In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is di...In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.展开更多
In this paper, a modified FPGA scheme for the convolutional encoder and Viterbi decoder based on the IEEE 802.11a standards of WLAN is presented in OFDM baseband processing systems. The proposed design supports a gene...In this paper, a modified FPGA scheme for the convolutional encoder and Viterbi decoder based on the IEEE 802.11a standards of WLAN is presented in OFDM baseband processing systems. The proposed design supports a generic, robust and configurable Viterbi decoder with constraint length of 7, code rate of 1/2 and decoding depth of 36 symbols. The Viterbi decoder uses full-parallel structure to improve computational speed for the add-compare-select (ACS) modules, adopts optimal data storage mechanism to avoid overflow and employs three distributed RAM blocks to complete cyclic trace-back. It includes the core parts, for example, the state path measure computation, the preservation and transfer of the survivor path and trace-back decoding, etc. Compared to the general Viterbi decoder, this design can effectively decrease the 10% of chip logic elements, reduce 5% of power consumption, and increase the encoder and decoder working performance in the hardware implementation. Lastly, relevant simulation results using Verilog HDL language are verified based on a Xinlinx Virtex-II FPGA by ISE 7.1i. It is shown that the Viterbi decoder is capable of decoding (2, 1, 7) convolutional codes accurately with a throughput of 80 Mbps.展开更多
The encoding/decoding scheme based on Fiber Bragg Grating (FBG) for Optical Code Division Multiple Access (OCDMA) system is analyzed and the whole process from transmitting end to receiving end is researched in detail...The encoding/decoding scheme based on Fiber Bragg Grating (FBG) for Optical Code Division Multiple Access (OCDMA) system is analyzed and the whole process from transmitting end to receiving end is researched in detail. The mathematical mode including signal transmission, summing, receiving and recovering are established respectively. One of the main sources of Bit Error Rate (BER) of OCDMA system based on FBGs is the unevenness of signal power spectrum, which leads to the chip powers unequal with each other. The Signal to Interfere Ratio (SIR) and BER performance of the system are studied and simulated at the case with uneven distribution of chips' powers.展开更多
The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)...The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.展开更多
Tea has a history of thousands of years in China and it plays an important role in the working-life and daily life of people.Tea culture rich in connotation is an important part of Chinese traditional culture,and its ...Tea has a history of thousands of years in China and it plays an important role in the working-life and daily life of people.Tea culture rich in connotation is an important part of Chinese traditional culture,and its existence and development are also of great significance to the diversified development of world culture.Based on Stuart Hall’s encoding/decoding theory,this paper analyzes the problems in the spreading of Chinese tea in and out of the country and provides solutions from the perspective of encoding,communication,and decoding.It is expected to provide a reference for the domestic and international dissemination of Chinese tea culture.展开更多
The Beijing-Hangzhou Grand Canal carries a wealth of Chinese cultural symbols,showing the lifestyle and wisdom of working people through ages.The preservation and inheritance of its intangible cultural heritage can he...The Beijing-Hangzhou Grand Canal carries a wealth of Chinese cultural symbols,showing the lifestyle and wisdom of working people through ages.The preservation and inheritance of its intangible cultural heritage can help to evoke cultural memories and cultural identification of the Canal and build cultural confidence.This paper applies Stuart Hall’s encoding/decoding theory to analyze the dissemination of intangible heritage tourism culture.On the basis of a practical study of the villages along the Beijing-Hangzhou Grand Canal,this paper analyses the problems in the transmission of its intangible cultural heritage and proposes specific methods to solve them in four processes,encoding,decoding,communication,and secondary encoding,in order to propose references for the transmission of intangible heritage culture at home and abroad.展开更多
Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been con...Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been conducted on two aspects.The first aspect is to encode speech frame by frame.The second aspect is to encode the whole speech to a vector.But the scale in these aspects is fixed.So,encoding speech with an adjustable scale for more latent information is worthy of investigation.But current alignment approaches only support frame-by-frame encoding and speech-to-vector encoding.It remains a challenge to propose a new alignment approach to support adjustable scale speech encoding.This paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector encoding.The speech feature fromourmodel achieves three functions.First,the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text length.Second,our model can get text embedding fromspeech,and the encoded speech feature is similar to the text embedding result.Finally,it can transfer the style of synthesis speech and make it more similar to the given reference speech.展开更多
The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance g...The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance gap between the aerial-view and the street-view images brings a huge challenge against this task.In this paper,we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street-view images.To bridge the domain gap between these two view images,we first use an inverse polar transform to make the street-view images approximately aligned with the aerial-view images.Then,the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information.Finally,we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars.Experiments on standard benchmark datasets show that our approach obtains 81.39%top-1 recall rate on the CVUSA dataset and 71.52%on the CVACT dataset,achieving the state-of-the-art performance and outperforming most of the existing methods significantly.展开更多
文摘In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.
