, and Pekcan, Gokhan
- Subjects
BLACK holes ,OPTIMIZATION algorithms ,PERFORMANCE-based design ,SEISMIC response ,CONSTRAINT satisfaction - Abstract
This paper presents a novel approach that facilitates the design of active controllers to mitigate seismically induced damage in structural systems. The proposed method is based on stochastic Modified Black Hole optimization algorithm. Two traditional controllers, namely Proportional-Integral-Derivative (PID) and Linear–Quadratic Gaussian (LQG) controllers were designed, and their performance was demonstrated on a benchmark 20-story steel-framed building. Evaluation criteria were defined to satisfy constraints on various response quantities, including drift, base shear, ductility, residual story drift, and control force. The constraint limits were defined in view of performance-based design requirements for the benchmark structure. The performance of the controllers was contrasted with that of traditional LQG, and significant reductions of all response quantities were achieved for design-level earthquakes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Design and analysis of event‐triggered predictive sliding mode control for discrete‐time constrained system.
- Author
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Meng, Huan, Zhang, Jinhui, and Li, Sihang
- Subjects
- *
SLIDING mode control , *DISCRETE-time systems , *CONSTRAINT satisfaction - Abstract
This paper presents a new approach for designing an event‐triggered predictive sliding mode control (SMC) for discrete‐time systems subject to constraints. The proposed controller is based on an existing reach‐law‐based SMC, and it formulates an optimization control problem (OCP) to generate predictive control actions that satisfy both state and input constraints. An event‐triggered strategy is introduced to reduce the frequency of OCP solving by taking into account the sliding mode controller and unmeasurable states between sampling instants. The paper also analyzes the recursive feasibility of the proposed controller, which ensures the feasibility of each OCP solution. Numerical simulations and comparison studies are performed to validate the theoretical results. The proposed approach offers an effective way to design event‐triggered predictive sliding mode controllers that reduce the computational burden of OCP solving while guaranteeing the satisfaction of system constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. Method for solving constrained 0-1 quadratic programming problems based on pointer network and reinforcement learning.
- Author
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Gu, Shenshen and Zhuang, Yuxi
- Subjects
REINFORCEMENT learning ,MACHINE learning ,QUADRATIC programming ,OPTIMIZATION algorithms ,DEEP learning ,CONSTRAINT satisfaction ,INTEGER programming - Abstract
The constrained 0-1 quadratic programming problem (CBQP) is an important problem of integer programming, and many combinatorial optimization problems can be converted to CBQP problem. Because BQP is NP-hard problem, the solving time and accuracy of traditional optimization algorithm are very dependent on the size of the problem, and the local optimal solution obtained by the heuristic algorithm is unstable. Deep learning algorithm has great advantages in solving such problems. In this paper, for the CBQP problem with linear constraints, we creatively apply two algorithms and models to solve it: the graph pointer network model (GPN) trained by hierarchical reinforcement learning (HRL), and the multi-head attention-based pointer network model trained by Advantage Actor-Critic (A2C), which greatly improves the solving speed, accuracy and constraint satisfaction rate of CBQP problems of different scales. At the same time, the bidirectional mask mechanism is innovatively introduced into the network so that the constraint satisfaction rate of the solution is very high. For the two algorithms, this paper solved the 0-1 knapsack (BKP) problem and the quadratic knapsack (QKP) problem, which are equivalent to the CBQP problem, and compared the results of the CBQP problem with different data distribution and scales. The experiment shows that no matter the objective function of the CBQP problem is linear or nonlinear, different data set distribution, or the scale, the pointer network trained by reinforcement learning in this paper has better results than traditional optimization algorithms in solving time, accuracy, stability and constraint satisfaction rate, and with the increase in the size of the problem, this advantage becomes more obvious. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
28. Attitude stabilization of spacecraft simulator based on modified constrained feedback linearization model predictive control.
