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Chaotic dynamics analysis of complex-variable chaotic systems (CVCSs) is an important problem in real secure communication and encryption. In this paper, a simple one-parameter chaotic system in complex field is proposed, whose nonlinear terms are the same as Lorenz system but the linear terms are much simpler. The proposed system has circular equilibria and therefore multi-stability can be measured by phase portraits, bifurcation diagrams and Lyapunov exponent spectrum. Its basin of attraction is filled with initial points leading to chaotic behaviors. The coexistence of infinitely many attractors is found in the proposed system, which is not reported in the existing complex-variable Lorenz system. Finally, two complexity indexes are used to measure dynamic characteristic with respect to parameter.

The aging population and an unhealthy lifestyle have led to a considerable proportion of chronic diseases in many countries. A new generation of emerging technologies has set off a new wave of revolution around the world, such as cloud computing, the Internet of Things, artificial intelligence, and so on. In recent years, the literature associated with chronic disease research driven by emerging technologies has grown rapidly, but a few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of this field. This paper is a bibliometric analysis of chronic disease research driven by emerging technologies, and this paper analyzed and visualized the time distribution, space distribution, literature co-citation, and research focus. Moreover, this paper visualized and determined the dynamic knowledge structure of chronic disease research driven by emerging technologies, which will be helpful in understanding the current research status and identifying the future research directions in this research field for e-health and medical informatics scholars.

To study power performance of large displacement UUV with solid oxy-hydrogen energy propulsion, a design solution of solid oxy-hydrogen power system is finished. Firstly, by using GA algorithm, structural optimization study for stream turbine is finished with maximizing inner efficiency in design condition, which matches the propulsion power requirements of the large displacement UUV. Secondly, the method of underwater rapid and controllable hydrogen and oxygen generation is designed, and whose mass flow is obtained according to the principle of minimum Gibbs free energy and Hess law. At last, calculations of cruising ability for large displacement UUV with solid oxyhydrogen energy propulsion is finished. Results show that solid oxy-hydrogen energy has doubly increased cruising ability and long-distance distribution ability for large displacement UUV. The scheme in this paper provides a new solution for the remote propulsion of large displacement UUV.

The accuracy and speed of object detection based on deep learning are much higher than that of human eyes, but the application of deep learning in object detection of fisheye image remains to be studied. Although the fisheye image has a wide field of vision, it has the problem of geometric distortion. In order to solve the problem that it is difficult to obtain features caused by fisheye image distortion, the 8-fold and 16-fold down-sampling feature images obtained by YOLOv3 for fisheye image detection were respective Maxpool to the same size as the 32-fold down-sampling feature images for fusion, so as to realize feature reuse and increase the detail features in the 32-fold down-sampling feature images. This paper constructs its own fisheye image data set and carries out experiments. The experimental results show the improved YOLOv3 algorithm has higher object detection accuracy for fisheye images in comparison to the original algorithm.

With the continuous progress of science and technology, the dense point cloud mapping has gradually replaced the traditional laser mapping and sparse point cloud mapping because of the rich environmental information and depth information. To address the ghosting problem of moving objects in dynamic environment, this paper introduce Mask R-CNN to extract moving objects mask in input frame. After the positioning accuracy are met, we proposes a robust mask truncated signed distance function (MTSDF) model to build dense map in dynamic indoor environment, which identifies the dynamic voxels by the truncation distance of the voxel and reset them, then, the dense point mapping is updated. We respectively tested on TUM RGB-D dataset and our lab, the results show that our method not only achieve better localization precision, but remove the ghosting generated by moving object in dense point cloud mapping well.

Existing methods cannot effectively optimize the pattern formation in the dynamic environment. Therefore, this paper presents algorithms to employ the convex quadratic programming to minimize the distance to obtain the optimal pattern parameters based on the dynamic environment information and the given pattern shape and then plan the collision-free path of each robot to the goal considering a preferred velocity. Simulation results verified the effectiveness of the proposed methodology for scenarios of letter formation.

The feature based visual SLAM self locates by detecting feature points in the image. However, it is difficult to extract effective feature points in the lack of texture environment, which is easy to cause tracking loss. In order to improve the robustness of slam in a thousand texture environment, some researchers put forward the slam method which combines feature points and feature lines. However, the extraction and matching of feature lines in these methods will consume a lot of computing resources. The paper proposes a direct tracking algorithm for line segment features, LSD algorithm is first used to extract the initial feature line segment in the key frame; in the ordinary frame, the direct tracking of line segment is realized by sampling, tracking and fitting steps. The algorithm is applied to the visual odometer, and experiments are carried out on the data sets of EuRoC and KITTI. The results show that the algorithm can improve the running speed of the visual odometer to 1.5 times compared with other algorithms, while maintaining similar trajectory accuracy.

