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| 1 | Steady-state Voltage Reactive Compensation Method for Half-wavelength Transmission Lines Considering Equivalent Power Supply Impedance显示文摘Half-wavelength AC transmission(HWACT)refers to transmission along lines with an electrical distance of half the power frequency wavelength.The voltage amplitude at the sending terminal is equal to that at the receiving terminal in steady-state.Furthermore,the voltage at the receiving terminal does not vary with load.However,the electrical distance has been extended in actual HWACT lines because of the impact of the equivalent power supply impedance,so the attractive characteristics no longer hold.As a result,reactive power compensation methods with constant power factor and constant voltage are needed and these are presented in this paper.The steady-state operational characteristics,the calculation methods of additional reactive power compensation and the admittance of a controllable shunt reactor are also given.The methods proposed in this paper are relatively simple to implement,and the calculations involved are straightforward,resulting in effective voltage stabilization.The results indicate that such compensation methods can increase the transmission capacity while maintaining a relatively low voltage along the line. | Jun Yi Wei Hu Zijie Zhang Lu Qiu Ansi Wang Bowen Yan Canbing Li Quanyou Yue | 2020 | CSEE Journal of Power and Energy Systems2020，6，4： | 2 |
| 2 | Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation显示文摘Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous data.Furthermore,flight data with random noise pose a significant challenge for anomaly detection.This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data.First,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model.Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge.Then,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner.Finally,the method's effectiveness is validated on real UAV flight data. | YANG Lei LI ShaoBo LI ChuanJiang ZHU CaiChao ZHANG AnSi LIANG GuoQiang | 2023 | Science China(Technological Sciences)2023，66，5： | 0 |
| 3 | Digital twin-enabled adaptive scheduling strategy based on deep reinforcement learning显示文摘The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning capabilities.While traditional scheduling methods cannot meet these needs due to their rigidity.Self-learning is an inherent ability of reinforcement learning(RL) algorithm inhered from its continuous learning and trial-and-error characteristics.Self-regulation of scheduling could be enabled by the emerging digital twin(DT) technology because of its virtual-real mapping and mutual control characteristics.This paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm,which was called explicit exploration and asynchronous update proximal policy optimization algorithm(E2APPO).Firstly,the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops,strengthening the self-regulation of the scheduling model.Secondly,an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling model.Lastly,the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms,such as wellknown scheduling rules and genetic algorithms,as well as other existing scheduling methods based on reinforcement learning.The comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy. | GAN XueMei ZUO Ying ZHANG AnSi LI ShaoBo TAO Fei | 2023 | Science China(Technological Sciences)2023，66，7： | 0 |
| 4 | A survey of unmanned aerial vehicle flight data anomaly detection:Technologies,applications,and future directions显示文摘Flight data anomaly detection plays an imperative role in the safety and maintenance of unmanned aerial vehicles(UAVs).It has attracted extensive attention from researchers.However,the problems related to the difficulty in obtaining abnormal data,low model accuracy,and high calculation cost have led to severe challenges with respect to its practical applications.Hence,in this study,firstly,several UAV flight data simulation softwares are presented based on a brief presentation of the basic concepts of anomalies,the contents of UAV flight data,and the public datasets for flight data anomaly detection.Then,anomaly detection technologies for UAV flight data are comprehensively reviewed,including knowledge-based,model-based,and data-driven methods.Next,UAV flight data anomaly detection applications are briefly described and analyzed.Finally,the future trends and directions of UAV flight data anomaly detection are summarized and prospected,which aims to provide references for the following research. | YANG Lei LI ShaoBo LI ChuanJiang ZHANG AnSi ZHANG XuDong | 2023 | Science China(Technological Sciences)2023，66，4： | 0 |
| 5 | Different levels of facial expression recognition in patients with firstepisode schizophrenia:A functional MRI study显示文摘Background The impairment of facial expression recognition has become a biomarker for early identification of first-episode schizophrenia, and this kind of research is increasing.Aims To explore the differences in brain area activation using different degrees of disgusted facial expression recognition in antipsychotic-na?ve patients with firstepisode schizophrenia and healthy controls.Methods In this study, facial expression recognition tests were performed on 30 first-episode, antipsychoticna?ve patients with schizophrenia(treatment group) and 30 healthy subjects(control group) with matched age, educational attainment and gender. Functional MRI was used for comparing the differences of the brain areas of activation between the two groups.Results The average response time difference between the patient group and the control group in the ‘high degree of disgust' facial expression recognition task was statistically significant(1.359(0.408)/2.193(0.625), F=26.65, p<0.001), and the correct recognition rate of the treatment group was lower than that of the control group(41.05(22.25)/59.84(13.91, F=19.81, p<0.001). Compared with the control group, the left thalamus, right lingual gyrus and right middle temporal gyrus were negatively activated in the patients with first-episode schizophrenia in the ‘high degree of disgust' emotion recognition, and there was a significant activation in the left and right middle temporal gyrus and the right caudate nucleus. However, there was no significant activation difference in the ‘low degree of disgust' recognition.Conclusions In patients with first-episode schizophrenia, the areas of facial recognition impairment are significantly different in different degrees of disgust facial expression recognition. | Chengqing Yang Ansi Qi Huangfang Yu Xiaofeng Guan Jijun Wang Na Liu Tianhong Zhang Hui Li Hui Zhou Junjuan Zhu Nan Huang Yingying Tang Zheng Lu | 2018 | General Psychiatry2018，31，5： | 0 |
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