- Author
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Khodaverdian, Maria and Malekzadeh, Maryam
- Subjects
ARTIFICIAL satellite attitude control systems ,SPACE vehicles ,PREDICTION models ,ANGULAR velocity ,ANGULAR momentum (Mechanics) ,CONSTRAINT satisfaction - Abstract
This paper aims to discuss the approach of constrained modified feedback linearization model predictive control for the spacecraft simulator. By utilizing the high accuracy and constrained properties of model predictive control (MPC), an optimum MPC is designed for the spacecraft feedback linearized system. The composite controller has the ability to control both the attitude and angular velocity of the reaction wheels (i.e. steering the angular momentum to zero at the end of the maneuver). The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform regulation maneuver with sufficient precision in the presence of external torques and actuator saturations. This paper aims to discuss the approach of constrained modified feedback linearization model predictive control (CMFLMPC) for the spacecraft simulator. The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform the regulation maneuver with sufficient precision in the presence of external torques and actuator saturations. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. Human-Centric Automation and Optimization for Smart Homes.
- Author
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Tay, Noel Nuo Wi, Botzheim, Janos, and Kubota, Naoyuki
- Subjects
HOME automation ,CONSTRAINT satisfaction ,PLANNING ,KNOWLEDGE representation (Information theory) ,ARTIFICIAL intelligence ,METHODOLOGY - Abstract
A smart home needs to be human-centric, where it tries to fulfill human needs given the devices it has. Various works are developed to provide homes with reasoning and planning capability to fulfill goals, but most do not support complex sequence of plans or require significant manual effort in devising subplans. This is further aggravated by the need to optimize conflicting personal goals. A solution is to solve the planning problem represented as constraint satisfaction problem (CSP). But CSP uses hard constraints and, thus, cannot handle optimization and partial goal fulfillment efficiently. This paper aims to extend this approach to weighted CSP. Knowledge representation to help in generating planning rules is also proposed, as well as methods to improve performances. Case studies show that the system can provide intelligent and complex plans from activities generated from semantic annotations of the devices, as well as optimization to maximize personal constraints’ fulfillment. Note to Practitioners—Smart home should maximize the fulfillment of personal goals that are often conflicting. For example, it should try to fulfill as much as possible the requests made by both the mother and daughter who wants to watch TV but both having different channel preferences. That said, every person has a set of goals or constraints that they hope the smart home can fulfill. Therefore, human-centric system that automates the loosely coupled devices of the smart home to optimize the goals or constraints of individuals in the home is developed. Automated planning is done using converted services extracted from devices, where conversion is done using existing tools and concepts from Web technologies. Weighted constraint satisfaction that provides the declarative approach to cover large problem domain to realize the automated planner with optimization capability is proposed. Details to speed up planning through search space reduction are also given. Real-time case studies are run in a prototype smart home to demonstrate its applicability and intelligence, where every planning is performed under a maximum of 10 s. The vision of this paper is to be able to implement such system in a community, where devices everywhere can cooperate to ensure the well-being of the community. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
30. Federated-Learning-Based Energy-Efficient Load Balancing for UAV-Enabled MEC System in Vehicular Networks.
- Author
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Shin, Ayoung and Lim, Yujin
- Subjects
REINFORCEMENT learning ,CONSTRAINT satisfaction ,MOBILE computing ,EDGE computing ,COMPUTER engineering ,DRONE aircraft - Abstract
At present, with the intelligence that has been achieved in computer and communication technologies, vehicles can provide many convenient functions to users. However, it is difficult for a vehicle to deal with computationally intensive and latency-sensitive tasks occurring in the vehicle environment by itself. To this end, mobile edge computing (MEC) services have emerged. However, MEC servers (MECSs), which are fixed on the ground, cannot flexibly respond to temporal dynamics where tasks are temporarily increasing, such as commuting time. Therefore, research has examined the provision of edge services using additional unmanned aerial vehicles (UAV) with mobility. Since these UAVs have limited energy and computing power, it is more important to optimize energy efficiency through load balancing than it is for ground MEC servers (MECSs). Moreover, if only certain servers run out of energy, the service coverage of a MEC server (MECS) may be limited. Therefore, all UAV MEC servers (UAV MECSs) need to use energy evenly. Further, in a high-mobility vehicle environment, it is necessary to have effective task migration because the UAV MECS that provides services to the vehicle changes rapidly. Therefore, in this paper, a federated deep Q-network (DQN)-based task migration strategy that considers the load deviation and energy deviation among UAV MECSs is proposed. DQN is used to create a local model for migration optimization for each of the UAV MECSs, and federated learning creates a more effective global model based on the fact that it has common spatial features between adjacent regions. To evaluate the performance of the proposed strategy, the performance is analyzed in terms of delay constraint satisfaction, load deviation, and energy deviation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
