For the optimization design of air circuit breaker (ACB), it is important and necessary to calculate the electro-dynamic repulsion force acting on the movable contact. A method based on 3-D FEM with the equations that describe the relationships among current, magnetic field and repulsion force, which takes the ferromagnet into account, is adopted to calculate the electro-dynamic repulsion force. The method enables one to analyze the factors that affect the electro-dynamic repulsion force, including the number of the movable conductor parallel branches as well as the location of the axis and the shape of the flexible connection. The discussion of the calculation results is also presented in this paper.

Much research which has shown the usage of social ties could improve the location predictive performance, but as the strength of social ties is varying constantly with time, using the movement data of user's close friends at different times could obtain a better predictive performance. A hybrid Markov location prediction algorithm based on dynamic social ties is presented. The time is divided by the absolute time (week) to mine the long-term changing trend of users' social ties, and then the movements of each week are projected to the workdays and weekends to find the changes of the social circle in different time slices. The segmented friends' movements are compared to the history of the user with our modified cross-sample entropy to discover the individuals who have the relatively high similarity with the user in different time intervals. Finally, the user's historical movement data and his friends' movements at different times which are assigned with the similarity weights are combined to build the hybrid Markov model. The experiments based on a real location-based social network dataset show the hybrid Markov location prediction algorithm could improve 15% predictive accuracy compared with the location prediction algorithms that consider the global strength of social ties.

Multipath energy capture and inter-symbol interference (ISI) are two intractable problems in high-data-rate Ultra-wideband (UWB) systems. To tackle the problems and simplify the receiver, we propose an adaptive interference avoidance scheme based on Pre-RAKE combining technique. The symbol repetition period (SRP) is regarded a changeable parameter in an ordered set to avoid severe interference paths and guarantee high data-rate. The set is known to both the transmitter and receiver. The index of the selected SRP is then sent to the receiver to coordinate the transmitter and receiver. The SRP can be updated adaptively according to the variations of the channels. Both theoretical analysis and simulations show that the ISI is mitigated and the transmission rate is improved simultaneously compared to the constant SRP transmission scheme.

To one double-breaker model, experimental investigation on blow open force was carried out. It demonstrates that the ratio between the emerging blow open force and arc power FB/ui decreases with the arcing time, the contact gap has less effect on FB/ui, and the characteristics of the blow open force are similar when the peak value of the short circuit current is beyond 4 kA. Then, according to the experimental data and conclusions, considering the influence of blow open force, the interruption process of molded case circuit breakers (MCCBs) was investigated. It demonstrates the blow open force has significant influence on interruption process and the proposed method is effective to evaluate new design of MCCBs.

The precision of magnetic field calculation is crucial to predict the arc behavior using magnetohydrodynamic (MHD) model. A integrated calculation method is proposed to couple the calculation of magnetic field and fluid dynamics based on the commercial software ANSYS and FLUENT, which especially benefits to take into account the existence of the ferromagnetic parts. An example concerning air arc is presented using the method.

In the design of nonlinear reliable controllers, one major issue is to solve for the solutions of the Hamilton-Jacobi inequality. In general, it is hard to obtain a closed form solutions due to the nonlinear nature of the inequality. In this paper, we seek for the existence conditions of quadratic type positive semidefinite solutions of Hamilton-Jacobi inequality. This is achieved by taking Taylor's series expansion of system dynamics and investigating the negative definiteness of the associated Hamilton up to fourth order. An algorithm is proposed to seek for possible solutions. The candidate of solution is firstly determined from the associated algebraic Riccati inequality. The solution is then obtained from the candidate which makes the truncated fourth order polynomial of the inequality to be locally negative definite. Existence conditions of the solution are explicitly attained for the cases of which system linearization possesses one uncontrollable zero eigenvalue and a pair of pure imaginary uncontrollable eigenvalues. An example is given to demonstrate the application to reliable control design problem.

Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.