This paper proposes a multi-robot formation control algorithm. The controller is designed by the difference between the distance and the angle of the two robots. It is proved that the algorithm can guarantee the stability of the formation. Furthermore, the obtained formation control algorithm is tested on the software platform and Pioneer3-DX platform. According to the multi-robot formation control algorithm, a control method of adaptive parameters is proposed. The existing parameters are replaced by fitting equations, which improves the efficiency of parameter adjustment. Simulation and experimental results verify the feasibility and effectiveness of the proposed method.

In the paper, a new hybrid variable selection algorithm for nonlinear regression multi-layer perceptron (MLP) is proposed. The proposed algorithm applies nonnegative garrote (NNG) to compress the input weights of the MLP. The zero input weights dependent variables will be removed from the initial dataset. Next, a further variable selection is carried out by extremal optimization (EO) algorithm. The new variable selection algorithm integrates powerful global selection ability of NNG and accurate local search ability of EO. Finally, two examples of artificial data sets and an industrial application for a debutanizer column are implemented to demonstrate the performance of the new algorithm. The simulation result demonstrates that the developed algorithm presents d better model performance along with less input variable selected than other state-of-art variable selection methods.

Dynamic flexible job shop scheduling (DFJSS) is one of the well-known combinational optimisation problems, which aims to handle machine assignment (routing) and operation sequencing (sequencing) simultaneously in dynamic environment. Genetic programming, as a hyper-heuristic method, has been successfully applied to evolve the routing and sequencing rules for DFJSS, and achieved promising results. In the actual production process, it is necessary to get a balance between several objectives instead of simply focusing only one objective. No existing study considered solving multi-objective DFJSS using genetic programming. In order to capture multi-objective nature of job shop scheduling and provide different trade-offs between conflicting objectives, in this paper, two well-known multi-objective optimisation frameworks, i.e. non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2), are incorporated into the genetic programming hyper-heuristic method to solve the multi-objective DFJSS problem. Experimental results show that the strategy of NSGA-II incorporated into genetic programming hyper-heuristic performs better than SPEA2-based GPHH, as well as the weighted sum approaches, in the perspective of both training performance and generalisation.

Dynamic flexible job shop scheduling (DFJSS) considers making machine assignment and operation sequencing decisions simultaneously with dynamic events. Genetic programming hyper-heuristics (GPHH) have been successfully applied to evolving dispatching rules for DFJSS. However, existing studies mainly focus on evolving deterministic dispatching rules, which calculate priority values for the candidate machines or jobs and select the one with the best priority. Inspired by the effectiveness of training stochastic policies in reinforcement learning, and the fact that a dispatching rule in DFJSS is similar to a policy in reinforcement learning, we investigate the effectiveness of evolving stochastic dispatching rules for DFJSS in this paper. Instead of using the "winner-takes-all" mechanism, we define a range of probability distributions based on the priority values of the candidates to be used by the stochastic dispatching rules. These distributions introduce varying degrees of randomness. We empirically compare the effectiveness of GPHH in evolving the stochastic dispatching rules with different probability distributions, as well as evolving the deterministic dispatching rules. The results show that the evolved deterministic rules perform the best. We argue that this is because unlike the traditional reinforcement learning methods, the current GPHH does not store the quality (value function) of any particular state and action during the simulation, and thus cannot fully take advantage of the feedback given by the simulation. In the future, we will investigate better ways to make better use of the information during the simulation in GPHH to further improve its effectiveness.

Multimedia transmission plays a dominant role in today's telecommunications, and one of the primary metrics is Quality of Service (QoS) which concentrates on the mobile devices and services without customers' experience. Besides, it is also necessary to reduce energy consumption of the service delivery. To optimize the trade-off between users' satisfying experience and energy efficiency, Quality of Experience (QoE) has become an important metric to be considered. However, QoE can be hardly measured in wireless network due to its unique communication characteristics, especially constrained energy. In this paper, we first present definitions of QoE with its essential characteristics in multimedia transmission. Then we discuss the existing approaches for achieving QoE-aware and energy-efficient video transmission from different perspectives such as resource allocation, traffic buffering and other scheduling strategies. Future challenges on the trade-off between QoE-aware and energy-efficient solutions are also discussed.

