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In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period post-disaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a real-world instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.

With the increasing capacity of wind turbines, the importance of the yaw system has gradually become more prominent. The supervisory control and data acquisition (SCADA) system of XEMC Windpower Co., Ltd. is used in this paper to analyze the yaw data of the No. 5 wind turbine of Baozhong Mountain Wind Farm in Hunan Province. In order to evaluate the power generation and the damage equivalent load (DEL) of the yaw bearing during the yaw process, an economic model predictive control (EMPC) yaw strategy based on light detection and ranging (LIDAR) was proposed. EMPC takes the yaw error and wind speed as the disturbance of the cost function (CF) at the same time by establishing the yaw bearing DEL look-up table in advance. Discrete adaptive yaw speed control set is proposed to adapt to different wind conditions sets. Finally, the effectiveness of EMPC is verified using the model of XE112-2000 wind turbine in simulation software Bladed.

With the development of manufacturing industry, the high precision size measurement for the holes on the surface of mechanical parts is required. A method for measuring size of the hole based on double projection is proposed in this paper. The CCD camera separately collects grating images which projected onto the part surface in two directions. The phase-shift method is used to calculate the phase of grating images in two directions, and the reconstructed three-dimensional shapes in the two directions are merged to measure the hole dimension. Experiment results show that the measurement accuracy of the thickness is 0.04 mm, and the hole size can reach to 0.1 mm.

The increase in the scale of distribution networks significantly reduces the efficiency of intelligent planning for distributed generation (DG). To improve the efficiency of intelligent DG planning and avoid the impact of uncertainty concerning renewable energy on it, this paper proposes a sensitivity index for the bus-embedded multi-objective estimation of distribution algorithm (MEDA) based on the semi-invariant probabilistic power flow approach to achieve an optimal solution. The sensitivity indices of the buses are comprehensively enabled to obtain a new index and determine their sensitivity sequences based on the area grey incidence decision-making method. Subsequently, according to the uncertainty of wind turbine generators and photovoltaics, a probability model is established, and the semi-invariant method is used to solve for the probabilistic power flow according to a correlation model. Finally, the sensitivity of the proposed bus-embedded MEDA to enhancing the efficiency of the solution is examined. The optimal DG allocation scheme is obtained with the goal of achieving the lowest total cost in the planning year. Finally, the feasibility and effectiveness of the proposed model and method are verified using simulations of the IEEE 33-bus, IEEE 69-bus, and IEEE 118-bus test systems.

Two-terminalt and tandem solar-cells have a high efficiency of power conversion. One of their main limitations is the operating current density as the two-terminal tandem solar-cell is equivalent to electrically connected series subcells. Increasing the top absorber layer’s thickness will lead to an increase in the top subcell current and a decrease in the bottom subcell’s current. The subcell with the minimum current forces the tandem cell to operate at its value, limiting the overall performance. In this paper, a proposed solution for such a problem is introduced using a bottom subcell consisting of germanium-telluride (GeTe), which gives a high current and matches the top subcell at a thicker absorber layer. A proposal of three different tandem cells with perovskite (MAPbI3)/CIGS, perovskite (MAPbI3)/GeTe, and perovskite (MAPbI3-xClx)/GeTe have been presented. The proposed perovskite (MAPbI3)/CIGS has an efficiency of 30.52%, whereas the replacement of the CIGS bottom subcell by GeTe led to a significant enhancement of the efficiency to reach 35.9%. High efficiency of 41.7% is obtained by replacing the perovskite (MAPbI3) top subcell with perovskite (MAPbI3-xClx). A modified numerical algorithm is proposed to obtain the optimum thickness of the top subcell to achieve higher power-conversion efficiency. The performance evaluation and simulation of the designed tandem cells were carried out using SCAPS-1D. The temperature effects on the proposed cells have been encountered in simulation. The results show that the proposed tandem solar-cells have comparable performance and higher efficiencies relative to the published works.

In the future, the population’s aging problem will become increasingly serious. For medical care, action-assisted vehicles are one of the solutions. A wheelchair is a common mobility assistance equipment, and the safety of the user is an important issue. For the ordinary wheelchairs, most of them use a fixed hand brake or disk brake to allow the users to stop the wheelchair. However, improper use of brakes tends to cause slippage, and there is no concept of the anti-lock braking system (ABS) for the wheelchair. Therefore, this paper applies this idea to intelligent wheelchairs with the aim of enhancing the safety of users in wheelchairs. The ABS architecture includes the core algorithms of the adaptive fuzzy-neural inference system and the friction coefficient estimation system. The friction coefficient estimation system uses a particle filter to quickly adapt to a non-linear state and unknown environment. The system provides more accurate braking control for the ABS according to the range of the friction coefficient based on the pavement type. In ABS, it uses the gyroscope to detect the acceleration and wheelchair angle information and then calculates the parameters of the wheelchair. The user clicks the stop command on the wheelchair to activate the brake system to achieve a simple and efficient mode of operation. It can efficiently reduce the braking time and braking distance and enhance the riding safety of wheelchairs.

