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For the linear discrete networked control system (NCS) which may suffer DoS attack on both sides of the controller, when the actuator has time-varying failure, the intelligent sensor unit uses wireless sensors to collect data. According to the large amount of data collected, the active fault tolerance/active passive capacity of linear discrete NCS under the discrete event-triggered communication mechanism (DETCS) is studied. The problem of cooperative controller design is discussed. Firstly, a linear discrete NCS model integrating DETCS, actuator fault, and network attack is established. Then, based on the idea of integral sliding mode control, an active fault-tolerant/attack active passive intrusion-tolerant cooperative controller is designed, and the actuator attack side network attack and sensor side network attack are extended to the state to obtain a new state vector. Then, an adaptive Kalman filter estimator (AKF) estimates the fault and attack information and then adjusts the initial fault-tolerant/intrusion-tolerant cooperative controller in real time according to the estimated information obtained by the adaptive Kalman filter estimator; finally, the MATLAB simulation example is used to verify the improvement of system performance by the designed control law and the saving of network resources by the introduction of DETCS.

This paper proposes a parallel computing analysis model HPM and analyzes the parallel architecture of CPU–GPU based on this model. On this basis, we study the parallel optimization of the ray-tracing algorithm on the CPU–GPU parallel architecture and give full play to the parallelism between nodes, the parallelism of the multi-core CPU inside the node, and the parallelism of the GPU, which improve the calculation speed of the ray-tracing algorithm. This paper uses the space division technology to divide the ground data, constructs the KD-tree organization structure, and improves the construction method of KD-tree to reduce the time complexity of the algorithm. The ground data is evenly distributed to each computing node, and the computing nodes use a combination of CPU–GPU for parallel optimization. This method dramatically improves the drawing speed while ensuring the image quality and provides an effective means for quickly generating photorealistic images.

The behaviors of dairy cows, such as feeding, ruminating, running, resting (standing, lying), head-shaking, drinking, and walking, can indicate their health status. In this study, a multi-sensor was used to collect data of cow’s multi-behaviors for research on behavior recognition. Firstly, a collar style data acquisition system equipped with geomagnetic and acceleration sensors to collect the behavioral data of dairy cows during their daily activities was designed. Secondly, the dairy cow behavioral recognition fusion model based on K-Nearest-Neighbors (KNN) and Random Forest (RF) models were used for behavior classification. To verify the accuracy of the fusion model, the algorithms of KNN, RF, Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Learning Vector Quantization (LVQ) were introduced for comparative recognition experiments with different algorithms. The KNN-RF fusion model had the highest average recognition accuracy of 98.51%, followed by the KNN model with an average recognition accuracy of 95.37%, and the LVQ model had the lowest average recognition accuracy of 80.81%. For the recognition and verification of each behavior, the KNN-RF fusion model had the most obvious improvement in the recognition of dairy cow feeding behavior, with a recognition accuracy of 99.34%, followed by the KNN model with a recognition accuracy of 95.07%. All six models had the lowest recognition accuracy for cow head-shaking behavior: a recognition accuracy of 89.11% with the KNN-RF model followed by the RF model with a recognition accuracy of 85.14%. The system can quickly and continuously collect cow behavior information, accurately recognize individual behaviors, and provide a scientific basis for the optimal design and efficient management of digital facilities and equipment for dairy cows.

Conventional maintenance mode for the traction power supply system (TPSS) is to perform scheduled regular maintenance activities for power supply equipment, while such maintenance mode may result in undue maintenance tasks and low efficiency due to different degradation processes of different sorts of equipment. To address this problem, this paper introduces a preventive opportunistic maintenance (POM) method for TPSS based on equipment reliability. Firstly, a POM model is established by considering the equipment reliability degradation process based on Weibull distribution. Then, by considering the total power outage time in the planned operation cycle of TPSS as the optimization objective, the optimal maintenance scheme of TPSS is formulated by iterative method of maintenance strategies. The proposed method is verified by introducing practical maintenance strategies and fault record data of the traction transformer, circuit breaker and disconnector in an actual TPSS of a railway administration. Results show that the presented method can make full use of the existing fault data to develop a POM scheme for TPSS. It can improve maintenance efficiency and reduce power outage time, providing guidance to formulate scientific maintenance strategies for TPSS.