We present an adaptive tracking control scheme and a parameter identification method for complex-variable chaotic systems (CVCSs) with bounded disturbances and different situations of unknown parameters. Firstly, we propose the tracking control scheme that can guarantee adaptive tracking for two arbitrary bounded CVCSs, and can be generalized to full state hybrid projective synchronization (FSHPS). Secondly, we give the definition of linear independence for complex-variable functions and derive the sufficient and necessary condition that allow unknown parameters to converge to their true values. Finally, we evaluate the developed schemes in simulations by taking the chaotic complex Chen system as the response system, and the chaotic complex Lu system as reference signals with interference and random noise. The simulation results verify our theoretical analysis and demonstrate the effectiveness and robustness of the proposed schemes.

Chaotic secure communication has several advantages over conventional crypto-based communication due to its simplicity, diversity and robustness. In this paper, we first present difference function projective synchronization (DFPS) for complex chaotic systems. It extends the category of synchronization and enables the development of applications based on complex chaotic signals instead of real signals, thereby increasing both the security and robustness for chaotic communications. We derive the control law using the active control theory based on Lyapunov stability analysis. A novel communication scheme is further designed based on DFPS. Its main idea is chaotic masking in essence, but the transmitted signal is the derivative of the sum of the information signal and chaotic signal. As the difference functions are complex and arbitrary, they are more unpredictable than real scaling functions, and the operations of complex numbers are complicated, thus greatly reducing the possibility that an interceptor extracts the information from the transmitted signal. The communication system can transmit both analog signal and digital symbols with fast transmission speed and high security, especially with low bit-error rate and strong robustness to noise for digital symbols. The main ideas of the proposed method are veri?ed by the example of coupled complex Lorenz system used as both transmitter and receiver.

Induction motors are the most popular drive equipment in industrial sector. In this paper, we address bearing fault detection of motors with detection coils and least squares support vector machine (LSSVM). Firstly, a detection scheme is proposed, where 8 pre-fixed coils sense the rotor displacements. Then, the serially connected coil pairs are processed with infinite impulse response (IIR) filters. Lastly, a LSSVM based algorithm is proposed to detect bearing faults. Experiments are carried out with an oil pump. The results convince the validity of the proposed approach.

By analyzing the drivers' main consideration about how to choose a parking space, this paper is committed to path optimization of parking lot by modified Dijkstra algorithm. The main factors that impact drivers choosing optimal parking lot are driving distance, the number of vehicle moved along motorway, and the occupancy situations of free parking space. Finally, an example would be analyzed to verify the feasibility of the method.

This paper revisits the distributed suboptimal LQR control problem for continuous-time multi-agent systems. Based on the system's topological structure, we design the distributed controllers in which each subsystem can use the information of its state and its neighbors'. The multi-agent system are described by an identical decoupled dynamical equation and a coupled performance index. Associating with the given matrix G and the designed suboptimal feedback gain matrices, the general distributed suboptimal controllers are obtained. By using the stable eigenvectors of the Hamiltonian matrix, the asymptotical convergence of the modified ARE is proved for infinite-horizon systems under mild conditions. To make this result more persuasively, the consideration for infinite-time distributed systems is presented and the suboptimal controllers are obtained.

In this paper, the distributed optimal consensus algorithms for general linear multi-agent systems are considered. The dynamics for the N agents are independent and homogeneous. The quadratic cost consists of three terms, one corresponds to the importance of approaching the destination (consensus value), the second term corresponds to the importance of formation maintenance, and the third term is the total expended energy. Under the following three conditions, the graph is connected, the subsystem is controllable, and the weighted matrices of the cost function satisfy that one Riccati equation and one Lyapunov equation have the same positive definite solution, we can get the distributed optimal controller and the system can be consensusable. The solutions of this paper consist of some existing results as the special cases.

This paper investigates the distributed control problem for a set of identical decoupled multi-agent systems with a performance index coupling the behavior between the multi-agents. Based on the optimal centralized control theory, an existence condition for optimal distributed controller is firstly proposed. It shows that the globally optimal distributed controller is dependent on the structure of penalty matrix. Based on the solutions of two single linear quadratic regulator problems, a suboptimal distributed controller is also proposed. The design procedure is employed by applying the decomposition of the global discrete-time algebraic Riccati equations and robustness of local controllers. The stability condition is given in terms of the spectrum of a matrix representing the desired sparsity pattern of the distributed controller. The computation complexity and communication load for each subsystem is only dependent on the number of the maximum vertex degree of the communication graph.