This paper proposes a novel deep learning architecture for estimating the remaining useful lifetime (RUL) of industrial components, which solely relies on the recently developed transformer architectures. The RUL estimation resorts to analysing degradation patterns within multivariate time series signals. Hence, we propose a novel shared temporal attention block that allows detecting RUL patterns with the progress of time. Furthermore, we develop a split-feature attention block that enables attending to features from different sensor channels. The proposed shared temporal attention layer in the encoder fulfils the goal of attending to temporal degradation patterns in the individual sensor signals before creating a shared correlation across the feature range. We develop two transformer architectures that are specifically designed to operate with multivariate time series data based on these novel attention blocks. We apply the architectures to the well known C-MAPSS benchmark dataset and provide various hyperparameter studies to analyse their impact on the performance. In addition, we provide a thorough comparison with recently presented state-of-the-art approaches and show that the proposed transformer architectures outperform the existing methods by a considerable margin. CA extern

With the explosive growth of information and communication technology and its services, some popular Websites currently generate an enormous amount of Internet traffic. A content delivery network (CDN) would then become imperative for supporting such services efficiently. In this paper, we propose joint optimizing approaches for replica server placement, content caching in selected servers, and content request load assignment among the servers, aiming to minimize the ratio of unserved content request load when the network resources and server capacity are both limited. For this, we develop a mixed integer linear programming (MILP) optimization model. To mitigate the computational complexity of the MILP model, we further decompose the optimization problem into three sub-problems, including: 1) choosing the replica server nodes optimally; 2) deciding the content items to be cached in the replica servers; and 3) allocating the content request loads from users onto different servers. For these sub-problems, we develop corresponding heuristic algorithms and show that the proposed approach is not only efficient but also performs very close to the MILP model. We also find that a number of system limitations, such as different numbers of replica servers placed, link capacity, server processing capacity, and server storage capacity, jointly affect the performance of the CDN. Saturation trends are observed on the performance, which indicate that as long as sufficient resources have already been provided, augmenting the resources further may only lead to marginal additional performance improvement.

In view of the instability and complexity of passenger flow change in urban rail transit, it is the key and the difficult point to use the prediction model to get more accurate number of short-term passenger flow. In view of this, this study proposes a hybrid forecasting model W-KELM, which combines wavelet transform (WT) and kernel extreme learning machine (KELM). The main idea of the model is to decompose passenger flow data into high-frequency and low-frequency sequences through WT and Mallat algorithm, and then use KELM approach to learn and forecast signals with different frequencies. Finally, different prediction sequences are reconstructed using WT. Through a case study of Beijing metro, we test the effectiveness of the model. The result shows that the W-KELM model has good prediction accuracy. In addition, this paper compare the prediction result of W-KELM model with those of BP neural network model, the single KELM method, and the hybrid model based on WT and BP neural network. It shows that the W-KELM model can effectively improve the prediction accuracy. Thus, providing a more accurate and real situation for monitoring and early warning of urban rail transit.

The use of Markov random fields (MRFs) is a common approach for performing image segmentation, where the problem is modeled using MRFs that incorporate priors on neighborhood nodes to allow for efficient Maximum a Posteriori inference. These local MRF models often result in smoothed segmentation boundaries, since they penalize the assignment of different labels to neighboring pixels and are limited in the use of long-range interactions. Although recent work on fully connected random fields and deep random fields has shown to be very promising in addressing these issues, both streams of approaches face certain limitations, which could affect inference performance and computational tractability. In this paper, we introduce the concept of deep randomly connected conditional random fields DRCRF, which fuse fully-connected random fields and deep random fields together to obtain benefits from long-range interactions while allowing for efficient inference using arbitrary potential functions. Leveraging random graph theory, the concept of stochastic cliques is incorporated into a deep CRF structure to take better advantage of long-range interactions while maintaining computational tractability. The experimental results demonstrate that the proposed DRCRF framework outperforms existing fully connected CRF frameworks and provides results comparable to the principled deep random field framework, which is among the state of the art in random field frameworks for image segmentation.

Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.

This research paper proposes a multi-band frequency selective surface (FSS) director using the aperture interdigital technique. The FSS unit cell has been designed based on a basic bandpass filter (BPF) connected with an interdigital structure. With the proposed structure, the FSS unit cell size can be reduced from $\lambda $ /2 to $\lambda $ /8 caused by the slow-wave effect of interdigital capacitance loaded at the end of the transmission line in the unit cell structure. Moreover, the capacitance loaded at the end of the transmission line can control the second and the third resonance frequencies to resonate as required. The unit cell has been designed at the fundamental frequency of 1.8 GHz and the controlled second and third resonance frequencies of 3.7 GHz and 5.2 GHz, respectively. The operating frequencies of the proposed FSS unit cell cover LTE band (0-1.9 GHz), Wi-MAX band (3.22GHz-4.7GHz), and WLAN band (5.15 GHz-5.95 GHz). The size of the unit cell is very compact which is 11.53 mm $\times10.77$ mm. The designed unit cells are then connected as an array of $8\times 8$ , resulting in an overall size of 92.31 mm $\times86.16$ mm. This array has been used as a director for dipole antennas designed at the frequencies of 1.8 GHz, 2.45 GHz, 3.7 GHz, and 5.2 GHz. From the simulation, the dipole antennas with the proposed FSS director have directional gains of 4.52 dB at 1.8 GHz, 3.83 dB at 3.7 GHz, and 3.29 dB at 5.2 GHz, respectively. Measurement results show the directional gains of 3.67 dB at 1.8 GHz, 3.16 at 3.7 GHz, and 3.42 dB at 5.2 GHz, respectively. The proposed FSS director has properties of multi-band operation with a compact size that can be developed for antenna covers and radomes of multi-band wireless communications.

Understanding how careers evolve is very important for us, so we can make better professional decisions; for companies, so they can better plan their internal organization career progressions; and also for the governments, so they can better plan their labour market and economic policies. In this paper, we model a career progression as a graph, called MCar, in which nodes represent occupations and edges correspond to the flow of professionals among occupations. Based on MCar, we introduce some key concepts to investigate career evolution, namely, career boundary, occupational poles, and occupational islands. Finally, we propose the use of community detection techniques on a real database with millions of professional backgrounds to objectively identify career boundaries in Brazil and to study the topologies of the resultant graph. The results obtained provide a quantitative basis for career models, showing, for instance, the presence of hubs suggesting that less regular careers are common.

