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Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1273-1286 doi: 10.1631/FITEE.2200304

Abstract: Next point-of-interest (POI) recommendation is an important personalized task in location-based social networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However, these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs. To address these limitations, a spatiotemporal trajectory (STT) model is proposed in this paper. We use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and introduce the user’s spatiotemporal information into the model in encoding. In the process of encoding information, an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance. In addition, we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user’s current trajectory and the candidate set. We evaluate the performance of our STT model with four real-world datasets. Experimental results show that our model outperforms existing state-of-the-art methods.

Keywords： Point-of-interest recommendation     Spatiotemporal effects     Long short-term memory (LSTM)     Attention mechanism    

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ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model Research Article

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12,   Pages 1551-1684 doi: 10.1631/FITEE.2000511

Abstract: Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements. The real-time nature of an electrocardiogram (ECG) and the hidden nature of the information make it highly resistant to attacks. This paper focuses on three major bottlenecks of existing deep learning driven approaches: the lengthy time requirements for optimizing the hyperparameters, the slow and computationally intense identification process, and the unstable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning feature representations directly from ECG time series. The proposed framework integrates deep bidirectional long short-term memory (BLSTM) and . The overall approach not only avoids the inefficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly evaluated in two ECG datasets, using two protocols, simulating the influence of electrode placement and acquisition sessions in identification. Comparing four recurrent neural network structures and four classical machine learning and deep learning algorithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The experimental results demonstrated an average identification rate of 97.71%, 99.41%, and 98.89% in training, validation, and test sets, respectively. Thus, this study proves that the application of APSO and LSTM techniques to biometric can achieve a lower algorithm engineering effort and higher capacity for generalization.

Keywords： 心电图生物特征；个体身份识别；长短期记忆网络；自适应粒子群优化算法    

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LDformer: a parallel neural network model for long-term power forecasting

田冉,李新梅,马忠彧,刘颜星,王晶霞,王楚

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1287-1301 doi: 10.1631/FITEE.2200540

Abstract: Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system’s reliable power supply and the grid economy’s reliable operation. However, most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of data. To address this challenge, we propose a parallel time-series prediction model called LDformer. First, we combine Informer with long short-term memory (LSTM) to obtain deep representation abilities in the time series. Then, we propose a parallel encoder module to improve the robustness of the model and combine convolutional layers with an attention mechanism to avoid value redundancy in the attention mechanism. Finally, we propose a probabilistic sparse (ProbSparse) self-attention mechanism combined with UniDrop to reduce the computational overhead and mitigate the risk of losing some key connections in the sequence. Experimental results on five datasets show that LDformer outperforms the state-of-the-art methods for most of the cases when handling the different long-time-series prediction tasks.

Keywords： Long-term power forecasting     Long short-term memory (LSTM)     UniDrop     Self-attention mechanism    

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Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China Article

Yuanyuan Man, Qinli Yang, Junming Shao, Guoqing Wang, Linlong Bai, Yunhong Xue

Engineering 2023, Volume 24, Issue 5,   Pages 230-239 doi: 10.1016/j.eng.2021.12.022

Abstract:

Runoff prediction is of great significance to flood defense. However, due to the complexity and randomness of the runoff process, it is hard to predict daily runoff accurately, especially for peak runoff. To address this issue, this study proposes an enhanced long short-term memory (LSTM) model for runoff prediction, where novel loss functions are introduced and feature extractors are integrated. Two loss functions (peak error tanh (PET), peak error swish (PES)) are designed to strengthen the importance of the peak runoff's prediction while weakening the weight of the normal runoff's prediction. The feature extractor consisting of three LSTM networks is established for each meteorological station, aiming to extract temporal features of the input data at each station. Taking the upper Huai River Basin in China as a case study, daily runoff from 1960–2016 is predicted using the enhanced LSTM model. Results indicate that the enhanced LSTM model performed well, achieving Nash–Sutcliffe efficiency (NSE) coefficient ranging from 0.917–0.924 during the validation period (November 2005–December 2016), outperforming the widely used lumped hydrological models (Australian Water Balance Model (AWBM), Sacramento, SimHyd and Tank Model) and the data-driven models (artificial neural network (ANN), support vector regression (SVR), and gated recurrent units (GRU)). The enhanced LSTM with PES as loss function performed best on extreme runoff prediction with a mean NSE for floods of 0.873. In addition, precipitation at a meteorological station with a higher altitude contributes more runoff prediction than the closest stations. This study provides an effective tool for daily runoff prediction, which will benefit the basin's flood defense and water security management.