文摘In this paper, a modified FPGA scheme for the convolutional encoder and Viterbi decoder based on the IEEE 802.11a standards of WLAN is presented in OFDM baseband processing systems. The proposed design supports a generic, robust and configurable Viterbi decoder with constraint length of 7, code rate of 1/2 and decoding depth of 36 symbols. The Viterbi decoder uses full-parallel structure to improve computational speed for the add-compare-select (ACS) modules, adopts optimal data storage mechanism to avoid overflow and employs three distributed RAM blocks to complete cyclic trace-back. It includes the core parts, for example, the state path measure computation, the preservation and transfer of the survivor path and trace-back decoding, etc. Compared to the general Viterbi decoder, this design can effectively decrease the 10% of chip logic elements, reduce 5% of power consumption, and increase the encoder and decoder working performance in the hardware implementation. Lastly, relevant simulation results using Verilog HDL language are verified based on a Xinlinx Virtex-II FPGA by ISE 7.1i. It is shown that the Viterbi decoder is capable of decoding (2, 1, 7) convolutional codes accurately with a throughput of 80 Mbps.
基金Supported by the Natural Science Research Foundation of Jiangsu Higher-Learning Insti-tution (No.04jkb510057).
文摘The encoding/decoding scheme based on Fiber Bragg Grating (FBG) for Optical Code Division Multiple Access (OCDMA) system is analyzed and the whole process from transmitting end to receiving end is researched in detail. The mathematical mode including signal transmission, summing, receiving and recovering are established respectively. One of the main sources of Bit Error Rate (BER) of OCDMA system based on FBGs is the unevenness of signal power spectrum, which leads to the chip powers unequal with each other. The Signal to Interfere Ratio (SIR) and BER performance of the system are studied and simulated at the case with uneven distribution of chips' powers.
基金supported by financial support from Universiti Sains Malaysia(USM)under FRGS Grant Number FRGS/1/2020/TK03/USM/02/1the School of Computer Sciences USM for their support.
文摘The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.
文摘Tea has a history of thousands of years in China and it plays an important role in the working-life and daily life of people.Tea culture rich in connotation is an important part of Chinese traditional culture,and its existence and development are also of great significance to the diversified development of world culture.Based on Stuart Hall’s encoding/decoding theory,this paper analyzes the problems in the spreading of Chinese tea in and out of the country and provides solutions from the perspective of encoding,communication,and decoding.It is expected to provide a reference for the domestic and international dissemination of Chinese tea culture.
基金supported by the National Social Science Fund Project (No.20BH151).
文摘The Beijing-Hangzhou Grand Canal carries a wealth of Chinese cultural symbols,showing the lifestyle and wisdom of working people through ages.The preservation and inheritance of its intangible cultural heritage can help to evoke cultural memories and cultural identification of the Canal and build cultural confidence.This paper applies Stuart Hall’s encoding/decoding theory to analyze the dissemination of intangible heritage tourism culture.On the basis of a practical study of the villages along the Beijing-Hangzhou Grand Canal,this paper analyses the problems in the transmission of its intangible cultural heritage and proposes specific methods to solve them in four processes,encoding,decoding,communication,and secondary encoding,in order to propose references for the transmission of intangible heritage culture at home and abroad.
基金supported by National Key R&D Program of China (2020AAA0107901).
文摘Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been conducted on two aspects.The first aspect is to encode speech frame by frame.The second aspect is to encode the whole speech to a vector.But the scale in these aspects is fixed.So,encoding speech with an adjustable scale for more latent information is worthy of investigation.But current alignment approaches only support frame-by-frame encoding and speech-to-vector encoding.It remains a challenge to propose a new alignment approach to support adjustable scale speech encoding.This paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector encoding.The speech feature fromourmodel achieves three functions.First,the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text length.Second,our model can get text embedding fromspeech,and the encoded speech feature is similar to the text embedding result.Finally,it can transfer the style of synthesis speech and make it more similar to the given reference speech.
基金National Natural Science Foundation of China,Grant/Award Number:62106177supported by the Central University Basic Research Fund of China(No.2042020KF0016)supported by the supercomputing system in the Supercomputing Center of Wuhan University.
文摘The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance gap between the aerial-view and the street-view images brings a huge challenge against this task.In this paper,we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street-view images.To bridge the domain gap between these two view images,we first use an inverse polar transform to make the street-view images approximately aligned with the aerial-view images.Then,the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information.Finally,we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars.Experiments on standard benchmark datasets show that our approach obtains 81.39%top-1 recall rate on the CVUSA dataset and 71.52%on the CVACT dataset,achieving the state-of-the-art performance and outperforming most of the existing methods significantly.