Computer-aided detection and diagnosis in ECG signals for heart diseases are gaining increasing attention. However, developing and selecting the highly performing diagnostic model suitable for clinical implications is still challenging. In this paper, we proposed a combined network of convolutional neural network (CNN) and Recurrent Neural Network (RNN), designed for the classification of ECG heart signals for diagnostic purpose. The proposed network consists of 2 convolutional layers with 5 × 5 kernels and ReLU activations, followed by 4 residual blocks, 2 bidirectional long short-term memory (biLSTM) layers, as well as 2 fully connected layers. Each residual block involved the structure of a Squeeze-and-Excitation Network (SENet) with lightweight property to recalibrate the feature map of the network. The last dense layer has 5 outputs, equivalent to the classes considered: non-ectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. To train and evaluate the combined CNN and RNN, we transferred the knowledge acquired on beat classification tasks in 2017 PhysioNet/CinC Challenge to that in PhysionNet's MIT-BIH dataset. The developed network achieved a recognition sensitivity of 95.90%, accuracy = 95.90% and specificity = 96.34% with classification time of single sample = 6.23 s in detecting 5 ECG classes. A comparative analysis proved the high performance of the proposed combined CNN and RNN against previous methods, demonstrating the potential of our proposed network in the analysis of beat patterns. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate cardiac health with the highest precision.

The use of supplementary controllers for mitigating subsynchronous interaction (SSI) in series compensated DFIG-based wind farms is quite promising due to high effectiveness and low cost. Implementation of such a controller requires effective communications between individual turbines and the wind farm controller, where the control performance is very much affected by the communication delays involved. This paper delivers the first detailed analysis on the impact of communication delays on SSI damping controller performance. A novel algorithm is proposed to calculate the stability delay margin (SDM) of the closed-loop system based on Rekasius Substitution and Guardian Map Theorem with the advantage of reduced computational burden particularly for high-order systems. Based on the proposed algorithm, the impacts of wind farm operating conditions and turbine control parameters on the SDM are investigated. To strengthen the SSI damping controller performance against communication delays, a Smith predictor scheme is also developed. The effectiveness of the proposed delay analysis framework and Smith predictor scheme based delay immune controller is validated through Electromagnetic Transient (EMT) simulations on a realistic test system with multiple series capacitor compensated lines considering various operation conditions.

Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.

Coded caching is widely regarded as an important tool to reduce pressure on the data transmission during the peak traffic times in heterogeneous wireless networks. In this paper, we will provide two concatenating constructions of coded caching schemes based on the schemes derived by Shangguan et al. in 2018 and Yan et al. in 2017, respectively. Then we show that our new schemes have good performance in reducing packet number. Furthermore, in some case, we present that our schemes have better performances on user number, memory ratio, packet number and transmission rate than the schemes obtained by Chittoor et al. in 2019 and Krishnan in 2018.

Non-orthogonal multiple access (NOMA) has been recognized as an essential technology for beyond fifth-generation (beyond-5G) wireless networks to increase connectivity, spectral efficiency, cell-edge throughput, and user fairness. In this paper, we propose two subcarrier-user assignment algorithms (SUAAs) for the downlink NOMA system to enhance the spectral efficiency, the fairness, the data rate of weak users, and the outage probability. The assignment order of the first proposed SUAA is based on the worst subcarrier first (WSF) to avoid selecting a user with the worst channel gain with any subcarrier and called (WSF-SUAA). On the other hand, the second proposed SUAA is based on spectral efficiency maximization (SEM) and called (SEM-SUAA), but requires exhaustive search. The assignment process of both algorithms is based on making the channel gain of the selected paired users per subcarrier as high as possible to increase the data rate of each user. Besides, the assignment process of strong users for all subcarriers is performed before the assignment process of weak users to increase the total system sum-rate. It is exposed throughout the simulation that the two proposed SUAAs can attain significant improvement in the total spectral efficiency, weak user data rate, outage probability, and user fairness compared to the existing algorithms. While the performances of the two proposed SUAAs are convergent, the computational complexity of WSF-SUAA is significantly lower than that of SEM-SUAA and slightly higher than that of the existing algorithms.

Human behaviors are known to spread through social contact. The diffusion process on social networks has also been leveraged to understand the spread of undesirable contagion. The contagion of malicious or even criminal behaviors in online social networks is just beginning to attract attention. Here, we study the social contagion problem of cheating behavior found in the massively multiplayer online role-playing game (MMORPG) that provides a lifelike environment with rich and realistic user interactions. Because cheating users boast an abnormal thus conspicuous degree of success, it has a strong chance of being noticed by their friends and leading them to cheat themselves. To detect and prevent cheating, it is beneficial to understand this dynamic as a contagion problem. In this paper, we show the existence of the contagion of cheating. We then explore various possible social reinforcement mechanisms after introducing several factors to quantify the effect of social reinforcement on the contagion and analyze the dynamics of bot diffusion in an extensive user interaction log from a major MMORPG.

In this paper, we describe improvements to the particle swarm optimizer (PSO) made by the inclusion of an unscented Kalman filter to guide particle motion. We show how this method increases the speed of convergence, and reduces the likelihood of premature convergence, increasing the overall accuracy of optimization. We demonstrate the effectiveness of the unscented Kalman filter PSO by comparing it with the original PSO algorithm and its variants designed to improve the performance. The PSOs were tested firstly on a number of common synthetic benchmarking functions and secondly applied to a practical three-dimensional image registration problem. The proposed methods displayed better performances for 4 out of 8 benchmark functions and reduced the target registration errors by at least 2mm when registering down-sampled benchmark brain images. They also demonstrated an ability to align images featuring motion-related artifacts which all other methods failed to register. These new PSO methods provide a novel, efficient mechanism to integrate prior knowledge into each iteration of the optimization process, which can enhance the accuracy and speed of convergence in the application of medical image registration.

