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International Conference on Systems and Informatics (ICSAI)

作者： Yi Chen Bin Sun Meiling Feng Junqing Sun Tianjin Key Laboratory of Intelligent Computing & Novel Software Tianjin University of Technology Tianjin China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

In recent years, “Internet +” and e-commerce technology have had a profound impact on the recycling industry. Consumer psychology, as the most important factor in the positive sales chain, has received little attention in the recycling field. Based on the consumer's acceptance of online recycling channels and sensitivity to recycling prices, this paper investigates a two-channel reverse supply chain system consisting of the brand owner and a dismantling center. Firstly, the pricing strategy model, which takes consumer psychology into account, is established based on the Stackelberg game. It is found that the consumer's acceptance of the online recycling channel will reduce the profit of the dismantling center, but has no effect on the profit of the brand owner. Consumers' sensitivity to price will increase the profits of both the brand owner and the dismantling center, and will also make the optimal pricing of both parties tend to be fixed. Finally, the simulation experiment is carried out to verify the influence of psychological indicators on the pricing strategy and profit of the brand owner and dismantling center, and the optimal pricing strategy of both models is obtained after the online acceptance index and price sensitivity index are determined.

关键词： Recycling Pricing Psychology Consumer electronics Games Analytical models Supply chains

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Study of Continuous Blood Pressure Measurement Based on Single lead Pulse Wave

Study of Continuous Blood Pressure Measurement Based on Sing...

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2017 2nd International Conference on Materials Science,Machinery and Energy Engineering(MSMEE 2017)

作者： Gang Zheng Lin-Lin Fan Jia Qi Tianjin Key Laboratory of Intelligent Computing and Software New Technology Tianjin University of technology School of computer science and engineering Tianjin University of technology

In order to achieve a non-invasive continuous measurement of blood pressure, a method of establishing a nonlinear blood pressure estimation model by BPNN(Back Propagation Neural Network)was proposed. By acquiring pulse wave, systolic and diastolic blood pressure value of every heart beat cycle were calculated based on the model, and continuous blood pressure was gotten as consequence. Experiments were done on 25 healthy young students and 33 patients in the ICU ward that introduced from MIMIC public database. Experiment result show that 95% systolic values and 96% diastolic values of health group were in confidence interval respectively, and that of patient group were 96.7% in systolic value and 95% in diastolic value respectively. And the blood pressure values measured by the proposed model met AAMI standard.

关键词： Pulse Wave Continuous Blood Pressure Measurement BPNN MIMIC

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TF-IDF keyword Extraction Method Combining Context and Semantic Classification

TF-IDF Keyword Extraction Method Combining Context and Seman...

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作者： Xinyun Wang Hongyun Ning Tianjin Key Laboratory of Intelligence Computing & New Software Technology Tianjin University of Technology

keyword extraction plays the same role as the cornerstone in the field of natural language processing. Text classification, information retrieval, abstract generation and text clustering are all based on keyword extraction. This article takes the research of keyword extraction model as the subject. First, it analyzes the principle and limitations of the traditional keyword extraction model TF-IDF when extracting keywords. Secondly, it focuses on the problem of ignoring context and word polysemy in the keyword extraction model. To improve, introduce the concept of context vector, construct a chain-extensible structure for polysemous words, and propose a new keyword extraction method of TF-IDF that combines context and semantic classification. The specific research contents are as follows: TF-IDF extracts keywords based on multiple texts. The target keywords are words that appear frequently in the current text, but appear in other articles that are significantly lower in frequency than the current article. This method takes into account the characteristic content of the article and makes the article distinct. However, this method ignores the influence of the context of the article and the problem of word polysemy. Obviously, the expression of the thought of the article will not only be affected by the context of the words, but also by the semantics of the words. In order to solve the above problems, this paper proposes a TF-IDF keyword extraction method that combines context and semantic classification. First, the text constructs a linked list expandable structure to solve the problem of polysemy;then, the influence of context is introduced. Before inserting a new word into the extended structure, the semantic similarity is calculated through the context vector. Finally, iterate over the entire expandable structure, sum the word frequency with semantic similarity under each semantic, and then sort the results to obtain TopN words as the text keywords of the correspond

关键词： Extraction TF-IDF Vector

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Research and Construction of Word Vector Extension Structure Based on Context

Research and Construction of Word Vector Extension Structure...

