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6th International Conference on Systems and Informatics, ICSAI 2019

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

ISBN: (纸本)9781728152561

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. © 2019 IEEE.

关键词： Profitability

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IGTP: A next point-of-interest recommendation method that integrates geospatial and temporal preferences

Research Square

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Research Square 2021年

作者： Jiao, Xu Xiao, Yingyuan Zheng, Wenguang Zhu, Ke School of General Education Tianjin Foreign Studies University Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

With the rapid development of location-based social networks(LBSNs), point-of-interest(POI) recommendation has become an important way to meet the personalized needs of users. The purpose of POI recommendation is to provide personalized POI recommendation services for users. However, general POI recommendations cannot meet the individual needs of users. This is mainly because the decision-making process for users to choose POIs is very complicated and will be affected by various user contexts such as time, location, etc. This paper proposes a next POI recommendation method that integrates geospatial and temporal preferences, called IGTP. Compared with general POI recommendation, IGTP can provide more personalized recommendations for users according to their context information. First, IGTP uses users’ preferences information to model users’ check-in histories to effectively overcome the challenge of extremely sparse check-in data. Secondly, IGTP takes into account the geographic distance and density factors that affect people’s choice of POIs, and limits POIs to be recommended to the potential activitive area centered on the current loca- tion of the target user. Finally, IGTP integrates geospatial and users’ temporal preferences information into a unified recommendation process. Compared with six advanced baseline methods, the experimental results demonstrate that IGTP achieves much better performance. © 2021, CC BY.

关键词： Location

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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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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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Research on character vector generation method based on words and context 2019

Research on character vector generation method based on word...

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2019 International Conference on Artificial Intelligence and Computer Science, AICS 2019

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

ISBN: (纸本)9781450371506

In order to solve the problem of character information limitation and the neglect of polysemy, a character vector generation method based on words and context is proposed. First of all, introduce the concept of context vector. Considering the fact that the same word has different meanings in different contexts, some even have the opposite meanings, therefore, to correctly understand the meaning of Chinese words, we must fully consider the context in which it is located. The sentence in which the character or word is located is expressed in the form of a vector called context vector. Secondly, construct the polysemous storage of words. Considering the importance of context and the polysemy of words, this paper expands the expression of the original word vector, and adds a context linked list for each word vector. The multiple semantics of words are stored in multiple context vectors in the linked list, so as to solve the polysemy problem of words. Finally, a character vector generation method based on words and context is proposed. The target character vector is calculated by using the word vectors of all words containing the target character and its context similarity. Meanwhile, attention mechanism is introduced. When calculating, the current character context and the word context are compared, according to the character and word context similarity to distribute weights, so that the words with more similar context make greater contribution to the generation of the character vector. This method not only add the word information to the character, but also distinguishes different semantics according to the context of the character, and makes all words participate in the calculation of the character vector according to different weights. The experiment uses the character vector evaluation method based on external tasks, taking the news title similarity task as an example, comparing the performance of the traditional CBOW method and the new words and context based chara

关键词： Semantics

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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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2019 International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2019

作者： Wang, Yiying Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing and New Software Technology Tianjin University of Technology Tianjin300384 China

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 words. Therefore, 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. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Semantics

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CFDIL: A context-aware feature deep interaction learning for app recommendation

Research Square

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Research Square 2021年

作者： Hao, Qingbo Zhu, Ke Wang, Chundong Wang, Peng Mo, Xiuliang Liu, Zhen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki Japan

The rapid development of Mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users’ preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method. © 2021, CC BY.

关键词： User profile

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Computer-Automated Design of Self-Aligned Multi-Ball Pitch Artifact with High Precision

SSRN

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SSRN 2024年

作者： Zhu, Hongjun Liu, Binghong Zhou, Sen Zhang, Xiaoyan Xin, Liu Chen, Kuo Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China Chongqing Academy of Metrology and Quality Inspection Chongqing401121 China School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland1010 New Zealand Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

This study presents a computational methodology for developing a multi-ball system that automatically aligns during assembly processes. The contributions of this work are threefold: Firstly, a comprehensive error analysis of the multi-ball system is performed. It is found that temperature and pressure-induced deformations should not be ignored when micrometer-level precision is required. Secondly, a novel process flow for designing the multi-ball pitch artifact is introduced, which enables optimization decision-making. Lastly, a series of experiments are conducted on standard bearing balls (grade G20). The resulting artifact meets the Class 1 standards. It is reasonable to assume that, by using balls of grade G3 precision, the multi-ball artifact can achieve accuracy at the sub-micrometer level. © 2024, The Authors. All rights reserved.

关键词： Decision making

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new Method of Traffic Flow Forecasting Based on QPSO Strategy for Internet of Vehicles

New Method of Traffic Flow Forecasting Based on QPSO Strateg...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： De-gan Zhang Jing-yu Du Ting Zhang Hong-rui Fan The Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Tianjin China The Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education Tianjin

ISBN: (数字)9781728165141

ISBN: (纸本)9781728165158

We propose a new method of traffic flow forecasting based on quantum particle swarm optimization strategy (QPSO) for Internet of Vehicles (IOV). Establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing method is applied to the quantum particle swarm method to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network can be used to obtain the required data. In addition, in order to compare the performance of the methods, a comparison study with other related methods such as QPSO-RBF is also performed. Our method can reduce prediction errors and get better and more stable prediction results.

关键词： Neural networks Simulated annealing Clustering algorithms Neurons Biological system modeling Genetics Linear programming

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new Algorithm of QoS Constrained Routing for Node Energy Optimization of Edge computing

New Algorithm of QoS Constrained Routing for Node Energy Opt...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Degan Zhang Jinyu Du Ting Zhang Peng Yang Hongrui Fan Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

We propose an oriented edge computing nodes energy optimized QoS constrained routing algorithm. The mechanism of this algorithm is to optimize the network energy consumption and extend the network life cycle by creatively combining the edge computing technology to preprocess the original data of the node, accelerating the transmission and processing of effective data, accelerating the algorithm convergence by using the way of automata interacting with the environment and controlling the dormant activation state of the node. Through experimental testing and comparison, this paper gave the Multi-QoS constrained routing algorithm for Edge computing and Node energy optimization algorithm (MQEN), which can meet the requirements of end-to-end delay and reliability service with multi-QoS constraints when significantly reducing network energy consumption.

关键词： Energy consumption Learning automata Quality of service Routing Acceleration Optimization Edge computing

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