In this paper, an efficient digital predistortion (DPD) architecture utilizing over-the-air (OTA) feedback is presented to linearize phased array transmitters. It places one additional observation antenna (OA) in the far field to capture the combined signal of the array outputs and uses the combined signal to build a nonlinear model that describes the joint nonlinear distortions of all the power amplifiers (PAs) in the phased array. Then, a corresponding DPD model is extracted by the typical indirect learning method to linearize the phased array. After introducing this DPD architecture, the relationship between the OA position and the performance of the extracted DPD model is specifically explored, and two significant results are found. First, when the OA is perfectly colocated in the main beam direction, the DPD coefficients extracted with an arbitrary steering angle for the phased array are also applicable to any other steering angle. Second, when the OA is not perfectly placed in the main beam direction, the linearization performance for the extracted DPD coefficients will degrade as the OA placement mismatch increases, and the phased array has an optimum steering angle at which the OA placement mismatch has a minimal influence on the DPD performance. Based on the above two results, a new DPD training strategy is proposed, which extracts only one set of DPD coefficients by making the phased array point at the optimum steering angle. The advantages of the proposed strategy are that the OA can be placed in a relatively wide range of positions and that the extracted coefficients will be applicable to any other steering angle. The two results and the proposed DPD training strategy are verified by simulations, which are conducted on a $1\times 4$ uniform linear array (ULA) and a $4\times 4$ uniform rectangular array (URA).

Frame arrival detection is the first crucial task for a digital communication receiver. In this paper, we propose a new frame arrival detection method based on the concept of matched filtering. The proposed training sequence consists of three repeated subsequences, each including a few repeated segments and exhibiting high sparsity in frequency domain. The proposed detection method includes the following two stages. The first matched filtering stage employs the subsequence as the filter coefficients, which matches the frequency sparsity of the received subsequence and could greatly improve the output signal to noise ratio. The second stage adopts delayed autocorrelation on the filtered signal to detect the presence of training sequence. It is demonstrated that the proposed method could outperform the conventional methods in terms of both missed detection and false alarm probability. We derive the theoretical analysis for both missed detection and false alarm performance. We further extend our performance analysis and numerical evaluation to the system with carrier frequency offset (CFO). The results illustrate accuracy of the analytical results and robustness of the proposed method against practical levels of CFO.

N-1 static security analysis (SSA) is an important method for power system stability analysis that requires solving N alternating-current power flows (ACPF) for a system with N elements to obtain strictly accurate results. Past researches have shown the potential of accelerating these calculations using an iterative solver with graphics processing unit (GPU). This paper proposes a GPU-based N-1 SSA algorithm with the preconditioned conjugate gradient (PCG) method. First, a shared preconditioner is selected to accelerate preprocessing of the iterative method for fast decoupled power flow (FDPF) in N-1 SSA. Second, it proposes a GPU-based batch-PCG solver, which packages a massive number of PCG subtasks into a large-scale problem to achieve a higher degree of parallelism and better coalesced memory accesses. Finally, the paper presents a novel GPU-accelerated batch-PCG solution for N-1 SSA. Case studies on a practical 10828-bus system show that the GPU-based N-1 SSA algorithm with the batch-PCG solver is 4.90 times faster than a sequential algorithm on an 8-core CPU. This demonstrates the potential of the GPU-based high-performance SSA solution with the PCG method under a batch framework.

In this paper, the design of reduced-order and full-order interval observers for discrete-time system is investigated. First, the original system is transformed into a descriptor system with the ability to estimate the unknown noise of sensors. Next, a reduced-order interval observer design method for descriptor system is presented. The state matrix of the system is obtained by linear transformation, and a Sylvester equation is constructed to find the appropriate observer gain matrix. Then, an existing design method of full-order interval observer is introduced in this paper. Finally, a simulation example is given to demonstrate that the reduced-order interval observer has more accurate interval estimation results than the full-order interval observer.

Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach.

Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

Directional modulation (DM) as a physical layer security technique has been studied from many different aspects recently. Normally all existing designs based on antenna arrays lead to varying weight coefficients for different antennas and for different signal symbols, which poses a particular challenge for feed circuits design in analogue implementation. In this paper, to reduce the implementation complexity, a constant magnitude constraint is proposed for the first time, and the resultant non-convex constraint can be modified to a convex form so that the problem can be solved conveniently by existing convex optimisation toolboxes. Design examples are provided to show the effectiveness of the proposed design.

Visual question answering is a challenging multimodal task, which has received increasing attention in recent years. One key solution to visual question answering is how to fuse the visual and textual features extracted from the image and questions, and thus, we can comprehensively employ the information from both modals and deliver correct answers. Bilinear pooling has been a powerful fusion approach owing to its exhausting interaction of each element of two modals, but its overuse of parameters limits its practical application. In this paper, we aim to retain the advantages of bilinear pooling for feature interaction and propose a novel multimodal feature fusion approach named multimodal local perception bilinear (MLPB) pooling, which can retain the second-order interactions between visual and textual features with limited learning parameters. To be specific, the MLPB utilizes local perception mechanism, which transforms the bilinear pooling between two high-dimensional raw features into multiple low-dimensional part features. To further reduce the computational cost, we propose to share the learning parameters of each local bilinear pooling. In this way, MLPB can achieve the complex interactions of the bilinear pooling without taking up too much computational resource. Extensive experiments show that the proposed method can achieve competitive or better performance than the state of the art.

