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	1. Moving Object Detection Algorithm Based on Dynamic Vision Sensor
	SUN Xue,SUN Xue,LIU Dengfeng,LIU Dengfeng
	Computer Science and Technology 11 April 2023
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	Abstract:Target detection and tracking mainly use RGB camera and deep learning algorithm. The information in the image is complex and redundant so the computing consumes a lot of resources. To solve the above problems, this paper proposes an improved spectral clustering algorithm to detect moving object. The algorithm is based on event data generated by dynamic vision sensors. In this paper, the cosine - Manhattan fusion distance is used to obtain a more accurate similarity matrix. The clustering results of some data are used to guide other data to speed up operation. The number of clusters is set adaptively to avoid the subjective influence of human beings. The results show that the accuracy of the improved algorithm on multiple data sets is more than 80%, and the time is significantly shortened. Spectral clustering algorithm based on dynamic vision sensor has great application potential in dealing with multi-target motion problems.
	TO cite this article:SUN Xue,SUN Xue,LIU Dengfeng, et al. Moving Object Detection Algorithm Based on Dynamic Vision Sensor[OL].[11 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4760291


	2. WFLNNet: Weighted Fusion of Linear and Nonlinear Predictions for Multivariate Time Series
	Dan Liu,Yuke Wang,Kun Xie,Ruotian Xie,Wei Liang,Dafang Zhang,Jigang Wen
	Computer Science and Technology 19 May 2022
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	Abstract:Multivariate time series forecasting has been widely used in finance, environment, transportation and other fields. However, traditional statistical prediction models usually assume that the time series conforms to a certain distribution or functional form, and cannot capture the complex nonlinear relationships. Although neural network based algorithms have powerful learning abilities, they usually ignore the linear features in time series. By weighted and fused both Linear and Nonlinear Predictions, this paper proposes a novel WFLNNet, where the linear prediction module is designed based on an autoregressive model while the nonlinear prediction module is designed based on the neural network and consists of a feature extraction encoder, an interactive attention network, and a fully connected layer to capture the most effective features in temporal and spatial correlations, as well as a mutual influence among multivariate time series. We have done experiments using 4 real datasets by comparing them with 6 baseline algorithms. The experimental results demonstrate that WFLNNet outperforms the 6 baseline algorithms with more accurate prediction.
	TO cite this article:Dan Liu,Yuke Wang,Kun Xie, et al. WFLNNet: Weighted Fusion of Linear and Nonlinear Predictions for Multivariate Time Series[OL].[19 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757819

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	3. An extended model of group chase and escape based on refuges
	ZHANG Xinglei,LIU Shaohua
	Computer Science and Technology 24 March 2021
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	Abstract:In this paper,an extended model is proposed to describe the motion trajectory of group chase and escape based on refuges.The rich dynamic behaviors of chaser and escaper are demonstrated by using a cellular automata model and the protective effect of refuge on escaper is explored in both long-term and short-term modes. The protective effect of different refuge density and distribution is compared in this paper. A critical refuge density which provides 100% protection for escaper is founded as refuge density increases and the optimal refuge distribution for prey\'s survival is concluded. These findings can provide references for the establishment of endangered animal refuges and the modeling of crowd evacuation under attack, and have profound significance on the topic of group chase and escape.
	TO cite this article:ZHANG Xinglei,LIU Shaohua. An extended model of group chase and escape based on refuges[OL].[24 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754164


	4. MobiPDF: Reconstructing PDF contents for Mobile Devices
	Peng Jin,Ligang He,Yilian Zhou,Cheng Chang,Liangtang Lei,Xiaorui Zhang,Hao Chen
	Computer Science and Technology 22 May 2020
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	Abstract:The PDF format is widely used in academia with its elegant style and unmodifiable features. With the popularity of mobile terminals, more and more people use mobile devices to browse and obtain documents. However, once the PDF document is generated, the style is fixed. It is difficult to read on a small-sized mobile screen. In this work, we develop a system called MobiPDF to reconstruct the content in a PDF document and render it in a way so that it can viewed properly on a mobile screen. In particular, the content of the PDF document is first decomposed into different areas. The PDF file is then converted into an HTML format file and also to an image file. Next, the texts and their rendering information are extracted from the HTML file according to the positions of the decomposed areas. In addition, the non-text information such as figures and tables is extracted from the image file based on its positions. The extracted text and non-text contents together with the rendering information are reconstructed as a new HTML file according to the size of the user\'s mobile screen. We have developed MobiPDF as a web service, which is hosted on the Tencent Cloud and can be accessed publically.
	TO cite this article:Peng Jin,Ligang He,Yilian Zhou, et al. MobiPDF: Reconstructing PDF contents for Mobile Devices[OL].[22 May 2020] http://en.paper.edu.cn/en_releasepaper/content/4752113


