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A variable message sign (VMS), which is a key component of intelligent transportation systems, has been frequently used in the management of urban roads and motorways to provide drivers with real-time traffic condition information about a road section or area. Nevertheless, there is a lack of unified regulations for VMS design, which makes it difficult to accurately determine the understandability and legibility of VMS information. In practice, inappropriate designs of VMSs are common, such as overload of VMS information and excessive number of phases, particularly on the urban roads of China. Building on our earlier findings obtained by surveys, in this study, field tests were conducted to assess the reliability of VMS information in terms of the visual perception characteristics of drivers. For a full visual perception, the information displayed on a VMS must be reliable, i.e., it should be easy for drivers to detect, obtain, and understand the messages they require. Two basic theories—information theory and visual perception theory—were introduced to build a quantification model of VMS information, select evaluation indices, and design the experimental process. A total of 24 drivers participated in the field experiments and questionnaires. Five indices—information obtainment rate, periodic validity, driver subjective scoring and the corresponding UI (represents information intelligibility), and CE (represents information legibility)—were analyzed to evaluate their relationships to VMS information reliability. The results confirmed that the amount of information, and the scrolling period considering redundant distance, significantly affect the reliability of VMSs information. For static VMSs, the information obtainment rate and the subjective scoring decrease with increasing amount of information. The recollection accuracy of the drivers significantly declines when the amount of information shown increases to 90 bits, corresponding to an information obtainment rate of less than 0.8 and UI equal to 0. For dynamic VMSs, the information reliability deteriorates as the scrolling period shortens and the number of phases increases. Unreasonable reliability is found when the periodic validity is less than 0.9, i.e., the actual scrolling period is more than 10% less than the calculated one, corresponding to CE equal to 0. The reliability of information was evaluated by combining the subjective scoring of the drivers and a data-based statistical analysis and considering driving safety. Accordingly, it was recommended that 90 bit is the maximum amount of information to be shown on a VMS and that the preferred scrolling period of each phase of a dynamic VMS is 5 s. The results of this study support the objective of providing reliable information to drivers by addressing the problems related to the amount of information and presentation time for VMSs. These findings provide a basis for determining the thresholds for VMS information to promote practical and user-friendly designs of VMSs on urban roads.

Battery pack performance is the main concern for electric vehicles and energy storage systems. However, charge imbalance is inevitable due to inconsistent manufacturing techniques and environmental conditions. Charge imbalance reduces the power performance and available energy of battery packs. Hence, it is necessary to perform battery equalization. This article proposes an active equalization circuit and a novel equalization strategy to achieve energy redistribution. A bidirectional equalization topology consisting of a forward transformer and switch matrix is proposed first. Then, an innovative equalization strategy based on clustering analysis and genetic algorithm (GA) is developed. Clustering analysis is introduced to identify the target cells to be balanced. To further increase the speed of the equalization process, GA is employed to optimize the classification results. Finally, a series of experiments were conducted to confirm the effectiveness of the proposed topology and the strategy. Both the simulation and experimental results validate that the proposed equalization strategy not only improves the inconsistency but also increases the equalization speed. In practical battery pack experiments, the pack capacity is improved by 16.84% after equalization, and the equalization time is decreased by 23.8% using the proposed method.

Recommendation systems have been widely developed and introduced in numerous applications. However, due to the lack of sufficient user feedback data, the recommendation performance of such systems is often affected by data sparsity. To address this problem, a multi-view fusion recommendation algorithm with an attentive deep neural network is proposed. A two-stage joint learning solution is designed in the proposed model, which combines user attributes, item attributes, and user-item interaction information into a unified framework. The convolutional neural network and attention mechanism are applied to improve effect of extracting features from user and item attributes. The extended deep neural network model based on matrix factorization and multiple-layer perception is used to enhance the feature extraction of user and item interaction information. Experimental results on the MovieLens-1 M and Book-Crossing real datasets show that the proposed algorithm can achieve the best recommendation accuracy compared with other classical recommendation algorithms, even with extremely sparse data.

Crowd counting has received increasing attention in the field of video surveillance and urban security system. However, many previous models are prone to poor generalization capability to unknown samples when limited labeled samples are available. To improve or mitigate the above weakness, we develop a novel Pseudo-label Growth Dictionary Pair Learning (PG-DPL) method for crowd counting. To be exact, we treat crowd counting as a task of classification and leverage dictionary learning-based (DL) strategy to target the task. Considering that being short of diverse training samples and imbalanced distribution across different classes in crowd scene inevitably result in large prediction deviation caused by the DL model, we propose to apply pseudo-label growth (PG) and adaptive dictionary size (ADS) to improve the accuracy of crowd counting with limited labeled samples. In the proposed method, PG optimizes the initial prediction via reconstructing the discriminant term to improve the robustness of learned dictionary, while ADS explores the imbalanced distribution among different classes to adapt to the size of class-specific dictionary. Extensive validation experiments on five benchmark databases indicate that the proposed PG-DPL can achieve compelling performance compared to other state-of-the-art methods.