In response to the difficulty of detecting and classifying pests and vegetable and fruit leaves with pests and diseases, this study proposes a novel vegetable and fruit leaf pest detection method called deep block attention SSD (DBA_SSD) for the identification of pests and diseases and classification of the degree of pests and diseases of vegetable and fruit leaves. We propose three vegetable and fruit leaf pest detection methods, namely, squeeze-and excitation SSD (Se_SSD), DB_SSD, and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected vegetable and fruit disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

Significant similarities exist between a spider web and wireless sensor network in terms of topology. Combining the unique advantages of the spider web in nature, such as invulnerability and robustness, with communication technology of a wireless sensor network presents high research value and broad development prospects. In this paper, a sort of a spiral artificial spider web based on 3D printing and its associated vibration testing device is proposed, which is used to study the transmission rule of vibration information of the spider web under given excitation conditions. It provides useful inspiration for establishment of an invulnerable communication rule of wireless sensor network. In order to investigate vibration transmission characteristics of the artificial spider web, vibration images are recorded and analyzed by a high-speed photography system, and vibration intensity is characterized by use of peak-to-peak value. Furthermore, vibration performance of the artificial spider web is studied under conditions of integrity and destruction, respectively. Our test observation reveals the vibration transmission rule of the unique structure of the spider web, providing a novel analysis method for improving invulnerability of the wireless sensor network.

Cabled seafloor observatories play an important role in ocean exploration for its long-term, real-time, and in-situ observation characteristics. In establishing a permanent, reliable, and robust seafloor observatory, a highly reliable cable switching and fault isolation method is essential. After reviewing the advantages and disadvantages of existing switching methods, we propose a novel active switching method for network configuration. Without additional communication path requirements, the switching method provides a way to communicate with a shore station through an existing power transmission path. A coded voltage signal with a distinct sequence is employed as the communication medium to transmit commands. The analysis of the maximum bit frequency of the voltage signals guarantees the accuracy of command recognition. A prototype based on the switching method is built and tested in a laboratory environment, which validated the functionality and reliability of the method.

Aiming at solving the problem of poor performance of airborne freestyle skiing balance, this paper presents the research of airborne freestyle skiing balance control based on ant colony algorithm. On the basis of defining the trajectory division of the airborne balance of freestyle skiing and the track of the center of gravity of the human body, a sensor is used to collect the data of the airborne balance of freestyle skiing, and the moving average, denoising, and normalizing processes are done. The training label of the ant colony algorithm is made by the analog signal matrix, the implementation foreground of the key posture frame of freestyle skiing is extracted, the disturbed area in the key posture frame is removed by the clustering algorithm, and the key posture area of freestyle skiing is obtained. The incremental clustering of the data of the key posture area of freestyle skiing is conducted, the incremental posture data mining model of freestyle skiing is established, the mining parameters are input into the mining model, and the incremental data mining is realized by the ant colony algorithm to complete the research on the control of the airborne balance of freestyle skiing. The results show that the proposed method has good reliability, good convergence, and strong response ability.

With the increasing popularity of electric vehicles, the disordered charging of large-scale electric vehicles will have a great impact on the safe operation of regional distribution network. In order to solve the security problems that may occur in the power grid, this paper uses the time-sharing pricing time division method for EV charging to meet the needs of EV users. Based on this method, a multi-objective optimization model is established, which takes the electric vehicle charging capacity and power as the constraints, and based on the minimum user charging cost and the minimum load curve variance. Then, the model is solved by non-dominated sorting genetic algorithm (NSGA -Ⅱ), and the optimal compromise solution is extracted by using fuzzy set theory. Finally, the correctness of the proposed model is verified by the example.

