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This paper considers a full-duplex relay-assisted system for covert communication, in which both the transmitter and the relay are the sender of covert messages. Different from current works that only consider the transmitter power optimization or relay power optimization individually, we propose a joint optimization approach for the transmitter and relay power, which is expected to enhance the system’s covert performance. We establish the minimum error detection probability using the Kullback-Leibler (KL) divergence, which serves as the foundation for formulating our joint optimization problem. The objective is to achieve the highest covert transmission rate within the constraints of covertness requirement and total power limitation. Employing a graphical method, we effectively transform inequality constraints into equality constraints, leading to the derivation of an optimal closed-form solution. The simulation results confirm the accuracy of the theoretical derivation and demonstrate that the proposed power allocation method is effective in determining the optimal power for both the transmitter and the full-duplex relay within a two-hop covert communication system.

Conformer has become one of the most popular models in the field of automatic speech recognition, achieving superior speech recognition performance by integrating a convolutional module into Transformer. However, existing Conformer models face challenges with high computational complexity in the attention mechanism when capturing global dependencies, and lack flexibility in dynamically adjusting local dependencies, which limits their efficiency in processing sequences. To address these challenges, we propose a new model called Ubranch Conformer, which combines Up-Down sampling and branch attention to enhance the ability to capture input information and reduce computational complexity. First, we designed a new architecture with an Up-Down sampling strategy that adjusts the sampling of embedded sequences between blocks in the model’s intermediate layers, reducing complexity without compromising efficiency. Second, we introduce a more efficient block structure that mainly contains branch attention and convolution gate modules, combining convolution with attention to improve the model’s ability and flexibility in capturing global and local dependencies. Experiments on multiple datasets show that our Ubranch Conformer achieves a more advanced level of performance compared to existing models, and achieves a good balance between performance and complexity.

Migration-based Dynamic Platform (MdyPlat) is a type of Moving Target Defense (MTD) techniques to protect the task executed on the platform. MdyPlat actively hinders sophisticated attacks through randomly and dynamically setting up a platform for executing the task. This paper aims to investigate how MdyPlat-related factors make quantitative impact on the capability of MdyPlat technique. We develop a semi-Markov model for describing the system dynamics and then derive metric calculation formulas for investigation. Compared to the existing analytical-modeling-based evaluation methods, our modeling approach can work even the times of all MdyPlat-related events follow any type of distribution. The comparison between simulation and numerical results validates the approximate accuracy of the model and formulas. Numerical experiment results uncover that 1) MdyPlat technique can effectively enhance the task security, particularly under high attack intensity; 2) The security risk of the executed task varies in different task-execution scenarios, which are denoted by the number of platforms and the probability distribution of the times of MDP-related events; and 3) Different cumulative distribution function of event time under the same mean value leads to different analysis result of the task security.

Centralized deepfake service providers have large amounts of computing power and training data, giving them the ability to produce high-quality deepfake content. However, once these service providers are attacked or malfunction, it may lead to the collapse of the entire deepfake ecosystem, making deepfake a potential threat to data security. This monopoly development has led to the uneven distribution of deepfake resources, which in turn has brought about the risk of single points of failure. To deal with the problem, this paper proposes a decentralized deepfake task management algorithm (DD-TMA) based on blockchain and edge computing. The blockchain in this algorithm can provide a decentralized storage and management platform to ensure that the data and models of deepfake tasks will not be tampered with or lost. Edge computing can distribute tasks to edge devices close to the data source for processing, reducing data transmission delays and bandwidth consumption, and improving the efficiency and security of deepfake tasks. The paper innovatively integrates blockchain, federated computing, and edge computing. Firstly, the algorithm establishes a decentralized computing platform based on blockchain. Subsequently, it enhances computing power during the execution of decentralized deepfake tasks through the integration of federated computing and edge computing. Finally, the algorithm increases the active performers of decentralized deepfake tasks through gamification, thereby improving task execution efficiency. Experiments conducted in this study on public data sets demonstrate that the algorithm is efficient, robust, and reusable. Compared with other algorithms, the efficiency of DD-TMA is improved by more than 20% and the stability is improved by more than 13%. This algorithm proves effective in solving the problems encountered in the execution of centralized deepfake tasks. The research provides new ideas for future evaluations of decentralized deepfake effects based on different strategies.

Existing visible light positioning (VLP) algorithms typically require multiple LEDs to achieve high-precision positioning. However, in practice, the number of available LEDs is influenced by various factors such as the layout of LEDs and the field-of-view of the receiver, and these factors can significantly affect the coverage of a VLP algorithm. To address this issue, this paper proposes an inertial sensor-assisted single view geometry (ISA-SVG) algorithm, which can achieve accurate positioning as little as one LED. In particular, we first establish the projection transformation model between the actual point and its projection on the image plane. Then, a pose fusion model that effectively fuses the inertial sensor information and VLP information is established. Finally, the position of the receiver based on the pose fusion model is estimated. IAS-SVG significantly improves the usability and coverage performance of visible light positioning algorithms. Simulation results show that the coverage rate of the ISA-SVG algorithm is over 95%. Experimental results show that the proposed method can achieve an average positioning error as low as 4.70 cm.

In this paper, the design methodology and implementation of single-band and multiple-band elliptic function bandpass filters (BPFs) are presented, based on the concept of bandstop resonator (BSR) sections. One or more single-mode and multiple-mode BSRs can be dangled from a non-resonant node. Each BSR can generate one reflection zeroes (RZ) and one transmission zeroes (TZ). Multiple BSR sections are used to flexibly and independently control the location and bandwidth of the stop bands and therefore the same of the passbands. The method to design single- and multiple-band elliptic function BPFs has been detailed using a number of examples based on waveguide technology. For proof of concept, a 6th-order single-band BPF with six BSR = 2 sections and a 3rd-order dual-band BPF using three BSR = 3 sections are designed and fabricated monolithically using a selective-laser-melting (SLM) 3-D printing technique. Excellent agreement between simulated and measured results verifies the proposed design methodology and its versatility as well as the additive-manufacture approach.