This paper investigates the adaptive fault-tolerant control problem for a class of nonlinear systems with unknown control directions and uncertain parameters. To solve this control problem, the homogeneous domination method based on backstepping and fault-tolerant control scheme is applied to the uncertain nonlinear systems. First, a homogeneous domination-based adaptive control scheme using Nussbaum-type function is designed. Then, a fault-tolerant controller is proposed to deal with the actuator faults. The adaptive controller ensures that the closed-loop system is bounded, and all the states of the system asymptotically converge to 0. Finally, a simulation example is applied to demonstrate the effectiveness of the new designed adaptive fault-tolerant control scheme.

Non-orthogonal multiple access (NOMA) is a promising spectrally-efficient technology to satisfy the massive data requirement of the next-generation wireless communication networks. In this paper, we consider cooperative non-orthogonal multiple access (CNOMA) networks with relaying user selection, where a two-stage user selection scheme is adopted to optimize the received signal-to-noise ratio (SNR) of the weak user. We derive analytical expressions of the outage probability of the CNOMA users under two different types of channel estimation errors. Afterwards, we investigate the asymptotic characteristics the outage probability of the CNOMA systems in the high SNR regimes. Our results show that for the case of constant variance of the channel estimation error, the outage probability of each user is saturated and cannot be improved by increasing the transmit power of the access point. In contrast, there is no performance bottleneck of the outage probability for all the users when the variance of the channel estimation errors decease with SNR, whilst fully diversity gain is achieved by the weak user.

Although hyperspectral remote sensing images have rich spectral features, for small samples of remote sensing images, feature selection, feature mining, and feature integration are very important. A single model is difficult to apply to multiple tasks such as feature selection, feature mining, and feature integration during training, resulting in poor classification results for small sample classification of hyperspectral images. To improve the classification of small samples, a sequential joint deep learning algorithm is proposed in this paper. (In this algorithm, the deep features of multiscale convolution under an attention mechanism are integrated by using Bidirectional Long Short-Term Memory(Bi-LSTM) and AML.) First, we used principal component analysis (PCA) to reduce the dimensionality of the hyperspectral data and retain their key features. Second, the model uses an integrated attention mechanism to distribute the probability weight of the key input feature. Third, the model uses multiscale convolution to mine features after the distribution weight to obtain deep features. Fourth, the model uses bidirectional long short-term memory (Bi-LSTM) to integrate the convolution results at different scales. Finally, the softmax classifier is used to complete the classification of multiclass hyperspectral remote sensing images. Experiments were carried out on three public hyperspectral data sets, and the results proved that our proposed AML algorithm is effective, thus demonstrating powerful performance in the prediction of hyperspectral images (HSIs) of small samples.

In this paper, the capacitated location-routing problem with fuzzy demands (CLRP-FD) is considered, which simultaneously solves two problems: locating the facilities and designing the vehicle routes among the established facilities and customers. In the CLRP-FD, the capacities of the employed vehicles and established facilities cannot exceed, and the demands of the customers are assumed to be triangular fuzzy variables. To model the CLRP-FD, a fuzzy chance constrained programming approach is designed using fuzzy credibility theory. To solve this problem, a hybrid particle swarm optimization (HPSO) algorithm, which includes a stochastic simulation and a local search strategy based on the variable neighborhood search algorithm, is proposed. Finally, the influence of the value of the dispatcher preference index ( DPI ) on the total distribution cost is analyzed by conducting numerical experiments. To evaluate the efficiency of the proposed HPSO and the performance of the CLRP-FD model, the results obtained using the HPSO were compared with their corresponding lower bound provided using the CPLEX solver. Moreover, we also evaluated the performance of HPSO through computational experiments on some well-known benchmark CLRP instances. The numerical results show that the proposed HPSO is competitive, and can give a satisfactory solution in a reasonable amount of time.

Next basket recommendation with consideration of user sequential shopping behaviors plays a significant role in E-commerce to improve the user experience and service quality. Recently, recurrent neural networks (RNNs), especially attention-based RNN, have been widely adopted in the next basket recommendation. However, existing fixed attention mechanisms are not designed to model the dynamic and diverse characteristics of user appetites. In this paper, we propose an inter-basket and intra-basket adaptive attention network (IIAAN) for the next basket recommendation. Specifically, the inter-basket adaptive attention acts on all historical user baskets to model user's diverse long-term preferences, while the intra-basket adaptive attention is designed to act on item-level in the most recent basket to model user's dynamic and different short-term preferences. Then, we further integrate inter-basket and intra-basket adaptive attentions together to improve recommendation effectiveness. Finally, we evaluate the proposed model IIAAN using three real-world datasets from various E-commerce platforms. Our experimental results show that our model IIAAN significantly outperforms the state-of-the-art approaches for the next basket recommendation.

Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.

With the popularity of Android smartphones, malicious applications targeted Android platform have explosively increased. Proposing effective Android malware detection method for preventing the spread of malware has become an emerging issue. Various features extracted through static and dynamic analysis in conjunction with machine learning algorithm have been the mainstream in large-scale malware identification. In general, static analysis becomes invalid in detecting applications which adopt sophisticated obfuscation techniques like encryption or dynamic code loading. However, dynamic analysis is suitable to deal with these evasion techniques. In this paper, we propose an effective dynamic analysis framework, called EnDroid, in the aim of implementing highly precise malware detection based on multiple types of dynamic behavior features. These features cover system-level behavior trace and common application-level malicious behaviors like personal information stealing, premium service subscription, and malicious service communication. In addition, EnDroid adopts feature selection algorithm to remove noisy or irrelevant features and extracts critical behavior features. Extracting behavior features through runtime monitor, EnDroid is able to distinguish malicious from benign applications with ensemble learning algorithm. Through experiments, we prove the effectiveness of EnDroid on two datasets. Furthermore, we find Stacking achieves the best classification performance and is promising in Android malware detection.