Keywords： Runoff prediction     Long short-term memory     Upper Huai River Basin     Extreme runoff     Loss function    

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Dynamic time prediction for electric vehicle charging based on charging pattern recognition Research Article

Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 299-313 doi: 10.1631/FITEE.2200212

Abstract: Overcharging is an important safety issue in the charging process of electric vehicle power batteries, and can easily lead to accelerated battery aging and serious safety accidents. It is necessary to accurately predict the vehicle's to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various s, the traditional prediction method often encounters modeling difficulties and low accuracy. In response to the above problems, data drivers and machine learning theories are applied. On the basis of fully considering the different electric vehicle battery management system (BMS) s, a prediction method with recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and fusion is proposed to classify vehicle s. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance prediction method is constructed by fully integrating and a strong tracking filter. Finally, the data run by the actual engineering system are verified for the proposed prediction algorithm. Experimental results show that the new method can effectively distinguish the s of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.

Keywords： Charging mode     Charging time     Random forest     Long short-term memory (LSTM)     Simplified particle swarm optimization (SPSO)    

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High-emitter identification for heavy-duty vehicles by temporal optimization LSTMand an adaptive dynamic threshold Research Article

Zhenyi XU, Renjun WANG, Yang CAO, Yu KANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1633-1646 doi: 10.1631/FITEE.2300005

Abstract: Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NO) in actual applications for environmental compliance, emitting more than 80% of NO and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six months or once a year, to filter out high-emission mobile sources at vehicle inspection stations. However, it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections, and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions. An is installed inside the vehicle and can record the vehicle’s emission data in real time. In this paper, we propose a long short-term memory (LSTM) and adaptive approach to identify heavy-duty high-emitters by using OBD data, which can continuously track and record the emission status in real time. First, a LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice. Then, the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria, calculated by an adaptive with changing driving conditions. Finally, a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results. Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.

Keywords： High-emitter identification     Temporal optimization     On-board diagnostic device (OBD)     Dynamic threshold    

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Short-term Load Forecasting Using Neural Network

Luo Mei

Strategic Study of CAE 2007, Volume 9, Issue 5,   Pages 77-80

Abstract:

Based on the load data of meritorious power of some area power system,  three BP ANN models,  namely SDBP, LMBP and BRBP Model,  are established to carry out the short-term load forecasting work, and the results are compared.  Since the traditional BP algorithm has some unavoidable disadvantages,  such as the low training speed and the possibility of being plunged into minimums local minimizing the optimized function,  an optimized L-M algorithm, which can accelerate the training of neural network and improve the stability of the convergence,  should be applied to forecast to reduce the mean relative error.  Bayesian regularization can overcome the over fitting and improve the generalization of ANN.

Keywords： short-term load forecasting(STLF)     ANN     Levenberg-Marquardt     Bayesian regularization     optimized algorithms    

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New results on impulsive type inertial bidirectional associativememory neural networks Research Articles

Chaouki AOUITI, Mahjouba Ben REZEG, Yang CAO

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 2,   Pages 324-339 doi: 10.1631/FITEE.1900181

Abstract: This paper is concerned with inertial bidirectional associative memory neural networks with mixed delays and impulsive effects. New and practical conditions are given to study the existence, uniqueness, and global exponential stability of anti-periodic solutions for the suggested system. We use differential inequality techniques to prove our main results. Finally, we give an illustrative example to demonstrate the effectiveness of our new results.