Utilizing Unmanned Aerial Vehicles (UAVs) plays a pivotal role in the localization of ground construction personnel targets within the specialized operational environments of the energy industry. To ensure the safety of personnel in these areas effectively, this study introduces an innovative dual UAV cooperative positioning system designed to address the challenges of personnel localization in various complex settings. In the first place, the research establishes a dual UAV collaborative remote sensing platform, specifically tailored for executing dual UAV flight control and ground personnel identification and localization tasks. Subsequently, this study proposes a high-precision ground personnel localization solution. This solution integrates the flight data of two UAVs with the pixel coordinates of targets in natural remote sensing imagery, enabling the precise localization of moving targets within complex environments. Moreover, due to the irregularity of ground personnel shapes in imagery, this research introduces an improved rotating target detection network based on the YOLOv7 architecture. This network is specifically optimized for detecting ground targets in remote sensing imagery, significantly enhancing the accuracy of target localization. Extensive experimental validation has demonstrated that this localization system can maintain target localization errors within a 5-meter threshold, effectively enhancing the safety and efficiency of personnel in specific operational areas of the energy industry.

For multi-object tracking (MOT), jointly learning the detector and embedding model (JDE) is one of the mainstream solutions. However, an inherent problem in this architecture arises as the tasks of target detection and appearance feature extraction compete with each other. FairMOT, as a representative method, attempts to address this issue by employing two homogeneous branches, but it overlooks the essential difference between these two tasks. Upon the original network architecture, we propose an adaptive dual decoder structure. Our objective is to separately learn more focused features for the target detection and the appearance feature extraction. Furthermore, we introduce a noise-adaptive Kalman filter based on the width estimation. In the motion information matching stage, we enhance the affinity matrix of motion information by employing an expanded-width strategy, combined with a more accurate overlap measure. We verify the effectiveness of our proposed approach through extensive experiments using the MOT17 dataset.

As a fundamental task in remote-sensing interpretation, semantic segmentation of remote-sensing images intends to allocate a definite class to each pixel in the image. Fast and efficient semantic segmentation of high-resolution remote-sensing images provides help to capture the real surface covering and plays an essential role in urban planning and dynamic monitoring. However, there are still some limitations in the previous remote-sensing image semantic segmentation model for urban scenes, such as the low weight of small target pixels and the tiny target size leading to the unsatisfactory recognition and segmentation results of the model for small target features. Meanwhile, the deeper and broader feature extraction module in the semantic segmentation network usually leads to more redundant parameters, which takes a lot of computation time. Thus, we propose a lightweight semantic segmentation network based on the knowledge distillation combined with a multiscale pyramidal pooling module and attention mechanism named KD-MSANet, which enhanced the ability to fuse and focus on shallow features. Then, we trained teacher—student models to obtain lightweight network models through a model pruning and distillation framework. Experiments on Vaihingen and Potsdam datasets demonstrated that the network we designed significantly reduces the number of parameters while ensuring almost constant accuracy. Compared with the precompression model, the student model reduced in size by 43.6% and the training efficiency was improved by 22.3%, while the accuracy reached 99.30% of the teacher model.

The optimal selections of supplier portfolios and supplier substitutes are important research contents of the supplier selection problem. However, most of the existing supplier selection methods are based on the efficiency of indicator capability of supplier, the complementary capability (i.e., weak similarity) between suppliers for the supplier portfolio, and the substitution capabilities (i.e., strong similarity) of supplier substitutes for the best supplier may not be considered. Therefore, two new supplier selection models, namely TOPSIS-NS considering the negative influence of similarity for supplier portfolio selection and TOPSIS-PS considering the positive influence of similarity for supplier substitute selection, are proposed based on TOPSIS and intuitionistic fuzzy set (IFS). Firstly, the efficiency of indicator capability of supplier is expressed by the membership degree of IFS obtained by TOPSIS, while the similarity among suppliers is expressed by the nonmembership degree of IFS obtained by the concordance correlation coefficient. Then, the process of TOPSIS-NS for supplier portfolio selection is constructed based on the score function of IFS, and the supplier portfolio selected by TOPSIS-NS can have higher complementary capabilities. Furthermore, the process of TOPSIS-PS for supplier substitute selection is constructed based on the accuracy function of IFS, and the supplier substitute selected by TOPSIS-PS can have higher substitution capabilities. Finally, two illustrative examples for optimal selections of supplier portfolio and supplier substitute are given respectively, and the results show the superiority of TOPSIS-NS in supplier portfolio selection and TOPSIS-PS in supplier substitute selection.

Presents corrections to the paper, (Corrections to “Two Methods With Bidirectional Similarity for Optimal Selections of Supplier Portfolio and Supplier Substitute Based on TOPSIS and IFS”).

Digital microfluidic, as an emerging and potential technology, diversifies the biochemical applications platform, such as protein dilution sewage detection. At present, a vast majority of universal cyberphysical digital microfluidic biochips (DMFBs) transmit data through wires via personal computers and microcontrollers (like Arduino), consequently, susceptible to various security threats and with the popularity of wireless devices, losing competitiveness gradually. On the premise that security be ensured first and foremost, calls for wireless portable, safe, and economical DMFBs are imperative to expand their application fields, engage more users, and cater to the trend of future wireless communication. To this end, a new cyber-physical DMFB called PortableLab is proposed in this paper, which guarantees data security through wireless sensors at low cost. After considering the security, computing consumption, and cost, a mobile module is added. In addition, the improved Advanced Encryption Standard (AES) and Cyclic Redundancy Check (CRC) algorithms are utilized to ensure the integrity and confidentiality of data transmission. Ultimately, all the security analysis, cost analysis, and experimental results on multiple protocols demonstrate the feasibility of the proposed PortableLab DMFB in time and space.