Due to the distributed and dynamic characteristics of the Internet of Vehicles (IoV) and the continuous growth in the number of devices, content-centric decentralized vehicular named data networking (VNDN) has become more suitable for content-oriented applications in IoV. However, the existing centralized architecture is prone to the failure of single points, which results in trust problems in key verification between cross-domain nodes and consuming more power and reducing the lifetime. Focusing on secure key management and power-efficient routing, this article proposes a blockchain-based key management and green routing scheme for VNDN. A blockchain-based key management scheme is presented to achieve secure and efficient distribution and verification of keys. Specifically, all trusted agencies (TAs) form a consortium blockchain for storing public key hashes to ensure the authenticity of users’ public keys. A green global routing scheme based on node relaying pressure (GGNRP) is proposed to save power consumption and reduce the forwarding delay. A new node relay pressure metric is introduced to assist with routing decisions. Detailed experiments and analysis show that, compared with the existing scheme, the proposed scheme can achieve secure key management and GGNRP can decrease the power consumption and average delay by 15.8% and 63.2%, respectively.

The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

Based on the current situation of digital map resource interconnection and mutual inspection, this paper studies three modes of unified retrieval of traditional guided data sources, and proposes a digital map resource retrieval model based on Web Services. This paper also designs digital map resources unified retrieval result fusion algorithm and metadata update algorithm based on Web Services in detail, which can be used for the development of digital map resource unified retrieval system.

In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.

Due to the volatility of natural resources, the power fluctuations of photovoltaic (PV) generation have serious negative impacts on the power quality. This paper reports a novel approach to resolve this problem in a combined cascade hydro-PV-pumped storage (CH-PV-PS) generation system through appropriate power distribution on a small time scale. According to the characteristics of power generation systems and multi-constraints, how to obtain the optimal smoothing effects with the small cost is a key challenge. For this purpose, wavelet packet decomposition is modified by constructing cost function and ideal power trajectory sequences to obtain a new adaptive power distribution method for the CH-PV-PS generation system considering the boundary conditions of the system in this paper. At the same time, to meet the real-time control requirements of actual systems, an additive function is presented to improve the optimization speed of the adaptive power distribution method. In the meantime, a fuzzy controller is designed to optimize the inevitable delay time of the power scheduling system, and the starting threshold is set to avoid the frequently mode conversion of pumped storage. Finally, the performance of the method is evaluated based on the PV station data in Xiaojin County, Sichuan Province, China. Simulation results based on Matlab-simulink indicate that the proposed method can effectively suppress PV power fluctuations and ensure normal operation of the CH-PV-PS generation system within the multiple constraints.

Purpose This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP (uRP). Design/methodology/approach Firstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation of uRP. Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of the uRP. Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions to uRP. Findings Results show that the proposed method can reasonably and objectively estimate the uRP of the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, the uRP of the selected industrial robot can be restricted by using the results of its key factors on uRP. Originality/value This paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting the uRP and thus useful in determining whether the RP of a tested industrial robot meets its requirements.

This study examines the problem of solving the Doppler ambiguity in analogue television (ATV)-based passive bistatic radar. When mismatched filtering is used to deal with the range ambiguity at 19.2 km, the Doppler ambiguity caused by the 50 Hz field frequency is equally rising because of the signal-to-noise ratio (SNR) loss in the main lobe. Aiming at this problem, an improved mismatched filtering algorithm is proposed. In this new algorithm, the Doppler ambiguity sidelobe suppressing is considered into the cost function, and the mismatched filtering factor is acquired based on minimising the SNR loss, the range sidelobes energy and the Doppler sidelobes energy. Moreover, a batch version is applied to lower the computation. Then the performance of the improved mismatched filtering algorithm is verified by the theoretical analysis. Finally, the application of real data proved the effectiveness of the improved algorithm.

In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

For the low-voltage transformer district with distributed generations (DGs), the traditional theoretical calculation method of line loss is not applicable. This paper presents a novel clustering method for line loss data of transformer district with DGs, which combined an improved Cuckoo Search algorithm and K-Means clustering algorithm. Firstly, the influence factors of line loss are screened based on the maximum information coefficient, and the line loss index system is established. Secondly, an improved cuckoo search clustering algorithm is proposed to cluster the sample data set to reduce the dependence on the initial clustering center. Finally, the simulation results of 410 samples from a certain area with photovoltaic power supply show the accuracy and effectiveness of the proposed method. The simulation results show that the proposed method is accurate and effective.