31. On the Impulsive Formation Control of Spacecraft Under Path Constraints.
- Author
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Shakouri, Amir
- Subjects
JENSEN'S inequality ,SPACE vehicles ,RELATIVE motion ,ARTIFICIAL satellite attitude control systems ,CONSTRAINT satisfaction - Abstract
This paper deals with the impulsive formation control of spacecraft in the presence of constraints on the position vector and time. Determining a set of path constraints can increase the safety and reliability in an impulsive relative motion of spacecraft. Specially, the feasibility problem of the position norm constraints is considered in this paper. Under assumptions, it is proved that if a position vector be reachable, then the reach time and the corresponding time of impulses are unique. The trajectory boundedness of the spacecraft between adjacent impulses are analyzed using the Gerschgorin and the Rayleigh–Ritz theorems as well as a finite form of the Jensen's inequality. Some boundaries are introduced regarding the Jordan–Brouwer separation theorem which are useful in checking the satisfaction of a constraint. Two numerical examples (approximate circular formation keeping and collision-free maneuver) are solved in order to show the applications and visualize the results. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
32. Two Improved Constraint-Solving Algorithms Based on lmaxRPC3 rm.
- Author
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Pan, Xirui, Cheng, Zhuyuan, and Zhang, Yonggang
- Subjects
CONSTRAINT satisfaction ,ARTIFICIAL intelligence ,ALGORITHMS ,DATA structures ,HEURISTIC - Abstract
The Constraint Satisfaction Problem (CSP) is a significant research area in artificial intelligence, and includes a large number of symmetric or asymmetric structures. A backtracking search combined with constraint propagation is considered to be the best CSP-solving algorithm, and the consistency algorithm is the main algorithm used in the process of constraint propagation, which is the key factor in constraint-solving efficiency. Max-restricted path consistency (maxRPC) is a well-known and efficient consistency algorithm, whereas the lmaxRPC3 r m algorithm is a classic lightweight algorithm for maxRPC. In this paper, we leverage the properties of symmetry to devise an improved pruning strategy aimed at efficiently diminishing the problem's search space, thus enhancing the overall solving efficiency. Firstly, we propose the maxRPC3 s i m algorithm, which abandons the two complex data structures used by lmaxRPC3 r m . We can render the algorithm to be more concise and competitive compared to the original algorithm while ensuring that it maintains the same average performance. Secondly, inspired by the RCP3 algorithm, we propose the maxRPC3 s i m R algorithm, which uses the idea of residual support to cut down the redundant operation of the lmaxRPC3 r m algorithm. Finally, combining the domain/weighted degree (dom/wdeg) heuristic with the activity-based search (ABS) heuristic, a new variable ordering heuristic, ADW, is proposed. Our heuristic prioritizes the selection of variables with symmetry for pruning, further enhancing the algorithm's pruning capabilities. Experiments were conducted on both random and structural problems separately. The results indicate that our two algorithms generally outperform other algorithms in terms of performance on both problem classes. Moreover, the new heuristic algorithm demonstrates enhanced robustness across different problem types when compared to various existing algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids.
- Author
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Cavus, Muhammed, Ugurluoglu, Yusuf Furkan, Ayan, Huseyin, Allahham, Adib, Adhikari, Kabita, and Giaouris, Damian
- Subjects
ENERGY management ,RECURRENT neural networks ,MICROGRIDS ,CONSTRAINT satisfaction ,ENERGY consumption - Abstract
Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system's operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model's computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model's accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Reference tracking via output feedback for constrained uncertain linear systems.
- Author
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Silveira Júnior, J. I. S. and Dórea, C. E. T.
