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Large scale quantum computers will break classical public-key cryptography protocols by quantum algorithms such as Shor’s algorithm. Hence, designing quantum-safe cryptosystems to replace current classical algorithms is crucial. Luckily there are some post-quantum candidates that are assumed to be resistant against future attacks from quantum computers, and NIST is considering standardizing them. Among these candidates, lattice-based cryptography sounds more interesting than others due to the performance results as well as confidence in the security. There are few works in the literature evaluating the performance of lattice-based cryptography in hardware. In this paper, we focus on Cryptographic Suite for Algebraic Lattices (CRYSTALS) key exchange mechanisms known as Kyber and provide an instruction-set hardware architecture and implement on Xilinx Artix-7 FPGA for performance evaluation and testing. Our proposed architecture provides an efficient and high-performance set of components to perform polynomial sampling, number-theoretic transform (NTT), and point-wise multiplication to speed up lattice-based post-quantum cryptography (PQC). This architecture implemented on ASIC outperforms state-of-the-art implementations.

The massive number of sensors deployed in the Internet of Things (IoT) produce gigantic amounts of data for facilitating a wide range of applications. Deep Learning (DL) would undoubtedly play a role in generating valuable inferences from this massive volume of data and hence will assist in creating smarter IoT. In this regard, exploring the potential of DL for IoT data analytics becomes highly crucial. This paper begins with a concise discussion on the Deep Neural Network (DNN) and its different architectures. The potential benefits that DL will bring to the IoT are also discussed. Then, a detailed review of DL-driven IoT use-cases is presented. Moreover, this paper formulates a DL-based model for Human Activity Recognition (HAR). It carries out a performance comparison of the proposed model with other machine learning techniques to delineate the superiority of the DL model over other techniques. Apart from enlightening the potential of DL in IoT applications, this paper will serve as an impetus to encourage advanced research in the realm of DL-driven IoT applications.

A finite element model (FEM) of the bracket arm of wrecker is developed and the mechanical behavior under six representative conditions is studied in this paper. Stress test experiment is carried out to validate the presented FEM. Comparison analyses show that results predicted from the FEM are in good agreement with those obtained from the test experiment. Moreover, the mechanical behavior analyses also give the danger working conditions for each component of the bracket arm. Based on the stress distributions under the danger working condition, structural improvement is conducted for the component which is prone to material failure. Mechanical behavior analyses of the improved structures are also carried out in this paper. Numerical analysis results show that the maximum stresses of each improved component of the bracket arm are all reduced to close or below the allowable stress. This indicted that the load bearing capacities of each component are improved effectively and meet the requirements of engineering applications.

Testing is the process of evaluating a system by manual or automated means. While Regression Test Selection (RTS) discards test cases and Test Suite Minimization (TSM) shows diminution in fault detection rate, Test Case Prioritization (TCP) does not discard test cases. Test Case Prioritization techniques can be coverage or historical information based or model based. It can also be cost-time aware or requirement-risk aware. GUI/Web applications need special prioritization mechanism. In this paper, 90 scholarly articles ranging from 2001 to 2018 have been reviewed. We have explored IEEE, Wiley, ACM Library, Springer, Taylor & Francis and Elsevier database. We have also described each prioritization method with their findings and subject programs. This paper includes a chronological catalogue listing of the reviewed papers. We have framed three research questions which sum up the frequently used prioritization metrics, regularly used subject programs and the distribution of different prioritization techniques. To the best of our knowledge, this is the first review with a detail report of the last 18 years of TCP techniques. We hope this article will be beneficial for both beginners and seasoned professionals.

Taking Uyghur character recognition as an example, this paper use a method of image segmentation which combines traditional methods with CNN, and transforms and implements the Uyghur character on intelligent devices. Firstly, after analyzing the application of several general segmentation methods, this paper finds some shortcomings but also some ideas for Uighur segmentation. Then, starting from the characteristics of Uyghur language, such as structure, word formation and input habits, the author studies the idea of Uyghur adhesive language segmentation from the perspective of language characteristics, puts forward the basic algorithm of Uyghur symbol segmentation, and applies the Uyghur character adhesion segmentation based on minimum spanning tree and multi-queue primitive merging model to improve the segmentation efficiency. In addition, in order to solve the limitations of the traditional handwriting recognition framework of “preprocessing + feature extraction + classifier”, this paper puts forward a new solution of Uyghur handwriting recognition technology combining prior domain knowledge with CNN, constructs a Uyghur handwriting recognition model on the basis of CNN and random elastic deformation, and also improves the recognition rate of the system by combining domain knowledge with CNN. According to the experimental results, we can conclude that the mixed method proposed in this paper can effectively break the technical bottleneck of traditional methods, thus improving the efficiency of segmentation and recognition.

With the rapid development of information and network technology, hospital information systems (HIS) has also become a hot research area. However, data could be subjected to the threat of security attacks, leakage, tampering, and forgery during medical data transmissions, data storage, and sharing based on public networks and cloud environments. The immutability, decentralization and anonymity of the blockchain provided new ways for solving the aforementioned problems. This paper proposes a complete medical information system model based on blockchain technology, to realize the goal of safe storage and sharing of medical data. A data collection system based on Internet of Things (IoT) was also developed that can simultaneously collect data from different types of non-invasive medical instruments to realize real-time collection of patient health record during surgery (SHR). This system designed an anonymous medical data sharing scheme based on cloud servers and proxy re-encryption algorithm to improve the security of private medical data sharing. The system was implemented based on the permissioned blockchain architecture Hyperledger Fabric, and a dual-channel Fabric deployment architecture and medical chaincode were designed for data management and access control. We also carried out computing overhead test, performance test and safety evaluation on the system, and the test results meet the requirements of actual medical production environment. The research work of this paper provides means for remote diagnosis and treatment, data mining and other practical applications based on the medical data on the blockchain.