Innovative product design is essentially an activity involving creative cognitive thinking. Therefore, research on the innovative design process and of methods, computer-aided innovation tools should be conducted based on systematic exploration based on the principles of innovative cognitive thinking. We aim to uncover some general principles that can serve as a systematic thinking framework for product designers, provide a feasible framework and method of innovative thinking for designers, and provide theoretical and methodological support for further development of computer-aided innovation platforms. This paper summarizes the sources and content of studies on the idea generation and design processes behind product innovation. Specifically, we break down the general innovative design process of products, outlining the cognitive mechanisms and propose an integrated application strategy that incorporates multiple methods. And taking “the solution program to the coking of oily sludge pyrolysis equipment” as an example, this paper demonstrated and verified the effectiveness and practicability of the method and strategy proposed. Based on the results on improvements in the equipment, the designers put forward some creative and implementable design schemes. The entire thinking process was relatively smooth and efficient. The example presented in this paper shows that the proposed method can effectively guide/assist/motivate designers to think creatively.

A high-performance multifunctional integrated composite wall panel system, which was composed of a high-performance thin-walled large cavity shell and multifunctional core, was proposed in consideration of the advantages of ultrahigh-toughness cement-based composite materials, the standard panel surface density was ≤75 kg/m2, and the minimum ultimate flexural load was 2,333 N/m2, and according to the thermal engineering theory, the heat transfer coefficient of modified polystyrene particles foamed concrete for the multifunctional core was calculated as 0.756W/(m2·K), and that of the designed three-layer (standard panel + 35 mm rock wool+ gypsum plasterboard) composite structure was 0.481W/(m2·K). By combining the characteristics of standard panel, customized exterior wall, sealing, waterproof, and other measures were proposed, and meanwhile, multiple structural combinations were constructed to meet the energy conservation design needs in different climatic regions.

In modal testing, the force transducer and accelerometer mounted on the test structure will introduce extra mass effects to the system and then affect the measured Frequency Response Functions (FRFs). This paper proposed a method for assessing sensor mass effects on the measured FRFs in shaker modal testing. The assessment method offers some distinct advantages where very few FRFs measurements are required, and more importantly it does not require calculations involving several measured FRFs, hence avoiding further contaminations of the measured data. In view of two different ways of response measurements, two cases have been discussed: (1) shaker + laser Doppler vibrometer and (2) shaker + accelerometer. In case (1), only force transducer mass need be assessed. In case 2, however, both force transducer and accelerometer masses should be considered. Especially in Case 2, the overall as well as the individual mass effects of the two transducers were assessed. It is found that the assessment method is quite effective in the experimental validation for Case 1. A simple numerical example for Case 2 presented also illustrates the good theoretical performance of the method. The same example is extended to incorporate simulated noise, simulating an experimental situation, and it is shown that the accuracy of assessment results will be affected to some extent by the noise. It is suggested that the measured FRFs be preprocessed using the curve-fitting procedure before applying the proposed method.

We study the optimal precoding for a full-duplex (FD) system, where one FD multi-antenna base station (BS) respectively transmits to and receives from two half-duplex single-antenna mobile users (MUs) on the same time slot and frequency band. At the FD BS, the received signal from the desired MU is severely affected by the extremely strong self-interference (SI) from its transmit antennas to the receive antennas. In the presence of residual SI after imperfect SI cancellation, the downlink transmission rate maximization problem subject to a targeted uplink rate is formulated as a non-convex optimization problem to characterize the achievable rate region for the considered system. Considering the case in which the SI channel is strongly correlated, the above problem is transformed into a convex problem by exploiting the rank-one property of the SI channel, which can be solved efficiently. Finally, numerical results validate the effectiveness of the proposed scheme.

Three-dimensional (3D) target reconstruction from inverse synthetic aperture radar (ISAR) data has a wide application in target scattering modelling, detection, and identification. In ISAR imaging of targets with complex motions such as the non-cooperative manoeuvring targets, the scattering centres on the target may rotate slowly in 3D space during the observation time. In this study, the authors have developed a new formulation for 3D target geometry reconstruction from the scattering centres high-resolution range (HRR) measurements, based on target motion features. First, after the translation compensation, the multi-view HRR of the scattering centres is extracted by HR spectral estimation technique. Then, the multi-view measurements data without correspondence information are associated using the multiple hypotheses tracking algorithm. Finally, the 3D target geometry and motion are reconstructed from the singular value decomposition of the correlated HRR data matrix. The effectiveness of the proposed algorithm is demonstrated by both simulated and real data experiment results.