The Hainan Island has a generally high biological diversity with a wide variety of plant species, some of which are listed as endemic to the island. It is time-consuming and difficult, even for the botanist experts to determine the name of species based on observations. Automated plant identification enables experts to process significantly greater numbers of plants with higher efficiencies in shorter periods of time. However, plant recognition is a kind of fine-grained visual recognition problem, which is relatively harder than conventional image recognition. In this paper, we employ a Deep Convolutional Neural Network (DCNN) trained on the ImageNet database, which contains millions of images, and then transfer the learning information for automated plant identification based on flower and fruit images. First, we modify the last three layers of the pre-trained network in order to adapt ResNet-50 model to our classification task, and replace the fully connected layer in the original pre-trained network with another fully connected layers, in which the output size represents the class of plants. Secondly, we use transfer experience and fine-tuned pre-trained DCNN for experiments using flower and fruit images. Finally, we evaluate the proposed network on two available botanical datasets: the Oxford flowers dataset with 102 classes and the HNPlant flowers and fruits dataset with 20 classes, and determine the optimal values of the associated hyperparameters to improve the overall performance. Experiment results demonstrate that the highest classification accuracies exhibited by the proposed model on the Oxford-102 and HNPlant-20 datasets are 92.4% and 95.0%, respectively, thus establishing their effectiveness and superiority.

Over the past decades, the development of single spacecraft for outer space exploration requires more complicated design and higher investment. From the modular design ideas of the International Space Station on-orbit service, an interface design for orbital replacement unit (ORU) provides a path to develop modular spacecraft with reduction of design period and cost for satellites. A review on the ORU interfaces has been made in order to summarize the development of grabbing interfaces, mechanical docking interfaces, and multifunctional docking interfaces of ORUs in the recent 30 years. The interface parameters for both grabbing and mechanical docking processes have been summarized and compared based on the design features of ORUs. The characteristics of mechanical, power, data, thermal flow, and fluid interfaces of the ORUs are discussed as well. The future direction, challenges, and applications of the ORU interfaces have been investigated, and advanced technologies such as target recognition and precise measurement and control are suggested for the development of autonomous and intelligent interfaces. This work provides fundamental knowledge and guidelines for the development of ORU interfaces.

Ubiquitous power Internet of Things (IoT) is a smart service system oriented to all aspects of the power system, and has the characteristics of universal interconnection, human-computer interaction, comprehensive state perception, efficient information processing, and other convenient and flexible applications. It has become a hot topic in the field of IoT. We summarize some existing research work on the IoT and edge computing framework. Because it is difficult to meet the requirements of ubiquitous power IoT for edge computing in terms of real time, security, reliability, and business function adaptation using the general edge computing framework software, we propose a trusted edge computing framework, named “EdgeKeeper,” adapting to the ubiquitous power IoT. Several key technologies such as security and trust, quality of service guarantee, application management, and cloud-edge collaboration are desired to meet the needs of the edge computing framework. Experiments comprehensively evaluate EdgeKeeper from the aspects of function, performance, and security. Comparison results show that EdgeKeeper is the most suitable edge computing framework for the electricity IoT. Finally, future directions for research are proposed.

Formal verification can mathematically prove whether a software satisfies the requirements described in its design. In traditional software development, even if the software systems, especially the operating system for Internet of Things in smart cities, passes the verification test, it is difficult to explain its correctness. The implementation of formal verification after the design and development process proves that the software system meets the expected requirements. This will become a trend for future software development. In this paper, we model the system state of the X86 architecture on the assembly layer, and use a micro operating system prototype for Internet of Things in smart cities as an example to explain the proposed method that can verify operating systems for Internet of Things in smart cities on the assembly layer. The verification result shows that this method is feasible.

Nearest Neighbor method (KNN) is a typical method to solve the problem of automatic image annotation (AIA). However, traditional AIA methods based on KNN only consider the relationships among images and labels. In this paper, we propose an improved KNN image annotation method based on a tag semantic extension model (TSEM). Our approach uses the convolutional neural network (CNN) to extract image features and predicts image tags automatically via nearest features. Different from existing work, the proposed method considers correlations among images, correlations between images and labels and those among labels. Additionally, a label quantity prediction (LQP) model is proposed to predict the number of tags, which further improves the tag prediction accuracy. Comparison experiments were performed on three typical image datasets Corel5k, ESP game and laprtc12. Experimental results show that the average F1 of our model is 0.427, which outperforms the state-of-the-art KNN image annotation methods.