This paper presents the design of a novel kinematic tensioning system to remedy the situation where the input-output relation in the cable transmission for a remotely operated multi-link end-effector is compromised due to the onset of slack. The actuation of multi-link end-effectors in remote manipulators via cable transmission exhibits loss of tension and slack generation on the non-driving side of the cable transmission. This paper outlines an analytical formulation of the kinematic component of slack resulting from the geometry of the end-effector links that are serially connected. If left unaddressed, cable slack adversely impacts transmission stiffness and produces backlash at the end-effector wherein for a fixed input, the output can move freely over a range of motion (i.e. dead-band). In case of articulating instruments for minimally invasive surgery, this limits the ability to position and orient the end-effector precisely, the ability to effectively transmit forces via the end-effectors to the surrounding tissues, and the tactile feedback to the user operating the instrument. To overcome these limitations, this paper presents a simple solution that effectively mitigates slack on the non-driving side of a cable transmission between a driving pulley and a multi-link end-effector with minimal cost or complexity. The proposed design uses a driving pulley modified with tensioner extensions to alter the transmission path, which provides a kinematic or geometric solution in contrast to the various spring based solutions that have several drawbacks. Based on a derivation of slack in the system, a design optimization is performed to generate the key dimensions of the tensioning system that minimizes slack without causing over-constraint. An experimental setup is designed and fabricated to validate the slack predictions in the cable transmission system for a multi-link end-effector, without and with the proposed tensioner. The experimental measurements demonstrate the efficacy of the proposed solution.

Metaheuristics are widely employed to solve hard optimization problems, like vehicle routing problems (VRP), for which exact solution methods are impractical. In particular, local search-based metaheuristics have been successfully applied to the capacitated VRP (CVRP). The CVRP aims at defining the minimum-cost delivery routes for a given set of identical vehicles since each vehicle only travels one route and there is a single (central) depot. The best metaheuristics to the CVRP avoid getting stuck in local optima by embedding specific hill-climbing mechanisms such as diversification strategies into the solution methods. This paper introduces a hybridization of a novel adaptive version of Iterated Local Search with Path-Relinking (AILS-PR) to the CVRP. The major contribution of this paper is an automatic mechanism to control the diversity step of the metaheuristic to allow it to escape from local optima. The results of experiments with 100 benchmark CVPR instances show that AILS-PR outperformed the state-of-the-art CVRP metaheuristics.

Serverless computing has seen a myriad of work exploring its potential. Some systems tackle Function-as-a-Service (FaaS) properties on automatic elasticity and scale to run highly-parallel computing jobs. However, they focus on specific platforms and convey that their ideas can be extrapolated to any FaaS runtime. An important question arises: do all FaaS platforms fit parallel computations? In this paper, we argue that not all of them provide the necessary means to host highly-parallel applications. To validate our hypothesis, we create a comparative framework and categorize the architectures of four cloud FaaS offerings, emphasizing parallel performance. We attest and extend this description with an empirical experiment that consists in plotting in deep detail the evolution of a parallel computing job on each service. The analysis of our results evinces that FaaS is not inherently good for parallel computations and architectural differences across platforms are decisive to categorize their performance. A key insight is the importance of virtualization technologies and the scheduling approach of FaaS platforms. Parallelism improves with lighter virtualization and proactive scheduling due to finer resource allocation and faster elasticity. This causes some platforms like AWS and IBM to perform well for highly-parallel computations, while others such as Azure present difficulties to achieve the required parallelism degree. Consequently, the information in this paper becomes of special interest to help users choose the most adequate infrastructure for their parallel applications., Comment: 19 pages, 15 figures, submitted to FGCS, revised

As the internal requirement of the development of China’s traditional manufacturing industry, intelligent transformation has gradually become the main goal of China’s regional manufacturing transformation. From the perspective of manufacturing value chain, this paper proposes a conceptual model of intelligent transformation of manufacturing industry. Taking 22 sample cities as cases, the paper uses fuzzy set qualitative comparative analysis method to identify the necessary factors of intelligent transformation of manufacturing industry and the configuration analysis of each factor combination, and then obtains three realization paths to promote intelligent transformation. The results show that intelligent technology innovation and intelligent industry investment are the necessary conditions for the intelligent transformation of manufacturing industry, and there are three configuration paths: collaborative R&D and processing, external factor driven and value chain climbing, which promote the intelligent transformation of manufacturing industry. This paper enriches the research on intelligent transformation of manufacturing industry from the perspective of value chain, and uses the qualitative comparative analysis method of fuzzy set to provide countermeasures and suggestions for the direction and path of intelligent transformation of manufacturing industry.