Keywords： Inertial neural networks     Anti-periodic solutions     Global exponential stability     Impulsive effect     Time-varying delay     Bidirectional associative memory    

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NIPAD: a non-invasive power-based anomaly detection scheme for programmable logic controllers Article

Yu-jun XIAO, Wen-yuan XU, Zhen-hua JIA, Zhuo-ran MA, Dong-lian QI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4,   Pages 519-534 doi: 10.1631/FITEE.1601540

Abstract: Industrial control systems (ICSs) are widely used in critical infrastructures, making them popular targets for attacks to cause catastrophic physical damage. As one of the most critical components in ICSs, the programmable logic controller (PLC) controls the actuators directly. A PLC executing a malicious program can cause significant property loss or even casualties. The number of attacks targeted at PLCs has increased noticeably over the last few years, exposing the vulnerability of the PLC and the importance of PLC protection. Unfortunately, PLCs cannot be protected by traditional intrusion detection systems or antivirus software. Thus, an effective method for PLC protection is yet to be designed. Motivated by these concerns, we propose a non-invasive powerbased anomaly detection scheme for PLCs. The basic idea is to detect malicious software execution in a PLC through analyzing its power consumption, which is measured by inserting a shunt resistor in series with the CPU in a PLC while it is executing instructions. To analyze the power measurements, we extract a discriminative feature set from the power trace, and then train a long short-term memory (LSTM) neural network with the features of normal samples to predict the next time step of a normal sample. Finally, an abnormal sample is identified through comparing the predicted sample and the actual sample. The advantages of our method are that it requires no software modification on the original system and is able to detect unknown attacks effectively. The method is evaluated on a lab testbed, and for a trojan attack whose difference from the normal program is around 0.63%, the detection accuracy reaches 99.83%.

Keywords： Industrial control system     Programmable logic controller     Side-channel     Anomaly detection     Long short-term memory neural networks    

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Temporality-enhanced knowledgememory network for factoid question answering Article

Xin-yu DUAN, Si-liang TANG, Sheng-yu ZHANG, Yin ZHANG, Zhou ZHAO, Jian-ru XUE, Yue-ting ZHUANG, Fei WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 104-115 doi: 10.1631/FITEE.1700788

Abstract: Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language. How to efficiently identify the exact answer with respect to a given question has become an active line of research. Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer. Most of these models suffer when a question contains very little content that is indicative of the answer. In this paper, we devise an architecture named the temporality-enhanced knowledge memory network (TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl. Unlike most of the existing approaches, our model encodes not only the content of questions and answers, but also the temporal cues in a sequence of ordered sentences which gradually remark the answer. Moreover, our model collaboratively uses external knowledge for a better understanding of a given question. The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.

Keywords： Question answering     Knowledge memory     Temporality interaction    

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Two-Way 4D Printing: A Review on the Reversibility of 3D-Printed Shape Memory Materials

Amelia Yilin Lee, Jia An, Chee Kai Chua

Engineering 2017, Volume 3, Issue 5,   Pages 663-674 doi: 10.1016/J.ENG.2017.05.014

Abstract:

The rapid development of additive manufacturing and advances in shape memory materials have fueled the progress of four-dimensional (4D) printing. With the right external stimulus, the need for human interaction, sensors, and batteries will be eliminated, and by using additive manufacturing, more complex devices and parts can be produced. With the current understanding of shape memory mechanisms and with improved design for additive manufacturing, reversibility in 4D printing has recently been proven to be feasible. Conventional one-way 4D printing requires human interaction in the programming (or shape-setting) phase, but reversible 4D printing, or two-way 4D printing, will fully eliminate the need for human interference, as the programming stage is replaced with another stimulus. This allows reversible 4D printed parts to be fully dependent on external stimuli; parts can also be potentially reused after every recovery, or even used in continuous cycles—an aspect that carries industrial appeal. This paper presents a review on the mechanisms of shape memory materials that have led to 4D printing, current findings regarding 4D printing in alloys and polymers, and their respective limitations. The reversibility of shape memory materials and their feasibility to be fabricated using three-dimensional (3D) printing are summarized and critically analyzed. For reversible 4D printing, the methods of 3D printing, mechanisms used for actuation, and strategies to achieve reversibility are also highlighted. Finally, prospective future research directions in reversible 4D printing are suggested.

