为了研究作业环境中噪声因素对人的安全注意力的影响,对安全注意力类型及品质特征进行分析,建立安全注意力水平模型。设计噪声条件下的安全注意力水平测量实验,通过实验中不同噪声环境下人的安全注意力水平变化,分析噪声对人的安全注意...为了研究作业环境中噪声因素对人的安全注意力的影响,对安全注意力类型及品质特征进行分析,建立安全注意力水平模型。设计噪声条件下的安全注意力水平测量实验,通过实验中不同噪声环境下人的安全注意力水平变化,分析噪声对人的安全注意力的影响作用。结果表明,噪声对人的安全注意力具有较为显著的影响作用;安全注意力的广度、稳定性、分配、转移均受噪声影响,安全注意力的稳定性及转移随噪声的增大而降低;当噪声值大于60 d B、70 d B时,噪声对安全注意力的广度及分配的影响较为显著。展开更多
In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design ou...In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design our model based on the Siamese network using deep Long Short-Term Memory (LSTM) Network. And we add the special attention mechanism to let the model give different words different attention while modeling sentences. The fully-connected layer is proposed to measure the complex sentence representations. Our results show that the accuracy is better than the baseline in 2016. Furthermore, it is showed that the model has the ability to model the sequence order, distribute reasonable attention and extract meanings of a sentence in different dimensions.展开更多
Detection of high impedance faults(HIFs)has been traditionally a main challenge in the protection of distribution systems,since they do not generate enough current to be reliably detected by conventional over-current ...Detection of high impedance faults(HIFs)has been traditionally a main challenge in the protection of distribution systems,since they do not generate enough current to be reliably detected by conventional over-current relays.Data-based methods are alternative HIF detection methods which avoid threshold settings by training a classification or regression model.However,most of them lack interpretability and are not compatible with various distribution networks.This paper proposes an object detection-based HIF detection method,which has higher visualization and can be easily applied to different scenarios.First,based on the analysis of HIFs,a Butterworth band-pass filter is designed for HIF harmonic feature extraction.Subsequently,based on the synchronized data provided by distribution-level phasor measurement units,global HIF feature gray-scale images are formed through combining the topology information of the distribution network.To further enhance the feature information,a locally excitatory globally inhibitory oscillator region attention mechanism(LEGIO-RAM)is proposed to highlight the critical feature regions and inhibit useless and fake information.Finally,an object detection network based You Only Look Once(YOLO)v2 is established to achieve fast HIF detection and section location.The obtained results from the simulation of the proposed approach on three different distribution networks and one realistic distribution network verify that the proposed method is highly effective in terms of reliability and generalization.展开更多
文摘为了研究作业环境中噪声因素对人的安全注意力的影响,对安全注意力类型及品质特征进行分析,建立安全注意力水平模型。设计噪声条件下的安全注意力水平测量实验,通过实验中不同噪声环境下人的安全注意力水平变化,分析噪声对人的安全注意力的影响作用。结果表明,噪声对人的安全注意力具有较为显著的影响作用;安全注意力的广度、稳定性、分配、转移均受噪声影响,安全注意力的稳定性及转移随噪声的增大而降低;当噪声值大于60 d B、70 d B时,噪声对安全注意力的广度及分配的影响较为显著。
文摘In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design our model based on the Siamese network using deep Long Short-Term Memory (LSTM) Network. And we add the special attention mechanism to let the model give different words different attention while modeling sentences. The fully-connected layer is proposed to measure the complex sentence representations. Our results show that the accuracy is better than the baseline in 2016. Furthermore, it is showed that the model has the ability to model the sequence order, distribute reasonable attention and extract meanings of a sentence in different dimensions.
基金supported by the National Key Research and Development Program of China(2017YFB0902800)Science and Technology Project of the State Grid Corporation of China(52094017003D).
文摘Detection of high impedance faults(HIFs)has been traditionally a main challenge in the protection of distribution systems,since they do not generate enough current to be reliably detected by conventional over-current relays.Data-based methods are alternative HIF detection methods which avoid threshold settings by training a classification or regression model.However,most of them lack interpretability and are not compatible with various distribution networks.This paper proposes an object detection-based HIF detection method,which has higher visualization and can be easily applied to different scenarios.First,based on the analysis of HIFs,a Butterworth band-pass filter is designed for HIF harmonic feature extraction.Subsequently,based on the synchronized data provided by distribution-level phasor measurement units,global HIF feature gray-scale images are formed through combining the topology information of the distribution network.To further enhance the feature information,a locally excitatory globally inhibitory oscillator region attention mechanism(LEGIO-RAM)is proposed to highlight the critical feature regions and inhibit useless and fake information.Finally,an object detection network based You Only Look Once(YOLO)v2 is established to achieve fast HIF detection and section location.The obtained results from the simulation of the proposed approach on three different distribution networks and one realistic distribution network verify that the proposed method is highly effective in terms of reliability and generalization.