In recent years, strengthening through using fibre reinforcing polymer (FRP), has been widely used in buildings, especially slab-column connections. This is due to several reasons; such as the application of additional loads, incorrect analysis and design and others. There are many standards to deal with the FRP application. Each standard has its own specification, which differs from others. No comparison between the standards has been conducted to advise the designer to choose his suitable standards to design slab-column connections. In this paper, an attempt is made to make a comparison between design standards in strengthening flat slab-column connections using FRP to explain the differences between standards in design. This comparison is based on the American Concrete Institute (ACI), Concrete Society technical report 55 and Japanese Society of Civil Engineers (JSCE), which are the only codes dealing with FRP plates or sheets. A numerical study was also conducted in this paper by using a finite element programme called ABAQUS to compare with the previous standards. As all the standards do not have a specific equation for the punching shear deals with FRP; a general equation derived by Farghaly and Ueda (2011) was used to address the slabs strengthened by FRP. From the results of the comparison, it can be seen that the Concrete Society technical report 55 gives the maximum value of the load, while the other codes result in a so closed values.

Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers; the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink., Comment: Accepted in IEEE Transactions on Information Theory; 34 pages

This paper presents a disturbance observer-based robust backstepping load-following control (DO-RBLFC) scheme for modular high-temperature gas-cooled reactors (MHTGRs) in the presence of actuator saturation and disturbances. Based on reactor kinetics and temperature reactivity feedback, the mathematical model of the MHTGR is first established. After that, a DO is constructed to estimate the unknown compound disturbances including model uncertainties, external disturbances, and unmeasured states. Besides, the actuator saturation is compensated by employing an auxiliary function in this paper. With the help of the DO, a robust load-following controller is developed via the backstepping technique to improve the load-following performance of the MHTGR subject to disturbances. At last, simulation and comparison results verify that the proposed DO-RBLFC scheme offers higher load-following accuracy, better disturbances rejection capability, and lower control rod speed than a PID controller, a conventional backstepping controller, and a disturbance observer-based adaptive sliding mode controller.

If robot uses 2D lidar or binocular camera to locate obstacles, there will be some problems such as missing obstacle information or inaccurate obstacle locating, which will affect the normal work of the robot. In order to obtain accurate 3D obstacle information, this paper proposes an algorithm for fusing 2D lidar and binocular vision to complete the obstacle location. In this paper, the depth value of the 2D lidar point cloud is used as a benchmark. By fitting the error equation of the binocular camera point cloud depth value, the depth value of the camera point cloud is modified to obtain an accurate 3D camera point cloud, thereby obtaining an accurate 3D obstacle information. Many experiments have proved that the fusion algorithm of 2D lidar and binocular vision can obtain accurate 3D obstacle information. The method of fusion 2D lidar and binocular vision can approximately achieve the measurement effect of 3D lidar, and the point cloud of obstacles is relatively dense, so the accurate 3D obstacle information can be obtained. This method can reduce the influence of single sensor on the robot locating obstacles, thus completing the accurate locating of obstacles, which is of certain significance to robot navigation.

In the production process of lithium battery, the quality inspection requirements of lithium battery are very high. At present, most of the work is done manually. Aiming at the problem of large manual inspection workload and large error, the robot visual inspection technology is applied to the production of lithium battery. In recent years, with the rapid development and progress of science and technology, the rapid development of visual detection hardware and algorithms, making it possible to screen defective products through visual detection algorithms. This paper takes lithium battery as the research object, and studies its vision detection algorithm. As a common commodity, the quality of lithium battery is the key for users to choose. With the increasing requirements of users for battery quality, how to produce high-quality battery is the key problem to be solved by manufacturers. However, at present, the defects of battery surface are mostly carried out manually. There are low efficiency and low detection rate in the process of manual detection. In this paper, the visual detection algorithm is studied to detect the defects such as pits, rust marks and broken skin on the surface of lithium battery, specifically to design the imaging experimental platform of lithium battery; use different lighting schemes to design different battery positioning and extraction algorithms; use Hough detection method to locate the battery surface, and design the battery defect algorithm for this, and compare the algorithm through experiments.

The once exclusive technology empowering immersive and interactive training systems is now more affordable and accessible to mainstream use cases. Its adoption in the manufacturing industry can help reshape training processes as an intrinsic part of production routines and reduce the mental resources required to complete a task. Current academic literature does not integrate components to describe the skills and attributes of workers with impairments. In contrast, the research in this paper addressed the design and evaluation of a new immersive and interactive training system that can effectively provide new human augmentation opportunities for workers with impairments by reducing the mental resources required to complete a task. Automated machine interpretation of tasks and actions of workers with impairments is still a long way off and one of the reasons is that individual skills are still difficult to describe formally. Therefore, in this paper, skill transfer is assessed through external evaluation. The results of the preliminary evaluation of our Cross Reality (XR) prototype for training and error minimization in the manufacturing of electrical cabinets confirmed significant productivity gains and high adoption by participants, validating the suitability of the solution for workers in industrial manufacturing processes.

A district heating network has generally more than one production unit, and then there are different ways to fulfill the energy demand of the network. This paper deals with the planning problem which is a problem of optimization under constraints. The production units are modeled using MINLP (mixed integer non-linear programming), in particular the following technical aspects are considered: storage capacity (for municipal wastes and biomass), allowable operating range, allowable variation of power over time, partial load performances, minimum working time when a unit runs, and minimum waiting time when it is in stand-by mode. The environmental constraint is the annual share of renewable energies in the global energy mix (for example, at least 50% over a year). The model aims to minimize the operating cost of the network. The problem cannot be solved by a one-shot optimization, so the paper proposes a sequential approach: first, a target of renewable share is calculated for each month of the year; secondly, the planning production is determined each day according to the calculated target. The results of a case study show that the planning strategy that only aims to minimize the operating cost violates the environmental constraint. Using the developed planning model raises a little bit the cost (4.5%) but fulfills the heat demand and environmental constraint at the same time. Considering a reduced VAT eligible in France when the constraint on renewable share is met, the proposed planning is even better in term of the total cost (10% lower).