Keywords： 4D printing     Additive manufacturing     Shape memory material     Smart materials     Shape memory alloy     Shape memory polymer    

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Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis Article

Yesheng Xu, Ming Kong, Wenjia Xie, Runping Duan, Zhengqing Fang, Yuxiao Lin, Qiang Zhu, Siliang Tang, Fei Wu, Yu-Feng Yao

Engineering 2021, Volume 7, Issue 7,   Pages 1002-1010 doi: 10.1016/j.eng.2020.04.012

Abstract:

Infectious keratitis is the most common condition of corneal diseases in which a pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues. Infectious keratitis is a medical emergency for which a rapid and accurate diagnosis is needed to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage; otherwise, it may develop a sight threatening and even eye-globe-threatening condition. In this paper, we propose a sequentiallevel deep model to effectively discriminate infectious corneal disease via the classification of clinical images. In this approach, we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis. In a comparison, the performance of the proposed sequential-level deep model achieved 80% diagnostic accuracy, far better than the 49.27% ± 11.5% diagnostic accuracy achieved by 421 ophthalmologists over 120 test images.

Keywords： Deep learning     Corneal disease     Sequential features     Machine learning     Long short-term memory    

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Short-Range Current Velocity Records to Apply in the Reference Period for Sutong Bridge

Su Hui,Gong Weiming,Liang Shuting

Strategic Study of CAE 2006, Volume 8, Issue 7,   Pages 42-46

Abstract:

The current velocity of reference period is determined by the local primitive data. A method is introduced to determine the current velocity of reference period through short-range current velocity records for the area that lacks of long-range current velocity records. According to short-range current velocity records of Sutong Bridge the probability distribute mode of the current velocity is established. Presuming the extreme value by way of minority sample，the current velocity of reference period is calculated.Correlation analysis is carried out, the rationality of the calculation is validated and the regression equation is established. The calculated results provide credible technical guide to devise and construct Sutong Bridge and ensure current and wave resistance safety.

Keywords： short-range records     the current velocity of reference period     correlation analysis     regression equation    

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Fractional-order memristive neural synaptic weighting achieved by pulse-based fracmemristor bridge circuit Research Articles

Yifei Pu, Bo Yu, Qiuyan He, Xiao Yuan,heqiuyan789@163.com,yuanxiao@scu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 862-876 doi: 10.1631/FITEE.2000085

Abstract: We propose a novel circuit for the fractional-order memristive neural synaptic weighting (FMNSW). The introduced circuit is different from the majority of the previous integer-order approaches and offers important advantages. Since the concept of memristor has been generalized from the classic integer-order memristor to the (), a challenging theoretical problem would be whether the can be employed to implement the or not. In this research, characteristics of the FMNSW, realized by a pulse-based bridge circuit, are investigated. First, the circuit configuration of the FMNSW is explained using a pulse-based bridge circuit. Second, the mathematical proof of the fractional-order learning capability of the FMNSW is analyzed. Finally, experimental work and analyses of the electrical characteristics of the FMNSW are presented. Strong ability of the FMNSW in explaining the cellular mechanisms that underlie learning and memory, which is superior to the traditional integer-order memristive neural synaptic weighting, is considered a major advantage for the proposed circuit.

Keywords： 分数阶微积分；分忆抗；分忆抗值；分数阶忆阻；分数阶记忆性突触    

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The brain areas and the neural mechanism involved in the Chinese paired-word associated learning and memory in healthy volunteers——a brain functional magnetic resonance imaging study

Zheng Jinlong,Shu Siyun,Liu Songhao,Guo Zhouyi,Wu Yongming,Bao Xinmin,Zhang Zengqiang,Jin Mei,Ma Hanzhang

Strategic Study of CAE 2008, Volume 10, Issue 5,   Pages 38-45

Abstract:

This paper is to investigate the activated brain areas and the neuronal mechanism of Chinese paired-word associated learning and memory in healthy volunteers by functional magnetic resonance imaging (fMRI) technique. 16 right-handed normal volunteers participated in a test of paired-word associated learning and memory, while the fMRI data were recorded. Control tasks were performed for the block-design. SPM 99 was used to analyze the data and to get the activated brain regions. 14 volunteers passed the paired-word associated learning and memory task. Both cortex and subcortical structures were activated. The brain cortex areas include the bilateral frontal lobes, the bilateral parietal lobes, the bilateral occipital lobes, the bilateral cingulate gyrus and the bilateral parahippocampal gyrus with extremely left hemisphere predominance and the left temporal lobe were activated by both coding and retrieval stages of the paired-word associated learning and memory task. The subcortical structures including the striatum and its marginal division (MrD) were activated with left predominance, the caudate and the thalamus were also activated during the tasks. However, the left occipital lobe and the middle and inferior frontal gyrus of the left frontal lobe were more activative than others in scope and brightness during the coding stage of the paired-word associated learning and memory task, while the left parietal lobe and dorsolateral part of the middle frontal gyrus were more activative than others in scope and brightness during the retrieval stage of the paired-word associated learning and memory task. The left middle and inferior frontal gyrus of the frontal lobe, the left lateral parts of the occipital lobe, the left superior lobule and supramarginal gyrus and the angular gyrus of the parietal lobe might play more important roles in the paired-word associated learning and memory task than the rest of the cortex. The MrD of the striatum was mainly involved in coding stages of the paired-word associated learning and memory task. The results of this study revealed that the subcortical structures mainly the striatum as well as the cortex were involved in the associated learning and memory of language in human brain. The transform of activated brain areas from the coding stage to the retrieval stage of the Chinese paired-word learning and memory was described and its neural mechanism was discussed.

Keywords： functional magnetic resonance imaging (fMRI) of human brain     paired-word     language     associated learning and memory     cortex and subcortical structures    

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Title Author Date Type Operation

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Journal Article

ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Journal Article

LDformer: a parallel neural network model for long-term power forecasting

田冉,李新梅,马忠彧,刘颜星,王晶霞,王楚

Journal Article

Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China

Yuanyuan Man, Qinli Yang, Junming Shao, Guoqing Wang, Linlong Bai, Yunhong Xue

Journal Article

Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE

Journal Article

High-emitter identification for heavy-duty vehicles by temporal optimization LSTMand an adaptive dynamic threshold

Zhenyi XU, Renjun WANG, Yang CAO, Yu KANG

Journal Article

Short-term Load Forecasting Using Neural Network

Luo Mei

Journal Article

New results on impulsive type inertial bidirectional associativememory neural networks

Chaouki AOUITI, Mahjouba Ben REZEG, Yang CAO

Journal Article

NIPAD: a non-invasive power-based anomaly detection scheme for programmable logic controllers

Yu-jun XIAO, Wen-yuan XU, Zhen-hua JIA, Zhuo-ran MA, Dong-lian QI

Journal Article

Temporality-enhanced knowledgememory network for factoid question answering

Xin-yu DUAN, Si-liang TANG, Sheng-yu ZHANG, Yin ZHANG, Zhou ZHAO, Jian-ru XUE, Yue-ting ZHUANG, Fei WU

Journal Article

Two-Way 4D Printing: A Review on the Reversibility of 3D-Printed Shape Memory Materials

Amelia Yilin Lee, Jia An, Chee Kai Chua

Journal Article

Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis

Yesheng Xu, Ming Kong, Wenjia Xie, Runping Duan, Zhengqing Fang, Yuxiao Lin, Qiang Zhu, Siliang Tang, Fei Wu, Yu-Feng Yao

Journal Article

Short-Range Current Velocity Records to Apply in the Reference Period for Sutong Bridge

Su Hui,Gong Weiming,Liang Shuting

Journal Article

Fractional-order memristive neural synaptic weighting achieved by pulse-based fracmemristor bridge circuit

Yifei Pu, Bo Yu, Qiuyan He, Xiao Yuan,heqiuyan789@163.com,yuanxiao@scu.edu.cn

Journal Article

The brain areas and the neural mechanism involved in the Chinese paired-word associated learning and memory in healthy volunteers——a brain functional magnetic resonance imaging study

Zheng Jinlong,Shu Siyun,Liu Songhao,Guo Zhouyi,Wu Yongming,Bao Xinmin,Zhang Zengqiang,Jin Mei,Ma Hanzhang

Journal Article