In this paper, a novel planar active artificial magnetic conductor (AAMC) frequency-reconfigurable antenna printed on an FR4 substrate is proposed. Its AAMC metal pattern in a unit cell consists of a compound-five-ring nesting (CFRN) structure. It is loaded with only one varactor diode connected to the ground plane by a metallic via. In comparison to the conventional single-circle-ring AMC, the dimension of the CFRN-AMC has been reduced by about 44% along with a lower reflection loss. The frequency-reconfigurable antenna is constructed by placing a triangle-shaped dipole antenna operating at 1.8 GHz over the proposed active CFRN-AMC ground plane consisted with 5×5 periodic unit cells. The prototype of the CFRN-AMC-backed antenna was fabricated and measured. The measured results agree well with the simulated data. It is found that by tuning the capacitance applied on the CFRN-AMC from 2.2 to 0.5 pF, the frequency band of the AMC-based antenna has been varied in the range of 1.51 through 2.12 GHz along with the highest radiation gain of 5.37 dBi at 1.74 GHz. This gain is about 3.16 dB higher than that of the antenna without using the AMC. In addition, the presented CFRN-AMC-based antenna remains a very low profile, since the separation distance between the antenna and the AMC plane is only 2 mm or 0.012λ, where the λ denotes the wavelength at 1.8 GHz in free space.

Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hence, a more accurate and effective wind power forecasting method is still required. In this paper, a new forecasting method is proposed by combining stacked auto-encoders (SAE) and the back propagation (BP) algorithm. First, an SAE with three hidden layers is designed to extract the characteristics from the reference data sequence, and the subsequent loss function is used in the pre-training process to obtain the optimal initial connection weights of the deep network. Second, after adding one output layer to the stacked auto encoders, the BP algorithm is used to fine tune the weights of the whole network. To achieve the best network architecture, the particle swarm optimization is adopted to decide the number of neurons of the hidden layer and the learning rate of each auto encoder. Experimental results show that, for short-term wind power forecasting, the proposed method achieves more stable and effective performance than the existing BP neural network and support vector machines. The improvement in accuracy is 12% on average under different time steps.

Distributed generation (DG) and electric vehicle (EV) are increasingly installed in the modern microgrids (MGs). This demands for an improvement in the power handling capacity of the existing distribution feeders. The power handling capacity of a feeder is limited by the voltage magnitude violation caused by overloading. The smart transformer (ST) is proposed as an effective solution for improving the performance of modern MG. This paper utilizes the power management capabilities of the ST with an integrated battery energy storage system (BESS) for improving the power handling capacity of a medium voltage (MV) 5-bus radial feeder. The proposed method demonstrates the ST's capability to control the active and/or reactive power flow at the MV grid for maintaining the steady-state voltage magnitude within the grid-specified limit. A sensitivity analysis based design study is proposed to determine the power rating requirements for different ST and BESS converters during distinct power control scenarios. The analysis shows the capability of ST in injecting/absorbing active and reactive powers in flexible combinations for achieving the desired voltage magnitudes. The findings are verified through simulation and experimental results.

By bringing content close to end-users, proactive caching plays a vital role in improving the user experience in wireless networks. Caching content at the network edge proactively has been particularly effective in fast-changing vehicular networks. The objective of this paper is to address the proactive caching problem at the next roadside unit (RSU) in vehicular networks using reinforcement learning techniques. The paper proposes two proactive caching algorithms based on multi-armed bandit (MAB) learning, namely non-contextual MAB-based (MAB) and contextual MAB-based (cMAB). Additionally, the paper also investigates the uncertainty associated with proactive caching systems in the form of entropy with a specifically extended Subjective Logic framework, providing an insight into the underlying link between prediction accuracy and uncertainty. Two cities: Las Vegas, USA with grid road layout and Manchester, UK with more complex and historical layout, are considered in the simulation. Results have shown the generality of the proposed schemes in cities with different road layouts. Performance of the two proposed MAB-based systems is compared with two non-contextual baseline system: Equal Probability-based and Probability-based, and one contextual baseline system named Compact Prediction Tree+ based. Both proposed systems outperformed their counterparts. In terms of the prediction accuracy, cMAB has reached 75% and 80\% accuracy in Las Vegas and Manchester respectively, and MAB reaches over 50% in both testing cities. Regarding the benefits to the vehicular network, cMAB and MAB perform similarly in both cities irrespective of the road layout. Particularly, the paper shows that on average 75% and 81% content fragments are proactively served with cMAB and over 50% with MAB in Las Vegas and Manchester, which is consistent with the prediction accuracy associated with the schemes.

This paper presents a compact, uni-planer wide band Multiple-Input-Multiple-Output (MIMO) antenna. The designed antenna array has a compact size of (26mm x 31mm) covering a wide frequency band from 3.1GHz - 11GHz. A partial ground plane shared by two radiating patches is used. A ground stub and a single column Electromagnetic Bandgap (EBG) structure in between the two radiating patches results in very low mutual coupling in the designed antenna. The proposed MIMO-antenna was evaluated through different performance metric indicators like channel capacity loss, far-field radiation pattern, S-parameters, envelope correlation coefficient, peak gain, diversity-gain and radiation efficiency. Our designed MIMO antenna has a very high isolation amongst the MIMO antennas ( $\text{S}_{21} < -25$ dB), a high Diversity-Gain (DG $>9.995$ dB), a very low Envelope Co-relation Coefficient of (ECC < 0.001) and low Channel-Capacity-Loss (CCL < 0.1bits/s/Hz). The said antenna has an average radiation efficiency of 85.5% and a peak gain of (5.67dB) within the ultra-wideband (UWB) spectrum. A 0.8mm thick FR-4 substrate is used to fabricate the MIMO-antenna and tested in an anechoic chamber. The measured results closely match with the simulated results.

