Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and efficiency.In this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic g...Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and efficiency.In this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants.A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types,and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential data.The fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types,and their future path was also predicted which is vital for predictive control of visually guided robotic grasping.High detection accuracy of 94.1%was obtained based on experimental results,and the error for path prediction was within 4.33 pixels 40 frames later.The average time to process a frame was between 28 and 62 frames per second,which also satisfied real-time performance.The results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency,and it can be readily extended to other fruit crops featuring high versatility.展开更多
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LST...In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.展开更多
The fuzzing test is able to discover various vulnerabilities and has more chances to hit the zero-day targets.And ICS(Industrial control system)is currently facing huge security threats and requires security standards...The fuzzing test is able to discover various vulnerabilities and has more chances to hit the zero-day targets.And ICS(Industrial control system)is currently facing huge security threats and requires security standards,like ISO 62443,to ensure the quality of the device.However,some industrial proprietary communication protocols can be customized and have complicated structures,the fuzzing system cannot quickly generate test data that adapt to various protocols.It also struggles to define the mutation field without having prior knowledge of the protocols.Therefore,we propose a fuzzing system named ICPFuzzer that uses LSTM(Long short-term memory)to learn the features of a protocol and generates mutated test data automatically.We also use the responses of testing and adjust the weight strategies to further test the device under testing(DUT)to find more data that cause unusual connection status.We verified the effectiveness of the approach by comparing with the open-source and commercial fuzzers.Furthermore,in a real case,we experimented with the DLMS/COSEM for a smart meter and found that the test data can cause a unusual response.In summary,ICPFuzzer is a black-box fuzzing system that can automatically execute the testing process and reveal vulnerabilities that interrupt and crash industrial control communication.Not only improves the quality of ICS but also improves safety.展开更多
As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the...As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.展开更多
基金National Key R&D Program(2020YFD1000101)and Special Funds for the Construction of Industrial Technology System of Modern Agriculture(Citrus)(CARS-26)Construction Project of Citrus Whole Course Mechanized Scientifific Research Base(Agricultural Development Facility 297[2017]19),Hubei Agricultural Science and Technology Innovation Action Project.
文摘Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and efficiency.In this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants.A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types,and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential data.The fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types,and their future path was also predicted which is vital for predictive control of visually guided robotic grasping.High detection accuracy of 94.1%was obtained based on experimental results,and the error for path prediction was within 4.33 pixels 40 frames later.The average time to process a frame was between 28 and 62 frames per second,which also satisfied real-time performance.The results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency,and it can be readily extended to other fruit crops featuring high versatility.
文摘In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.
文摘The fuzzing test is able to discover various vulnerabilities and has more chances to hit the zero-day targets.And ICS(Industrial control system)is currently facing huge security threats and requires security standards,like ISO 62443,to ensure the quality of the device.However,some industrial proprietary communication protocols can be customized and have complicated structures,the fuzzing system cannot quickly generate test data that adapt to various protocols.It also struggles to define the mutation field without having prior knowledge of the protocols.Therefore,we propose a fuzzing system named ICPFuzzer that uses LSTM(Long short-term memory)to learn the features of a protocol and generates mutated test data automatically.We also use the responses of testing and adjust the weight strategies to further test the device under testing(DUT)to find more data that cause unusual connection status.We verified the effectiveness of the approach by comparing with the open-source and commercial fuzzers.Furthermore,in a real case,we experimented with the DLMS/COSEM for a smart meter and found that the test data can cause a unusual response.In summary,ICPFuzzer is a black-box fuzzing system that can automatically execute the testing process and reveal vulnerabilities that interrupt and crash industrial control communication.Not only improves the quality of ICS but also improves safety.
基金supported in part by the National Natural Science Foundation of China(61533019,91720000)Beijing Municipal Science and Technology Commission(Z181100008918007)the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles(pICRI-IACVq)
文摘As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