A measurement system based on binocular vision and industrial robots was proposed to meet in-situ high-efficiency and high-precision measurement of large-scale high reflective components. Through target detection, the system could accurately segment the region of interest where the marking points were located, effectively reduce the false extraction caused by high reflective surface, and improve the robustness and measurement efficiency of binocular vision measurement system. At the same time, through controlling the movement of the robot end effectors, the measurement of the whole component in multi-position was completed, and then the measurement results of different positions were unified in the same coordinate system through the coordinate transformation relationship in multi-position. The experimental results show that in the range of 1.2 m × 1 m, the root-mean-square (RMS) of vision measurement precision can reach to 0.049 mm in nine positions. The whole measurement system can effectively complete the high-efficiency and high-precision measurement of the simulated cabin components.

Modern HPC applications pose high demands on I/O performance and storage capability. The emerging non-volatile memory (NVM) techniques, such as Phase Change Memory and STT-RAM, offer low-latency, high bandwidth, and persistence for HPC applications. However, the existing I/O stack, including OS, high level library, I/O middleware, and applications, are designed and optimized based on an assumption of disk-based storage. To effectively use NVM, we must re-examine the existing I/O sub-system to properly integrate NVM into it. Using NVM as a fast storage, the previous assumption on the inferior performance of storage (e.g., hard drive) is not valid any more. The performance problem caused by slow storage may be mitigated; the existing mechanisms to narrow the performance gap between storage and CPU may be unnecessary and result in large overhead. Thus fully understanding of the impact of introducing NVM into the HPC software stack demands a thorough performance study. In this paper, we analyze and model the performance of I/O intensive HPC applications with NVM as a block device. We study the performance from three perspectives: (1) the impact of NVM on the performance of traditional page caches; (2) a performance comparison between MPI individual I/O and POSIX I/O; and (3) the impact of NVM on the performance of collective I/O. We reveal the diminishing effects of page caches, ignorable performance difference between MPI individual I/O and POSIX I/O, and performance disadvantage of collective I/O on NVM due to unnecessary data shuffling. We model the performance of MPI collective I/O and study the complex interaction between data shuffling, storage performance, and I/O access patterns. Extensive experiments have been conducted to verify our analysis.Keywords: NVM, page cache, MPI I/O, Collective I/O

Overhead transmission line faults are mostly single‐phase grounding faults, and to ensure the power supply reliability of photovoltaic power plants in the cases of tie line transient faults, a feasible solution is to employ the single‐phase reclosing. However, for the single‐phase reclosing of the tie line, photovoltaic power source may face the over‐voltage problem during the non‐full phase operation, which will seriously affect the success rate of the single‐phase reclosing. This paper, according to different non‐full phase operation control strategies of photovoltaic power source, establishes the corresponding sequence network analysis models, and proposes the theoretical calculation method of non‐full phase operation voltage, which takes account of different neutral point grounding modes of step‐up transformer and different load levels. Meanwhile, the closing impulse problem of the single‐phase reclosing is discussed. The correctness of the theoretical method is verified by digital simulation results. On this basis, the applicable scope and application suggestions of the single‐phase reclosing are given. The research conclusions can provide important guidance and reference for the application of single‐phase reclosing technology in tie line of renewable energy power plant.

In this article, a concise and practical analytical method of transmitted torque calculation for pre-design of Axial Permanent Magnetic Coupler (APMC) by combining Faraday's law with magnetic circuit model is proposed. The proposed method is able to consider the three-dimension (3D) edge magnetic effect as well as the induced magnetic field generated by eddy currents. The correctional skin depth factor is also taken into consideration. In order to verify the validity of the proposed method, the 3D finite element method (FEM) is employed. The results present that the relative error between the proposed method and 3D FEM in the normal working slip range is within 5.2%. Finally, detailed analysis is carried out to investigate the influence of the common parameters on the transmitted torque for APMC. Moreover, the deficiencies of this proposed method and the corresponding design considerations are also discussed, which offers useful and auxiliary information for the pre-design of APMCs.

A novel multiresonant dc–dc converter with notch filter function is proposed in this article. The converter can regulate the output voltage over a wide range and has significant advantages in terms of gain. Due to its gain characteristics, the multiresonant converter has better inherent soft startup capability and over-current protection function. In addition, the switches of the converter can realize zero-voltage switching over the entire output-voltage range, and the output rectifier diode can realize zero-current switching. Compared with the traditional LLC converter, the multiresonant converter can use third harmonic to transmit the active power, and the loss caused by the reactive power cycle of the converter is reduced, which improve the efficiency. The converter parameters are optimized from the perspective of gain and efficiency in this article. Finally, a 2.5-kW experimental prototype is designed and the peak efficiency is 97.27%. The correctness of the theoretical analysis and the rationality of the parameter design are proved by the experimental results.

