Search

An analytical plate bending model is used in a novel measurement method that is being developed for feedback control of material properties in paper manufacturing. The model involves an efficient material model for paper yet allowing a selected level of material complexity. In this study, the effects of the simplifications are studied by comparing the results with a discretized model applied in finite element methods. The boundary conditions required for specifying the censoring location in the real system proves to be challenging and a specific combined load distribution might be necessary to apply the developed model.

Scientific research projects play a positive role in the development of scientific researches. Although researchers continue to study the paper output of projects, the correlation between projects and papers in research contents and topics is still unclear. Based on the data set NSFCD from China, the topics of papers are divided and the matching degree between projects and paper topics is defined to reveal the relationship between projects and papers. The results show that the research contents of projects and papers have some relevance, but the low values of matching degree indicate that the correlation between them is not evident. This work proposes a quantitative method for evaluating projects with paper topics and suggests that the quality of projects’ outputs needs to be strengthened. The papers published by project participants should be related to project contents.

This paper presents a techno-economic analysis of an absorption-based CO2capture process using Monoethanolamine (MEA) as the solvent for pulp and paper mill limekiln. The flue gas specifications were obtained from published limekiln data of a theoretical pulp and paper mill. The process was simulated in Aspen Plus and linked to CAPCOST using a python script for the cost calculations. The CO2capture cost estimates were compared to the only CO2capture costs data available in the literature for limekiln flue gas. Comparing the cost breakdown between the published data and this study, the capital cost difference was found to be highest for the stripper and the compression and dehydration sections. Further, the capture cost sensitivity analysis, evaluating the impacts of key parameters, including flue gas CO2mol%, MEA, electricity, and steam, showed that the capture costs varied from $70 to $82 per tonne of CO2captured.

Jan Willems introduced the system-theoretic notion of dissipativity in his foundational two-part paper which appeared in the Archive of Rational Mechanics and Analysis in 1972. Even earlier, in a likewise pivotal 1971 IEEE Transactions on Automatic Control paper, he investigated infinite-horizon least-squares optimal control and the algebraic Riccati equation from a dissipativity point of view. This note revisits infinite-horizon optimal control leveraging strict dissipativity. We discuss the interplay between dissipativity and stability properties in continuous-time infinite-horizon problems without assuming linear dynamics or quadratic cost functions. Finally, we compare our recent results from Faulwasser and Kellett (2021) to the original findings of Willems (1971).

Image enhancement is the most fundamental steps used to manipulate an image so that the result is more acceptable than the original image, with a try for preserving every detail, in the field of digital image processing. With the high demand of computer visual technology, digital image processing concepts have been rapidly used in many real world applications for information gathering, such as: industrial productions, medical images, video monitoring, intelligent transportations, etc. However, image captured under low light has some issues such that it may contains noise, undergoes color distortion and also may undergoes with some information loss. Again industry like medical and satellite we can't afford single information to be miss, so for that purpose low light image enhancement is a must needed concept for current emerging world. Here in this paper, we analyze different methods of image enhancement and their result, mainly this paper focus on different fusion-based methods, other methods such as retinex based methods, fuzzification etc.

Today, we have many options for hardware and software technology solutions for the migration of the manufacturing sector to an industry 4.0. However, there are many cases in which the adoption of new technologies has been slowed mainly due to uneven growth in the industry, little standardization, poor systems’ integration, lack of knowledge, and the lack of providers who understand the real needs of companies. The purpose of this work is to present a methodology for selecting the proper technology that was developed after characterizing and quantifying the industry's requirements. Our methodology presents criteria for evaluating and selecting technology providers that focus on four dimensions: technical criteria, purchase criteria, additional services for the operation of the products, and operating costs. This methodology represents a practical way to qualify technology providers, assigning them a quantitative score so that the decision-making process is much faster, more convincing, and compelling, especially when adopting new technology. The methodology was built and used into a paper mill for selecting sensor suppliers to implement a condition monitoring system that captures vibration signals at different points of paper presses. The evaluation was successful, resulting in a winning provider being selected and the IoT sensor-based system implemented.