Due to the prevalence of human activity in urban spaces, recommending ROIs (region-of-interests) to users, especially irregular ROIs, becomes an important task in location-based social networks. A fundamental problem is how to aggregate users’ preferences over POIs (point-of-interests) to infer the users’ region-level mobility patterns. The majority of existing studies ignore the users’ implicit interactions with individual POIs when addressing this issue. For example, a user check-in a region cannot provide any specific information about how the user likes this region (we call this phenomenon “ROI-level” implicitness) and which POI in this region the user is interested in (i.e., “POI-level” implicitness). Furthermore, existing studies adopt predefined strategies for region-level preference aggregation, that is, initializing the importance of different POIs with identical weights, which is insufficient to model the reality of social networks. We emphasize two facts in this paper: (1) there simultaneously exists ROI-level and POI-level implicitness that blurs the users’ underlying preferences; and (2) individual POIs should have non-uniform weights and more importantly, the weights should vary across different users. To address these issues, we contribute a novel solution, namely GANR 2 (Graph Attentive Neural Network for Region Recommendation). Specifically, to learn the user preferences over irregular ROIs, we provide a principled neural network equipped with two attention modules: the POI-level attention module, to select the informative POIs of one ROI, and the ROI-level attention module, to learn the ROI preferences. Moreover, we learn the interactions between users and ROIs under the NGCF (Neural Graph Collaborative Filtering) framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

Aiming at arbitrary failures of actuators and network attacks, the design problems of passive fault-tolerant/intrusion-tolerant robust H∞ controllers for island AC microgrid inverter systems are studied. The controller has a good ability to suppress external limited energy disturbances. First, a closed-loop system model including actuator failure was established; then, the network attack on the actuator is described as a special external interference to design a H∞ robust passive fault-tolerant/intrusion-tolerant controller, and the Lyapunov equation and linear matrix inequality are used to obtain sufficient conditions for the progressive stability of the closed-loop fault system. Finally, the feasibility and effectiveness of the designed control law are verified through MATLAB simulation.

This paper is mainly based on the big electric power data of 5900 industrial enterprises above designated size in Shanghai. By combining a complex network and a hidden Markov model, the prosperity index of 164 medium-sized industries in Shanghai is constructed. Specifically, we use complex networks to describe the correlation between different industries, in order to mine the upstream and downstream drivers that affect industrial power consumption, and on this basis, consider the external factors that affect power consumption to establish a hidden Markov model that predicts changes in power consumption in the industry. Further, we use the state probability output by the Hidden Markov Model to define the industrial prosperity index, hoping that the index can fully reflect the economic operation of various industries in Shanghai and become a “barometer” and “wind vane” for economic development.

The quality control of smart meters exists in production enterprises and power grid companies respectively. Their data are not connected, which leads to the lack of comprehensive control in the whole life cycle of smart meter design, manufacturing and operation scrap, which results in the overall quality level of smart meter industry is limited. The NQI service cloud platform in the field of smart grid measurement takes cloud computing technology as the core and NQI quality service as the process to mine and analyze the quality data of smart meters in the whole life cycle, so as to achieve the overall quality improvement of smart meter industry. In this paper, the service cloud platform architecture is designed by analyzing the service scenario, the quality evaluation index system and the data model of intelligent power meter.

In order to achieve the purpose of predicting the batch life index of intelligent meters in using, this paper analyzes the history failure data of the products, comprehensively considers the stresses such as temperature and humidity, then proposes an improved Weibull distribution statistical analysis method based on adaptive weighting coefficient, which is applied to the batch life prediction of intelligent meters. The prediction error of stage failure rate is less than 0.0002 fit.