As an advanced metrology technology, three-dimensional (3D) gear measurement using linear laser techniques is susceptible to the influence of multiple coupling factors. Consequently, there exists an urgent need for new principles to effectively decouple the effects of each factor. Digital twins realize the connection between physical and virtual spaces. By fusing multi-source data from the existing system, a high-fidelity digital model is established to realize the simulation of the gear measurement process in this paper. Moreover, simulation of the light reflections of tooth flanks and the dynamic error coupling process can effectively clarify the coupling mechanism of measurement errors. This paper presents a method of constructing a digital twin model of a 3D measurement system for gears and realizes the expression of the actual measurement processing. It provides the foundation for subsequently studying the reflection characteristics of the tooth flank at different incident angles and searching for the optimum incident attitude of the line structured light sensor.

The human-machine function allocation (HFA) strategy of manned submersibles is an important factor that affects the reliability of oceanauts. However, the uncertainty of the HFA strategy makes its optimization very complicated. To this end, a non-cooperative game-based HFA method is proposed, which transforms the multi-objective optimization model of mental workload and situation awareness (SA) under the allocation strategy into a non-cooperative game model and forms a mapping relationship. Mental workload and situation awareness are used as non-cooperative game players. Assignable functions are attributed to the players by fuzzy clustering analysis and combined with non-assignable functions to construct the utility matrix by utility functions. The optimal allocation strategy combination is obtained through the Nash equilibrium analysis of the utility matrix. The proposed method was applied to the optimization of the HFA strategy of the manned submersible during the navigation.

Coal gangue recognition of top coal caving is one of the important links in the process of intelligent coal mine construction. However, the recognition accuracy of this technology in practical application is still challenging, because the recognition model is not perfect in the following aspects: 1) the filtering scale is not suitable for signal noise reduction;, 2) the selected features have no obvious difference in vibration signals of coal and gangue; and 3) the overall relevance of the model input data to the target classification is insufficient. The purpose of this paper is to establish a coal gangue recognition model with effective filtering, feature extraction and classification capabilities, which can adaptively carry out purposive feature extraction while retaining relevant information to improve the recognition accuracy. Firstly, hierarchical filtering (HF) method was proposed. Secondly, an effective information correlation fusion based coal gangue recognition model (EICF-coal gangue recognition model) was established by coupling wrapper feature selection method and recognition algorithm. Then, 2223 groups of vibration impact tests were carried out on the coal gangue mixture with gangue content of 0 to 50%, and two kinds of coal gangue recognition sample sets of “caving category” and “shutdown category” were established. Finally, coal gangue recognition experiments were carried out on 9 hierarchical filter sample sets by coupling wrapper and 5 recognition algorithms. Under the combined effect of the HF method, wrapper feature selection method and Stacking, coal gangue recognition accuracy reaches 99%. This paper demonstrates the effectiveness of the EICF-coal gangue recognition model.

Non-invasive voltage measurement of transmission lines plays an important role in the process of power system digitization. Aiming at the phase to phase coupling effect of the field source structure of the transmission line, the paper proposed a self-decoupling voltage measurement method based on the field source structure parameters of the measuring point, to achieve the accurate measurement of three-phase voltage by a single sensor. Voltage measurement is achieved at the measuring point of the sensor by utilizing a three-dimensional electric field induction probe. The principle of voltage self-decoupling measurement is introduced in this paper. The measurement model of voltage self-decoupling is analyzed. Simulation calculations are then performed to calculate and adjust the coefficient of the self-decoupling matrix. Finally, the voltage measurement and decoupling effect of this method are verified using a three-phase horizontal distribution transmission line as an example. The results show that the voltage measurement error under steady-state and transient conditions is less than 2.8% and 2.2% respectively. This method can be used for voltage measurement of transmission lines.

As the core technology in the field of imaging satellite application, imaging satellite mission planning has received more and more attention. Aiming at this problem, this paper proposes a distributed imaging satellite mission planning method (DISMPA) based on multi-agent system. Firstly, a distributed imaging satellite mission planning model with variable collaborative division of labor is constructed based on multi-agent system theory. The model defines the intelligence level of satellites in the satellite cluster and the interaction mode between satellites. Secondly, the cooperation mechanism between satellite agents is established based on blackboard model. Two negotiation strategies of worker serial activation and worker parallel activation are proposed. In order to improve the efficiency of negotiation, the targets are preassigned before negotiation. Finally, a hybrid discrete multi-verse optimization algorithm is proposed to solve the mission planning problem of worker agents in the model. Simulation experiments show that the average fitness values obtained by DISMPM using serial activation strategy and DISMPM using parallel activation strategy are up to 20% higher than those obtained by the centralized algorithm with the best solution effect in this paper, and the average negotiation times of the two DISMPM methods are both reduced by more than 80% compared with the contract network algorithm, effectively reducing the system traffic and reducing the communication burden. It is proved that DISMPA is suitable for distributed imaging satellite mission planning.