The development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition during the manufacturing process. In particular, the characteristics of sensor data are temporal. Most the existing approaches for time series classification are not applicable to adaptively extract the effective feature from a large number of sensor data, accurately detect the fault, and provide the assignable cause for fault diagnosis. This study aims to propose a multiple time-series convolutional neural network (MTS-CNN) model for fault detection and diagnosis in semiconductor manufacturing. This study incorporates data augmentation with sliding window to generate amounts of subsequences and thus to enhance the diversity and avoid over-fitting. The key features of equipment sensor can be learned automatically through stacked convolution-pooling layers. The importance of each sensor is also identified through the diagnostic layer in the proposed MTS-CNN. An empirical study from a wafer fabrication was conducted to validate the proposed MTS-CNN and compare the performance among the other multivariate time series classification methods. The experimental results demonstrate that the MTS-CNN can accurately detect the fault wafers with high accuracy, recall and precision, and outperforms than other existing multivariate time series classification methods. Through the output value of the diagnostic layer in MTS-CNN, we can identify the relationship between each fault and different sensors and provider valuable information to associate the excursion for fault diagnosis.

The traditional wind tunnel strain balance design cycle is a manual iterative process. With the experience and intuition of the designer, one solution that meets the design requirements can be selected among a small number of design solutions. This paper introduces a novel software integration-based automatic balance design optimization system (ABDOS) and its implementation by integrating professional design knowledge and experience, stepwise optimization strategy, CAD-CAE software, self-developed scripts and tools. The proposed two-step optimization strategy includes the analytical design process (ADP) and the finite element method design process (FEDP). The built-in optimization algorithm drives the design variables change and searches for the optimal structure combination meeting the design objectives. The client-server based network architecture enables local lightweight design input, task management, and result output. The high-performance server combines all design resources to perform all the solution calculations. The development of more than 10 balances that have been completed and a case study show that this method and platform significantly reduce the time for design evaluation and design-analysis-redesign cycles, assisting designers to comprehensively evaluate and improve the performance of the balance.

A hybrid full-bridge converter that consists of a secondary-side phase-shift full-bridge converter (SPS-FBC) and a full-bridge LLC converter is proposed in this study. The wide zero-voltage switching (ZVS) range of the primary switches can be achieved and the series inductor of the SPS-FBC converter does not need to be specially designed to help achieving the soft switching operation with a set of shared high frequency inverter circuits. The dual outputs of the proposed hybrid converter are connected in series, reducing the voltage stress of the power device in the secondary side. The whole dc-output voltage can be regulated by primary-side phase-shift and secondary-side phase-shift dual-mode control scheme within the desired voltage range, by which the two output voltages are regulated independently and free from cross-regulation. Therefore, the proposed converter is suitable for high voltage, high power and wide output voltage range applications. The working principle and steady-state characteristics of the proposed converter are presented and verified in experiment of a 3 kW prototype. The experimental results show that the proposed converter maintains high efficiency throughout the voltage range, and the peak efficiency is 97.43%.

Near-field acoustical holography (NAH) based on equivalent source method (ESM) is an efficient technique for sound source identification. Conventional ESM with Tikhonov regularization (TRESM), ESM based on CVX MATLAB toolbox (CVX) and wideband acoustic holography (WBH) are commonly used methods for calculating equivalent source strengths. However, all of them have their respective limitations. To address some of these, an alternating iterative algorithm for sound source identification based on equivalent source method (AIESM) is proposed in this article, which is a combination of alternating direction method and a non-monotone line search technique. The method makes use of sparse regularization under the principle of compressive sensing (CS) to calculate equivalent source strengths. Moreover, inspired by the idea of functional beamforming (FB), AIESM with order n can yield an improved dynamic range when detecting the source location. Numerical simulations are carried out at different frequencies, and the results suggest that the computational efficiency of the proposed method is close to that of TRESM. In addition, AIESM has a better reconstruction accuracy than TRESM and WBH in a relatively wide frequency range. Compared with ESM based on CVX, AIESM is slightly better in reconstruction accuracy and has a higher computational efficiency. Meanwhile, AIESM with order n can provide more accurate source position and better resolution. The validity and practicality of the proposed method are further supported by experimental results.

