SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

退役动力电池回收研究进展及未来趋势——基于CiteSpace可视化分析

基于中国知网(CNKI)数据库和Web of Science核心数据库,运用文献计量软件CiteSpace对2000—2023年退役动力电池回收研究的发文趋势、高产作者、高产机构、关键词共现和聚类等进行可视化分析。结果发现:自2000年以来,动力电池回收研究呈持续上升趋势,国外高产作者聚集且合作关系紧,国内高产作者分散且合作关系疏;国外高产机构集中于发达国家,国内高产机构多来自经济发展水平较高的城市;关键词共现和聚类分析表明,退役动力电池回收工艺、梯次利用和回收体系是研究焦点。未来应积极开展跨区域、跨学科和跨产业合作,加强高效、智能、安全的退役动力电池回收体系及其梯次利用产业链构建研究,以促进新能源汽车产业链的健康发展。

BER Performance Analysis of OTFS Systems with Power Allocation

Orthogonal time frequency space(OTFS),as a novel 2-D modulation technique,has been proposed to achieve better BER performances over delayDoppler channels.In this paper,we propose two different power allocation(PA)algorithms in OTFS systems with zero forcing(ZF)or minimum mean square error(MMSE)equalization,where general formulas with PA are derived in advance under the condition of minimum BER(MBER)criterion.On one hand,a suboptimal MBER power allocation method is put forward to achieve better BER performances,and then analytical BER expressions are derived with proposed PA strategy.Considering the case of MMSE equalization,a combined subsymbol allocation(SA)and PA strategy is raised,where some subsymbols may be abandoned due to worse channel conditions,and then it is proven effectively to improve BER performances through theoretical and simulation results.Furthermore,BER performances with proposed joint SA and PA strategy are also investigated in delay-Doppler channels,where an improved message passing(MP)receiver based on equivalent channel matrix with PA is given.

Rural Power System Load Forecast Based on Principal Component Analysis

Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.

Effect of eccentric and inclined loading on the bearing capacity of strip footing placed on rock mass

This paper deals with the bearing capacity determination of strip footing on a rock mass in hilly area by considering the influence of inclined and eccentric loading. Applying the generalized HoekBrown failure criterion, the failure behavior of the rock mass is modeled with the help of the power cone programming in the lower bound finite element limit analysis framework. Using bearing capacity factor(Ns), the change in bearing capacity of the strip footing due to the occurrence of eccentrically inclined loading is presented. The variations of the magnitude of Ns are obtained by examining the effects of the Hoek-Brown rock mass strength parameters(uniaxial compressive strength(sci), disturbance factor(D), rock parameter(mi), and Geological Strength Index(GSI)) in the presence of different magnitudes of eccentricity(e) and inclination angle(λ) with respect to the vertical plane, and presented as design charts. Both the inclined loading modes, i.e., inclination towards the center of strip footing(+λ) and inclination away from the center of strip footing(-λ), are adopted to perform the investigation. In addition, the correlation between the input parameters and the corresponding output is developed by utilizing the artificial neural network(ANN). Additionally, from sensitivity analysis, it is observed that inclination angle(λ) is the most sensitive parameter. For practicing engineers, the obtained design equation and design charts can be beneficial to understand the bearing capacity variation in the existence of eccentrically inclined loading in mountain areas.

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.

High-quality development of China’s power industry: Measurement, spatial pattern, and improvement paths

As the largest manufacturing country,China is striving to improve the development quality of its power industry with the goal of Carbon Peaking and Carbon Neutrality,in order to sustain its high-quality economic growth.In this regard,it is of importance to reveal both the regional development level of China’s power sector and its characteristics in terms of inspiring the next improvement direction.Motived by this purpose,this paper constructs an evaluation indicator system from three dimensions at the province level based on the connotation of high-quality development of the power industry(HDPI).Next,it calculates the HDPI indexes of 30 provinces and explore their development trend and spatial pattern.The results indicate that the total comprehensive performance of all regions was improved in general in the recent decade,but the spatial distribution characteristics of clean,low-carbon,safe and efficient are different.In the aspects of improvement space in future,not only do actively ameliorate the related management regimes or technical fields so as to improve the corresponding indicators’value,but also passively rely on the macro-development such as China’s urbanization level improvement,technological level improvement,and industrial structure upgrading as usual.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Analysis of cooperation equilibrium of participants in power battery recycling chains considering information barrier

In the era of large-scale retirement of power batteries,there are information barriers and high recovery costs in their recycling.In view of this,in this study we constructed a tripartite evolutionary game model of the cooperation between power battery production and recycling enterprises and government participation.We analyzed the strategic choice of the three parties in the process of power battery recycling and simulated the influence of participants'willingness and information barriers on the strategic choices of the parties.The results showed that power battery production and recycling enterprises,and the government are affected by each other's willingness to participate at different degrees.The willingness of power battery manufacturers and recycling enterprises to cooperate with each other decreased with increases in information barriers.By analyzing the impact of information barrier on power battery recycling,some suggestions are put forward to provide decision-making reference for promoting the sustainable development of power battery industry.

Network Analysis of Research Project Members in Grant-in-Aid for Scientific Research in Japan

The researcher network that appeared in research projects funded by the Japanese government was analyzed. Several static and dynamic network analysis methods were applied to the data for 20 years to explore the fine structure of the researcher’s network for grants. Our analysis shows that the long-term trend of researchers’ group sizes has become smaller, particularly rapidly decreasing in recent years. Some findings on researcher behavior in joining a project have also been reported.