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作者： Yiying Wang Hongyun Ning Tianjin Key Laboratory of Intelligence Computing & New Software Technology Tianjin University of Technology

The CBOW(Continuous Bag-of-Words) model is a three-layer forward neural network that predicts the central word vector by the fixed-size window information. In fact,context has a very important role in understanding the meaning of words. However, the context information of the fixed window size is partial and it is not enough to represent the whole context. Due to the polysemy of Chinese words, the same word may have different semantics in different contexts, while the traditional CBOW method ignores the polysemy of ***, this paper proposes a context-based word vector extension structure for the above problems. The main contents are as follows:(1) Introducing the concept of context vector. The entire sentence in which the target word is located is represented by a vector.(2) Constructing the polysemy storage method of words, adding a contextual list to each word vector, so that the multiple semantics of the words can be effectively distinguished.(3) Based on the word vector extension structure of this paper, a new character vector generation model is proposed, and the effect of the new model and the traditional CBOW model in the news headline similarity sorting task is compared. The experimental results show that the new character vector generation model based on the extension structure of the word vector obtains better result.

关键词： Vector Research and Construction of Word Vector Extension Structure Based on Context

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Study of hybrid strategy for ambulatory ECG waveform clustering

Journal of Software

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Journal of software 2011年第7期6卷 1257-1264页

作者： Zheng, Gang Yu, Tian Laboratory of Biological signal and Intelligent Processing Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Computer and Communication Engineering Tianjin University of Technology Tianjin China Laboratory of Biological signal and Intelligent Processing Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

A hybrid strategy has been proposed to reduce the wrong clustering on Ambulatory ECG (electrocardiogram). Since Ambulatory ECG is usually composed by 24 hours data, the number of individual ECG waveform can reach to 100,000, the request for accurate clustering result is highly required. The proposed strategy adopted some intelligent algorithms to solve the above problem. It clusters ECG waveform sample (selected from Ambulatory ECG) synchronously by Max-Min distance clustering algorithm, K-means algorithm and Simulated annealing algorithm first. And then, it adopted all three outputs from the above three algorithms as input on Back Propagation Artificial Neural Network (BP ANN). In the end, we got more accurate clustering result from the output of ANN. For testing the results, data of MIT/BIH arrhythmia database were used for experiments. After the controlled trial on MIT/BIH data, it can be safely concluded that the clustering result achieved by improved strategy can got more accurate than that by the traditional clustering algorithm. An average accuracy ratio is about 94.6%, 1.6% higher than k-means algorithm averagely and 1.3% higher than Simulated Annealing algorithm averagely. © 2011 ACADEMY PUBLISHER.

关键词： Electrocardiograms

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A Method of Concept Similarity Computation Based on Semantic Distance

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Procedia Engineering 2011年 15卷 3854-3859页

作者： Wenjie Li Qiuxiang Xia Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education Tianjin 300384China

Calculating the similarity between the concepts is the key in the process of the ontology mapping. Based on the analysis of the traditional method of calculating semantic similarity, a semantic distance-based concept similarity computation method is proposed. In order to keep the effectiveness and comprehensiveness of this method, the main factors which impact the semantic distance are considered fully, the factors of concept semantic contact ratio and depth differences between concepts nodes are also taken into account. By calculating test data, the experiment result shows that the method can calculate concepts similarity effectively.

