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
With the expansion of the Internet market,the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle,tedious work,and difficult system mainte...With the expansion of the Internet market,the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle,tedious work,and difficult system maintenance.Therefore,to improve software development efficiency,this study uses residual networks and bidirectional long short-term memory(BLSTM)networks to improve the Pix2code model.The experiment results show that after improving the visual module of the Pix2code model using residual networks,the accuracy of the training set improves from 0.92 to 0.96,and the convergence time is shortened from 3 hours to 2 hours.After using a BLSTM network to improve the language module and decoding layer,the accuracy and convergence speed of the model have also been improved.The accuracy of the training set grew from 0.88 to 0.92,and the convergence time was shortened by 0.5 hours.However,models improved by BLSTM networks might exhibit overfitting,and thus this study uses Dropout and Xavier normal distribution to improve the memory network.The results validate that the training set accuracy of the optimized BLSTM network remains around 0.92,but the accuracy of the test set has improved to a maximum of 85%.Dropout and Xavier normal distributions can effectively improve the overfitting problem of BLSTM networks.Although they can also decrease the model’s stability,their gain is higher.The training and testing accuracy of the Pix2code improved by residual network and BLSTM network are 0.95 and 0.82,respectively,while the code generation accuracy of the original Pix2code is only 0.77.The above data indicate that the improved Pix2code model has improved the accuracy and stability of code automatic generation.展开更多
This paper studies a high-speed text-independent Automatic Speaker Recognition(ASR)algorithm based on a multicore system's Gaussian Mixture Model(GMM).The high speech is achieved using parallel implementation of t...This paper studies a high-speed text-independent Automatic Speaker Recognition(ASR)algorithm based on a multicore system's Gaussian Mixture Model(GMM).The high speech is achieved using parallel implementation of the feature's extraction and aggregation methods during training and testing procedures.Shared memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR algorithm.The experimental results show speed-up improvements of around 3.2 on a personal laptop with Intel i5-6300HQ(2.3 GHz,four cores without hyper-threading,and 8 GB of RAM).In addition,a remarkable 100%speaker recognition accuracy is achieved.展开更多
Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice...Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice were trained to return to the home arm and remain there during a delay period. Software was designed to enable automatic training in all its phases, including habituation, shaping, and learning. Using this system, we trained mice to successfully perform a spatially delayed nonmatch to sample task, which tested spatial cognition,working memory, and decision making. Performance depended on the delay duration, which is a hallmark of working memory tasks. The HASS enabled a human operator to train more than six mice simultaneously with minimal intervention, therefore greatly enhancing experimental efficiency and minimizing stress to the mice.Combined with the optogenetic method and neurophysiological techniques, the HASS will be useful in deciphering the neural circuitry underlying spatial cognition.展开更多
Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these...Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.展开更多
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.
基金supported by National Natural Science Foundation of China(No.62062063)the Science and Technology Research Project of Jiangxi Provincial Department of Education,China(No.GJJ202310)the Jiangxi Provincial Natural Science Foundation,China(No.20224BAB202022).
文摘With the expansion of the Internet market,the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle,tedious work,and difficult system maintenance.Therefore,to improve software development efficiency,this study uses residual networks and bidirectional long short-term memory(BLSTM)networks to improve the Pix2code model.The experiment results show that after improving the visual module of the Pix2code model using residual networks,the accuracy of the training set improves from 0.92 to 0.96,and the convergence time is shortened from 3 hours to 2 hours.After using a BLSTM network to improve the language module and decoding layer,the accuracy and convergence speed of the model have also been improved.The accuracy of the training set grew from 0.88 to 0.92,and the convergence time was shortened by 0.5 hours.However,models improved by BLSTM networks might exhibit overfitting,and thus this study uses Dropout and Xavier normal distribution to improve the memory network.The results validate that the training set accuracy of the optimized BLSTM network remains around 0.92,but the accuracy of the test set has improved to a maximum of 85%.Dropout and Xavier normal distributions can effectively improve the overfitting problem of BLSTM networks.Although they can also decrease the model’s stability,their gain is higher.The training and testing accuracy of the Pix2code improved by residual network and BLSTM network are 0.95 and 0.82,respectively,while the code generation accuracy of the original Pix2code is only 0.77.The above data indicate that the improved Pix2code model has improved the accuracy and stability of code automatic generation.
文摘This paper studies a high-speed text-independent Automatic Speaker Recognition(ASR)algorithm based on a multicore system's Gaussian Mixture Model(GMM).The high speech is achieved using parallel implementation of the feature's extraction and aggregation methods during training and testing procedures.Shared memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR algorithm.The experimental results show speed-up improvements of around 3.2 on a personal laptop with Intel i5-6300HQ(2.3 GHz,four cores without hyper-threading,and 8 GB of RAM).In addition,a remarkable 100%speaker recognition accuracy is achieved.
基金supported by the Instrument Developing Project of the Chinese Academy of Sciences(YZ201540)the National Science Foundation for Distinguished Young Scholars of China(31525010)+4 种基金the General Program of the National Science Foundation of China(31471049)the Key Research Project of Frontier Science of the Chinese Academy of Sciences(QYZDB-SSW-SMC009)China–Netherlands CAS-NWO Programme:Joint Research Projects,The Future of Brain and Cognition(153D31KYSB20160106)the Key Project of Shanghai Science and Technology Commission(15JC1400102,16JC1400101)the State Key Laboratory of Neuroscience,China
文摘Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice were trained to return to the home arm and remain there during a delay period. Software was designed to enable automatic training in all its phases, including habituation, shaping, and learning. Using this system, we trained mice to successfully perform a spatially delayed nonmatch to sample task, which tested spatial cognition,working memory, and decision making. Performance depended on the delay duration, which is a hallmark of working memory tasks. The HASS enabled a human operator to train more than six mice simultaneously with minimal intervention, therefore greatly enhancing experimental efficiency and minimizing stress to the mice.Combined with the optogenetic method and neurophysiological techniques, the HASS will be useful in deciphering the neural circuitry underlying spatial cognition.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2022-0-00377,Development of Intelligent Analysis and Classification Based Contents Class Categorization Technique to Prevent Imprudent Harmful Media Distribution).
文摘Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.