With the development of deep learning, autonomous robot systems grow rapidly and require better performance. Robot Operating System 2 (ROS2) has been widely adopted as the main communication framework in autonomous robot systems. However, the performance of ROS2 has become the bottleneck of these real-time systems. From our observations, we find that it can take a large amount of time to serialize complex message in communication, especially for some high-level programming languages, including Python, Java and so on. To address this challenge, we propose a novel technique, called adaptive two-layer serialization algorithm, which can achieve good performance in communication for different kinds of messages. Experimental results show that our algorithm can achieve significant performance improvement over traditional methods in ROS2, up to 93% improvement in our framework. We have successfully applied our proposed techniques in a real autonomous robot system.

To accurately detect defects in patterned fabrics, a novel detection algorithm combining template correction with primitive decomposition (TCPD) method is proposed in this study. First of all, the fabric image is segmented into lattices according to variation regularity. Then, the authors propose an effective anisotropy correction method to reduce the interference of stretching and distortion between lattices. On the basis of the proposed PD method, the corrected lattice is further divided into graphic elements with smaller particle size. The smaller primitives make the boundary of the detection results more accurate. Moreover, a self-supervised threshold selection strategy is presented, which utilises the defect-free regions to obtain threshold. Furthermore, this strategy makes each primitive has corresponding criteria for judging defects. Extensive experiments demonstrate that TCPD method achieves 0.8127 true positive rate, 0.3889 positive predictive value and 0.5261 f value in star-patterned fabrics.

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

The data-driven interwell simulation model (INSIM) has been recognized as an effective tool for history matching and interwell-connectivity characterization of waterflooding reservoirs. INSIM-FT-3D (FT: front tracking) was recently developed to upgrade the applicationdimension of INSIM series data-driven models from two-dimensional (2D) to three-dimensional (3D). However, INSIM-FT-3D cannot accurately infer the dynamic change of well-connectivity and predict well's bottom-hole pressure (BHP). The main purpose of this study intends to expand the capability of INSIM-FT-3D to empower for the assimilation of BHPs, the reliable prediction of water breakthrough and the characterization of dynamic interwell-connectivities.The default setting of well index (WI) in INSIM-FT-3D based on Peaceman's equation does not yield accurate BHP estimates. We derive a WI that can honor the BHPs of a reference model composed of a set of 1D connections. When history matching BHPs of a 3D reservoir, we show that the derived WI is a better initial guess than that obtained from Peaceman's equation. We also develop a flow-path-tracking (FPT) algorithm to calculate the dynamic interwell properties (allocation factors and pore volumes (PVs)). Besides, we discuss the relationship between the INSIM-family methods and the traditional grid-based methods, which indicates that the INSIM-family methods can calculate the transmissibility of the connection between coarse-scale cells in a more accurate manner. As an improvement of INSIM-FT-3D, the newly proposed data-driven model is denoted as INSIM-FPT-3D.To verify the correctness of the derived WI, we present a 1D problem and a T-shaped synthetic reservoir simulation model as the reference models. BHPs and oil production rates are obtained as the observed data by running these two reference models with total injection/production-rate controls. An INSIM-FPT-3D model is created by specifying the transmissibilities and PVs that are the same as those in the reference model. By applying the derived WIs in INSIM-FPT-3D, the resulting BHPs and oil rates obtained agree well with the reference model without further model calibration. Applying INSIM-FPT-3D to a synthetic multi-layered reservoir shows that we obtain a reasonable match of both BHPs and oil rates with INSIM-FPT-3D. Compared with the FrontSim model, the INSIM-FPT-3D model after history matching is shown to match the dynamic PVs from FrontSim reasonably well and can correctly predict the timing of water breakthrough.By allowing for the assimilation of BHP data, we enable INSIM-FPT-3D to history match a green field with limited production history and forecast the timing of water breakthrough. The improved INSIM-FPT-3D leads to more accurate characterization of the interwell connectivities.

Autonomous navigation systems consist of multiple software modules, such as sensing, object detection, and planning, to achieve traffic perception and fast decision making. Such a system generates a large amount of data and requires data processing and communication in real-time. Although accelerators, such as GPUs, have been exploited to speed up data processing, communicating GPU messages between modules is still lacking support, leading to high communication latency and resource contention. For such a latency-sensitive and resource-limited autonomous navigation system, high performance and lightweight message communication are crucial and demanding. To obtain both high performance and low resource usages, we first propose a novel pub-centric memory pool and an on-the-fly offset conversion algorithm to avoid unnecessary data movement. Secondly, we combine these two techniques and propose an efficient message communication on a single GPU. Finally, we extend this approach to multi-GPU and design a framework that natively supports GPU message communication for Inter-Process Communication. With comprehensive evaluation, results show our approach is able to reduce communication latency by 53.7% for PointCloud and Image messages compared to the state-of-the-art approach. Moreover, in the real autonomous navigation scenario, our approach reduces the end-to-end latency by 29.2% and decreases resource usage up to 58.9%.

