Psychological studies on human subjects show that contrast detection learning promote learner＇s sensitivity to visual stimulus contrast. The underlying neural mechanisms remain unknown. In this study, three cats （Fe...Psychological studies on human subjects show that contrast detection learning promote learner＇s sensitivity to visual stimulus contrast. The underlying neural mechanisms remain unknown. In this study, three cats （Felis catus） were trained to perform monocularly a contrast detection task by two-altemative forced choice method. The perceptual ability of each cat improved remarkably with learning as indicated by a significantly increased contrast sensitivity to visual stimuli. The learning effect displayed an evident specificity to the eye employed for learning but could partially transfer to the naive eye, prompting the possibility that contrast detection learning might cause neural plasticity before and after the information from both eyes are merged in the visual pathway. Further, the contrast sensitivity improvement was evident basically around the spatial frequency （SF） used for learning, which suggested that contrast detection learning effect showed, to some extent, a SF specificity. This study indicates that cat exhibits a property of contrast detection learning similar to human subjects and can be used as an animal model for subsequent investigations on the neural correlates that mediate learning-induced contrast sensitivity improvement in humans.展开更多
Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for position...Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.展开更多
基金Supported by Natural Science Foundation of Anhui Province(070413138)the foundation of Key Laboratory of Anhui Province and the Key Research Foundation from Education Department of Anhui Province(KJ2009A167)
文摘Psychological studies on human subjects show that contrast detection learning promote learner＇s sensitivity to visual stimulus contrast. The underlying neural mechanisms remain unknown. In this study, three cats （Felis catus） were trained to perform monocularly a contrast detection task by two-altemative forced choice method. The perceptual ability of each cat improved remarkably with learning as indicated by a significantly increased contrast sensitivity to visual stimuli. The learning effect displayed an evident specificity to the eye employed for learning but could partially transfer to the naive eye, prompting the possibility that contrast detection learning might cause neural plasticity before and after the information from both eyes are merged in the visual pathway. Further, the contrast sensitivity improvement was evident basically around the spatial frequency （SF） used for learning, which suggested that contrast detection learning effect showed, to some extent, a SF specificity. This study indicates that cat exhibits a property of contrast detection learning similar to human subjects and can be used as an animal model for subsequent investigations on the neural correlates that mediate learning-induced contrast sensitivity improvement in humans.
基金supported by the National Key R&D Program of China(No.2018AAA0100804)the Talent Project of Revitalization Liaoning(No.XLYC1907022)+5 种基金the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the Capacity Building of Civil Aviation Safety(No.TMSA1614)the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Scientific Research Project of Liaoning Provincial Department of Education(Nos.L201705,L201716)the High-Level Innovation Talent Project of Shenyang(No.RC190030)the Second Young and Middle-Aged Talents Support Program of Shenyang Aerospace University.
文摘Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.
