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.

Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.

The power spectrum and correlation of flow noise for an axisymmetric body in water

Understanding the physical features of the flow noise for an axisymmetric body is important for improving the performance of a sonar mounted on an underwater platform. Analytical calculation and numerical analysis of the physical features of the flow noise for an axisymmetric body are presented and a simulation scheme for the noise correlation on the hydrophones is given. It is shown that the numerical values of the flow noise coincide well with the analytical values. The main physical features of flow noise are obtained. The flow noises of two different models are compared and a model with a rather optimal fore-body shape is given. The flow noise in horizontal symmetry profile of the axisymmetric body is non-uniform, but it is omni-directional and has little difference in the cross section of the body. The loss of noise diffraction has a great effect on the flow noise from boundary layer transition. Meanwhile, based on the simulation, the noise power level increases with velocity to approximately the fifth power at high frequencies, which is consistent with the experiment data reported in the literature. Furthermore, the flow noise received by the acoustic array has lower correlation at a designed central frequency, which is important for sonar system design.

An Improved Empirical Mode Decomposition for Power Analysis Attack

Correlation power analysis(CPA) has become a successful attack method about crypto-graphic hardware to recover the secret keys. However, the noise influence caused by the random process interrupts(RPIs) becomes an important factor of the power analysis attack efficiency, which will cost more traces or attack time. To address the issue, an improved method about empirical mode decomposition(EMD) was proposed. Instead of restructuring the decomposed signals of intrinsic mode functions(IMFs), we extract a certain intrinsic mode function(IMF) as new feature signal for CPA attack. Meantime, a new attack assessment is proposed to compare the attack effectiveness of different methods. The experiment shows that our method has more excellent performance on CPA than others. The first and the second IMF can be chosen as two optimal feature signals in CPA. In the new method, the signals of the first IMF increase peak visibility by 64% than those of the tradition EMD method in the situation of non-noise. On the condition of different noise interference, the orders of attack efficiencies are also same. With external noise interference, the attack effect of the first IMF based on noise with 15dB is the best.

Dynamic inhomogeneous S-Boxes in AES: a novel countermeasure against power analysis attacks

Substitution boxes (S-Boxes) in advanced encryption standard (AES) are vulnerable to attacks bypower analysis.The general S-Boxes masking schemes in circuit level need to adjust the design flow andlibrary databases.The masking strategies in algorithm level view each S-Box as an independent moduleand mask them respectively,which are costly in size and power for non-linear characteristic of S-Boxes.The new method uses dynamic inhomogeneous S-Boxes instead of traditional homogeneous S-Boxes,andarranges the S-Boxes randomly.So the power and data path delay of substitution unit become unpre-dictable.The experimental results demonstrate that this scheme takes advantages of the circuit character-istics of various S-Box implementations to eliminate the correlation between crypto operation and power.Itneeds less extra circuits and suits resource constrained applications.

Influences of Institutional Pressures on Corporate Social Performance： Empirical Analysis on the Panel Data of Chinese Power Generation Enterprises

Institutional theory has proved the influence of institutional pressures on organization practices and structures. Meanwhile, with the soaring use of corporate social performance （CSP）, more researchers are focusing on exploring the relationship between institution pressures and CSP which is still not completely understood yet. Against this background, the paper aims to fill the gap through generally hypothesizing that different types of institutional pressures individually and collectively affect CSP via the mediating effect of corporate environmental strategy. First, based on the previous and extensive literature review, the theoretical framework and research hypotheses are constructed. Next, canonical correlation analysis about the panel data of 51 Chinese large-scale power generation enterprises from 2004 to 2009 is made to test the relevant hypotheses. Finally, based on the data analysis results, the study draws some conclusions and policy implications for promoting the CSP of Chinese enterprises, including enhancing the steering function of government policies and industry regulations and emphasizing the intermediary role of media.

Nonlinear Dynamic Analysis of MPEG-4 Video Traffic

The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The priraeipal compohems analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts.So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear be havior and can be characterized by its power spectral density,principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.

基于两阶段聚类和MCMC算法的风光出力序列建模方法

针对风光出力的随机性建模问题,提出一种基于两阶段聚类和双层马尔科夫链模型的风光相关出力序列建模方法。首先采用两阶段聚类得到不同的风光典型日出力模式,第1阶段采用自组织映射聚类方法识别不同气象条件下的光伏出力类型;第2阶段采用近邻传播聚类方法对不同光伏出力类型对应的风电出力样本进行聚类。其次,建立双层马尔科夫链模型描述风光出力的相依变化,上层建立单变量马尔科夫链模型描述风光出力模式的日间转移,下层建立双变量马尔科夫链模型描述风光出力日内相邻时刻的状态转移。最后,采用MCMC模拟方法得到指定时间长度的风光出力序列。仿真算例表明,所提方法在各项评价指标上均优于传统MCMC方法及Copula模型,能生成更符合风光实际相关性的出力序列。