关键词： ontology semantic distance concept semantic similarity

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Topic search ranking algorithm based on user's query intent

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Journal of Computational Information Systems 2013年第17期9卷 6955-6962页

作者： Zhao, Dexin Zhang, Meizhen Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

It is a hot issue that how to achieve information retrieval rapidly and accurately in accordance with the user's query intent on the internet information retrieval research. In order to solve the theme drift problem existing in PageRank algorithms, this paper presents a topic sorting algorithm named as SC-PageRank, which is based on the user's query analysis. We use Markov chain model to compute the correlation between the user's query intent and the website theme. Simulation results demonstrate the effectiveness of the proposed method. © 2013 Binary Information Press.

关键词： Information retrieval

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A search ranking algorithm based on user preferences

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Journal of Computational Information Systems 2012年第21期8卷 8969-8976页

作者： Zhao, Dexin Zhang, Meizhen Zhang, Degan Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

To investigate search ranking method for providing good service to users, the original PageRank algorithm model was studied to describe the hyperlink relationship based on Web pages. It was computed with a single vector using the hyperlink structure to improve the ranking of search query results. However, more and more topics are being discussed on the Web and the vocabulary of users remains relatively stable, the search engine usually returns the same results to users who enter the same keyword, which might not be related to each user's preference. A new method named UPPM based on user preferences was proposed. It was supposed to optimize initial query with user's query logs that including his click history and feedback, and the search results were reordered for particular user's need with updated query. The experiment shows that the new method can understand user's search subject behind his query and improve ordering of top results in the real Web search. © 2012 Binary Information Press.

关键词： Websites

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Block linked list index structure for large data full text retrieval

Block linked list index structure for large data full text r...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yang Yang Hongyun Ning Tianjin Key Laboratory of Intelligence Computing & New Software Technology Tianjin University of Technology Tianjin China

In order to solve the problems of the existing Chinese full-text retrieval algorithms in terms of large data, for example, data structure is difficult to expand, not suitable for incremental index, and the retrieval efficiency is low. Based on the traditional inverted index structure, this paper proposes an index structure, which can support large data storage with extension ability and update in real time: block linked-list index structure. Firstly, the new algorithm introduces the management concept of the block unit, the block unit is responsible for the management of the document set, and it creates an index for each term in the master index, then the index linked-list maps the term index and the block unit. This block linked-list index structure can greatly improve the ability of index expansion. Secondly, the main index and document index are both using the fixed-length storage with the same length, the positions of the terms' index information are both stationary in the master index file and the document index file of the block unit. According to the method, it can effectively solve the problems of the incremental index update and improve the update efficiency of the index. Finally, in the experiments, 350000 documents (about 1.46TB data) are randomly selected from the internet corpus (SogouT), which is used for comparing two index algorithms in three aspects, including the capability of initial datasets index creation, multitudinous files update and the retrieval of massive data sets. The results show that the new index algorithm has higher processing performance, especially in the efficiency of updating, with nearly 10% improvement.

关键词： Data structures Indexing Memory Periodic structures Computational efficiency Fuzzy systems

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Chinese keyword Extraction Method Based on Context and Word Classification

Chinese Keyword Extraction Method Based on Context and Word ...

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Computer Information and Big Data Applications (CIBDA), International Conference on

作者： Xinyun Wang Hongyun Ning Tianjin Key Laboratory of Intelligence Computing & New Software Technology Tianjin University of Technology Tianjin China

ISBN: (数字)9781728198378

ISBN: (纸本)9781728198385

The traditional keyword extraction algorithm TextRank increases the weight of the central word through the correlation between the words and the central word in the co-occurrence window, but the number of words in the co-occurrence window is limited, which will cause the poor expression of the semantic information of the word, and then affect accuracy of the keyword extraction results. In order to solve the limitation of word information in traditional keyword extraction methods and the problem of ignoring polysemy, this paper proposes a Chinese keyword extraction method based on context and semantic classification.

关键词： Data mining Microsoft Windows Semantics Classification algorithms Context Sorting Context modeling

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