Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
为探究在集散式控制系统(distributed control system,DCS)危险排除过程中控制员不同信息搜索策略对排险任务绩效的影响及认知负荷的中介效应,基于虚拟现实技术、皮肤电采样和眼动追踪技术构建模拟DCS工控平台,招募20名相关专业被试参...为探究在集散式控制系统(distributed control system,DCS)危险排除过程中控制员不同信息搜索策略对排险任务绩效的影响及认知负荷的中介效应,基于虚拟现实技术、皮肤电采样和眼动追踪技术构建模拟DCS工控平台,招募20名相关专业被试参与模拟排险实验并对其认知负荷及排险绩效进行量化,使用眼动轨迹匹配法判断被试的信息搜索模式,研究认知负荷的中介效应及中介机理。研究结果表明:不同信息搜索策略会显著影响任务绩效;认知负荷对该影响的中介效应高达89.66%,表明信息搜索策略主要通过影响认知负荷来间接作用于排险任务绩效,认知负荷越高,任务绩效越低;逻辑系统搜索策略能通过高效图式匹配减少认知资源消耗,显著抑制认知负荷增长,任务绩效表现最佳;空间系统搜索较难抑制认知负荷,任务绩效较差;随机搜索被试认知负荷显著高于其他组,绩效表现最差;此外,不同认知负荷水平下被试的信息搜索策略没有明显转变倾向。研究结果可为DCS控制人员的考核和培训提供理论支撑。展开更多
研究基于DCS(Distributed Control System)的燃气-蒸汽联合循环机组运行智能控制系统,确保机组安全运行的同时,提高机组整体运行效率。构建基于DCS的燃气-蒸汽联合循环机组运行智能控制框架,过程控制层的Mark VI系统、DCS系统根据监测...研究基于DCS(Distributed Control System)的燃气-蒸汽联合循环机组运行智能控制系统,确保机组安全运行的同时,提高机组整体运行效率。构建基于DCS的燃气-蒸汽联合循环机组运行智能控制框架,过程控制层的Mark VI系统、DCS系统根据监测数据变化实现机组设备、旁路等自动控制。SIS层接收联合循环机组监测数据后,将其作为基于深度神经网络故障诊断模型的输入,实现机组设备故障的识别。在检测到故障时触发联锁保护子系统动作,将停机指令下达给自动启停控制子系统,使机组停止运行。实验结果表明,该系统可实现燃气-蒸汽联合循环机组设备故障识别,在100次训练后,训练损失为0.1左右,F-Score指标最大值为0.93;故障工况下,该系统可根据预定逻辑实现燃气-蒸汽联合循环机组自动停机。展开更多
为降低核安全级数字化控制系统(Digital Control System,DCS)关键芯片在工作过程中的温升,提高系统的可靠性,本研究提出利用机器学习方法对核安全级DCS关键芯片进行布局优化。首先,试验测得DCS在事故工况(环境温度55℃)下的芯片稳态温度...为降低核安全级数字化控制系统(Digital Control System,DCS)关键芯片在工作过程中的温升,提高系统的可靠性,本研究提出利用机器学习方法对核安全级DCS关键芯片进行布局优化。首先,试验测得DCS在事故工况(环境温度55℃)下的芯片稳态温度,随后结合有限元分析计算模拟试验过程。基于有限元模型生成100组随机芯片排布下的中央处理器(Central Processing Unit,CPU)和可编程逻辑门阵列(Field Programmable Gate Array,FPGA)稳态温度数据,利用多输出支持向量回归(Multi-output Support Vector Regression,M-SVR)算法建立温度预测模型,结合粒子群优化(Particle Swarm Optimization,PSO)算法计算出温升最小的芯片位置坐标。进一步,利用有限元分析验证该优化位置坐标下的芯片稳态温度。研究结果表明,有限元模型能较好反映试验现象,SVR-PSO算法优化得到的芯片布局使CPU和FPGA的稳态温度分别降低2.4℃和2.5℃。因此,本研究提出的算法能够实现芯片布局优化,有效降低其工作温升,提升核安全级DCS系统可靠性。展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