- Subjects
UNCERTAIN systems ,CONSTRAINT satisfaction ,LINEAR systems ,INVARIANT sets ,ROBUST control ,ADAPTIVE control systems - Abstract
In this paper we propose a robust output feedback control scheme for constant reference tracking in uncertain linear systems subject to state and control constraints. First, we establish conditions under which a polyhedral set is Output Feedback Controlled Invariant with respect to a linear system subject to polytopic uncertainties, i.e. a set guaranteeing robust constraint satisfaction via output feedback. Then, we build a dynamic output feedback controller for the uncertain model based on set-membership observers, allowing the reduction of the set of possible states consistent with the measurements. Finally, to reduce the tracking error, we propose a model update procedure that adjusts the nominal model used for tracking to the output measurements. Numerical experiments illustrate the ability of the proposed controller to achieve reference tracking with reduced offset for the uncertain systems under consideration. To the best of our knowledge, it is one of the first works that addresses constant reference tracking in uncertain linear systems under constraints with output feedback. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. Time-Aware Multi-Application Task Scheduling With Guaranteed Delay Constraints in Green Data Center.
- Author
-
Yuan, Haitao, Bi, Jing, Zhou, MengChu, and Ammari, Ahmed Chiheb
- Subjects
PRODUCTION scheduling ,CONSTRAINT satisfaction ,DATA libraries ,ENERGY consumption ,ITERATIVE methods (Mathematics) - Abstract
A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks’ delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners—This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
36. Predefined-time stabilization of stochastic nonlinear systems with application to UAVs.
- Author
-
Qiu, Lifang, Zhao, Junsheng, Sun, Zong-Yao, and Xie, Xiangpeng
- Subjects
- *
NONLINEAR systems , *BACKSTEPPING control method , *LYAPUNOV functions , *CONSTRAINT satisfaction , *ANGLES - Abstract
The paper presents a new Lyapunov-type predefined-time stabilization control algorithm for stochastic high-order nonlinear systems with asymmetric output constraints. In contrast to stochastic finite-time and fixed-time stabilization, the average value of the settling-time function for stochastic predefined-time stabilization control is independent of both the initial value and the control factors. To mitigate the significant uncertainties arising from the asymmetric output constraint, a tan-type barrier Lyapunov function is formulated. Furthermore, by harnessing the previously mentioned barrier Lyapunov function and integrating the power integrator technique, a controller design strategy is formulated based on the backstepping method. The rigorous analysis in this study proves that the designed controller ensures both the attainment of predefined-time convergence of the system states to the origin in probability and the satisfaction of the output constraint. Finally, an example of a roll angle subsystem for quadrotor UAVs and a numerical illustration are presented to corroborate the theoretical analysis. • A novel Lyapunov-type stochastic predefined-time stable control algorithm is presented in Theorem 1. • Controller design based on the backstepping method is formulated through the power integrator technique. • The tan-type barrier Lyapunov function extends the order range and absorbs the inherent properties of the nonlinear terms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective.
- Author
-
Laguel, Yassine, Malick, Jérôme, and van Ackooij, Wim
- Subjects
BILEVEL programming ,CONVEX functions ,PYTHON programming language ,RANDOM variables ,CONSTRAINT satisfaction ,STOCHASTIC programming - Abstract
Chance constraints are a valuable tool for the design of safe decisions in uncertain environments; they are used to model satisfaction of a constraint with a target probability. However, because of possible non-convexity and non-smoothness, optimizing over a chance constrained set is challenging. In this paper, we consider chance constrained programs where the objective function and the constraints are convex with respect to the decision parameter. We establish an exact reformulation of such a problem as a bilevel problem with a convex lower-level. Then we leverage this bilevel formulation to propose a tractable penalty approach, in the setting of finitely supported random variables. The penalized objective is a difference-of-convex function that we minimize with a suitable bundle algorithm. We release an easy-to-use open-source python toolbox implementing the approach, with a special emphasis on fast computational subroutines. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Searching for impossible subspace trails and improved impossible differential characteristics for SIMON-like block ciphers.