In this paper, a mode-switching sliding controller with the nonlinear gain generated by a fuzzy logic controller is put forward to enhance the tracking performance in an electro-pneumatic position control system which includes four solenoid ON-OFF valves and a double rod-end cylinder. The proposed method is designed to increase the response speed and reduce the overshoots by utilizing the nonlinear gain which is determined by a fuzzy logic controller. In addition, several improvements are made in the mode-switching sliding controller originally from previous works. The mode-switching strategy is totally redesigned and a hysteresis control scheme is introduced to the new mode-switching sliding controller. Moreover, the improved controller is properly combined with the fuzzy nonlinear gain. Simulation and experiments with various kinds of input are carried out to verify the effectiveness of the proposed method. All the experiments are conducted on a field programmable gate array platform, and the experimental results validate the reliability of the novel scheme.

For the nonlinear, time-variable and unpredictable environment (such as the manipulator workspace that has many random and moveable obstacles), a Hammerstein adaptive (HA) impedance controller based on a Hammerstein impedance model is proposed in this paper. The model consists of a static nonlinear element and a linear dynamic element, which can describe the nonlinearity of the environment. Then, an adaptive law is designed by a Lyapunov function, which can adjust the parameters of the static nonlinear element, so that the model has good adaptability and robustness for the environment. By introducing a parameters self-adjust model-free adaptive controller as the inner position controller, the HA impedance control system is built, which does not require the knowledge of the robot dynamics. In order to verify the force-control performance of the proposed control scheme, some simulations and experiments are done on a bionic wrist joint actuated by pneumatic muscles. Compared with the conventional impedance controller with constant parameters, experimental results demonstrate that HA impedance controller has better force tracking characteristic and can restrict the contact force effectively, no matter how the external environment changes, which increases the safety between robots and humans or operation objects.

Source location based on polarization angle information is a research hot spot in the field of shallow distributed source localization. The near-field P-wave and S-wave aliasing are severe and the polarization characteristics of the wave group are poor, making it difficult to extract the polarization angle, an important source localization parameter. To address the above difficulty, this paper proposes a method that is based on high-resolution parabolic Radon transform (HRP-Radon) and incorporates adaptive covariance matrix (ACM) to extract the polarization angle information of the wavefront. First, a data set is constructed from the data acquired by the sensor array, and this data set is morphologically corrected against the first break time information. Second, the time domain data set is converted into the Radon domain by HRP-Radon, and the direct P-wave is extracted from the aliasing information by use of the focusing characteristics of the P-wave and S-wave and the phase characteristics of the far-field P-wave. Third, a model is built using the ACM algorithm to extract the direct P-wave angle information and the angle extraction is assessed using a metric-polarization method. The numerical simulation and experimental results show that the proposed method is able to extract the wavefront angle information of the blasting near-field aliasing signal and determine the source location from the polarization angle information. This method is of practical value to the field of underground space.

This paper presents an angular acceleration planning for joint configuration spaces of vision-based kinematically redundant robot manipulators to achieve real-time tracking tasks in dynamic environment. In accordance with the proved proposition, the joint velocity formed by the inverse kinematics mapping of the defined pose error is regarded as the system error, and utilized to further deduce the planned joint angular acceleration and with kinematic redundancy for obstacle avoidance simultaneously. Highly-complex nonlinearities in the angular acceleration are equivalent to a system perturbation represented by a synthetic form, thereby simplifying the tedious calculations of the angular acceleration theoretically and converting the planning problem into a control problem. Hyperbolic tangent-based super twisting algorithm, as the control input of manipulator system, is designed to resist the synthetic perturbation. The collision-free movement of redundant manipulators for tracking tasks is thus achieved through the integration of the control input in real time. An error-based S-function is proposed as the internal parameter of the planned joint acceleration to prevent the integral saturation. Lyapunov theoretical analysis proves the stability of the proposed super twisting algorithm. Simulation and contrast experiments in dynamic-obstacles environments indicate that the proposed joint angular acceleration planning for kinematically redundant manipulators owns feasibility, smoothness and practicability.

With the increase of network services, how to avoid link congestion and make full use of limited bandwidth resources in network virtualization environment have become the key challenges. In this paper, we introduce the cognitive method into virtual network topology reconfiguration and modify the connectivity to reach the target topology by sensing the traffic. Then, we formulate an optimization problem to maximize the ratio of cumulative saved resources to the square of changed link number (CSR/SCLNR) and determine which virtual link to be added or deleted. Finally, a heuristic algorithm called cognitive virtual network topology reconfiguration method based on traffic prediction and link importance (CVNTRM-TPLI) is proposed to solve this optimization problem. In the CVNTRM-TPLI, the link importance is presented in link deletion as the topological factor to avoid the ping pang effect. Also, a hybrid traffic prediction algorithm based on optimal parameter selection is put forward, where immune optimization is introduced to select the optimal parameters and the virtual network topology can react promptly to the traffic fluctuation without adding or deleting virtual link frequently. Simulation results show that the CVNTRM-TPLI not only has the highest CSR/SCLNR, but also solves the link congestion and makes full use of the limited bandwidth resources.