	5. Model of dynamic division of distribution area based on ant colony algorithm
	Zhang Yazhou,Zhuang Yufeng
	Computer Science and Technology 16 April 2020
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	Abstract:In recent years, with the rapid popularization of mobile terminals, China\'s express delivery has developed rapidly, and with the continuous increase in express delivery volume, the pressure of express delivery has increased year by year. Under the existing distribution area division model, problems such as warehouse outages have become increasingly serious. This article mainly proposes a model of dynamic division of distribution areas from the perspective of dynamic adjustment of express delivery volume to solve existing problems. The main idea of the entire model is to first ensure that the delivery volume of the delivery point is not overstock as possible under the conditions of limited delivery capacity. the express delivery volume forms a dynamic balance between express delivery points, and then optimizes the delivery distance under this condition to solve the problems mentioned in this article.
	TO cite this article:Zhang Yazhou,Zhuang Yufeng. Model of dynamic division of distribution area based on ant colony algorithm[OL].[16 April 2020] http://en.paper.edu.cn/en_releasepaper/content/4751656


	6. Research on Cache Strategy of Edge Image Based on Popularity Prediction
	ZOU Sheng,LIU Liang
	Computer Science and Technology 25 March 2019
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	Abstract:With the advent of the era of Internet of Everything, the edge computing used to make up for the lack of cloud computing comes into being. However, due to the light-weighting of edge clouds in edge computing, the problem of limited resources is caused, especially in terms of storage resources. At the same time, considering the problem that there are a lot of redundant images in the virtual machine or the container, this paper solves the problem of insufficient storage resources from the perspective of optimizing the edge image cache. To this end, this paper chooses Kubernetes as the edge platform, and uses the value of Baidu index as the popularity value, and proposes an edge image cache algorithm based on popularity prediction, namely b-GRU. Firstly, based on the feature analysis of the acquired data, the prediction of the image popularity based on GRU is performed. Then, the image cache replacement based on the popularity prediction is performed. Finally, the comparison experiment of b-GRU shows that the storage space of b-GRU is only 41% of LRU and LFU storage space under the condition of guaranteeing a certain cache hit ratio, which proves the effectiveness of this strategy.
	TO cite this article:ZOU Sheng,LIU Liang. Research on Cache Strategy of Edge Image Based on Popularity Prediction[OL].[25 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4748074


	7. A GDSF-M Cache Replacement Algorithm Based on Enterprise Reporting System
	LIANG Yuwei,XU Tong,ZHANG Lei,ZHANG Lejian
	Computer Science and Technology 14 December 2018
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	Abstract:Enterprise-level reporting systems play a key role in the operation of E-business platforms.The enterprise business reporting system is characterized by large amount of data and regular operation. The core of the cache replacement strategy of the Web proxy server is the cache replacement algorithm, which can effectively improve the performance of the proxy server. In this paper we implement a business reporting system and study the traditional web cache replacement algorithm. Based on the GDSF algorithm, an improved GDSF-M algorithm is proposed, which considers the cache size. Factor, a report type weight function is added to the target value function to apply to the current application scenario. Finally, the performance of GDSF-M algorithm is verified by Squid proxy server. Compared with LRU, LFU and GDSF algorithm, it is proved that the improved GDSF-M algorithm has better improvement in hit rate and byte hit ratio. At the same time, the hit rate in the current scene has better stability than other algorithms.
	TO cite this article:LIANG Yuwei,XU Tong,ZHANG Lei, et al. A GDSF-M Cache Replacement Algorithm Based on Enterprise Reporting System[OL].[14 December 2018] http://en.paper.edu.cn/en_releasepaper/content/4746709


