通过记录物理地址扩展(Physical Address Extensions,PAE)T6T/T6R与三汇内话SVCS3000之间的IP语音(Voice over IP,VoIP)连接的互联互通性,测试在正常、网络中断、实时传输协议(Real-time Transport Protocol,RTP)打包时长不匹配、修改...通过记录物理地址扩展(Physical Address Extensions,PAE)T6T/T6R与三汇内话SVCS3000之间的IP语音(Voice over IP,VoIP)连接的互联互通性,测试在正常、网络中断、实时传输协议(Real-time Transport Protocol,RTP)打包时长不匹配、修改主备模式以及远程电台控制等情况下SVCS3000与PAE的VoIP互联互通性,解决VoIP运营商之间通信存在的协议转移问题。展开更多
为提升含高比例电力电子设备的电力系统暂态稳定性,提出一种基于广域测量系统(wide area measurement system,WAMS)的静止无功补偿器(static var compensator,SVC)优化控制策略。通过同步相量测量单元实时获取发电机母线电压相量,构建...为提升含高比例电力电子设备的电力系统暂态稳定性,提出一种基于广域测量系统(wide area measurement system,WAMS)的静止无功补偿器(static var compensator,SVC)优化控制策略。通过同步相量测量单元实时获取发电机母线电压相量,构建以发电机电功率与机械功率偏差最小为目标函数的粒子群优化模型,并结合电压幅值与相角灵敏度系数动态计算SVC最优无功功率注入量。创新性引入灵敏度系数刻画发电机有功功率对SVC无功功率的依赖关系,实现多发电机转子角振荡协同阻尼。在DIgSILENT PowerFactory平台搭建IEEE 14节点系统,仿真结果表明,所提策略可有效降低转子角振荡幅度及斜率,使临界清除时间延长40 ms。与传统本地控制方法相比,所提方法突破了单一振荡阻尼限制,利用WAMS信息实现多机协调控制,为提升复杂电力系统暂态稳定性提供了新思路。展开更多
研究基于谐波抑制的配电网静止无功补偿器(Static Var Compensator,SVC)的优化设计,提出一种改进型三相四桥臂SVC拓扑结构。该拓扑结构通过辅助桥臂降低主桥臂开关管电流应力,增强系统的谐波抑制能力。在电感-电容(Inductor Capacitor,...研究基于谐波抑制的配电网静止无功补偿器(Static Var Compensator,SVC)的优化设计,提出一种改进型三相四桥臂SVC拓扑结构。该拓扑结构通过辅助桥臂降低主桥臂开关管电流应力,增强系统的谐波抑制能力。在电感-电容(Inductor Capacitor,LC)滤波器参数优化方面,基于多目标优化理论,设计在保证良好滤波效果的同时具有较小系统损耗和良好经济性的最优参数组合。功率器件方面,选用了第三代宽禁带半导体器件SiC-MOSFET。仿真验证结果表明,所提出的优化方案不仅增强了系统的无功补偿精度与动态响应能力,还在多频段谐波抑制、健壮性增强以及电能质量改善等方面展现出良好应用前景。展开更多
Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing ...Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression(LR), Decision Tree(DT), Support Vector Machine(SVM),Random Forest(RF), and Gradient Boosting(GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree(DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and66.12% for testing. For validation, the Gradient Boosting(GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest(RF) and Gradient Boosting(GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.展开更多
为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,...为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,针对经典TS模糊预测算法应用于波动负荷时出现的输出异常置0情况,提出一种范围自适应修正的改进方法,该方法能消除一类算法应用机理导致的异常值,从而提高TS模糊算法对波动负荷无功功率预测的可靠性和准确性;最后,基于模型训练时间约束,建立无功功率半周期滚动预测控制模型,提前10 ms预测无功功率,改善了SVC传统控制系统响应的滞后特性。仿真结果表明,相比于SVC传统控制方法,所提方法的平均闪变改善率提高了54.17%,验证了所提方法对闪变现象的抑制效果提升显著。展开更多
The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capable...The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.展开更多
文摘通过记录物理地址扩展(Physical Address Extensions,PAE)T6T/T6R与三汇内话SVCS3000之间的IP语音(Voice over IP,VoIP)连接的互联互通性,测试在正常、网络中断、实时传输协议(Real-time Transport Protocol,RTP)打包时长不匹配、修改主备模式以及远程电台控制等情况下SVCS3000与PAE的VoIP互联互通性,解决VoIP运营商之间通信存在的协议转移问题。
文摘为提升含高比例电力电子设备的电力系统暂态稳定性,提出一种基于广域测量系统(wide area measurement system,WAMS)的静止无功补偿器(static var compensator,SVC)优化控制策略。通过同步相量测量单元实时获取发电机母线电压相量,构建以发电机电功率与机械功率偏差最小为目标函数的粒子群优化模型,并结合电压幅值与相角灵敏度系数动态计算SVC最优无功功率注入量。创新性引入灵敏度系数刻画发电机有功功率对SVC无功功率的依赖关系,实现多发电机转子角振荡协同阻尼。在DIgSILENT PowerFactory平台搭建IEEE 14节点系统,仿真结果表明,所提策略可有效降低转子角振荡幅度及斜率,使临界清除时间延长40 ms。与传统本地控制方法相比,所提方法突破了单一振荡阻尼限制,利用WAMS信息实现多机协调控制,为提升复杂电力系统暂态稳定性提供了新思路。
文摘研究基于谐波抑制的配电网静止无功补偿器(Static Var Compensator,SVC)的优化设计,提出一种改进型三相四桥臂SVC拓扑结构。该拓扑结构通过辅助桥臂降低主桥臂开关管电流应力,增强系统的谐波抑制能力。在电感-电容(Inductor Capacitor,LC)滤波器参数优化方面,基于多目标优化理论,设计在保证良好滤波效果的同时具有较小系统损耗和良好经济性的最优参数组合。功率器件方面,选用了第三代宽禁带半导体器件SiC-MOSFET。仿真验证结果表明,所提出的优化方案不仅增强了系统的无功补偿精度与动态响应能力,还在多频段谐波抑制、健壮性增强以及电能质量改善等方面展现出良好应用前景。
文摘Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression(LR), Decision Tree(DT), Support Vector Machine(SVM),Random Forest(RF), and Gradient Boosting(GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree(DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and66.12% for testing. For validation, the Gradient Boosting(GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest(RF) and Gradient Boosting(GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.
文摘为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,针对经典TS模糊预测算法应用于波动负荷时出现的输出异常置0情况,提出一种范围自适应修正的改进方法,该方法能消除一类算法应用机理导致的异常值,从而提高TS模糊算法对波动负荷无功功率预测的可靠性和准确性;最后,基于模型训练时间约束,建立无功功率半周期滚动预测控制模型,提前10 ms预测无功功率,改善了SVC传统控制系统响应的滞后特性。仿真结果表明,相比于SVC传统控制方法,所提方法的平均闪变改善率提高了54.17%,验证了所提方法对闪变现象的抑制效果提升显著。
基金Princess Nourah bint Abdulrahman University and Researchers Supporting Project Number(PNURSP2024R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.