- Author
-
Wang, Xuzi, Wu, Baofeng, Hou, Lin, and Lin, Dongdai
- Subjects
CYBERTERRORISM ,INTERNET security ,QUADRATIC differentials ,CONSTRAINT satisfaction ,SUBSPACES (Mathematics) - Abstract
In this paper, we greatly increase the number of impossible differentials for SIMON and SIMECK by eliminating the 1-bit constraint in input/output difference, which is the precondition to ameliorate the complexity of attacks. We propose an algorithm which can greatly reduce the searching complexity to find such trails efficiently since the search space exponentially expands to find impossible differentials with multiple active bits. There is another situation leading to the contradiction in impossible differentials except for miss-in-the-middle. We show how the contradiction happens and conclude the precondition of it defined as miss-from-the-middle. It makes our results more comprehensive by applying these two approach simultaneously. This paper gives for the first time impossible differential characteristics with multiple active bits for SIMON and SIMECK, leading to a great increase in the number. The results can be verified not only by covering the state-of-art, but also by the MILP model. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
39. Matching events and activities by integrating behavioral aspects and label analysis
- Author
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Jan Mendling, Claudio Di Ciccio, Thomas Baier, and Mathias Weske
- Subjects
Matching (statistics) ,Process (engineering) ,Business process ,Computer science ,102013 Human-computer interaction ,Process mining ,02 engineering and technology ,Constraint satisfaction ,computer.software_genre ,Conformance checking ,Business Process Model and Notation ,Business process discovery ,502050 Wirtschaftsinformatik ,020204 information systems ,102001 Artificial intelligence ,0202 electrical engineering, electronic engineering, information engineering ,ddc:00 ,Declare ,Business process intelligence ,102022 Softwareentwicklung ,Institut für Informatik und Computational Science ,Special Section Paper ,Natural language processing ,Business process modeling ,502050 Business informatics ,102022 Software development ,Event mapping ,Modeling and Simulation ,020201 artificial intelligence & image processing ,Data mining ,computer ,process mining / event mapping / business process intelligence / constraint satisfaction / DECLARE / natural language processing ,Software - Abstract
Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.
- Published
- 2018
40. Linear Optimal Noncausal Control of Wave Energy Converters.
- Author
-
Zhan, Siyuan and Li, Guang
- Subjects
LINEAR control systems ,WAVE energy ,OCEAN waves ,CLOSED loop systems ,CONSTRAINT satisfaction - Abstract
This paper addresses the fundamental theoretical development of a linear optimal noncausal control for wave energy converters (WECs) in a closed analytic form. It is well known that the WEC control is a noncausal control problem, i.e., the future wave information contributes to the present control action. This paper provides a reliable, efficient, and simple linear optimal controller with guaranteed stability for the WEC control problem. The proposed WEC linear optimal control consists of a causal linear state feedback part and an anticausal linear feedforward part to incorporate the influence of future incoming waves. The stability of the closed-loop WEC control system with the proposed linear optimal controller is proven. The contribution of the noncausal term using wave prediction information to the optimal control and the energy output is analyzed quantitatively. The proposed linear controller can be more preferred when constraint satisfaction becomes a less important issue for mild sea states and some well-designed WECs with an ample operation range. The proposed optimal control strategy can be extended for a generic class of energy maximization problems. Numerical simulations are presented to justify the efficacy of the proposed WEC optimal control. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
41. Rigorous packing of unit squares into a circle.
- Author
-
Montanher, Tiago, Neumaier, Arnold, Csaba Markót, Mihály, Domes, Ferenc, and Schichl, Hermann
- Subjects
CIRCLE packing ,CONSTRAINT satisfaction ,INTERVAL analysis ,MATHEMATICAL optimization ,ALGORITHMS - Abstract
This paper considers the task of finding the smallest circle into which one can pack a fixed number of non-overlapping unit squares that are free to rotate. Due to the rotation angles, the packing of unit squares into a container is considerably harder to solve than their circle packing counterparts. Therefore, optimal arrangements were so far proved to be optimal only for one or two unit squares. By a computer-assisted method based on interval arithmetic techniques, we solve the case of three squares and find rigorous enclosures for every optimal arrangement of this problem. We model the relation between the squares and the circle as a constraint satisfaction problem (CSP) and found every box that may contain a solution inside a given upper bound of the radius. Due to symmetries in the search domain, general purpose interval methods are far too slow to solve the CSP directly. To overcome this difficulty, we split the problem into a set of subproblems by systematically adding constraints to the center of each square. Our proof requires the solution of 6, 43 and 12 subproblems with 1, 2 and 3 unit squares respectively. In principle, the method proposed in this paper generalizes to any number of squares. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