Using deep learning and machine learning techniques for network intrusion detection is of great significance for enhancing the defense capability of network security systems. Given the characteristics of generative adversarial networks, such as the approximate consistency of generated samples with the input data distribution but with a random distribution within a certain bounded interval, and in response to the problem of insufficient classification performance and detection omission caused by the imbalance of different degrees of data categories and quantities in network intrusion traffic, and in light of the fact that the effectiveness of existing classification algorithms based on unbalanced traffic data still has some room for improvement, this paper proposes a network intrusion detection strategy based on auxiliary classifier generative adversarial networks. The data expansion experiments are conducted with the intrusion detection dataset NSL-KDD. The data are classified into twenty-three categories before and after the expansion by binary classification validation. The results show that the expansion of the generated samples for unbalanced network traffic data improve the subsequent recognition effect significantly. Finally, five classification performance index verification experiments are conducted. The results prove that the strategy of this paper performs better in accuracy, precision, recall rate and F-value indexes, and is capable of obtaining a large number of features from limited samples and inferring complete data distribution based on fewer features. The model as a whole has stronger generalization ability and defense effect.

Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificial bee colony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.

With the advent of IoT technology, the dynamic nature of IoT devices has introduced new obstacles to access control. It is essential to consider the security requirements of the actual physical environment, rendering the traditional access control approach centered on the information space. In the IoT ecosystem, there are several issues such as the dynamics of devices frequently entering and leaving, the lack of computing and storage capacity, and distributed deployment. To address these challenges, this paper proposes the Domain Attribute Based Access Control(DABAC) that incorporates domain elements to implement the physical location limitation of dynamic devices. Moreover, an intelligent gateway is utilized to divide the physical area and act as a proxy to achieve regional device management, automatic networking of devices in the domain, and the dynamic expansion of the sensor network resulting from device entry or exit. Then, given the distributed deployment of devices, smart contracts are employed to deploy access control mechanisms and construct a trusted environment to mitigate threats such as single points of failure. Finally, the DABAC is implemented on the Ethereum platform, simulating a smart medical situation. The experimental results demonstrate that the proposed solution effectively addresses the problem of access control of device dynamics in an untrusted IoT environment while maintaining system security.

Regenerative braking is one of the most important methods for improving energy utilization in electric vehicles. Electric vehicles with front-rear, individually driven configurations exhibit significant potential and flexibility for recovering braking energy. To improve the system’s high-power impact tolerance, a high-power density hydraulic energy storage system can be incorporated to facilitate a full-drive dual-motor electric–hydraulic hybrid (DMEHH) powertrain. The DMEHH system is composed of an independently driven electric–hydraulic hybrid front axle and a purely electric rear axle. In this study, a method for distributing braking torque to minimize energy loss was devised based on the proposed DMEHH powertrain. Power loss models for both the electric and hydraulic subsystems have been developed. Loss minimization control was adapted for the power loss model of the permanent magnet synchronous motors in the powertrain, and the front-rear and front electric–hydraulic torque braking distribution maps were calculated using the energy-loss minimization rule. The application of torque distribution maps resulted in energy losses less than those of the general ideal torque distribution for all braking conditions. The energy loss decreased by 27.2% when the loss minimization control method was used in the WLTC cycle, and decreased by 29.1%, 25.5%, and 21.6% in UDDS, NEDC, and US06 driving cycles, respectively.

Although harmonic drives have been adopted by all sorts of industrial environments, the mathematical expression of its dynamics has not yet been fully solved. An intelligent algorithm based on data acquisition is used to analyze the kinematic parameter of the flexible manipulator offline to improve the accuracy of predicting output torque. In this study, a method is proposed to capture the dynamics of harmonic drive systems, such as temperature, speed, and load. A friction model is proposed, and the model parameters are estimated by MCMC (Markov chain Monte Carlo) approximation. The accuracy of the estimated parameters in different conditions is calculated, and the sensitivity to parameter uncertainty of the proposed model is considered.

With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve si-multaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is noncon-vex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

In this work, we present the optimal design method of multimodal traffic strategies in emergency evacuations by considering the background traffic. First, the lane use configuration of public-oriented transport in multimodal evacuation that considers the background traffic is presented. Then, the feasible strategies are proposed based on the application conditions of five evacuation traffic strategies in feasible strategy proposal. Furthermore, the feasible strategies are transformed into optimal strategies by using the optimal strategy design model to minimize the weighted sum of public transport, private cars, and background traffic target values. Finally, the previous case study of Wenling is used to discuss the strategy evolution process, compare other strategies, and analyze the sensitivity of weights. The results presented in this work provide a reference for other researchers on the design and formulation of multimodal evacuation traffic strategies.

We proposed a dual-chirp microwave photonic radar based on compressive sensing for distance and velocity measurement. This radar can generate different chirp linear frequency modulated (LFM) signals using a dual-parallel Mach-Zehnder modulator (DPMZM). In the receiving part, a dual-drive Mach-Zehnder modulator (DDMZM) and a Mach-Zehnder modulator (MZM) are cascaded for the optical mixing and de-chirp processing with a pseudo-random bit sequence (PRBS). Then the mixed signal can be gathered by an analog-to-digital converter (ADC) at a sampling rate that is well below the Nyquist sampling rate. Using fewer sampling points, the reconstruction algorithm can recover the de-chirped signal accurately with a compression ratio of 8. A proof-of-concept experiment demonstrates that when the target is stationary, the distance measurement error is about 1.560 cm. The signal-to-noise ratio (SNR) of the recovery signal is enhanced to 30.725 dB. When the target is moving, the simulation results present that the maximum distance error is 1.2 cm, and the velocity error is below 0.140 m/s. This compressive sensing radar reduces the pressure of a massive amount of data storage or processing and guarantees the accuracy of signal recovery. At the same time, it breaks the limitation of operation bandwidth and increases the speed of operation.