According to the recently published report by the Federal Communications Commission, the spectrum regulatory authority in the United States, traffic distribution across the radio spectrum is extremely uneven. The phenomenon can be utilized to improve the multimedia content sharing. This paper proposes a novel model of multimedia sharing in wireless communication networks. The proposed multimedia sharing mechanism offer services to users who want to access the multimedia networks. At the same time, it does not affect the QoS of existing paying users. The numerical results show that the proposed sharing mechanism provides higher throughput and quality of service than the existing system.

Model the uplink chance-constrained resource allocation problem.We propose HPSO which combines the SVM and PSO.Utilize SVM to compute the probabilistic interference constraint condition. Resource allocation plays a critical role to enhance the performance of cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to lack the cooperation between cognitive network and primary network, the channel state information (CSI) between cognitive radio (CR) user and primary user (PU) could not be estimated precisely. In this work, a resource allocation problem over the power and subcarrier allocation based on chance-constrained programming is formulated to maximize the average weighted sum-rate throughput and guarantee the probabilistic interference constraint condition for PU. In order to solve the above resource allocation problem, the probabilistic interference constraint condition is computed by support vector machine (SVM) and we combine particle swarm optimization (PSO) and SVM to develop hybrid particle swarm optimization (HPSO). Simulation results verify HPSO not only yields the higher average weighted sum-rate throughput than other algorithms, but also satisfies the probabilistic interference constraint condition.

In this paper, we propose a high-definition (HD) video real-time edge detection system, also presenting its hardware design and implementation results. Traditional median filtering blurs image details while removing noise, therefore we propose the improved median filtering algorithm which not only effectively removes impulse noise, but also retains more image details. For the problem that traditional sobel edge detection algorithm easily loses graphic details. This paper proposes the improved sobel edge detection algorithm which extends the gradient direction to eight, introducing local adaptive threshold and non-maximum suppression. The proposed algorithms were designed with Verilog-HDL, and the real-time operation was verified and evaluated using an field programmable gate array (FPGA) based test system at an operating frequency of 200MHz for 1280×720 HD video.

With an ever-increasing resolution of optical remote-sensing images, how to extract information from these images efficiently and effectively has gradually become a challenging problem. As it is prohibitively expensive to label every object in these high-resolution images manually, there is only a small number of high-resolution images with detailed object labels available, highly insufficient for common machine learning-based object detection algorithms. Another challenge is the huge range of object sizes: it is difficult to locate large objects, such as buildings and small objects, such as vehicles, simultaneously. To tackle these problems, we propose a novel neural network based remote sensing object detector called full-coverage collaborative network (FCC-Net). The detector employs various tailored designs, such as hybrid dilated convolutions and multi-level pooling, to enhance multiscale feature extraction and improve its robustness in dealing with objects of different sizes. Moreover, by utilizing asynchronous iterative training alternating between strongly supervised and weakly supervised detectors, the proposed method only requires image-level ground truth labels for training. To evaluate the approach, we compare it against a few state-of-the-art techniques on two large-scale remote-sensing image benchmark sets. The experimental results show that FCC-Net significantly outperforms other weakly supervised methods in detection accuracy. Through a comprehensive ablation study, we also demonstrate the efficacy of the proposed dilated convolutions and multi-level pooling in increasing the scale invariance of an object detector.

High-penetration grid-connected photovoltaic (PV) systems can lead to reverse power flow, which can cause adverse effects, such as voltage over-limits and increased power loss, and affect the safety, reliability and economic operations of the distribution network. Reasonable energy storage optimization allocation and operation can effectively mitigate these disadvantages. In this paper, the optimal location, capacity and charge/discharge strategy of the energy storage system were simultaneously performed based on two objective functions that include voltage deviations and active power loss. The membership function and weighting method were used to combine the two objectives into a single objective. An energy storage optimization model for a distribution network considering PV and load power temporal changes was thus established, and the improved particle swarm optimization algorithm was utilized to solve the problem. Taking the Institute of Electrical and Electronic Engineers (IEEE)-33 bus system as an example, the optimal allocation and operation of the energy storage system was realized for the access of high penetration single-point and multi-point PV systems in the distribution network. The results of the power flow optimization in different scenarios were compared. The results show that using the proposed approach can improve the voltage quality, reduce the power loss, and reduce and smooth the transmission power of the upper-level grid.

