查看更多>>摘要：Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index.Objective To explore the utility of Internet of Things in power transformer fault diagnosis system.Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system.Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.
查看更多>>摘要：Abstract Medium-length LDPC codes are in demand in certain areas such as mobile environment (Wi-Fi and Mobile WiMAX) and in telecommand links from the ground to space because of their lower latency properties. However, because of the length of these codes is rather short, decoding error rates are worse than those of long-length codes. In this paper, we show that the combination of shortened LDPC codes, whose shortened positions are properly selected, and ordered statistic decoding (OSD) significantly improves the decoding error. For the best choice of shortening positions, we used the integer programming approach. In particular, we utilized Feldman–Wainwright–Karger code polytope for this purpose. Some studies have independently reported the efficiency of shortened LDPC codes and OSD methods. This paper emphasizes that their combination results in multiplicative effectiveness.
查看更多>>摘要：Abstract With the rapid development of cross-border e-commerce, the improvement of consumer satisfaction has become the focus of cross-border e-commerce platform optimization. Relying on advanced algorithm technology, it can realize the accurate and efficient matching between massive information and users, which is conducive to improving the user experience. Based on the consideration of consumer satisfaction, this paper constructs a dual channel supply chain composed of cross-border suppliers, cross-border e-commerce enterprises, retailers and consumers and studies the revenue and cost sharing contract of the supply chain under the conditions of centralized decision and decentralized decision. The research shows that cross-border e-commerce enterprises can choose to form revenue and expenditure sharing contracts through online and offline channels, optimize decentralized decision-making, and achieve win–win cooperation among supply chain entities.
查看更多>>摘要：Abstract With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.
查看更多>>摘要：Abstract The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.
查看更多>>摘要：Abstract In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The optimal identification and detection of power disturbance are realized. The simulation results show that the algorithm has higher accuracy and higher recognition ability, which improves the accurate probability of power disturbance detection and the anti-disturbance ability of power test.
查看更多>>摘要：Abstract High-tech industrial agglomeration is conducive to boosting technological progress, promoting industrial structure upgrading and realizing economic transformation, and certainly affects the overall industrial environmental efficiency. However, few recent studies have focused on its impact on industrial environmental efficiency from a green perspective. In the context of promoting the development of green economy, it is of great significance to clarify the links between high-tech industrial agglomeration and industrial environmental efficiency. In this research, we first analyzed the theoretical mechanism of the impact of high-tech industrial agglomeration and its spatial spillover effects on industrial environmental efficiency and then made an empirical analysis based on the panel data of 29 provinces and cities in China from 2003 to 2016. During the research, Super-DEA method, ESDA method and spatial Dubin model are used. The result shows that: (1) There is a significant spatial positive correlation between China's industrial environmental efficiency and high-tech industrial agglomeration; (2) high-tech industrial agglomeration has improved the local industrial productivity and industrial technology level through scale effects and technical effects, which has accordingly significantly enhanced the corresponding environmental efficiency; (3) through the association of regional industries, the cross-regional cooperation of enterprises and the formation of innovation networks, high-tech industrial agglomeration promotes the spillover of knowledge and technology among regions, improves the level of industrial technology in neighboring regions, and enhances the industrial environmental efficiency in neighboring regions. All these three is helpful to re-evaluate the development mode of high-tech industry agglomeration and to formulate relevant government policies.
查看更多>>摘要：Abstract In this paper, we investigate the secrecy performance of short-packet transmissions in ultra-reliable and low-latency communications (URLLC). We consider the scenario where a multi-antenna source communicates with a single-antenna legitimate receiver requiring ultra-high reliability and low latency, in the presence of a single-antenna eavesdropper. In order to safeguard URLLC, the source transmits the artificial noise (AN) signal together with the confidential signal to confuse the eavesdropper. We adopt a lower bound on the maximal secrecy rate as the secrecy performance metric for short-packet transmissions in URLLC, which takes the target decoding error probabilities at the legitimate receiver and the eavesdropper into account. Using this metric, we first derive a compact expression of the generalized secrecy outage probability (SOP). Then, we formally prove that the generalized SOP is a convex function with respect to the power allocation factor between the confidential signal and the AN signal. We further determine the optimal power allocation factor that minimizes the generalized SOP. The results presented in this work can be useful for designing new secure transmission schemes for URLLC.
查看更多>>摘要：Abstract Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.
NSTL
Springer Nature