Aiming at the problems with low efficiency and low standardization in the establishment of complex product Research & Development(R&D) process, a combination and optimization method for R&D activities is proposed in this paper. Firstly, process module is established by combining related R&D activities using process network diagram and description text. Then, a method is established for similarity representation between process module and design requirements, on which the module reorganization is based. And optimal combination scheme of modules is obtained by an adaptive genetic algorithm. Finally, the effectiveness of proposed method is verified through the example of a product R&D process.

This paper provides a conceptual analysis of the role of business continuity management (BCM) as a key enabler of supply chain resilience (SCRE). Through an analysis of the extant literature, the authors show how BCM can facilitate and enhance key constructs that are directly linked to SCRE. The results show that the existing BCM lifecycle – already widely used in the industry – has the potential to play a decisive role in boosting agility, flexibility, velocity, visibility, and collaboration, as well as response and recovery capabilities.

Nowadays, technologies and strategies to bring processes toward the Industry 4.0 paradigm go beyond simple paper proposals and focus on their implementation. This article presents a case study of University-Business collaboration focused on technology transfer as part of a viable solution to real problems in the company. The objective of the collaborative project was the transfer of knowledge and use of technologies by the research group and, together with the knowledge based on the experience of the company's maintenance department, to solve a real problem in the industry. Therefore, we set out with the industry the objective of implementing a system for early detection of failures in press bearings to ensure that maintenance personnel performs maintenance actions on time. We combined agile methodologies and design thinking for the implementation because agile methodologies allow controlling the development progressively, and design thinking focuses on the user's needs. The implementation process was executed in six phases: empathize, ideate, define, test, prototype, and implement. The project was successfully implemented in six months and met industry requirements. Combining agile methodologies and design thinking was fundamental for the implementation, as it was possible to get feedback from industry personnel and to have materialized progress in each meeting.

Machining distortion caused by residual stresses is one of the major challenges in the production of thin-walled monolithic parts, which are widely used in the aerospace industry. The relevant influencing factors include the selected process parameters, the machining sequence as well as the used fixture system. This paper presents an adaptive fixture system for reducing machining distortion caused by residual stresses in milling. To validate the advantage of this system, machining distortion experiments are conducted on monolithic parts made of Ti-6A1-4V using a conventional and the adaptive fixture system.

Intrinsically, many real control problems are mathematically challenging. Thus, we often relax the original problem to obtain a tractable problem. In this paper, we consider a computational technique to solving a nonlinear stochastic optimal control problem in a direct manner. Our computational approach employs the idea of the recently developed technique, called deep unfolding. Deep unfolding is a deep learning method that is model-based, used to accelerate iterative algorithms. For the finite-horizon optimal control problem, we regard each state transition of the discrete-time dynamical system with control inputs and disturbances as an iteration step. Then, we unfold those state transitions into the layers to construct a deep neural network so that each of those layers contains a trainable parameter of the control input. This produces a computational graph. Once the computational graph is fixed, the control inputs are computed by training the deep neural network. This open-loop method is then integrated into model predictive control to close the loop. The advantages of our computational technique are not only the ability of handling nonlinear state transitions and non-Gaussian disturbances, but also its simplicity. The feasibility and benefit of the proposed technique are demonstrated by numerical experiments using a continuous stirred tank reactor model, which is highly nonlinear.

In this paper, adaptive radial basis function neural network based sliding mode control of a fast acting superconducting magnetic energy storage (SMES) is reported. With the converter interface SMES is installed and connected with the wind-diesel micro grid to carry the required power exchange to improve the system frequency. With sliding surface design and neural network using a radial basis function, a sliding mode controller action is used to control the converter, and achieve the desired operation of SMES. Computer simulations are performed and presented to show the superiority of the proposed methodology with the system subjected to load and wind power variations.