The consensus optimal control problem for a class of linear multi-agent systems with directed communication networks is studied in this paper using adaptive dynamic programming. To overcome the restrictions of the agent’s low processing capability and to extend the actuator’s lifetime, consider the event-triggered. In the beginning, a dynamic event-triggered is provided, with several existing static event-triggered serving as special examples. When using the dynamic event-triggered, a longer interval can be shown between any two consecutive event-triggered. Designing a dynamic event-triggered control law becomes more challenging when implemented in directed networks. In addition, on the basis of dynamic event-triggered, a novel adaptive dynamic programming is used to construct a suitable dynamic event-triggered control law, which employs just the interaction information between agents and does not need model dynamics, overcoming the difficulty of solving the algebraic Riccati equation. Finally, the effectiveness of the proposed method is verified by simulation results, and no agent exhibits Zeno behavior.

With the rapid development of Social Networking Services (SNSs), linking online user IDs is becoming increasingly important to internet service providers. Existing methods can achieve matching adjacent IDs between different services, where adjacent IDs mean the IDs that send message loggings at the same physical location. However, nonadjacent IDs also need to be matched in reality, which is a key challenge. In this paper, a new method based on users social behaviors and contact graph is put forward to realize linking of IDs across domains. This method can be used for matching both adjacent IDs and nonadjacent IDs. Specifically, all the IDs are mapped to contact graph. And we utilize a set matching algorithm based on the contact graph to find out the set of candidate IDs and generate confidence score by means of this algorithm to select the most appropriate matching. Our experimental results show that our algorithm is capable of identifying not only the set of adjacent IDs that belong to one same user but also the set of nonadjacent IDs that belong to one same user.

The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, such as flood inundation maps and insurance claims, becomes available only weeks and months after the floods have receded. Crisis response managers, however, need near-real-time data to prioritize emergency response. This time lag creates a need for rapid flood impact assessment. Accordingly, community-scale big data (such as satellite imagery) has been utilized for the early estimation of end flood impacts. Some recent studies have shown promising results for using human activity fluctuations as indicators of flood impacts. Existing studies, however, used mainly a single community-scale activity feature for the estimation of flood impacts and have not investigated their temporal importance for indicating flood impacts. Hence, in this study, we examined the importance of heterogeneous human activity features (such as human mobility, visits to points-of-interest, and social media posts) in different flood event stages. Using four community-scale big data categories we derived ten features related to the variations in human activity (e.g., travel, credit card transactions, and online communications) and evaluated their temporal importance for rapid assessment of flood impacts. Using multiple random forest models, we examined the temporal importance of each feature in indicating the extent of flood impacts (measured by the flood insurance claims and flood inundations) in the context of the 2017 Hurricane Harvey in Harris County, Texas. Our findings reveal that 1) fluctuations in human activity index and percentage of congested roads are the most important indicators for rapid flood impact assessment during response and recovery stages; 2) variations in credit card transactions assumed a middle ranking in both response and recovery stages; and 3) patterns of geolocated social media posts (Twitter) were of low importance across flood stages. Insights derived from data analysis reveal the potential for harnessing community-scale data characterizing human activity fluctuations for rapid assessment of flood impacts. The results of this research could rapidly forge a multi-tool enabling crisis managers to identify hotspots with severe flood impacts at various stages then to plan and prioritize effective response strategies.

Due to the randomness of load and renewable energy generation (REG), microgrids face multiple uncertainties. These uncertainties lead to the uncertainty of microgrid operation and bring more challenges to the economic evaluation of microgrids. In this paper, an economic evaluation method for determining microgrid revenue distribution is proposed. Considering the dual uncertainties of source-load and forecast, and temporal autocorrelation of time series, the probabilistic model of uncertainties is established by multivariate kernel density estimation (KDE). Then the random scenarios including forecasting values are generated and used in optimal dispatch calculation for the detailed production simulation. The probabilistic revenue is derived with a method based on Monte Carlo method. Finally, a case study is carried out based on the real data of an industrial park. The results demonstrate the necessity and effectiveness of the probabilistic revenue analysis proposed in this paper. This method can reveal the actual values of each component of a microgrid (e.g., device or algorithm) in specific scenes and provides more insights into investment decisions.

A polarimeter for optical rotation (OR) detection and optical active media measurement based on m=1 Zero-Order Liquid Crystal Polymer Vortex Half-Wave Retarder (LCPVR) and Digital Image Processing (DIP) was proposed in this letter. In this research, we demonstrate the principle of measuring optical rotation with LCPVR polarimeter system which is based on Jones vectors and matrices. Two opposing wedge-shaped dark areas will appear in the output image when the uniform distribution polarized light passes the LCPVR. DIP is used to find the darkest line in the opposing wedge-shaped dark areas, then the optical rotation angle is calculated, which is from −90 degree to +90 degree. The polarimeter is used to measure the concentration of sucrose solutions at the range of 1000-10000mg/dl L. We also present the fabricated method of LCPVR by Digital Micro-mirror Device (DMD) system, and the LCPVR will be low price in the fabricated processing. Compared with existed polarimeter, our polarimeter with LCPVR has simpler optical structure and less size. The key innovation of this system is that the optical rotation angle can be displayed with visualized image except digital. And the rotation angle is calculated by DIP without mechanical adjust a 1/4 wave plate, which lead to higher detection speed and stronger stability.

The three-coil loosely coupled inductive power transfer (IPT) charging system can achieve constant current (CC) to constant voltage (CV) output automatically and smoothly during battery charging process. However, due to cross-coupling that existed between the assistive and receiver coils, a voltage deviation will generate in CV output compared with the ideal CV charging voltage threshold of battery. In this paper, the practical effect of the cross-coupling between the assistive and receiver coils and the impedance matching at full load for efficiency optimization are further taken into consideration based on three-coil coupler design. Four design schemes of the assistive coil are compared by finite-element analysis (FEA) and an effective design approach is provided. Then, the proposed approach is theoretically analyzed to relieve the cross-coupling issue on the CV output even with efficiency optimization. Also, it decreases the space occupancy of the three-coil coupler. Finally, an experimental prototype is built to verify the proposed design method and the theoretical analysis.