In modular steel building (MSB), connection is a critical part in the structural behavior and ability of load transferring. Previous MSB joints mainly use inter-connection plate or plug-in device, which ignore practical difficulties to hoist and install the modular unit. Therefore, this paper proposed an innovative corner-fitting connection, which is fully prefabricated and liftable. The seismic performance of the novel connection was analyzed by using finite element (FE) software ABAQUS. Numerical study of the novel joint was based on Simplified FE model and Detailed FE model. The developed FE model was verified by comparing with the test results of innovative connections in the previous references. The Simplified model for the joint was developed by using springs to connect the components. Five quasi-static cyclic loading FE analysis were performed on the innovative MSB connection to explore its load transferring capacity and seismic behavior. Results showed that weakening at the beam end can transfer the plastic hinge to the weakened position with little effect on the overall strength. The developed Simplified model can accurately predict the load–displacement curve and Von-mises stress of the joint. With these results, the cyclic response can be incorporated into the Simplified global model by defining the stiffness and dashpot coefficient of the spring. This paper proposed an engineering practical joint and Simplified FE model for seismic analysis of MSB, which is useful for the rapid development of this promising construction method.

Aiming at the security issues during the multi-types data storage and data transmission in autonomous Internet of Things (IoT) systems, this paper proposes an AI algorithm for data enhanced encryption used in the ends and the intermediate nodes of IoTs. The algorithm in this paper first constructs a three-dimensional Arnold transformation matrix for data unit value encryption in the end of IoTs, and designs a quantum logic intelligent mapping that effectively diffuses the encrypted data units to reduce the linear correlation of the image data and to improve the security performance of IoT edge data. Furthermore, the algorithm designs an AI access strategy for scrambling sequence nodes and builds a random-access route for the elements of the scrambling sequence which can reduce the calculation cost and improve the operating efficiency of IoT system in the ends and intermediate nodes. Finally, the data shared matrix is used to share the encrypted data to achieve the (k, n) threshold strategy. Experimental results prove that the algorithm has high plaintext and key sensitivity and can effectively resist brute force attacks, statistical analysis and differential attacks. The algorithm in this paper provides an AI solution for data security encryption in the ends and the intermediate nodes of autonomous IoT systems.

With the development of the information and Internet technology, the public opinions with big data will rapidly emerge in an online-offline social network, and an inefficient management of public opinions often will lead to the security crisis for either firms or governments. To unveil the interaction mechanism among a large number of agents between the online and offline social networks, in this paper we propose the public opinion dynamics model in an online-offline social network context. Next, in the theory aspect we investigate the analytical conditions to form a consensus in the public opinion dynamics model. Furthermore, we conduct the extensive simulations to investigate how the online agents impact the dynamics of public opinion formation, and unfold that the online agents shorten the steady-state time, decrease the number of opinion clusters, and smoothen the opinion changes in the opinion dynamics. The increase in the size of the online agents often enhances these effects. The results in this paper can provide a basis for the management of the public opinions in the Internet age.

Internet of Things (IoT) embodies a vision of merging heterogeneous objects to establish seamless interaction among physical and virtual entities. IoT has given the Internet a shift from connecting networks to interconnecting the physical world. The IoT devices are capable of sensing, processing, communicating and storing the data acquired from the physical world. Most of the applications of IoT are pervasive in nature, the pervasive IoT environment poses many challenges due to constrained resources in these miniature and unattended IoT devices. This paper presents a survey of physical and virtual resource management in IoT systems. The main focus of the paper is on resource management in pervasive IoT environment with limited resources. This paper also presents a use case of IoT based Body Area Network and proposes a model for resource management in personal and community healthcare.

Nowadays smartphones become an indispensable tool in many people's everyday life that makes themselves attractive targets for attackers. Among various malware targeting at smartphones, SMS-based malware is one of the most notorious ones. Though a number of Android dynamic analysis frameworks have been proposed to analyze SMS-based malware, most of these frameworks or some Android tools, such as Google Android Emulator, do not support an app or malware to send SMS messages to a real smartphone; hence, security researchers cannot use them directly to analyze the behavior of SMS-based malware. In our previous work, SMS Helper, we designed an application layer tool to allow an app or malware in an Android emulator to send and receive SMS messages to or from a real smartphone. Based on SMS Helper, this paper proposes an Android dynamic analysis framework, called SMS Observer, to assist security researchers to analyze SMS-based malware. SMS Observer integrates SMS Helper into it as a client agent, meanwhile, and it maintains the integrity of system logs. This paper also figures out a way to detect whether an app is executed in an emulator and describes how to use SMS Observer to prevent such evasion. Experimental results using real-world malware samples show SMS Observer is much more effective in detecting SMS-related behavior of SMS-based malware than existing frameworks, such as Google Android Emulator, Andrubis, CopperDroid, and DroidBox. SMS Observer can analyze sophisticated SMS-based malware samples and provide a comprehensive view of malicious behavior.

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

Internet technology, as a product of the development of the network era, connects a large number of hardware devices, but the connection of many devices makes its corresponding security be greatly tested. The network security technology and the corresponding computer hardware security technology have become the focus of people's attention. At the same time, the construction of multimedia database based on this related technology has become the development trend of people's research and attention. Most of the conventional computer security technology focuses on the security construction of the operating system, which has certain openness, so its corresponding reliability is very low, but also uncontrollable. Based on this, this paper proposes a design scheme of the public security architecture on the embedded terminal, and realizes the design of the multimedia security broadcast control system based on this scheme, and realizes the play and storage of multimedia files, as well as the interaction between the corresponding media servers. The wireless network embedded security terminal designed in this paper includes client terminal identity authentication, corresponding media content protection and storage, and real-time broadcast content monitoring. Finally, the experiment is carried out based on the proposed design scheme. The experimental results show that the embedded security terminal proposed in this paper has obvious performance advantages, and its correctness and availability have been verified in practice.