RWG based method of moment (MOM) is invalid in calculating the radar cross section (RCS) of large-scale chaff cloud. It causes unimaginable computational burden. This paper studies in detail the principle and solution process of the method of moments using pulse basis function and obtains the RCS of chaff cloud in a relatively short time. Compared with the MoM with RWG basis function, the proposed method greatly reduces the number of unknowns and thus improves the computational efficiency.

Intelligent reflecting surface (IRS) has been considered as a promising technology in the next-generation wireless communication systems, since it can improve the coverage and power efficiency because of its ability to reflect the signal with a controlled phase shift. Inspired by this, in our proposed cooperative radar-communication system, the base station (BS) signal is also used for detection at the radar, while the IRSs are utilized at the radar and BS to enhance the signal transmission and receiving simultaneously. A successive IRS phase shift optimization method is proposed to get the suboptimal phase shift with low computational complexity. The convergence performance and quantization of IRS phase shift are discussed. The simulation results verify the effectiveness of utilizing the IRS and BS to improve the detection performance regarding the detector’s SNR.

Echo estimation of chaff cloud before they are put into use is a crucial issue in anti-electromagnetic interference. It is quite difficult to calculate the echo of the chaff cloud that contains millions of chaff elements. Comparing with the general numerical method in time-domain, a more efficient method to calculate the echo of the chaff cloud is proposed in this paper. A superposition strategy is introduced to calculate the echo by summarizing the echo of a single chaff element that is pre-stored in the database. This strategy is available when the interval length between any two elements in cloud is larger than two wavelengths. Then, we give the echo of the chaff cloud illustrated by two modulated incident signals.

Aiming at the traffic safety problem caused by sudden agglomerate fog on the highway, this paper designs an early warning system of the robot for monitoring agglomerate fog on the highway based on Beidou short message and MQTT protocol. This system monitored environmental parameters such as visibility, location information, temperature and humidity in real time by the robot for monitoring agglomerate fog on the highway, and adopts the Beidou short message and MQTT protocol dual-channel communication mode to achieve two-way data communication with the cloud platform and server. The problem of data transmission loss caused by no mobile communication network is solved. Use the Internet of Things cloud platform and WEB application development technology to realize a system with the functions of monitoring data visualization, data monitoring and remote control of robot movement. The experimental results show that the communication that data interaction and remote control between the cloud platform and the robot is stable in the system, and the early warning of the level of fog is timely and effective, which improve the accuracy of monitoring agglomerate fog with broadly application foreground.

This paper addresses the design and control of an intelligent dual-arm mobile robot named Baymax-I. The mechanism design of Baymax-I is based on modular concepts, which is mainly composed of three parts, including main body, dual-arms and a mobile chassis. The main body is designed as human-like structure, carrying multiple sensors. UR3 manipulators are adopted as two arms. Each manipulator has six degrees of freedom. A two-finger grip pawl is mounted on the end of each manipulator. A MIR mobile chassis drives the Baymax-I motion with high maneuverability. As for software systems, the robot is designed based on hierarchical structure. The bottom driving layer is used to perform specific movements. The middle layer is responsible for processing sensor data. The top layer is algorithm execution layer. In the end, three experiments including SLAM, pressing the elevator and opening the fridge are presented to verify the feasibility and effectiveness of the proposed system design and control methods of the Baymax-I robot.

The Data Distribution Service (DDS) standard enables integration and scalability of common distributed simulation systems, and its flexible data communication mechanism can improve the communication efficiency. We propose an overall architecture for a distributed simulation system based on DDS, including the communication interface adapted to the EXata platform and the system clock synchronization module. The proposed simulation system can simulate a large-scale network and satisfies the demands of communications simulations.

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large ‘sample sizes’. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.

Multi-Camera multi-target tracking is essential in the research field of urban intelligence traffic. It shows that the task becomes challenging due to differences of illumination, angle, and occlusion under different cameras. In this paper, we propose an efficient multi-camera vehicle tracking system, which contains a model trained with multi-loss to extract appearance features, and a filter with spatial-temporal information between cameras. The proposed system includes three parts. Firstly, we generate tracklets in a single-camera with different views by vehicle detection and multi-target tracking. Secondly, we extract the appearance feature of each tracklet through the trained vehicle ReID model. Thirdly, we innovatively propose a matching strategy that calculates several factors, the similarity of appearance features, the time information, and the space information of target ID between adjacent cameras. The proposed system ranks the sixth place in the City-Scale Multi-Camera Vehicle Tracking of AI City 2021 Challenge (Track 3) with a score of 0.5763.

