Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频...目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频率调节(Dynamic Voltage Frequency Scaling,DVFS)技术,研究异构多核实时系统中基于任务同步的节能调度问题,提出同步感知的最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First,SA-LESF)。该算法针对所有任务的速度配置进行迭代优化,直至所有任务均达到其最大限度节能的速度配置。此外,进一步提出基于动态松弛时间回收的同步感知最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First with Dynamic Reclamation,SA-LESF-DR)。该算法在保证实时任务可调度的同时,实施相应的回收策略,进一步降低系统能耗。实验结果表明,SA-LESF与SA-LESF-DR算法在能耗表现上具有优势,在相同任务集下,相比其他算法可节省高达30%的能耗。展开更多
在实现“双碳”目标的背景下,城市轨道交通需要实现节能低碳运行。现有牵引供电系统(traction power supply system,TPSS)采用下垂控制策略存在再生制动能量利用率低、缺乏对系统潮流主动有效控制及优化手段等问题。该文分析双向变流器...在实现“双碳”目标的背景下,城市轨道交通需要实现节能低碳运行。现有牵引供电系统(traction power supply system,TPSS)采用下垂控制策略存在再生制动能量利用率低、缺乏对系统潮流主动有效控制及优化手段等问题。该文分析双向变流器的下垂参数对系统用电量的影响,进而提出一种实现牵引负荷功率最优分配的协同控制方法,该方法对双向变流器的下垂参数实时优化和调整,促进机车再生制动能量的流动,有效提高再生制动能量利用率,显著提升系统协同控制性能。经仿真计算,采用该方法可年节约0.17亿k W·h电量,有效助力城市轨道交通牵引供电系统安全、经济运行。展开更多
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
文摘目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频率调节(Dynamic Voltage Frequency Scaling,DVFS)技术,研究异构多核实时系统中基于任务同步的节能调度问题,提出同步感知的最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First,SA-LESF)。该算法针对所有任务的速度配置进行迭代优化,直至所有任务均达到其最大限度节能的速度配置。此外,进一步提出基于动态松弛时间回收的同步感知最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First with Dynamic Reclamation,SA-LESF-DR)。该算法在保证实时任务可调度的同时,实施相应的回收策略,进一步降低系统能耗。实验结果表明,SA-LESF与SA-LESF-DR算法在能耗表现上具有优势,在相同任务集下,相比其他算法可节省高达30%的能耗。
文摘在实现“双碳”目标的背景下,城市轨道交通需要实现节能低碳运行。现有牵引供电系统(traction power supply system,TPSS)采用下垂控制策略存在再生制动能量利用率低、缺乏对系统潮流主动有效控制及优化手段等问题。该文分析双向变流器的下垂参数对系统用电量的影响,进而提出一种实现牵引负荷功率最优分配的协同控制方法,该方法对双向变流器的下垂参数实时优化和调整,促进机车再生制动能量的流动,有效提高再生制动能量利用率,显著提升系统协同控制性能。经仿真计算,采用该方法可年节约0.17亿k W·h电量,有效助力城市轨道交通牵引供电系统安全、经济运行。