There exists an increasing need for precision measurement & pointing control and extreme motion stability for current and future space systems, e.g., Ultra-Performance Spacecraft (UPS). Some notable technologies of realizing Ultra-Pointing (UP) ability have been developed particularly for Ultra-accuracy Ultra-stability Ultra-agility (3U) spacecraft over recent decades. Usually, Multilevel Compound Pointing Control Techniques (MCPCTs) are deployed in aerospace engineering, especially in astronomical observation satellites and Earth observation satellites. Modern controllers and/or algorithms, which are a key factor of MCPCTs for 3U spacecraft, especially the jitter phenomena that commonly exist in a UPS Pointing Control System (PCS), have also been effectively used in some UP spacecraft for a number of years. Micro-vibration suppression approaches, however, are often proposed to deal with low-level mechanical vibration or disturbance in the microgravity environment that is common for UPS. This latter approach potentially is one of the most practical UP techniques for 3U tasks. Some emerging advanced Disturbance-Free Payload (DFP) satellites that exploit the benefits of non-contact actuators have also been reported in the literature. This represents an interesting and highly promising approach for solving some challenging problems in the area. This paper serves as a state-of-the-art review of UP technologies and/or methods which have been developed, mainly over the last decade, specifically for or potentially could be used for 3U spacecraft pointing control. The problems discussed in this paper are of reference significance to UPS and millisecond optical sensors, which are involved in Gaofeng Project, deep space exploration, manned space flight, and gravitational wave detection.

The Resilient Slip Friction Joint (RSFJ) is a relatively new friction damper developed and introduced in New Zealand. It is used in structures to dissipate the earthquake energy with the advantage of re-centring the structure back to its upright position. This paper investigates the experimental and analytical behaviour of the RSFJ when it is affected by the second-order effect (SOE) both in the damper in-plane and out-of-plane cases due to possible inherent imperfection in reality. The main consequences of SOE is firstly the reduction of the axial force capacity, stiffness of the damper and in return the structure, change in damping ratio. All of these may end up changing the effective period and seismic response of the structure. The ultimate catastrophic effect of SOE might be the failure of the damper if it is not properly designed for it in advance. From this perspective, this paper proposes some analytical expressions for the prediction of the damper response when it is affected by SOE. Furthermore, it investigates the effect of boundary conditions on SOE and provides some recommendations so that the SOE is minimized.

Since project monitoring aims to make decisions, which have future impacts on a project’s success, accurate forecasting of project characteristics is of greater importance. This paper proposes the detection of problems in scheduling projects by introducing the forecasting of successors and renewable resource features. For this purpose, this paper presents the novel applications of forecasting models ”long short term memory” (LSTM) and ”gated recurrent unit” (GRU) in this study. Subsequently, forecasting results of successors and renewable resource characteristics of projects are determined by covering historical records’ observed values from real-life engineering projects’ datasets. Results show that LSTM and GRU models’ proposed applications can reduce errors from one up to seven steps-ahead forecastings. Moreover, forecasting the increased number of successors may require more renewable resources to effectively complete the jobs in projects. Generally, the proposed models are reliable and robust to application in forecasting the project scheduling task.

Wildfires can pose a major threat to the secure operation of power networks. Chile, California, and Australia have suffered from recent wildfires that have induced considerable power supply cuts. Further, as power systems move to a significant integration of variable renewable energy sources, successfully managing the impact of wildfires on the power supply can become even more challenging due to the joint uncertainty in wildfire trajectories and the power injections from wind and solar farms. Motivated by this, this paper develops a practical decision-support approach that concatenates a stochastic wildfire simulation method with an attacker-defender model that aims to find a worst-case realization for (i) transmission line and generator contingencies, out of those that can potentially be affected by a given wildfire scenario, and for (ii) wind and solar power trajectories, based on a max-min structure where the inner min problem represents a best adaptive response on generator dispatch actions. Further, this paper proposes an evaluation framework to assess the power supply security of various power system topology configurations, under the assumption of limited transmission switching capabilities, and based on the simulation of several wildfire evolution scenarios. Extensive computational experiments are carried out on two representations of the Chilean power network with up to 278 buses, showing the practical effectiveness of the proposed approach for enhancing wildfire resilience in highly renewable power systems.

Person re-identification is to recognize a pedestrian who has been observed at different places in monitoring system, which is done by computer automatically. Important practical significance can be found in this paper on improving the intelligence of monitoring system. There are two challenging problems needed to be solved for person re-identification, which is feature representation and matching. Designing a suitable feature representation method is both challenging and crucial. Ideally, the extracted features should be robust enough to cope with the various situations of the monitoring environment, such as posture difference, illumination changing, and shooting angle difference, etc. There are a lot of challenges for feature matching too, such as small sample size, inter-class confusion, and intra-class variation, etc. In order to meet these challenges, a new person re-identification scheme was put forward in this paper. The following three aspects would be used to describe it. First, an improved BOF feature extraction algorithm based on SURF was put forwarded, SURF algorithm extracts the preliminary feature descriptor and generates visual dictionary, so the influence factors such as illumination and scale invariants can be deal with. And then, covariance descriptor was adopted by the algorithm. It has high robustness, and matching accuracy can be improved too for the case of small sample. Finally, an effective classifier was designed by LIBSVM based on improved BOF algorithm, so the efficiency of the person re-identification algorithm can be improved. The proposed method was compared with the current mainstream algorithm through experiment, and it can be found through the experimental results that it is effective to solve the difficult problems for person re-identification.