基于相关性分析的电网非同步监测数据场景谐波责任划分

针对传统谐波责任划分方法需采用专门同步设备监测数据,且需基于等值电路模型划分谐波责任,工程应用较为复杂等不足,采用现有谐波监测装置非同步测量数据,提出一种综合考虑了数据非同步性、场景划分和数据相关性的谐波责任划分方法。首先,对原始非同步监测数据集采用分段聚合近似算法进行降噪预处理,利用形状动态时间规整算法(shape dynamic time warping,ShapeDTW)实现数据匹配对齐;然后,利用点排序识别聚类结构的聚类算法(ordering points to identify the clustering structure,OPTICS)划分场景以处理电力系统中因负荷投切和无功补偿装置切换等情况导致的谐波责任变化;最后,基于相关性分析构建场景谐波责任和总谐波责任指标,在指标构建的过程中引入了场景时长占比这一因素以得到更加科学合理的总谐波责任值。通过仿真验证和电网实例验证,该方法能基于现有非同步性监测数据实现各用户合理时间尺度动态谐波责任划分,可为工程上的快速谐波责任划分提供一定的新思路和新方法。

对同步流密码设备的相关性功耗分析(CPA)攻击

利用同步流密码的再同步弱点,提出了用相关性功耗分析(CPA)攻击同步流密码的技术,该技术采用统计学中的相关系数来分析密钥并实施攻击。概括了CPA技术的攻击过程,给出了CPA攻击两个同步流密码A5/1和E0的步骤和实验。实验证明,用CPA攻击同步流密码是可行的。最后给出了有效的防御对策来抵抗这种攻击。

针对AES密码芯片的CPA攻击点选择研究

为解决AES密码芯片的相关性能量攻击(CPA)的攻击点选择问题,提出了一种CPA攻击点的选择方法,搭建了验证CPA攻击点有效性的仿真平台,并针对AES密码芯片进行了选择分析和验证实验。针对AES密码芯片提出了两种CPA攻击点选择——异或(XOR)操作攻击点和S盒(Sbox)操作攻击点。验证结果表明,两种选择都有效,其中后者的效果更好。

针对DES密码芯片的CPA攻击仿真

为研究密码芯片抗功耗分析性能,构造了一个功耗分析研究平台,结合DES算法在平台上进行了相关性功耗分析(CPA)攻击仿真实验。根据猜测部分密钥时的模拟功耗与猜测整个密钥时模拟功耗之间的相关系数大小来确定猜测密钥的正确性,由此可以确定整个密钥。这种功耗分析仿真方法,能够揭示未经防御的DES算法面临CPA攻击时的脆弱性。

面向ASIC实现的CPA研究平台及其应用

差分功耗分析(DPA)是一种非侵入式边信道攻击技术,对各种密码芯片的安全构成了极大威胁。为了能够快速地评估密码算法ASIC实现方式的算法级抗功耗分析攻击措施的实际效果,将门级功耗分析方法应用于功耗分析攻击评估技术中,搭建了基于PrimeTimePX和MATLAB的相关性功耗分析(CPA)研究平台。该平台具有较强的通用性,只需修改算法攻击功耗模型部分,即可快速完成对不同密码算法ASIC实现中算法级防护措施的评估。作为应用,利用该平台分别对普通AES算法实现和基于Threshold技术的AES算法实现进行了相关性攻击实验,证明了该平台的有效性和便捷性。

结合EDCA和CPA的容错双向选择攻击

当所设计的攻击方案带有容错功能时,往往需要在非常大的候选空间中挑出正确的密钥。如何有效地实现这个目标是侧信道攻击中非常重要且具有挑战性的问题。针对这一问题,以AES-128为目标研究了结合欧式距离增强碰撞攻击(EDCA)和相关能量分析攻击(CPA)的容错双向选择攻击。为了提高碰撞检测的成功率,提出了EDCA,与传统的相关增强碰撞攻击(CCA)相比,EDCA利用欧式距离来区分两组能量迹之间的相似性,其碰撞检测的成功率更高,从而使优化更加实用和有意义。除此之外,结合EDCA和CPA,将密钥以及对应的碰撞对做分组处理,然后进行双向筛选,得到最优的碰撞链,大大减少了候选空间,从而降低了密钥枚举的复杂性,有效地恢复密钥。实验结果表明,在低信噪比SNR=-3 dB和SNR=-6 dB的条件下,设置碰撞对的阈值ThΔ=5,所提出的方案在3000条能量迹时成功率达到98.78%和80.25%,均优于现有方案。