42. Scaling with Confidence: Entity Resolution under Weighted Constraints.
- Author
-
Qing Wang and Zeyu Shen
- Subjects
CONSTRAINT satisfaction ,ROBUST control ,DATABASE management ,SCALABILITY ,ELECTRONIC data processing - Abstract
Constraints ubiquitously exist in many real-life applications for entity resolution. However, it is always challenging to effectively specify and use such constraints for performing ER tasks. In particular, not every constraint is equally robust. Adding weights to express the "confidence" on constraints thus becomes a natural choice. In this paper, the authors study entity resolution (ER), the problem of determining which records in one or more databases refer to the same entities, in the presence of weighted constraints. They propose a unified framework that allows us to associate a weight for each constraint, capturing the confidence for its robustness in an ER model. The authors develop an approach to learn weighted constraints based on domain knowledge, and investigate how effectively and efficiently weighted constraints can be used for generating an ER clustering and for determining a propagation order across multiple entity types. Their experimental study shows that using weighted constraints can lead to improved ER quality and scalability. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
43. Exploiting Functional Constraints in Automatic Dominance Breaking for Constraint Optimization.
- Author
-
Lee, Jimmy H. M. and Zhong, Allen Z.
- Subjects
MATHEMATICAL optimization ,CONSTRAINT algorithms ,CONSTRAINT satisfaction ,PROBLEM solving ,ARTIFICIAL intelligence - Abstract
Dominance breaking is a powerful technique in improving the solving efficiency of Constraint Optimization Problems (COPs) by removing provably suboptimal solutions with additional constraints. While dominance breaking is effective in a range of practical problems, it is usually problem specific and requires human insights into problem structures to come up with correct dominance breaking constraints. Recently, a framework is proposed to generate nogood constraints automatically for dominance breaking, which formulates nogood generation as solving auxiliary Constraint Satisfaction Problems (CSPs). However, the framework uses a pattern matching approach to synthesize the auxiliary generation CSPs from the specific forms of objectives and constraints in target COPs, and is only applicable to a limited class of COPs. This paper proposes a novel rewriting system to derive constraints for the auxiliary generation CSPs automatically from COPs with nested function calls, significantly generalizing the original framework. In particular, the rewriting system exploits functional constraints flattened from nested functions in a high-level modeling language. To generate more effective dominance breaking nogoods and derive more relaxed constraints in generation CSPs, we further characterize how to extend the system with rewriting rules exploiting function properties, such as monotonicity, commutativity, and associativity, for specific functional constraints. Experimentation shows significant runtime speedup using the dominance breaking nogoods generated by our proposed method. Studying patterns of generated nogoods also demonstrates that our proposal can reveal dominance relations in the literature and discover new dominance relations on problems with ineffective or no known dominance breaking constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. A generalized three-dimensional cooperative guidance law for various communication topologies with field-of-view constraint.
- Author
-
Kang, Honglong, Wang, Pengyu, and Song, Shenmin
- Subjects
TELECOMMUNICATIONS laws & regulations ,COMMUNICATION in law ,CONSTRAINT satisfaction ,COMMUNICATION laws ,PROPORTIONAL navigation - Abstract
In this paper, a generalized three-dimensional (3D) cooperative guidance law with seeker's field-of-view (FOV) constraints is designed for multiple missiles to achieve salvo attack against a stationary target. The proposed generalized guidance law is composed of two parts: a proportional navigation guidance (PNG) component for target capture and a biased feedback component for simultaneous arrival. Two novel auxiliary functions are integrated into the biased feedback component to guarantee the satisfaction of FOV constraint. Furthermore, a time-varying lower bound that contains the impact time error is utilized to avoid the guidance command singularity. The proposed guidance law can be easily applied to various missile communication topologies, including centralized connected, distributed connected, and no-connected topologies. The convergence of impact time error, FOV constraint satisfaction, and command non-singularity of the proposed guidance law are theoretically analyzed and proved. Extensive numerical simulations with comparative studies are conducted to demonstrate the effectiveness and advantages of the proposed guidance law. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. T-norms driven loss functions for machine learning.