In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.

Considering the scarcity of Uyghur sentiment resources, in this paper proposed a new combined unsupervised sentiment classification method for Uyghur text without any labeled corpora. In the first part, a Uyghur sentiment dictionary, UYSentiDict, was adopted to classify the sentences. For the sentiment vocabulary matching, both the matching of the original word and the stem were considered, and the influence of sentence patterns, negation words, and degree adverbs were further considered as well. Based on different thresholds, the sentences with higher sentiment values were selected from the lexicon-based classification results as a pseudo-labeled dataset. In the second part, different sentiment characteristics were learned from the pseudo-labeled dataset by the machine learning classifier, and the remaining categorical data were further classified. It can be concluded that the method proposed in this paper has good classification efficiency in Uyghur sentiment corpora in four different fields, and some results were performed better than the classification results of machine learning classifier. Moreover, this method is not restricted by the field of data and does not need to be marked in advance with good training corpus, and can solve the resource shortage problem in the field of Uyghur sentiment classification effectively.

Image Mosaicing is that the method of mixing 2 or additional pictures of identical scene into one image and generate panorama of high-resolution image. In this paper, we've represented the essential strategies used to generate panorama image. Our aim is to supply different strategies and algorithms used to generate wide angle image. This paper is principally for those who want to try to learn and implement image mosaicing.

To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress.

This paper presents a new approach to the multi-modal medical image fusion based on Principal Component Analysis (PCA) and Singular value decomposition (SVD).The main objective of the proposed approach is to facilitate its implementation on a hardware unit, so it works effectively at run time. To evaluate the presented approach, it was tested in fusing four different cases of a registered CT and MRI images. Eleven quality metrics (including Mutual Information and Universal Image Quality Index) were used in evaluating the fused image obtained by the proposed approach, and compare it with the images obtained by the other fusion approaches. In experiments, the quality metrics shows that the fused image obtained by the presented approach has better quality result and it proved effective in medical image fusion especially in MRI and CT images. It also indicates that the paper approach had reduced the processing time and the memory required during the fusion process, and leads to very cheap and fast hardware implementation of the presented approach.

The establishment of secure secret keys ahead of transmissions is one of the key issues in the field of information security. The security of traditional cryptographic secret key establishment mechanisms is seriously challenged by computing-intensive attacks, with the fast growth of high-performance computing. As an alternative, considerable efforts have been made to develop physical (PHY) layer security measures in recent years, such as link-signature-based (LSB) secret key extraction techniques. Those mechanisms have been believed secure, based on the fundamental assumption that wireless signals received at two locations are uncorrelated when separated by more than half a wavelength. However, this assumption does not hold in some circumstances under latest observations, rendering LSB key extraction mechanisms vulnerable to attacks. To address this problem, the formal theoretical analysis on channel correlations in both real indoor and outdoor environments is provided in this paper. Moreover, this paper proposes empirical statistical inference attacks (SIA) against LSB key extraction, whereby an adversary infers the signature of a target link. Consequently, the secret key extracted from that signature has been recovered by observing the surrounding links. In contrast to prior literature that assumes theoretical link-correlation models for the inference, our study does not make any assumption on link correlation. Instead, we employ machine learning (ML) methods for link inference based on empirically measured link signatures. We further propose a countermeasure against the SIAs, called forward-backward cooperative key extraction protocol with helpers (FBCH). In the FBCH, helpers (other trusted wireless nodes) are introduced to provide more randomness in the key extraction. Our experimental results have shown that the proposed inference methods are still quite effective even without making assumptions on link correlation. Furthermore, the effectiveness of the proposed FBCH protocol is validated by our experiment results.

The paper presents digital measurement twin (DMT) for CMM based on STEP-NC. DMT is acting as a mirror between the physical and virtual measuring world. In this paper, the measurement system with a ...

In this paper, we apply the Z-type control method to a crop-pest–natural enemy model. We consider the indirect Z-controller in the natural enemy population and investigate the mathematical properties of the model. Furthermore, our analytical results are also numerically validated. Our paper supports that the pest population can be controlled by using an indirect Z-control mechanism in the natural enemy population. Investigations on the crop-pest–natural enemy model also highlight how the Z-control method acts in respect to different dynamical regimes of the uncontrolled model.

Many methods achieve the visual object tracking task with deep learning technologies. As the deep features of different levels contain various semantic information and functions, this paper presents a multi-hierarchy feature aggregation approach to tackle the specific issues in the tracking task, which consists of two aspects. On one hand, this paper integrates the features captured by the offline and online classifiers at the score level, which constructs complementary roles of these classifiers to enhance the stability of classification. Besides, the proposed offline classifier is continuously optimized with different levels of features to reinforce classification constraints. On the other hand, we design a butterfly attention module to promote the capacity of multi-hierarchy feature aggregation in the regression network, which aims to fuse and strengthen the multi-scale features by attending to their spatial information. It can capture more spatial contexts by utilizing the self-attention mechanism during the fusion procedure, and preserve the hierarchy of the features during the strengthening process. Extensive experiments on four public datasets, i.e., VOT2018, OTB100, NFS and LaSOT datasets, demonstrate the effectiveness of the proposed methods.