In order to solve the problems of copyrights infringement, high cost and complex process of rights protection in current media convergence center, a digital rights management system based on blockchain technology and IPFS (Inter Planetary File System) technology is proposed. Considering that large files such as video and audio cannot be stored on the blockchain directly, IPFS technology is adopted as the data expansion scheme for the data storage layer of the Ethereum platform, IPFS protocol is further used for distributed data storage and transmission of media content. In addition, smart contract is also used to uniquely identify digital rights through NFT (Non-fungible Tokens), which provides the characteristics of digital rights transferability and traceability, and realizes an open, transparent, tamper-proof and traceable digital rights management system for media convergence center. Several experimental results show that it has higher transaction success rate, lower storage consumption and transaction confirmation delay than existing scheme.

Existing noise inference algorithms neglected the smooth characteristics of noise data, which results in executing slowly of noise inference. In order to address this problem, we present a noise inference algorithm based on fast context-aware tensor decomposition (F-CATD). F-CATD improves the noise inference algorithm based on context-aware tensor decomposition algorithm. It combines the smoothness constraint with context-aware tensor decomposition to speed up the process of decomposition. Experiments with New York City 311 noise data show that the proposed method accelerates the noise inference. Compared with the existing method, F-CATD reduces 4-5 times in terms of time consumption while keeping the effectiveness of the results.

This letter presents a novel two-dimensional (2-D) defected ground array (DGA) for planar circuits, which has horizontal and vertical periodicities of defect structure. The defect unit cell of DGA is composed of a Sierpinski carpet structure to improve the effective inductance. Measurements show that the proposed DGA provides steeper cutoff characteristics, lower cutoff frequency, and higher slow-wave factors than the conventional periodic defected ground structure in the same occupied surface.

Gas flow in arc quenching chamber has an important effect on the interruption capability of low voltage circuit breakers. In this paper, based on a simplified model of arc chamber with a single break, which can be opened by the electro-dynamics repulsion force automatically, the effect of different vent configurations including middle vent and side vent on the interruption performance is investigated. First, the experiments are carried out to compare the different performance in the interruption process between middle vent type and side vent type. In addition, according to the experimental model, a 3-D magneto-hydrodynamic model was developed by adapting and modified the commercial computational fluid dynamics software FLUENT. The simulation results show the same trend in arc motion as explained in the experimental conclusions in theory.

The combination of multiple-input multiple-output (MIMO) technology and sparse code multiple access (SCMA) can significantly enhance the spectral efficiency of future wireless communication networks. However, the receiver design for downlink MIMO-SCMA systems faces challenges in developing multi-user detection (MUD) schemes that achieve both low latency and low bit error rate (BER). The separated detection scheme in the MIMO-SCMA system involves performing MIMO detection first to obtain estimated signals, followed by SCMA decoding. We propose an enhanced separated detection scheme based on lightweight graph neural networks (GNNs). In this scheme, we raise the concept of coordinate point relay and full-category training, which allow for the substitution of the conventional message passing algorithm (MPA) in SCMA decoding with image classification techniques based on deep learning (DL). The features of the images used for training encompass crucial information such as the amplitude and phase of estimated signals, as well as channel characteristics they have encountered. Furthermore, various types of images demonstrate distinct directional trends, contributing additional features that enhance the precision of classification by GNNs. Simulation results demonstrate that the enhanced separated detection scheme outperforms existing separated and joint detection schemes in terms of computational complexity, while having a better BER performance than the joint detection schemes at high Eb/N0 (energy per bit to noise power spectral density ratio) values.

In this paper, we study the problem of joint resource allocation in the two-way relay system, where a pair of multi-antenna users wish to exchange information via multi-antenna amplify-and-forward relay under orthogonal frequency-division multiplexing (OFDM) modulation. We formulate a sum-rate maximization problem subject to a limited power constraint for each user and relay. Our resource allocation strategy aims at finding the best pairing scheme and optimal power allocation over subchannels in frequency and space domains. This turns out to be a mixed integer programming problem. We then derive an asymptotically optimal solution though the Lagrange dual decomposition approach. Finally, simulation results are provided to demonstrate the performance gain of the proposed algorithms.