With the rapid development of smart power distribution network, the overall distribution network presents a distributed feature. The architecture of the traditional centralized distribution network has been unable to meet the massive distribution data access and real-time computing needs generated by these scenarios. In this paper, an intelligent distribution architecture for edge interoperability is designed. The distribution network is decomposed into a network composed of a backbone network and multiple edges. The architecture includes four parts: physical architecture, information architecture, edge object model, and edge intelligent mode. The proposed architecture replaces the original centralized distribution network architecture by edge division, which reduces the pressure of centralized storage, calculation, and transmission. Edge interoperability can be realized by the edge object model and information architecture corresponding to the physical architecture.

The small signal stability of grid-connected inverter is of great importance, and the state space model is one of the most efficient modeling methods to analyze the small disturbance stability of grid-connected inverters. The proposed small signal modeling of the grid-connected inverter system takes the time delays caused by digital control into consideration, and converts the time delay factor into a rational polynomial with the pade approximation method, so as to establish the state space model of the entire system. Eigenvalue analysis method is used to compare the small disturbance stability of the grid-connected inverter system with or without considering time delays under different environments. Numerical simulation results show that in the grid-connected inverter system with time delays considered, when the strength of the power grid changes, the system has discontinuous stable regions and the small signal model of the system with time delay considered is more accurate and more sensitive to changes in the controller parameters. Finally, Matlab/Simulink simulation results verify the correctness of the proposed theoretical analysis.

Blood Pressure (BP) is a rhythmic biological parameter which reflects the health state of individual's cardiovascular system. In this paper, the issue of duty cycle optimization for BP sensor in a Wireless Body Area Network (WBAN) scenario is studied. The sticking point is how to achieve a balance between monitoring reliability and energy conservation according to the BP status. Motivated by this, we use the peak and the trend of BP values over a period of time to characterize the BP status of a human, and establishes a two-dimensional BP evolution model based on the circadian and the seasonal variation of BP. Then we formulate the duty cycle optimization problem as a Markov Decision Process (MDP) and a Q-learning based algorithm is proposed as the solution. To test the validity of the algorithm, BP values of 180 days are generated based on the basic medical statistics. Simulation results show that the proposed algorithm significantly reduces energy consumption and effectively restrains delay in sensing abnormality.

Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R2, Kling–Gupta efficiency (KGE), structural similarity index (SSIM), spectral angle mapper (SAM), and peak signal-to-noise ratio (PSNR) of the simulated Sentinel-2 images generated by different methods. The results show that the simulated Sentinel-2 images generated by the MCcGAN have a higher quality and accuracy than those produced via the previous methods.

In recent years, the application of Electric-Gas Integrated Energy System(EG-IES) has continued to develop, the coupling and connection between energy sources have become more and more close, and the interaction between power system and natural gas system has become complex. The change of power system network state will not only affect its own system, but also affect the natural gas system through coupling devices, which will cause huge economic loss when it is serious. This paper first analyzes the impact of voltage sag events in the power system on the electricity-gas integrated energy system, and then considers the natural gas system user economic loss assessment based on the process parameter immune time curve to provide a basis for subsequent voltage sag control methods. Finally, an electric-gas integrated energy system composed of IEEE14-node and 10-node natural gas systems is selected as an example to verify the correctness and effectiveness of the evaluation method.

In this study, the performance of the mixed radio-frequency (RF)/free-space optical (FSO) system with co-channel interference (CCI) is investigated. The RF links are modelled as Rayleigh fading, whereas FSO links follow M-distribution with pointing errors. Assume the amplify-and-forward relay is corrupted by multiple CCIs, and both fixed and variable gain relay schemes are considered. Novel expressions for the end-to-end outage probability (OP), symbol error rate (SER) and outage capacity in different cases are presented and analysed. Numerical results demonstrate the existence of OP and SER floors in the presence of CCIs, which implies system performance is dominant by RF links, and interference is the main factor affecting the system performance. Additionally, the fixed gain scheme provides a performance enhancement compared with the variable gain scheme in the authors' system.