Online product recommendations (OPRs) which include provider recommendations (PRs) and consumer reviews (CRs) are widely used in electrical environment to enhance customer loyalty. In this paper, the consumer shopping efficiency consists of screening efficiency and evaluation efficiency while the products are also classified as search products and experience products. We extend a rich theoretical framework which explains the mechanisms how quality of OPRs influences consumers' shopping efficiency and how product type plays the moderating role. Using a survey research with 182 participants, our findings provide strong support for the proposed model. The empirical analyses reveal that higher quality of OPRs is associated with higher consumer shopping efficiency which leads to higher consumer loyalty. What's more, the impacts of quality of PRs on screening efficiency are stronger for experience products than search products. However, the moderating effect of product type on the relationship between quality of CRs and evaluation efficiency is not significant.

In this paper, the distributed control of the LQR problem for continuous-time multi-agent systems is considered. Based on the centralized optimal control, we prove that the solution of the ARE is the maximal solution of the algebraic Riccati inequality. The algebraic relations of the solutions of the AREs for different weighted matrices are shown and the two distributed controllers are designed: the fully distributed one and the interconnected distributed one. They can give the upper bounds and the lower bounds of the centralized optimal cost function. The distributed optimal closed-loop systems are also asymptotic stable.

The Artificial Bee Colony (ABC) algorithm is a new swarm optimization algorithm with good numerical functions optimization results. In order to enhance the performance ability of ABC algorithm, a hybrid ABC (HAB) algorithm is presented where swarming behavior of bacterial foraging optimization algorithm is introduced into the ABC algorithm to do local search. The performance of the proposed method is examined on well-known six numerical benchmark functions and the obtained results are compared with basic ABC algorithm and BFO algorithm. The experimental results show that the proposed approach is very effective method for solving numeric benchmark functions and successful in terms of solution quality and convergence to the global optimum, especially on the multimodal functions.

Software plagiarism, an act of illegally copying others' code, has become a serious concern for honest software companies and the open source community. In this paper, we propose LoPD, a program logic based approach to software plagiarism detection. Instead of directly comparing the similarity between two programs, LoPD searches for any dissimilarity between two programs by finding an input that will cause these two programs to behave differently, either with different output states or with semantically different execution paths. As long as we can find one dissimilarity, the programs are semantically different, but if we cannot find any dissimilarity, it is likely a plagiarism case. We leverage symbolic execution and weakest precondition reasoning to capture the semantics of execution paths and to find path dissimilarities. LoPD is more resilient to current automatic obfuscation techniques, compared to the existing detection mechanisms. In addition, since LoPD is a formal program semantics-based method, it can provide a guarantee of resilience against many known obfuscation attacks. Our evaluation results indicate that LoPD is both effective and efficient in detecting software plagiarism.

Under the condition of low signal to clutter ratio, track-before-detect algorithm (TBD) based on dynamic programming (DP) can detect ground dim SAR moving targets. However, target diffusion and strong clutter in the background may result in high false alarm rate and low detection rate in the accumulation processing of TBD, so this paper proposes the state-weighted DP-TBD. The method utilizes the characteristic that displacement of the moving target is nearly equal between adjacent frames. When states in the kth frame are accumulated, we use accumulation information between the (k-1)th and (k-2)th to add a weight value to merit function in search window of the (k-1)th so that the target can accumulate along the real trajectory better. After accumulation, we can obtain the detection result through morphological processing. Simulation results show that the method in this paper can improve the detection performance in low signal-to-clutter ratio.

The design and optimization of the distributed controller for the multi-agent scalar systems are considered in this paper. Based on the system topological structure, the distributed controller for discrete-time system is designed. Using the “averaged” optimization approach, the optimal feedback gain scaling factors are presented and the distributed suboptimal controllers are obtained. The uniqueness of the suboptimal distributed controller is showed and an illustrative example is also given.

In this paper, the distributed suboptimal LQR control problem for multi-agent systems is considered. The distributed controllers which only use the information of its state and its neighbors' are designed based on the topological structure of the system. The suboptimal feedback gain matrices are presented and the distributed suboptimal controllers are obtained by using the “averaged” optimization approach. The asymptotically convergence of the modified Riccati equation is proved for the infinite-time systems. Accordingly, the infinite-time distributed suboptimal controllers are presented.

This paper investigates the distributed LQR problem for the scalar identical discrete-time multiple systems. Based on the optimal centralized control, the existence condition for the distributed optimal control is first proposed. It reveals that the globally optimal distributed controller is dependent on the structure of the penalty matrix related to the state. When the proposed condition cannot holds, an suboptimal design method with the aid of the decomposition of the discrete algebraic Riccati equations and robustness of the local controllers is also proposed by using the Riccati equation decomposition approach.