This paper presents a novel and practical face-assisted video coding scheme to enhance the visual quality of the face region in videophone applications. A low-complexity skin-color-based face detection and tracking scheme is proposed to locate the human face regions in realtime. After classifying the macroblocks (MBs) into the face and non-face regions, we present a dynamic distortion-weighting adjustment (DDWA) scheme to skip encoding the static non-face MBs, and the saved bits are used to compensate the face region by increasing the distortion weighting of the face MBs. The quality of the face regions will thus be largely enhanced. Moreover, the computation originally required for encoding the skipped MBs can also be saved. The experimental results show that the proposed method can significantly improve the PSNR and the subjective quality of face regions, while the degradation introduced on the non-face areas is relatively invisible to human perception. The proposed algorithm is fully compatible with the H. 263 standard, and the low complexity feature makes it well suited to be implemented for real-time applications.

The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.

Due to security demand of society development, real-time face recognition has been receiving more and more attention nowadays. In this paper, a real-time face recognition system via Local Binary Pattern (LBP) plus Improved Biomimetic Pattern Recognition (BPR) has been proposed. This system comprises three main steps: real-time color face detection process, feature extraction process and recognition process. Firstly, a color face detector is proposed to detect face with eye alignment and simultaneous performance; while in feature extraction step, LBP method is adopted to eliminate the negative effect of the light heterogeneity. Finally, an improved BPR method with Selective Sampling construction is applied to the recognition system. Experiments on our established database named WYU Database, PUT Database and AR Database show that this real-time face recognition system can work with high efficiency and has achieved comparable performance with the state-of-the-art systems.

A multiple-antenna receiving and combining scheme is proposed for high-data-rate transmitted-reference (TR) Ultra-Wideband (UWB) systems. The nonlinearity of the inter-symbol interference (ISI) model is alleviated via simple antenna combining. Under the simplified ISI model, frequency domain equalization (FDE) is adopted and greatly reduces the complexity of the equalizer. A simple estimation algorithm for the simplified ISI model is presented. Simulation results demonstrate that compared to the single receive antenna scheme, the proposed method can obtain a significant diversity gain and eliminate the BER floor effect. Moreover, compared to the complex second-order time domain equalizer, FDE showed better performance robustness in the case of imperfect model estimation.

In this letter, we simulate GNSS/LEO measurements and propose a process strategy for LEO-augmented GNSS medium length baseline RTK. Experiments show that, the performance of GNSS medium length baseline RTK can be significantly improved by introducing LEO satellites. The convergence speed of LEO-augmented GPS or BDS float solution maybe better than GPS/BDS combined under the conditions of similar satellite geometry. Besides, the RMS error of fixed solutions are improved to better than 4cm from sub-decimeter level.

Prior studies have shown that the performance of amplify-and-forward (AF) relay systems can be considerably improved by using multiple antennas and low complexity linear processing at the relay nodes. However, there is still a lack of performance analysis for the cases where the processing is based on limited feedback (LFB). Motivated by this, we derive the closed-form expression of the outage probability of AF relay systems with LFB beamforming in this letter. Simulation results are also provided to confirm the analytical studies.

In this study, we propose a complete architecture based on digital watermarking techniques to solve the issue of copyright protection and authentication for digital contents. We apply visible and semi-fragile watermarks as dual watermarks where visible watermarking is used to establish the copyright protection and semi-fragile watermarking authenticates and verifies the integrity of the watermarked image. In order to get the best tradeoff between the embedding energy of watermark and the perceptual translucence for visible watermark, the composite coefficients using global and local characteristics of the host and watermark images in the discrete wavelet transform (DWT) domain is considered with Human Vision System (HVS) models. To achieve the optimum noise reduction of the visibility thresholds for HVS in DWT domain, the contrast-sensitive function (CSF) and noise visible function (NVF) of perceptual model is applied which characterizes the global and local image properties and identifies texture and edge regions to determine the optimal watermark locations and strength at the watermark embedding stage. In addition, the perceptual weights according to the basis function amplitudes of DWT coefficients is fine tuned for the best quality of perceptual translucence in the design of the proposed watermarking algorithm. Furthermore, the semi-fragile watermark can detect and localize malicious attack effectively yet tolerate mild modifications such as JPEG compression and channel additive white Gaussian noise (AWGN). From the experimental results, our proposed technique not only improves the PSNR values and visual quality than other algorithms but also preserves the visibility of the watermark visible under various signal processing and advanced image recovery attacks.