With the increase of travel demands in the networked metro system, more and more metro transfer stations are suffering from oversaturated situations, leading to the accumulation of passengers in the transfer corridor and boarding area with potential accident risks. To further improve the transfer efficiency and passenger accumulation safety at transfer stations, the joint operation strategy is adopted. Metro trains are allowed to run across different metro lines and passengers switch other lines without transfer. This paper proposes 1) a meaningful, yet simple, way for defining the running strategy of trains for joint operation with two intersecting metro lines and 2) an optimal model for the train frequency to minimize indicators associated with the passenger service and train capacity. Regulation constraints such as waiting time of through and transfer passengers, number of available trains, load factor, and departure interval are taken into account. Finally, a case study of the joint operation in the Beijing metro system is implemented to demonstrate the performance and effectiveness of the proposed approaches.

Automatic License Plate Recognition (ALPR) is one of the applications that hugely benefited from Convolutional Neural Network (CNN) processing which has become the mainstream processing method for complex data. Many ALPR research proposed new CNN model designs and post-processing methods with various levels of performances in ALPR. However, good performing models such as YOLOv3 and SSD in more general object detection and recognition tasks could be effectively transferred to the license plate detection application with a small effort in model tuning. This paper focuses on the design of experiment (DOE) of training parameters in transferring YOLOv3 model design and optimising the training specifically for license plate detection tasks. The parameters are categorised to reduce the DOE run requirements while gaining insights on the YOLOv3 parameter interactions other than seeking optimised train settings. The result shows that the DOE effectively improve the YOLOv3 model to fit the vehicle license plate detection task.

Visual tracking remains an open challenge, as it requires real-time and long-term accurate target prediction. Siamese network has been widely studied due to its excellent accuracy and speed. Since long-term tracking may lead to model degradation and drift, most existing algorithms cannot well solve this problem. This article proposes a new Siamese Network based on Fast Attention Network named SiamFA. This method designs an attention model, which can enhance the key and global information of the target, to obtain a more robust target model and achieve long-term tracking. At the same time, the attention model is used to obtain the potential position information of the target when calculating the similarity between the template and the search area. In addition, the attention network we design reduces many redundant operations and effectively improves computational efficiency. We utilize a multi-layer perceptron to forecast the bounding box to avoid excessive hyper-parameters. In order to verify the effectiveness of our network, we conduct tests on many commonly used datasets, such as OTB100, GOT-10k, LaSOT, TrackingNet, UAV123. Our method can achieve a success rate of 62.7% and the precision rate of 64.3% on LaSOT. At the same time, it can run at about 100fps, which exceeds the comparison network, proving that our network can run in real-time.

Regenerative braking is the key to achieve efficient use of energy and extend the driving range in pure electric vehicles. This study proposes a new predictive control method integrating adaptive cubic exponential prediction and dynamic programming to address the problem of efficient energy recovery during the regular braking process of four-wheel pure electric vehicles. The method considers the dynamic characteristics of an electro-hydraulic combined braking system. The adaptive cubic exponential prediction is adopted to predict the vehicle velocity and braking intensity. The dynamic programming is employed to optimize the motor braking torques and wheel cylinder pressures under the condition of braking regulations, road constraints, and vehicle constraints. To verify the effectiveness of the new predictive control method, the ideal and multi-stage braking force distribution methods are employed for comparison. The results confirm that, under gradual braking conditions, the energy recovery efficiency achieved via the proposed method is improved by 1.55% and 6.40% considering the ideal and multi-stage braking force distribution methods, respectively.

Over the years, probabilistic nature of renewable energy sources (RES) and its influence on power system adequacy have been well studied. However, rather less attention has been paid to the impact of RES unit itself's and its power conversion system's (PCS') reliability, as well as their various connection topologies. This paper devises a comprehensive sensitivity study on how each of these elements can affect overall generating system reliability. given the plethora of RES configurations and components, it is of import to identify the most vulnerable element in RES. In this work, component importance is extended, for the first time, to generating capacity adequacy assessment (HLI). Measurement index is the centerpiece in reliability importance. New indices have to be introduced to facilitate the study. While the physical meaning of previously developed indices is lost, in this study indices are proposed based on traditional importance measures, of which the physical meaning are strictly retained and consistent with the definitions. With the proposed assessment technique, components in various RES configuration can be ranked according to their reliability importance. It is found in the numerical study that different importance measures (such as risk-achievement based measures and risk-reduction based measures) can result in different rankings. Studies on contributing factors of the reliability importance are also performed. As more and more RES gaining foothold in generating systems, the proposed technique assist to achieve targeted reliability level of the system, by easily identifying and prioritizing reliability improvement tasks among various units/components in the increasing complex system.

This paper proposes a novel adaptive range composite differential evolution (ARCoDE) algorithm to efficiently and accurately solve optimal reactive power dispatch (ORPD) problem. Because of a novel adaptive range strategy for control parameters, the proposed ARCoDE possesses superior exploration and exploitation capabilities that can efficiently handle the ORPD problem involving complicated constraints and discrete and continuous variables. This has been demonstrated in case studies using the IEEE optimal power flow testbeds considering complex wind and demand scenarios. The superior performance of ARCoDE has been further validated through comparisons with several award-winning algorithms in 2014 IEEE Competition on “Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems”, given limited iterations of in evolutionary optimization process.