Traditionally, production optimization is performed based on repeatedly running a full-scale reservoir simulation model, which is quite time-consuming. Recently, we developed a computationally efficient data-driven model, which is referred to as interwell-numerical-simulation model (INSIM), for history matching, characterizing interwell connectivity, monitoring, and optimization for a waterflood. In this research, we develop a new workflow for fast optimization of waterflooding performance which only requires one single INSIM forward run. Specifically, the optimized well injection rate and production rate are determined with the help of the new INSIM-derived injection efficiency and the existing injector-centered allocation factor. The basic idea of the proposed optimization workflow is to recover more oil by injecting more water to those injector-producer pairs that have more remaining oil left; whereas decrease water injection for those connections without much oil left. The major criterion to tune the injection rate is based on the injection efficiency calculated with INSIM, a value to measure the effectiveness of injected water from an injector. The larger the injection efficiency is, the more oil can be produced by injecting a unit amount of water. The advantage of this method is that the well controls are optimized based on the reservoir-engineering point of view which is easy to understand and visualize. Also, the new workflow is very easy to implement and is much more computationally efficient than the traditional optimization workflow. We test the proposed workflow with two examples including a large-scale field example. The computational results indicate that, by using the proposed workflow, the field oil production is increased with less producing water.

In this work, a dynamic multi-market Cournot model is introduced based on a multi-markets’ specific inverse demand function. Puu’s incomplete information approach, as a realistic method, is used to contract the corresponding dynamical model under this function. Therefore, some stability analysis is used by the model to detect the stability and instability conditions of the system’s Nash equilibrium. Based on the analysis, some dynamic phenomena such as bifurcation and chaos are found. Numerical simulations and the Maximum Lyapunov exponent are used to provide experimental evidence for the complicated behaviors of the system evolution. It is observed that the equilibrium points of the system can loose stability via flip bifurcation or Neimark–Sacher bifurcation and time-delayed feedback control is used to stabilize the chaotic behaviors of the system.

A novel method for the eight most common driver’s distraction actions recognition is presented in this paper. To this end, a semi-cascade network (SCN) with very lightweight architecture is designed. The approach recognizes the morphology of the human face and hands to make judgments about the driver’s actions rather than just judging facial information. In order to subdivide similar actions, a SCN structure which effectively reduces the network’s scale is employed. A joint training approach is proposed for training the network and achieving 95.61% accuracy. In addition, to verify the validity of the method, a dataset containing 100,000 samples is created. Finally, a warning strategy is provided for our system and 93.9% warning rate for the driver’s distraction behavior is achieved.

More than 25% of vehicle kinetic energy can be recycled under urban driving cycles. A single-pedal control strategy for regenerative braking is proposed to further enhance energy efficiency. Acceleration and deceleration are controlled by a single pedal, which alleviates driving intensity and prompts energy recovery. Regenerative braking is theoretically analyzed based on the construction of the single-pedal system, vehicle braking dynamics, and energy conservation law. The single-pedal control strategy is developed by considering daily driving conditions, and a single-pedal simulation model is established. Typical driving cycles are simulated to verify the effectiveness of the single-pedal control strategy. A dynamometer test is conducted to confirm the validity of the simulation model. Results show that using the single-pedal control strategy for electric vehicles can effectively improve the energy recovery rate and extend the driving range under the premise of ensuring safety while braking. The study lays a technical foundation for the optimization of regenerative braking systems and development of single-pedal control systems, which are conducive to the promotion and popularization of electric vehicles.

Supercomputers’ capability is approaching the exascale level, which enables large computing systems to run more jobs concurrently. Since modern data-intensive scientific applications can sometimes produce millions of I/O requests per second, I/O systems always suffer from heavy workloads and impede the overall performance. How to allocate I/O resources and guarantee the QoS (Quality of Service) for each individual application is becoming an increasingly important question. In this paper, we propose SDQoS, a software-defined QoS framework with the token bucket algorithm, aiming to meet the I/O requirements of concurrent applications contending for the I/O resources and improve the overall performance of the I/O systems. Evaluation shows that SDQoS can effectively control the I/O bandwidth within a 5%–10% deviation and improve the performance by 20% in extreme cases.

Visual place recognition (VPR) is a fundamental but challenging problem that has not been solved completely for a long time, especially in a kaleidoscopic environment. Recent advanced works which exploit ConvNet landmarks as a representation of an image for the VPR have demonstrated promising performance under condition and viewpoint changes. In this paper, we propose an improved ConvNet landmark-based VPR with better robustness and higher matching efficiency by extending this method from two aspects. First, we introduce hashing to find global optimal landmark matches for each landmark in the query image to boost the quality of matched landmark pairs. Second, we apply the sequence search for finding the best matches basing on the temporal information attached in both query and reference images. The experiments which conducted on four challengeable benchmark datasets show that our approach significantly enhances the robustness of traditional ConvNet landmark-based VPR and outperforms the state-of-the-art ConvNet feature-based VPR named SeqCNNSLAM. Moreover, our method has higher computing efficiency than previous ConvNet landmark-based VPR.