Stabilization problem of inverted pendulum system is one of the challenging issues to the researchers. To stabilize this unstable inverted pendulum system, linear quadratic regulator (LQR) controller has been designed along with the Kalman filter. Kalman filter estimates the state variables of the system and LQR controller takes these estimated state variables as inputs. The performance of this proposed controller has been tested for both stabilization and tracking problem of inverted pendulum system. Both the stabilization and tracking responses of the closed-loop system satisfies the required performance specifications. The responses of the closed loop system with Kalman filter and without Kalman filter also have been compared. Kalman filter based closed-loop system eliminates the effects of process and sensor noises that are present in the outputs of the system. The performance of this proposed controller is also compared with LMI based robust controller that had been earlier designed for the same system.

The paper describes various brachistochrone problem statements. In the beginning an approach to solving the brachistochrone problem with a nonzero initial velocity using optimal control methods is presented. The substantiation of the transition to zero initial velocity is also provided. The second type of problem statement takes into account explicit restrictions on the curvature of the trajectory. The solution is obtained by using the Pontryagin’s Maximum Principle. The paper demonstrates that the optimal solution includes both regular and singular trajectory parts.

The paper presents an identification strategy to obtain relations between time delays of a transport model for an evaporating liquid film and corresponding flow/liquid properties. To this end, step-response based pilot plant experiments referring to the falling film evaporator process are conducted. In this context, the time-delay relations are identified offline by minimizing the output error between the model and experiment. In subsequent validation experiments, we observe that modeled and measured outputs are in good accordance. Within this framework, we conclude that the influence of evaporation on the liquid’s time-delay behavior can be neglected.

Community detection plays an important role in understanding and exploiting the structure of complex systems. Many algorithms have been developed for community detection using modularity maximization or other techniques. In this paper, we formulate the community detection problem as a constrained nonsmooth optimization problem on the compact Stiefel manifold. A Riemannian projected proximal gradient method is proposed and used to solve the problem. Numerical experimental results on synthetic benchmarks and real-world networks show that our algorithm is effective and outperforms several state-of-art algorithms.

In this work we present a novel classification model that can detect kick-scooters from inertial and pressure sensors. The detection is performed with kick-scooters being trained with other activities and transportation modes including still, walking, biking, taking bus and tramway. Results show that kick-scooters can be precisely detected up to 99% for three different sensor placements: on-foot, waist-attached and in the trouser’s pocket. Thus, this paper provides a first contribution where kick-scooters can be classified and studied for further applications such as mobility behavior analysis and navigation.

Intelligent manufacturing and automated systems several complex control tasks must be carried out. A possible way for improving the performance level of the systems is the application of machine-learning-based agents, e.g. neural networks in the control loop. A novel challenge of these complex systems is to provide analysis and synthesis methods, with which their performance levels can be assessed. The paper proposes analysis method for tracking control systems, whose control loop contains feed-forward neural networks. Through the method the asymptotic stability and the tracking performance through decay rate are assessed. The proposed method is based on the linear approximation of the closed-loop system, and thus, a polytopic set of linear systems is resulted. Using the resulted polytopic system an analysis method based on an optimization is formed, whose result approximates the decay rate of the system. The effectiveness of the method is illustrated through a benchmark example, i.e. the torque control of a one-degree-of-freedom robotic arm.

Modern engineering systems include many components of different types and functions. Verifying that these systems satisfy given specifications can be an arduous task, as most formal verification methods are limited to systems of moderate size. Recently, contract theory has been proposed as a modular framework for defining specifications. In this paper, we present a contract theory for discrete-time dynamical control systems relying on assume/guarantee contracts, which prescribe assumptions on the input of the system and guarantees on the output. We then focus on contracts defined by linear constraints, and develop efficient computational tools for verification of satisfaction and refinement based on linear programming. We exemplify these tools in a simulation example, proving a certain safety specification for a two-vehicle autonomous driving setting.

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results.