This paper proposes a novel brake mechanism in robot joints, especially for space robot joints which require low power consumption and high reliability. The key point of the brake mechanism is that the torque spring is replaced with the Nd-Fe-B permanent magnet. Due to the position and force relationship between the permanent magnet and the electromagnet in the brake mechanism, the stroke of the brake armature can be extended so that the bolt type brake structure is adapted and the braking torque can be infinite theoretically. Meanwhile, the holding current in the electromagnet can be decreased to save energy and reduce heat generation. The magnetic circuit models of the electromagnet and permanent magnet in the brake mechanism are established and analyzed. In order to improve the response speed of the brake mechanism, a novel H-bridge brake power supply circuit is proposed. To verify the benefits of the design, a permanent magnet brake prototype with the H-bridge power supply circuit is set up. While the holding current is consistent with the peak current in the conventional torque spring motor brake, the proposed permanent magnet brake mechanism can decrease the holding current below 0.1 A.

A phase unwrapping algorithm assisted by light field without additional time coding is proposed, which can be used in various aspects of optical imaging and metrology. The phase consistency between the light field and the industrial camera is the sticking point to convert the angular resolution of the light field into the order of phase. By virtue of the angle information recorded by the light field, the correct order can be uniquely determined from several candidate orders without confusion in the measurement volume. The mandatory requirements for system fixation and pre-calibration and the connection between the light field and industrial camera are prerequisites for order uniqueness. The mapping relationship established by the phase implies coordinate conversion, lens distortion and other factors, thus providing a high degree of stability for the transmission of phase order information. The achieved results demonstrate that our method is robust and can be taken as an effective tool for means of phase unwrapping.

The integrated energy system can realize the coupling and complementation of various energy sources such as cold, heat and electricity, and plays an important role in the consumption of renewable energy. This paper proposes a bi-level optimal design method for integrated energy system from both economic and carbon emissions aspects. The upper model aims at maximizing the system economy and optimizes the selection and capacity allocation of renewable energy power generation, storage and conversion equipment to meet the demands in the region. The lower model aims at maximizing the environment-protection performance and minimizes the system’s carbon emissions. Since the lower model contains binary variables to characterize trading states and charging and discharging states, the model cannot be transformed into a mathematical program with equilibrium constraints. To effectively handle this problem, the reformulation and decomposition method is adopted. Case studies show that this bi-level model can effectively consider the influence of the objective function in the lower model on the optimal capacity configuration in the upper model, avoid the influence of different objective weights when the multi-objective model is converted to the single-objective model, and obtain the global optimal solution.

The low-power design of software and hardware is crucial for the efficiency and sustainability of embedded systems. Accurate energy consumption measurement plays a vital role in evaluating the energy performance of software and hardware, which also provides design insights to develop new paradigms for algorithm or code optimization in terms of energy efficiency and system cost. A few literature has surveyed this research field, but it lacks multi-faceted comparisons and comprehensive analysis. In this paper, we first show the necessity of accurate energy consumption analysis for embedded systems. Then, we study major methods in literature for measuring energy consumption of embedded systems, which can be summarized with three categories: 1) measurement-based energy profiling, 2) model-based energy estimation, and 3) simulator-based energy estimation. Some subcategories are further made based on characteristics of these approaches. The pros and cons of each category have been reviewed and evaluated through multi-faceted comparisons. Finally, for transparent energy analysis and improving the energy efficiency of embedded systems, we come up with some contributing factors that matter the energy consumption measurements and discuss the challenges and future research directions.

Under the protection of the patent system, an inventor can patent his or her invention, to make an economic profit from the patent, thus encouraging further invention. However, a patent is territorial. To get an international patent, the invention should be patented several times in all target patent offices, even with the help of international conventions like PCT, leading to inefficiency, expensiveness, and uncertainty. Therefore, we construct an international patent application system based on a permissioned blockchain named PABC. Patent applications can be approved or rejected without relying on one patent office. Each patent office in a country or region manages a peer node of the system, reaching agreements of all the applications. First, we build the model of the system and detail the three-layer architecture of PABC Core. Then, we implement PABC on a permissioned blockchain platform - Hyperledger Fabric. In addition, experiments about its throughput, transaction latency, and failure rate were carried out. The results show that PABC can meet current needs.

Global navigation satellite system (GNSS) based passive radar has been applied in the detection of moving targets. However, the low signal power of GNSS on the earth's surface limits the application of this technology for the long-range or low-observable target detection. Increasing the observation time can effectively improve the detection capability. But the target motion involves the range cell migration (RCM) and the Doppler frequency migration (DFM) over the long observation time, which results in the integration gain loss and lower the detection performance. This article proposes a new hybrid coherent and noncoherent integration method named the keystone transform and Lv's distribution. The proposed method not only compensate the RCM and the DFM but also provide coherent and noncoherent integration gains to increase the signal-to-noise ratio. The simulated results and the field trial results demonstrate that the detection performance of the proposed method is superior to the other two known moving target detection methods. And the analysis of the computational complexity shows that the proposed method and the other two methods are in the same order of O(N3logN).

Image registration is a fundamental and important task in remote sensing. In this article, we focus on feature-based image registration. Existing attempts often require estimating a transformation model or imposing relaxed geometric constraints to establish reliable feature correspondences. However, a parametric model cannot handle image pairs undergoing complex transformations, and relaxed methods discard a lot of structure information and the results are often coarse. To solve the above issues, we propose a local structure visualization descriptor to preserve the original structure information, and cast the feature matching task into an evaluation of the consensus of visual structure under a convolutional neural network. This strategy can effectively measure the similarity of neighborhood structure for mismatch removal. In summary, our method does not depend on a specific transformation model and can process arbitrary remote sensing images (e.g., different deformations, severe outliers, various rotations, and scaling changes). To demonstrate the robustness of our strategy for image registration, extensive experiments on various real remote sensing images for feature matching are conducted and compared against nine state-of-the-art methods, where our method gives the best performances in most scenarios.