This paper introduces a cyber-physical system for building automation and control that is developed based on a distributed model predictive control. The implemented distributed method significantly reduces computation overhead with respect to the centralized methods. However, continuous data transfer between subsystems, which are often far from each other, is required when using this method. Information transmission between subsystems is very often subject to the limitations of transmission bandwidth and/or short communication range resulting in significant communication overhead. This causes significant time latency between making measurements and applying control commands, which adversely affects the control performance. Therefore, the distributed method used in this paper implements a two-level communication architecture to reduce the communication overhead. In order to avoid collision in communication inside neighborhood using this method, the TDMA-OFDMA scheme is used for wireless communication between distributed devices. Under these assumptions, the communication overhead is calculated. Then, a novel algorithm for finding the size of neighborhoods resulting in the lowest time latency between making measurements and applying control commands is presented for a typical office building. Finally, the satisfactory performance of the proposed cyber-physical system for the temperature control of a typical office building in the presence of disturbance and model inaccuracy is illustrated using computer simulations.

Context and rationale Intrusion Detection, the ability to detect malware and other attacks, is a crucial aspect to ensure cybersecurity. So is the ability to identify this myriad of attacks. Objective Artificial Neural Networks (as well as other machine learning bio-inspired approaches) are an established and proven method of accurate classification. ANNs are extremely versatile – a wide range of setups can achieve significantly different classification results. The main objective and contribution of this paper is the evaluation of the way the hyperparameters can influence the final classification result. Method and results In this paper, a wide range of ANN setups is put to comparison. We have performed our experiments on two benchmark datasets, namely NSL-KDD and CICIDS2017. Conclusions The most effective arrangement achieves the multi-class classification accuracy of 99.909% on an established benchmark dataset.

The Asteroid (469219) 2016 HO 3 , one of the targets of the potential small celestial body missions, is a typical small-sized stable quasi-satellite of the Earth. Compared with the targets of previous impact missions, it is more challenging for a hypervelocity impact on 2016 HO 3 due to its much smaller size. Besides, the time durations for autonomous navigation and maneuver correction are greatly narrowed because the impactor can hardly capture the small-sized target until they are close enough. This paper suggests a high-precision autonomous navigation system and proposes a minimum-impact-error correction strategy for the targeting maneuver to solve the challenges. The navigation system utilizes traditional optical navigation assisted by a radar ranging measurement unit, which has been proved of high precision and short convergence time during the approach phase (better than 10 m in 100.0 s). After obtaining the relative positions and velocities, the targeting maneuvers are needed to correct the impactor’s orbit for a final successful impact. Therefore, a minimum-impact-error correction strategy is proposed to determine the optimal number and correction schedule of targeting maneuvers, which can minimize the impact error under the condition that the total velocity increments are limited. In the case of the 2016 HO 3 impactor, this paper conducts numerical Monte-Carlo simulations for validation. The results are encouraging, and the proportion of successful impact is above 99.1 percent. The correction strategy proposed in this paper is meaningful for future hypervelocity impact on small celestial bodies.

Internet of Things (IoT) is considered as a key enabler of health informatics. IoT-enabled devices are used for in-hospital and in-home patient monitoring to collect and transfer biomedical data pertaining to blood pressure, electrocardiography (ECG), blood sugar levels, body temperature, etc. Among these devices, wearables have found their presence in a wide range of healthcare applications. These devices generate data in real-time and transmit them to nearby gateways and remote servers for processing and visualization. The data transmitted by these devices are vulnerable to a range of adversarial threats, and as such, privacy and integrity need to be preserved. In this paper, we present LightIoT, a lightweight and secure communication approach for data exchanged among the devices of a healthcare infrastructure. LightIoT operates in three phases: initialization, pairing, and authentication. These phases ensure the reliable transmission of data by establishing secure sessions among the communicating entities (wearables, gateways and a remote server). Statistical results exhibit that our scheme is lightweight, robust, and resilient against a wide range of adversarial attacks and incurs much lower computational and communication overhead for the transmitted data in the presence of existing approaches., This paper has been accepted for publication by the IEEE Transactions on Green Communications and Networking. The copyright is with the IEEE

Single-object tracking is regarded as a challenging task in computer vision, especially in complex spatio-temporal contexts. The changes in the environment and object deformation make it difficult to track. In the last 10 years, the application of correlation filters and deep learning enhances the performance of trackers to a large extent. This paper summarizes single-object tracking algorithms based on correlation filters and deep learning. Firstly, we explain the definition of single-object tracking and analyze the components of general object tracking algorithms. Secondly, the single-object tracking algorithms proposed in the past decade are summarized according to different categories. Finally, this paper summarizes the achievements and problems of existing algorithms by analyzing experimental results and discusses the development trends.

This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process. A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is often a critical variable to track in many chemical, petrochemical, metallurgical, and oil industries. This method utilizes less than 100 images for creating an environment, from which the agent generates its own data without the need for expert knowledge. Unlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires far fewer parameters, which naturally reduces its maintenance cost. Besides its frugal nature, the agent is robust to environmental uncertainties such as occlusion, intensity changes, and excessive noise. From a closed-loop control context, an interface location-based deviation is chosen as the optimization goal during training. The methodology showcases RL for real-time object-tracking applications in the oil sands industry. Along with a presentation of the interface tracking problem, this paper provides a detailed review of one of the most effective RL methodologies: actor–critic policy.