Context: The inclusion of grey literature (GL) is important to remove publication bias while gathering available evidence regarding a certain topic. The number of systematic literature reviews (SLRs) in Software Engineering (SE) is increasing but we do not know about the extent of GL usage in these SLRs. Moreover, Google Scholar is rapidly becoming a search engine of choice for many researchers but the extent to which it can find the primary studies is not known. Objective: This tertiary study is an attempt to i) measure the usage of GL in SLRs in SE. Furthermore this study proposes strategies for categorizing GL and a quality checklist to use for GL in future SLRs; ii) explore if it is feasible to use only Google Scholar for finding scholarly articles for academic research. Method: We have conducted a systematic mapping study to measure the extent of GL usage in SE SLRs as well as to measure the feasibility of finding primary studies using Google Scholar. Results and conclusions: a) Grey Literature: 76.09% SLRs (105 out of 138) in SE have included one or more GL studies as primary studies. Among total primary studies across all SLRs (6307), 582 are classified as GL, making the frequency of GL citing as 9.23%. The intensity of GL use indicate that each SLR contains 5 primary studies on average (total intensity of GL use being 5.54). The ranking of GL tells us that conference papers are the most used form 43.3% followed by technical reports 28.52%. Universities, research institutes, labs and scientific societies together make up 67.7% of GL used, indicating that these are useful sources for searching GL. We additionally propose strategies for categorizing GL and criteria for evaluating GL quality, which can become a basis for more detailed guidelines for including GL in future SLRs. b) Google Scholar Results: The results show that Google Scholar was able to retrieve 96% of primary studies of these SLRs. Most of the primary studies that were not found using Google Scholar were from grey sources.

Polygon shape is an important factor affecting the efficiency of vector polygon overlay analysis. Aiming at the problems that polygon shape is difficult to accurately measure and different algorithms have different sensitivities to the same polygon in vector polygon overlay analysis, 27 shape variables are selected from literature to establish the shape complexity models for Vatti, Greiner-Hormann and Martinez algorithms by multiple linear regression analysis in this paper. These three algorithms are suitable for arbitrary polygon clipping and include intersection, union, difference and symmetric difference operators. The experimental results show that the calculated performances of the intersection, union, difference and symmetric difference operators belonging to the same algorithm are similar to their sensitivity to the same polygon shape. The fitting correlation coefficient R2 of the shape complexity model based on the Greiner-Hormann algorithm is the highest among the three algorithms, which can explain 99% of the performance of this algorithm.

Many hazardous industrial incidents can occur due to the inadequate and inefficient monitoring of the offshore plants. Manual inspections of the offshore plants on a regular basis is not only time consuming but also dangerous regarding to human safety. For considering the safety measurement and alleviating the burden of the manual inspection, unmanned aerial vehicles (UAVs) can be effectively utilized to collect data from the remote industrial environment. In an industrial scenario, less delay is required for emergency packets and high throughput is needed for monitoring packets. This paper proposes a priority-aware fast MAC (PF-MAC) protocol for UAV-assisted industrial Internet-of-things (IIoT) systems, ensuring fast and robust data delivery. At first, the IoT devices under the UAV communication range transmit a reservation frame to the UAV to catch transmission opportunities using CSMA/CA. The devices utilize static traffic priority and a novel adaptive backoff mechanism during CSMA/CA. After receiving the reservation frames from the IoT devices, the UAV calculates the dynamic device priority based on their static traffic priority, communication duration, sampling frequency, and remaining energy. Then, time slot is assigned by the UAV to each device for data transmission. To ensure fairness, if a device fails in contention during the CSMA/CA period, the static traffic priority is raised in the next retransmission. There is no prior work in the literature that considers both the traffic priority and the device priority to ensure Quality of Service in IIoT and related systems. According to our performance study, the proposed PF-MAC outperforms the conventional protocols in terms of delay and throughput.

Location estimation is significant in mobile and ubiquitous computing systems. Considering the influence of measurement error caused by time difference of arrival (TDOA)/angle of arrival (AOA) hybrid location algorithm and the nonlinear optimization problem encountered in the location estimation, in this paper, a particle swarm optimization (PSO) algorithm based on the chaos theory is proposed for the hybrid location of mobile location estimation. Taking the TDOA/AOA hybrid location algorithm for mobile location estimation as the object, the proposed algorithm greatly improves the location performance and accuracy of mobile location estimation. First, the estimation function of the mobile station is obtained by the maximum likelihood method, and then the initial population of PSO is generated by using the estimation function of the mobile station as a fitness function. The chaotic optimized particle swarm optimization algorithm (CPSO) is used to solve the optimal solution of the optimal position of the population and obtain the optimal mobile location position estimation, which makes the TDOA/AOA location algorithm have better location performance. The simulation results have demonstrated that the performance of the proposed method compared with the traditional Chan algorithm, the Taylor algorithm, and the TDOA/AOA hybrid location algorithm, the proposed algorithm can reduce the impact of error on the location accuracy, achieve a balance of global and local search capabilities, and have a faster convergence speed and more accurate positioning accuracy.

Lithium-ion batteries are widely used in new energy vehicles, especially electric vehicles. Temperature estimation is very important for battery life and safety. However, current temperature measurement methods cannot accurately measure the battery internal temperature. In this paper, a new method for battery temperature estimation based on an improved magnetic nanoparticle thermometer (MNPT) is proposed. The influence of dc magnetic field on temperature accuracy of a MNPT is firstly studied, the optimal dc magnetic field is found out under limitation of maximum temperature sensitivity and minimum temperature error, a new model of an improved MNPT is also established based on the ratio of first and second harmonics, and then the Lithium-ion battery temperature is estimated by use of the improved MNPT, the simulation and experiment results show that the improved MNPT can accurately estimate the battery internal temperature, which provides a new method for monitoring battery temperature of a new energy vehicle.