As one of the most popular mobile platforms, the Android system implements an install-time permission mechanism to provide users with an opportunity to deny potential risky permissions requested by an application. In order for both users and application vendors to make informed decisions, we designed and built Permlyzer, a general-purpose framework to automatically analyze the uses of requested permissions in Android applications. Permlyzer leverages the combination of runtime analysis and static examination to perform an accurate and in-depth analysis. The call stack-based analysis in Permlyzer can provide fine-grained information of the permission uses from various aspects include location, cause and purpose. More importantly, Permlyzer can automatically explore the functionality of an application and analyze the permission uses. Our evaluation using 51 malware/spyware families and over 110,000 Android applications demonstrates that Permlyzer can provide detailed permission use analysis and discover the characteristics of the permission uses in both benign and malicious applications.

This paper studies the distributed optimal control design problem for the continuous-time systems. We consider an identical decoupled dynamical equation of the multi-agent system and a coupled performance index which is described through the Laplacian matrix. Using the LQR method, the optimal feedback gain matrix is got. It is proved that the optimal feedback matrix corresponds to a complete undirected graph for the Laplacian matrix in the cost function. We aim to get the distributed control that only uses the information of its state and the states of its neighbors. Then the existence conditions of the distributed optimal control for special cost function are given and the asymptotic stability of the system is discussed. It is also showed that optimal feedback matrix corresponds to a complete undirected graph when the conditions don't satisfy. Examples are given to show the efficiency of the proposed results.

JavaScript based attacks have been reported as the top Internet security threats in recent years. Since most of the Internet users rely on anti-virus software to protect themselves from malicious JavaScript code, attackers exploit JavaScript obfuscation techniques to evade the detection of anti-virus software. To better understand the obfuscation techniques adopted by malicious JavaScript code, we conduct a measurement study. We first categorize observed JavaScript obfuscation techniques. Then we conduct a statistic analysis on the usage of different categories of obfuscation techniques in real-world malicious JavaScript samples. We also study the detection effectiveness of 20 most popular anti-virus software against obfuscation techniques. Based on the results, we analyze the cause of the popularity of obfuscation in malicious JavaScript code; the reason behind the choice of obfuscation techniques and the difference between benign obfuscation and malicious obfuscation. Moreover, we also provide suggestions for designing effective obfuscation detection approaches in future.

In this paper, a novel image quality assessment, called Structure Analysis Image Quality Measurement (SAIM), is proposed by evaluating the difference of the distorted image from the reference image on three aspects: the average luminance, edge intensity and orientation. It is sensitive to edge change, which consists well with the perceptual characteristics of human vision. The experiment results demonstrate its superiority over the most popular metrics, such as PSNR and MSSIM, on “LIVE Image Quality Assessment Database”. And furthermore, SAIM can find a wide application in various fields, because of its capability of estimating the similarity between any two signals.

The contour matching is a hot issue and a difficult problem of image processing, and the accuracy and the efficient of the algorithms are the most two critical factors. To obtain the most likely matching part,a curve matching algorithm that based on the multiscale space is proposed. Firstly the line length and the angle are calculated as the feature vectors, secondly by setting different angle threshold to reduce the number of feature points, lastly the contour matching is measured by hausdorff distance. Experiments show that the algorithm is effective and stable.

The basic situation was introduced firstly on the wheel double axis fatigue (abbreviated to WDAF) testing. Then the basic process to carry out the finite element analysis and the fatigue life prediction on the WDAF testing were introduced briefly. Subsequently, a typical wheel was taken as an example to implement the finite element fatigue life prediction for the WDAF testing of the aluminum alloy wheel. Finally, an initial criteria was proposed for the WDAF life prediction based on the analytical results from FEA for several wheels.

The blind source separation (BSS) using a two-stage sparse representation approach is discussed in this paper. The first challenging task of this approach is how to estimate the unknown mixing matrix precisely, to solve this problem, the algorithm based on linear membership function is proposed. And then, we proposed the optimization algorithm based on integral to get the max value of the function. Compared with the classical methods, this proposed method can estimate the unknown mixing matrix faster and more precisely. Meanwhile for the good locality of the function, the required key condition on sparsity of the sources can be considerably relaxed and the noise problem which the classical method can't solve can be solved well using the method proposed. Another contribution described in this paper is the discussion of the impact of noise on the estimating the mixing matrix. Given the impact of noise, we set weights to put more emphasis on the more reliable data. Several experiments involving speech signals show the effectiveness and efficiency of this method.