- Author
-
Giannini, Francesco, Diligenti, Michelangelo, Maggini, Marco, Gori, Marco, and Marra, Giuseppe
- Subjects
MACHINE learning ,TRIANGULAR norms ,ARTIFICIAL intelligence ,FIRST-order logic ,CONSTRAINT satisfaction ,SUPERVISED learning - Abstract
Injecting prior knowledge into the learning process of a neural architecture is one of the main challenges currently faced by the artificial intelligence community, which also motivated the emergence of neural-symbolic models. One of the main advantages of these approaches is their capacity to learn competitive solutions with a significant reduction of the amount of supervised data. In this regard, a commonly adopted solution consists of representing the prior knowledge via first-order logic formulas, then relaxing the formulas into a set of differentiable constraints by using a t-norm fuzzy logic. This paper shows that this relaxation, together with the choice of the penalty terms enforcing the constraint satisfaction, can be unambiguously determined by the selection of a t-norm generator, providing numerical simplification properties and a tighter integration between the logic knowledge and the learning objective. When restricted to supervised learning, the presented theoretical framework provides a straight derivation of the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by neural-symbolic methods. In addition, the presented methodology allows the development of novel classes of loss functions, which are shown in the experimental results to lead to faster convergence rates than the approaches previously proposed in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
46. 约束可满足性中求解 RB 模型实例的算法综述.
- Author
-
杨 易, 王晓峰, 莫淳惠, 庞立超, 杨 澜, and 赵星宇
- Subjects
- *
CONSTRAINT satisfaction , *PHASE transitions , *NP-complete problems , *CONSTRAINT algorithms , *ARTIFICIAL intelligence , *METAHEURISTIC algorithms - Abstract
Constraint satisfaction problem is one of the most basic NP-complete problems in artificial intelligence. Over the years, with the in-depth study of constraint satisfaction problem, scholars at home and abroad have proposed a variety of case models. RB model is an example of growth domain constraint satisfaction problem with precise phase transition, which is very challenging to solve. In order to find a new efficient algorithm and promote the research of RB model algorithm for constraint satisfiability problem, firstly, this paper analyzed the model development and solving techniques of constraint satisfaction problem. Secondly, it sorted out various examples of RB model solving algorithms, and divided the solving algorithm literature into backtracking heuristic, information propagation and meta-heuristic related improved algorithms. This paper compared and summarized the algorithm principles, improved strategies, convergence and accuracy. Finally, it gave the research trend and development direction of solving RB model example algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. A survey of intelligent optimization algorithms for solving satisfiability problems.
- Author
-
Yang, Lan, Wang, Xiaofeng, Ding, Hongsheng, Yang, Yi, Zhao, Xingyu, and Pang, Lichao
- Subjects
OPTIMIZATION algorithms ,SWARM intelligence ,EVOLUTIONARY algorithms ,PROBLEM solving ,HEURISTIC algorithms ,CONSTRAINT satisfaction - Abstract
Constraint satisfaction problems have a wide range of applications in areas such as basic computer theory research and artificial intelligence, and many major studies in industry are not solved directly, but converted into instances of satisfiability problems for solution. Therefore, the solution of the satisfiability problem is a central problem in many important areas in the future. A large number of solution algorithms for this problem are mainly based on completeness algorithms and heuristic algorithms. Intelligent optimization algorithms with heuristic policies run significantly more efficiently on large-scale instances compared to completeness algorithms. This paper compares the principles, implementation steps, and applications of several major intelligent optimization algorithms in satisfiability problems, analyzes the characteristics of these algorithms, and focuses on the performance in solving satisfiability problems under different constraints. In terms of algorithms, evolutionary algorithms and swarm intelligence algorithms are introduced; in terms of applications, the solution to the satisfiability problem is studied. At the same time, the performance of the listed intelligent optimization algorithms in applications is analyzed in detail in terms of the direction of improvement of the algorithms, advantages and disadvantages and comparison algorithms, respectively, and the future application of intelligent optimization algorithms in satisfiability problems is prospected. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. Multi-agent system-based fuzzy constraints offer negotiation of workflow scheduling in Fog-Cloud environment.