集散控制系统(distributed control system,DCS)是复杂的分布式控制系统,网络安全威胁多种多样,可能会针对DCS的不同层面进行攻击,如网络通信、数据存储、应用程序等,使网络风险安全监测变得更加复杂。为此,设计了一种燃煤电厂DCS的网...集散控制系统(distributed control system,DCS)是复杂的分布式控制系统,网络安全威胁多种多样,可能会针对DCS的不同层面进行攻击,如网络通信、数据存储、应用程序等,使网络风险安全监测变得更加复杂。为此,设计了一种燃煤电厂DCS的网络风险安全监测方法。基于电厂DCS构建了基于改进卡尔曼滤波的网络风险监测模型。试验结果表明,所提方法应用中网络环境的安全性得到明显改善,且监测的误报率始终低于3%。展开更多
DCS系统(Distributed Control System)作为热工控制系统的核心部分,其性能指标对发电机组安全稳定运行有着至关重要的影响。DCS系统在长期运行中常存在如卡件故障、信号干扰等问题,需依据行业技术标准,通过科学的测试手段,对其主要安全...DCS系统(Distributed Control System)作为热工控制系统的核心部分,其性能指标对发电机组安全稳定运行有着至关重要的影响。DCS系统在长期运行中常存在如卡件故障、信号干扰等问题,需依据行业技术标准,通过科学的测试手段,对其主要安全性指标如网络性能、控制器性能、响应时间、控制器冗余监测、控制器切换、抗干扰能力等进行测试,可有效判断出DCS长周期运行过程中存在的安全隐患,并进行针对性运行维护,以提升其安全可靠性。展开更多
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
文摘为探究在集散式控制系统(distributed control system,DCS)危险排除过程中控制员不同信息搜索策略对排险任务绩效的影响及认知负荷的中介效应,基于虚拟现实技术、皮肤电采样和眼动追踪技术构建模拟DCS工控平台,招募20名相关专业被试参与模拟排险实验并对其认知负荷及排险绩效进行量化,使用眼动轨迹匹配法判断被试的信息搜索模式,研究认知负荷的中介效应及中介机理。研究结果表明:不同信息搜索策略会显著影响任务绩效;认知负荷对该影响的中介效应高达89.66%,表明信息搜索策略主要通过影响认知负荷来间接作用于排险任务绩效,认知负荷越高,任务绩效越低;逻辑系统搜索策略能通过高效图式匹配减少认知资源消耗,显著抑制认知负荷增长,任务绩效表现最佳;空间系统搜索较难抑制认知负荷,任务绩效较差;随机搜索被试认知负荷显著高于其他组,绩效表现最差;此外,不同认知负荷水平下被试的信息搜索策略没有明显转变倾向。研究结果可为DCS控制人员的考核和培训提供理论支撑。
文摘研究基于DCS(Distributed Control System)的燃气-蒸汽联合循环机组运行智能控制系统,确保机组安全运行的同时,提高机组整体运行效率。构建基于DCS的燃气-蒸汽联合循环机组运行智能控制框架,过程控制层的Mark VI系统、DCS系统根据监测数据变化实现机组设备、旁路等自动控制。SIS层接收联合循环机组监测数据后,将其作为基于深度神经网络故障诊断模型的输入,实现机组设备故障的识别。在检测到故障时触发联锁保护子系统动作,将停机指令下达给自动启停控制子系统,使机组停止运行。实验结果表明,该系统可实现燃气-蒸汽联合循环机组设备故障识别,在100次训练后,训练损失为0.1左右,F-Score指标最大值为0.93;故障工况下,该系统可根据预定逻辑实现燃气-蒸汽联合循环机组自动停机。
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘集散控制系统(distributed control system,DCS)是复杂的分布式控制系统,网络安全威胁多种多样,可能会针对DCS的不同层面进行攻击,如网络通信、数据存储、应用程序等,使网络风险安全监测变得更加复杂。为此,设计了一种燃煤电厂DCS的网络风险安全监测方法。基于电厂DCS构建了基于改进卡尔曼滤波的网络风险监测模型。试验结果表明,所提方法应用中网络环境的安全性得到明显改善,且监测的误报率始终低于3%。
文摘DCS系统(Distributed Control System)作为热工控制系统的核心部分,其性能指标对发电机组安全稳定运行有着至关重要的影响。DCS系统在长期运行中常存在如卡件故障、信号干扰等问题,需依据行业技术标准,通过科学的测试手段,对其主要安全性指标如网络性能、控制器性能、响应时间、控制器冗余监测、控制器切换、抗干扰能力等进行测试,可有效判断出DCS长周期运行过程中存在的安全隐患,并进行针对性运行维护,以提升其安全可靠性。