A real-time mouse cursor control system using static gestures was designed to achieve the goal of human-computer interaction (HCI) in this paper. The system uses the computer’s own camera or external USB camera to collect video data, detect and recognize gestures of people in the video, and control the cursor movement or click in real time based on the gesture. To improve the recognition accuracy of hand gesture images detection, a method of hand gesture images detection based on PHOG + Improved LBP + K-NN was proposed in this paper. In order to improve the real-time performance of the system, the system determines whether the current frame has human hands by skin color detection. When human hands are detected, Pyramid Histogram of Oriented Gradients (PHOG) features and the improved Local Binary Pattern (LBP) features are further extracted. After fusing PHOG features and improved LBP features, k-nearest neighbor classification (K-NN) is used to implement gesture recognition. Six different gestures were tested 50 times with different angles, different lights and no skin tone in the background. The experimental results show that the system has good recognition performance.

This study examines the problem of direction of arrival (DOA) estimation in FM-based passive bistatic radar using a four-element Adcock antenna array. The authors investigate the influence of the target echo, involved in the reference signal, on the DOA estimation of the corresponding target considered. Analysis shows that, unlike the pure reference signal case, a weak target echo involved in the reference signal may not affect the detection of the corresponding target. It can often have a significant impact on the estimation of a target's DOA; thus typical DOA estimate methods degrade heavily. A robust method is proposed to cope with this problem. Simulation and experimental results demonstrate the effectiveness of the method.

Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common state sequences are extracted from the model and clustered as driving patterns. The Next Generation SIMulation dataset on the I-80 highway is used for learning and evaluating. The experimental results demonstrate high accuracy of car-following model fitting.

In order to implement a high-efficiency bridgeless power factor correction converter, a new topology and operation principles of continuous conduction mode (CCM) and DC steady-state character of the converter are analyzed, which show that the converter not only has bipolar-gain characteristic but also has the same characteristic as the traditional Boost converter, while the voltage transfer ratio is not related with the resonant branch parameters and switching frequency. Based on the above topology, a novel bridgeless Bipolar-Gain Pseudo-Boost PFC converter is proposed. With this converter, the diode rectifier bridge of traditional AC-DC converter is eliminated, and zero-current switching of fast recovery diode is achieved. Thus, the efficiency is improved. Next, we also propose the one-cycle control policy of this converter. Finally, experiments are provided to verify the accuracy and feasibility of the proposed converter.

In order to improve the accuracy and efficiency of robot grasping, we propose a new method for transparent object detection and location that utilize depth image, RGB image and IR image. In detection process, an active depth sensor (RealSense) is firstly employed to retrieve the transparent candidates from the depth image and the corresponding candidates in the RGB image and IR image are then extracted separately. A transparent candidate classification algorithm is subsequently presented that uses SIFT features to recognize the transparent ones from the candidates. In location process, we obtain a new group of RGB images and IR images by adjusting camera orientation to make its optical axis perpendicular to the normal direction of the plane on which the object is placed. The object contours in RGB image and IR image are then extracted, respectively. The three-dimensional object is finally reconstructed by means of stereo matching of the two contours, and the current pose information of the object is calculated in the end. In order to verify the feasibility of the method, we built a hand-eye test system with a movable industrial robot to detect and capture transparent objects at different locations. The final test results demonstrate that the method is more general and effective than the traditional one.

The reason of cloud data with long and narrow surface lack was explored, and the reconstruction method was researched. In order to get the best reconstructed surface, the surface trend and the surface cloud data errors in different surface orders were compared during the process of surface reconstruction. The results showed that the reconstruction of cloud data with long and narrow surface lack was realized. The free surface was generated through sectional breakpoint cloud data which was cut by the cloud data nearby the lack cloud data. The surface of the original cloud data was obtained through the adjustment of control points of free surface. The reconstruction of cloud data with long and narrow surface lack was finished through the generation of surface to cloud data. The research will provide new methods for the defect of the cloud data to realize surface reconstruction, and the research on reconstruction of the abridged cloud data with narrow and long surface by Imageware software will provide theoretical foundation for development and application of reverse engineering.