- Author
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Marwa, Mokni, Hajlaoui, Jalel Eddine, Sonia, Yassa, Omri, Mohamed Nazih, and Rachid, Chelouah
- Subjects
PRODUCTION scheduling ,NEGOTIATION ,WORKFLOW management systems ,WORKFLOW ,TIME management ,CONSTRAINT satisfaction ,FUZZY sets ,ECOLOGY - Abstract
This paper presents the multi-agent system fuzzy-constraints offer negotiation of Workflow Scheduling in Fog-Cloud environment, called (Fuzzy-Cone) approach, to solve the workflow scheduling problem with conflicting constraints in Fog-Cloud IT infrastructures. A client agent and a supplier agent are created to represent the client and supplier sides respectively, with a win–win strategy based on negotiation. The novelty of this approach is the design of a multi-agent system with agents supervised by a strategy based on a fuzzy inference system modeling all possible cases, thus facilitating decision-making. The workflow scheduling problem is treated as a set of fuzzy constraint satisfaction problems (FCSP). Each agent has an FCSP modeling a set of fuzzy constraints based on the negotiation with other agents by proposing offers or counter-offers. The proposed negotiation approach is implemented to respect all the imposed restrictions and represent the imprecise preferences of the approach entities by pre-defining the fuzzy constraints and optimizing the workflow scheduling solution in terms of time and cost. of compiling. The proposed approach has been tested with different experiments and compared with state-of-the-art algorithms. The experimental results show that the negotiation between the solutions, of mutually satisfactory scheduling, considerably improved the values of time and cost of compilation, while respecting the set of the imposed constraints. The proposed approach achieves a workflow scheduling scheme that reduces compilation time by 37% and increases cost by 6% compared to state-of-the-art algorithms. The different solutions we have proposed respect the constraints of time and budget by executing workflows of different sizes in reasonable time and cost. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
49. Optimal Hybrid Precoding Based QoE for Partially Structured Massive MIMO System.
- Author
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Samklang, Farung, Uthansakul, Peerapong, Uthansakul, Monthippa, and Anchuen, Patikorn
- Subjects
DIGITAL communications ,MIMO systems ,DIGITAL signal processing ,CONSTRAINT satisfaction ,WEB services ,TRAFFIC flow ,QUALITY of service ,RADIO frequency - Abstract
Precoding is a beamforming technique that supports multi-stream transmission in which the RF chain plays a significant role as a digital precoding at the receiver for wireless communication. The traditional precoding contains only digital signal processing and each antenna connects to each RF chain, which provides high transmission efficiency but high cost and hardware complexity. Hybrid precoding is one of the most popular massive multiple input multiple output (MIMO) techniques that can save costs and avoid using complex hardware. At present, network services are currently in focus with a wide range of traffic volumes. In terms of the Quality of Service (QoS), it is critical that service providers pay a lot of attention to this parameter and its relationship to Quality of Experience (QoE) which is the measurement of the overall level of user satisfaction. Therefore, this paper proposes hybrid precoding of a partially structured system to improve transmission efficiency and allocate resources to provide network services to users for increasing the user satisfaction under power constraints that optimize the quality of baseband precoding and radio frequency (RF) precoding by minimizing alternating algorithms. We focus on the web browsing, video, and Voice over IP (VOIP) services. Also, a Mean Opinion Score (MOS) is employed to measure the level of user satisfaction. The results show that the partially structured system provides a good user satisfaction with the network's services. The partially structured system provides high energy efficiency up to 85%. Considering web service, the partially structured system for 10 users provides MOS at 3.21 which is higher than 1.75 of fully structured system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
50. Factories of the future: challenges and leading innovations in intelligent manufacturing.
- Author
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Romero, David, Jardim-Goncalves, Ricardo, and Grilo, Antonio
- Subjects
INNOVATIONS in business ,BUSINESS enterprises ,MANUFACTURED products ,CONSTRAINT satisfaction ,INTERNETWORKING - Published
- 2017
- Full Text
- View/download PDF
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