A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

Low-frequency oscillation of train-network system considering traction power supply mode

The low-frequency oscillation(LFO)has occurred in the train-network system due to the introduction of the power electronics of the trains.The modeling and analyzing method in current researches based on electrified railway unilateral power supply system are not suitable for the LFO analysis in a bilateral power supply system,where the trains are supplied by two traction substations.In this work,based on the single-input and single-output impedance model of China CRH5 trains,the node admittance matrices of the train-network system both in unilateral and bilateral power supply modes are established,including three-phase power grid,traction transformers and traction network.Then the modal analysis is used to study the oscillation modes and propagation characteristics of the unilateral and bilateral power supply systems.Moreover,the influence of the equivalent inductance of the power grid,the length of the transmission line,and the length of the traction network are analyzed on the critical oscillation mode of the bilateral power supply system.Finally,the theoretical analysis results are verified by the time-domain simulation model in MATLAB/Simulink.

Analysis of Potential Causes of Safety Failure of New Energy Vehicle Power Batteries

The aim of this paper is to analyze the potential reasons for the safety failure of batteries for new-energy vehicles.Firstly,the importance and popularization of new energy batteries are introduced,and the importance of safety failure issues is drawn out.Then,the composition and working principle of the battery is explained in detail,which provides the basis for the subsequent analysis.Then,the potential impacts of factors such as overcharge and over-discharge,high and low temperature environments,internal faults,and external shocks and vibrations on the safety of the batteries are analyzed.Finally,some common safety measures and solutions are proposed to improve the safety of new energy batteries,in hopes of improving the safety of batteries for new-energy vehicle.

Channel estimation in integrated radar and communication systems with power amplifier distortion

To reduce the negative impact of the power amplifier(PA)nonlinear distortion caused by the orthogonal frequency division multiplexing(OFDM)waveform with high peak-to-average power ratio(PAPR)in integrated radar and communication(RadCom)systems is studied,the channel estimation in passive sensing scenarios.Adaptive channel estimation methods are proposed based on different pilot patterns,considering nonlinear distortion and channel sparsity.The proposed methods achieve sparse channel results by manipulating the least squares(LS)frequency-domain channel estimation results to preserve the most significant taps.The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion.Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns.In addition,the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.

Development and Application of a Power Law Constitutive Model for Eddy Current Dampers

Eddy current dampers (ECDs) have emerged as highly desirable solutions for vibration control due to theirexceptional damping performance and durability. However, the existing constitutive models present challenges tothe widespread implementation of ECD technology, and there is limited availability of finite element analysis (FEA)software capable of accurately modeling the behavior of ECDs. This study addresses these issues by developing anewconstitutivemodel that is both easily understandable and user-friendly for FEAsoftware. By utilizing numericalresults obtained from electromagnetic FEA, a novel power law constitutive model is proposed to capture thenonlinear behavior of ECDs. The effectiveness of the power law constitutive model is validated throughmechanicalproperty tests and numerical seismic analysis. Furthermore, a detailed description of the application process ofthe power law constitutive model in ANSYS FEA software is provided. To facilitate the preliminary design ofECDs, an analytical derivation of energy dissipation and parameter optimization for ECDs under harmonicmotionis performed. The results demonstrate that the power law constitutive model serves as a viable alternative forconducting dynamic analysis using FEA and optimizing parameters for ECDs.

Power BI在我院药品数据可视化中的应用

目的加强医院药品精细化管理,提升药师对药品的数据分析能力,实现数据分析自动化及查询功能,为集采药品、麻精药品的动态监管提供数据支撑。方法使用数据可视化工具Power BI搭建服务,制作报表,能够查询药品使用情况和对比情况,实现各科室药品使用情况的可视化管理。结果通过该软件,药师可以精确查询任意时间维度下药品的使用数量、金额等情况,实现数据分析自动化。结论借助数据管理工具能够帮助药师提升数据分析能力和工作效率,有助于加强医院药品的精细化管控。

Analysis on Sensitivity of Power System Stability to Generator Parameters

The sensitivity of power system stability (including transient and dynamic stabilities) to generator parameters (including parameters of generator model, excitation system and power system stabilizer) is analyzed in depth by simulations. From the tables and plots of the resultant simulated data, a number of useful rules are revealed. These rules can be directly applied to the engineering checking of generator parameters. Because the complex theoretical analyses are circumvented, the checking procedure is greatly simplified, remarkably promoting the working efficiency of electrical engineers on site.

Impact Analysis of the 2011 Fukushima Nuclear Power Plant Accidents by Running Spectrum Analysis on Newspaper

Huge amount of digital data of the Great East Japan Earthquake is provided by the highly-developed digital data technology. But the method and technique for analysis of these huge digital data are not developed sufficiently. This paper proposes a running spectrum technique for text data and analyzing changes of disaster phase during the disaster management cycle. Impact analysis of the nuclear power plant accidents have been performed by using Fukushima Minpo newspaper for its verification. The result shows the dynamic characteristics of the nuclear power plant accidents. As the time interval B becomes longer, the analysis data is used from wide range period along with the smoothing effect. When observing different time intervals B, fewer keywords have been ranked in the longer time intervals of B. The proposed technique is a powerful tool to effective and efficient disaster response and management. analyze effectively the huge amount of digital data for the