Cloud computing can be considered as a disruptive technology that is making life easier for Cloud users. Determining a focus of research in a specific subject area is sometimes challenging. A systematic map enables a synthesis of a scheme for categorizing data in a field of interest. The goal of this research paper is to carry out a systematic mapping study of policy language and programming models on the cloud. The mapping involved contribution category such as method, research category such as evaluation and major topics extracted from the abstracts of primary studies. The result indicated there are more publications on evaluation research in term of security with 8.9%. There were more papers published on validation research, solution proposal and experience research on the topic of paradigms with 7.53%, 6.85% and 4.11% respectively. Also, there were more publications on philosophical research in terms of privacy with 4.11%. In addition, there were more articles published on opinion research in terms of the survey with 4.11%. On the other hand, there were no articles on metric in terms of framework, paradigms and accountability, and reliability to the best of the researchers’ knowledge. The outcome of this systematic study will be of benefit to cloud users, researchers, practitioners and providers.

In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accurately track. However, because the model of the robot can not be accurately obtained, some researchers have proposed using a neural network to approximate the unknown term. The parameters of the traditional RBF neural network are usually selected through the empirical and trial-and-error method, which maybe biased and inefficient. In addition, due to the end-effector of the mechanical arm trajectory will be constantly changing according to the needs of the task, when the neural network of compact set cannot contain the whole input vector, the neural network cannot achieve the ideal approximation effect. In this paper, the broad neural network is used to approximate the unknown terms of the robot. This method can reuse the motion controller that has been learned and complete other motions in the robot operating space without relearning its weight parameters. In this paper, the effectiveness of the proposed method is proved by the ultrasound scanning task.

This paper presents a large-scale integrated modelling framework that can capture the relationships between travel mode choice, departure time choice and route choices simultaneously. Conventional transportation models have typically been applied to small scale networks to avoid the complexity of the large-scale simulation. While there are different approaches and strategies that researchers have presented to tackle the transportation congestion challenges, such strategies need to be accurately evaluated at the full system scale before implementation. The proposed framework integrates an econometric model for travel mode and departure time choice with a simulation-based dynamic traffic and transit assignment model. It addresses the interactions between the traffic and transit networks in addition to their interactions with the mode and departure time choice model. A case study of the proposed modelling framework is presented in this paper, where the effects of replacing the current flat transit fare used in the City of Toronto with a time-based transit fare structure are captured. It is found that an increase in the fare during the middle of the peak results in a reduction in transit vehicles crowdedness. The scenario analysis also shows that 1.85% of transit users will shift their departure time from the (congested) middle period of the peak to the (less costly) shoulders of the peak, whilst 2.6% of transit users opt-out of using transit in favour of either driving or accompanying another driver.

Most of the papers published so far on fractional discrete systems are related to theoretical results on their chaotic behaviors or to applications based on the mathematical modeling of the chaotic phenomena. No paper has been published to date regarding the hardware implementation of hyperchaotic fractional maps. This manuscript makes a contribution to the topic by presenting the first example of hardware implementation of hyperchaotic fractional maps. In particular, by exploiting the Grunwald–Letnikov difference operator, the paper introduces a new version of the fractional Grassi–Miller map. The system dynamics are analyzed via bifurcation diagrams and Lyapunov exponents, showing that the conceived map is hyperchaotic when the fractional order μ belongs to the interval [ 0 . 966 , 1 ] . Finally, a hardware implementation of the fractional map is illustrated, with the aim to concretely highlight the presence of hyperchaos in physical systems described by fractional difference equations. Arduino, an open-source platform, has been used to illustrate the simplicity as well as the feasibility of the implementation.

This paper presents an automated, domain-independent, metamorphic testing platform called MTKeras. In this paper, we report on an investigation demonstrating the effectiveness and usability of MTKeras through five case studies in the four domains of image classification, sentiment analysis, search engines and database management systems. We also report on the effectiveness of combining metamorphic relation (input) patterns in individual metamorphic relations, enhancing the failure-finding abilities of the individual relations. The results of our experiments support combining patterns, and the use of MTKeras. The research reported in this paper shows the applicability of metamorphic relation patterns, and introduces a practical tool for the research community.

For impulse transfers, the solution of two-impulse rendezvous is generally obtained by solving the Lambert orbit problem, and the search for the exploration window of the target celestial body is a two-dimensional optimization based on velocity increment calculation. In order to assess the accessibility and exploration window of large-number asteroids, this paper approximates the transfer orbit from the Earth to the target asteroid as a co-planar two-impulse transfer orbit and gives an effective method. Based on the theory of cotangential transfer orbit, this paper proposes a method to express the transfer orbit with one single variable (the aphelion argument of the transfer orbit, θT), which can also determine the start time of the transfer. After that, the paper combines the two-impulse orbit transfer theory (without considering the rendezvous) with the calculation method of the exploration window (considering the rendezvous), proposing the concept of the rendezvous difference angle eR. eR is defined as the mean anomaly angle difference between the spacecraft and the target object when the spacecraft enters the target orbit. The rendezvous difference angle eR has the same minimum point as the velocity increment in exploration window search, while it can be directly calculated from the transfer orbit and the transfer time. When cotangential transfer orbits are used, eR can be expressed as a function of θT. Taking eR as the optimal target variable, the exploration window search can be simplified to a one-dimensional problem. This method is suitable for the large-scale accessibility calculation for asteroids with low inclinations, so as to quickly determine the accessible target group.