Cracks and bugholes (surface air voids) are common factors that affect the quality of concrete surfaces, so it is necessary to detect them on concrete surfaces. To improve the accuracy and efficiency of the detection, this research implements a novel deep learning technique based on DeepLabv3+ to detect cracks and bugholes on concrete surfaces. Firstly, in the decoder, the $3\times 3$ convolution of the feature fusion part is improved to a 3-layer depth separable convolution to reduce the information loss during up sampling. Secondly, the original expansion rate combination is changed from 1, 6, 12, 18 to 1, 2, 4, 8 to improve the segmentation effect of the model on the image. Thirdly, a weight value is added to each channel of the Atrous Spatial Pyramid Polling (ASSP) module, and the feature maps that contribute significantly to the target prediction are learned and screened. To use this method, a database is built containing 16, $662\,\,256\times256$ pixel images of bugholes and cracks on concrete surfaces. The two defects included in those images are labeled manually. The DeepLabv3+ architecture is then modified, trained, validated and tested using this database. A strategy of model-based transfer learning is applied to optimize and accelerate the learning efficiency of the model. The weights and biases of the Xception part of the model are initialized by the pretrained backbones. The results are 97.63% (crack), 93.53% (bughole) Average Precision (AP), 95.58% Mean Average Precision (MAP) and 81.87% Mean Intersection over Union (MIoU). A comparative study is conducted to verify the performance of the proposed method, and the results demonstrate that the proposed approach performs significantly better in crack and bughole detection on concrete surfaces.

In most of the existing work for activity recognition, 3D ConvNets show promising performance for learning spatiotemporal features of videos. However, most methods sample fixed-length frames from the original video, which are cropped to a fixed size and fed into the model for training. In this manner, two problems limit the model performance for recognition. First, the cropped video clips are incomplete or even distorted in appearance, resulting in a large gap between the feature representation and semantics of human activity. Second, the useful features of longer video frame sequences are weakened by the repeated stacking of 3D convolution over deep networks due to the limitations of GPU memory and computing ability. This article proposes a method based on a 3D backbone network for multi scale spatial feature representation, which uses a pyramid pooling layer to allow the input of video frames at different scales, and then aggregates short-term spatial–temporal features into a long-term video-level representation. Objection detection is used as a component of model testing to explore the improvement of activity recognition considering the large amount of space–time redundancy in real life videos. An experiment is performed on the principal video dataset, UCF101, and the proposed method presents a competitive performance.

The massive data generated by large-scale dynamic systems makes its optimization facing a tough challenge. Traditional White Box-based methods directly model the internal operating mechanism of the system, so massive amounts of measured data need to be handled, which is costly and time-consuming. The poor interpretability of the Black Box-based methods makes it difficult to adapt to the dynamic environment. Thus we propose a novel Gray Box-based approach namely Deep Reinforcement Learning-enabled Constraint Set Inversion Algorithm (DRESIA), which establishes a quantitative model of the nonlinear interoperability effects of system internal states which simplifies the White Box's complex mechanism of reconstruction and prediction and retains the interpretability of the model, therefore improves the prediction efficiency of feasible region while also improving the generalization ability. It further improves the dynamic adaptability of the modeling environment, which provides a new performance balancing scheme for system modeling. Under the premise that the large-scale 5G Cyber-Twin system satisfies the given Quality of Service (QoS) requirements, we perform DRESIA to realize the efficient and dynamic optimal search of feasible region, the results show that the DRESIA reduces the computational cost, and balances the accuracy and robustness of the feasible region, which validate the effectiveness and superiority of Gray Box-based approach.

Coalition-based unmanned aerial vehicle (UAV) swarms havebeen widelyused in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analyses are made and a scheduling method based on the shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problems are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.

The atmosphere-land exchange inverse disaggregation (DisALEXI) algorithm is a multi-scale energy balance model that estimates evapotranspiration (ET) using land-surface temperature (LST) as a driving remote sensing input. Using LST products from ECOSTRESS, a thermal radiometer mounted on the International Space Station, DisALEXI ET products have been produced over the contiguous United States (CONUS) at 70 m resolution. The goal of this study is to demonstrate the accuracy of the CONUS-wide ET produced by the Jet Propulsion Laboratory (JPL) and to compare the results with the original DisALEXI ET produced by researchers at the United States Department of Agriculture (USDA). DisALEXI-USDA has been produced ad-hoc using Landsat LST, and is routinely produced over six target sites using ECOSTRESS LST. DisALEXI-JPL was implemented in order to expand the spatial coverage. DisALEXI-JPL was evaluated at 26 CONUS eddy covariance sites, showing good correlation, with R2 = 0.80 and RMSE = 0.81 mm/day, which is comparable to previous DisALEXI validation studies (RMSE ∼1 mm/day). The two DisALEXI implementations compared well, with R2 = 0.92. This article evaluates DisALEXI-JPL and shows that the algorithm is valid over a larger segment of CONUS. We also show the impact of quality flags, as pixels with high view zenith angles or high aerosol optical depth showed greater deviation from field measurements. As a product demonstration, we show a regional map of fine-scale ET, where the fine-scale variation over wider areas can detect small areas of stress much sooner than products with coarse resolution representing average conditions.

Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superior coding performance, many state-of-the-art block-based compression standards such as High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are designed primarily for YUV 4:2:0 format, where U and V components are subsampled by considering the human visual system. This paper investigates various DLEC designs to support YUV 4:2:0 format by comparing their performance against the main profiles of HEVC and VVC standards under a common evaluation framework. Moreover, a new transform network architecture is proposed to improve the efficiency of coding YUV 4:2:0 data. The experimental results on YUV 4:2:0 datasets show that the proposed architecture significantly outperforms naive extensions of existing architectures designed for RGB format and achieves about 10% average BD-rate improvement over the intra-frame coding in HEVC.