计及相似日和时间相关性的深度学习短期电力负荷预测

针对特征提取不足、负荷数据噪声大等问题,提出一种基于多因素相似日聚类、时间相关性分析、两层分解降噪的多分支组合负荷预测方法。首先,利用皮尔逊相关系数和最大互信息系数综合分析日负荷影响因素的线性相关性和非线性相关性,增强对重要特征的筛选。将筛选出的高相关性气象因素、日期因素和24 h日负荷数据通过主成分分析方法降维后,进行K-medoids相似日聚类。然后,对各聚类相似日的负荷、气象和日期等数据进行多维分析、多特征提取,构建多特征提取矩阵块以增强数据的周期性规律和时空特性,并结合变分模态分解及经验小波变换提取原始数据的多尺度波动规律、增加数据细节特征,同时降低数据的非线性程度。利用组合预测模型中不同输入分支的门控残差卷积模块充分挖掘数据间的局部相关性,提取局部短时依赖、获取高维特征;利用输入分支并联的双向长短期记忆网络提取数据间的时序特征、挖掘长期依赖关系。最后,对不同类型的特征进行综合集成、强化,实现短期电力负荷预测。实验结果表明:在短期电力负荷单步预测中,用所提的多特征提取、多模型组合方法可获得较高的预测精度;在多步预测中,用所提策略能大幅提升预测精度。所提方法整体预测效果优异。

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.

Inferential Statistics and Machine Learning Models for Short-TermWind Power Forecasting

The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation.Inferred statistics are utilized in this research to infer general features based on the selected information,confirming that there are differences between two forecasting categories:Forecast Category 1(0-11 h ahead)and Forecast Category 2(12-23 h ahead).In z-tests,the null hypothesis provides the corresponding quantitative findings.To verify the final performance of the prediction findings,five benchmark methodologies are used:Persistence model,LMNN(Multilayer Perceptron with LMlearningmethods),NARX(Nonlinear autoregressive exogenous neural networkmodel),LMRNN(RNNs with LM training methods)and LSTM(Long short-term memory neural network).Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model,LMNN,NARX network,and LMRNN,and the 23-steps forecasting accuracy has improved by 19.61%.

科技强省评价指标体系构建与监测研究

科技强省建设评价监测是评估科技强省建设各方面目标实现程度的重要手段。通过面向湖北科技强省建设需求,确定评价监测目标和内容,在此基础上开展评价监测指标体系研究。实证分析表明,湖北在科技强省建设五个要素方面表现较为平衡,但与先进地区相比有较大差距,要建成科技强省,还需要在各方面进行全面提升。还分析了湖北推进科技强省建设的优势以及关键制约因素,通过评价和持续监测分析,能提高科技主管部门制定科技政策和发展战略的科学性,对提高区域科技创新治理能力和治理水平起到重要支撑作用。

基于注意力特征融合时空图网络的超短期风电功率预测

为提高风电功率的预测精度,综合考虑时间和空间多维度因素的影响,提出了一种基于注意力机制和多阶段特征融合的时空图神经网络(Spatio-temporal graph neural network with attention mechanism and multistage feature fusion,AMF-STGNN)的超短期风电功率预测方法。首先基于Pearson相关系数法对数据特征进行降维,确定影响风电功率的关键因素。然后构建AMF-STGNN预测模型。该模型主要由时空关联网络构建模块和多维度时空特征抽取模块组成。通过时空关联网络构建模块构建时空图,以揭示风电气象因素的空间连接关系。通过多维度时空特征抽取模块应用时间卷积和图卷积挖掘数据的时空特征,并利用注意力机制学习重要特征。此外,该方法还引入残差结构和多阶段特征融合结构提高模型的表达能力。最后以宁夏3个风电场真实数据为例,验证了所提方法在提升风电功率预测精度方面的有效性。

基于PSD特征的FBCCA脑电信号识别方法

当前基于稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)的脑机接口(brain-computer interfaces,BCIs)使用的都是单一识别算法,针对不同时间长度的识别准确率较低。提出了一种基于滤波器组的典型相关分析(filter bank canonical correlation analysis,FBCCA)与功率谱密度(power spectral density,PSD)分析相结合的SSVEP识别算法,可以提高SSVEP识别的普适性与准确率。该方法使用FBCCA寻找高相似度的参考频率信号,再通过多组PSD分析来锁定最终的响应频率,完成频率识别。该方法无需经过训练就能得到较高的识别准确率。实验结果表明:在刺激时长为1 s时,该方法能达到86.61%的准确率,比PSD分析方法提升了5.44%,比典型相关性分析方法(canonical correlation analysis,CCA)提升了10.38%的准确率,比FBCCA提升了8.86%的准确率。