	8. Visual Word Based Similar Image Retrieval \Optimization By Hamming Distance
	ZHUANG Huang, WEI Yi-Fei, SONG Mei
	Computer Science and Technology 11 May 2017
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	Abstract:In this paper we present a new method for visual word based similar image retrieval by comparing content of a query image with images stored in a database. The retrieval consists of three main steps: feature extraction, indexing and query optimization. The feature extraction step is based on SURF algorithm. For indexing, we use the K-Means algorithm and the Bag-of-Visual-Words model. The last step is very significant and we associate TF-IDF with Hamming Distance to query. Our method is tested on the highly diverse opening images and has proved a better retrieval accuracy based on the experimental results.
	TO cite this article:ZHUANG Huang, WEI Yi-Fei, SONG Mei. Visual Word Based Similar Image Retrieval \Optimization By Hamming Distance[OL].[11 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4734318


	9. Audio Visual Speech Recognition with Multimodal Recurrent Neural Networks
	Weijiang Feng, Naiyang Guan, Yuan Li, Xiang Zhang, Zhigang Luo
	Computer Science and Technology 04 May 2017
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	Abstract:Studies on nowadays human-machine interface have demonstrated that visual information can enhance speech recognition accuracy especially in noisy environments. Deep learning has been widely used to tackle such audio visual speech recognition (AVSR) problem due to its astonishing achievements in both speech recognition and image recognition. Although existing deep learning models succeed to incorporate visual information into speech recognition, none of them simultaneously considers sequential characteristics of both audio and visual modalities. To overcome this deficiency, we proposed a multimodal recurrent neural network (multimodal RNN) model to take into account the sequential characteristics of both audio and visual modalities for AVSR. In particular, multimodal RNN includes three components, i.e., audio part, visual part, and fusion part, where the audio part and visual part capture the sequential characteristics of audio and visual modalities, respectively, and the fusion part combines the outputs of both modalities. Here we modelled the audio modality by using a LSTM RNN, and modelled the visual modality by using a convolutional neural network (CNN) plus a LSTM RNN, and combined both models by a multimodal layer in the fusion part. We validated the effectiveness of the proposed multimodal RNN model on a multi-speaker AVSR benchmark dataset termed AVletters. The experimental results show the performance improvements comparing to the known highest audio visual recognition accuracies on AVletters, and confirm the robustness of our multimodal RNN model.
	TO cite this article:Weijiang Feng, Naiyang Guan, Yuan Li, et al. Audio Visual Speech Recognition with Multimodal Recurrent Neural Networks[OL].[ 4 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4732586


	10. Smart-phone-assisted Human Motion Recognition Based on Wavelet Transform
	Tian Yaning,Yin Sixing,Qu zhaowei
	Computer Science and Technology 03 November 2016
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	Abstract:Human motion recognition is becoming a research upsurge, which aims at understanding human behavior, and plays an increasingly important role in a number of applications, such as health care and smart home. In this paper, we collect datasets by using the built-in sensors of a mobile phone and propose an approach to extract features based on wavelet transform. In contrast to the existing related works, our work intends to recognize the physical activities when the phone's orientation and position are varying. The activities' true acceleration is inferred by using the phone's pitch, yaw and roll angles. After preprocessing, the continuous original time series data is segmented into discrete training samples by the sliding windows of proper size. Then statistical features such as wavelet coefficients are extracted through the wavelet transform. Support Vector Machine (SVM) is employed as classifier to recognize five types of motion: jumping, walking, running, stepping upstairs and stepping downstairs. We find a proper wavelet basis function to extract the features and achieve an average recognition accuracy of 90.71%. We can distinguish the five kinds of motion clearly, so the results show that it is feasible to use wavelet transform to extract features in human motion recognition.
	TO cite this article:Tian Yaning,Yin Sixing,Qu zhaowei. Smart-phone-assisted Human Motion Recognition Based on Wavelet Transform[OL].[ 3 November 2016] http://en.paper.edu.cn/en_releasepaper/content/4708069

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