Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.

Mining association rules in incomplete information systems

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

New data association technique based on ACO with directional information considered

Due to the advantages of ant colony optimization （ACO） in solving complex problems, a new data association algorithm based on ACO in a cluttered environment called DACDA is proposed. In the proposed method, the concept for tour and the length of tour are redefined. Additionally, the directional information is incorporated into the proposed method because it is one of the most important factors that affects the performance of data association. Kalman filter is employed to estimate target states. Computer simulation results show that the proposed method could carry out data association in an acceptable CPU time, and the correct data association rate is higher than that obtained by the data association （DA） algorithm not combined with directional information.

The Legal Regulation of the Information Exchange for Trade Association in China

In the modem society where information is efficient, information exchange is an important affair and function of trade association. However, information exchange behaviors of industry association will have double and legal effect on market and competition, which can not only promote the communication among members of the industry to find a solution to the industry benefit maximization, but also can produce inhibition and barriers to competition in the market. So in this paper, the industry association analysis will be made on the pros and cons of the information exchange, and the legislative defects and the insufficiency of information exchange industry association in our country. According to the practice in our country, the author puts forward the system design of our country industry association information exchange behavior.

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.

Approximate querying between heterogeneous ontologies based on association matrix

An approximate approach of querying between heterogeneous ontology-basedinformation systems based on an association matrix is proposed. First, the association matrix isdefined to describe relations between concepts in two ontologies. Then, a methodof rewriting queriesbased on the association matrix is presented to solve the ontology heterogeneity problem. Itrewrites the queries in one ontology to approximate queries in another ontology based on thesubsumption relations between concepts. The method also uses vectors to represent queries, and thencomputes the vectors with the association matrix; the disjoint relations between concepts can beconsidered by the results. It can get better approximations than the methods currently in use, whichdonot consider disjoint relations. The method can be processed by machines automatically. It issimple to implement and expected to run quite fast.

Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

Study on data association algorithm of multi-passive-sensor location system

Aiming at three-passive-sensor location system, a generalized 3-dimension （3-D） assignment model is constructed based on property information, and a multi-target programming model is proposed based on direction-finding and property fusion information. The multi-target programming model is transformed into a single target programming problem to resolve, and its data association result is compared with the results which are solved by using one kind of information only. Simulation experiments show the effectiveness of the multi-target programming algorithm with higher data association accuracy and less calculation.

Visualizing Association Rules Using Linked Matrix,Graph, and Detail Views

Although association rule mining is an important pattern recognition and data analysis technique, extracting and finding significant rules from a large collection has always been challenging. The ability of information visualization to enable users to gain an understanding of high dimensional and large-scale data can play a major role in the exploration, identification, and interpretation of association rules. In this paper, we propose a method that provides multiple views of the association rules, linked together through a filtering mechanism. A visual inspection of the entire association rule set is enabled within a matrix view. Items of interest can be selected, resulting in their corresponding association rules being shown in a graph view. At any time, individual rules can be selected in either view, resulting in their information being shown in the detail view. The fundamental premise in this work is that by providing such a visual and interactive representation of the association rules, users will be able to find important rules quickly and easily, even as the number of rules that must be inspected becomes large. A user evaluation was conducted which validates this premise.

Target Ship Identification Algorithm Based on Comprehensive Correlation Discriminant and Information Entropy

Ship type identification is an important part of electronic reconnaissance. However, in the existing methods, such as statistical-based methods and fuzzy-mathematics-based methods, the information acquired by the passive sensor is not fully utilized, and there is a certain ambiguity in the assignment relationship of the emitters-ship. They can’t conclude the accurate and reliable assignment relationship of the emitters-ship. Therefore, this paper proposes a comprehensive correlation discriminant method to obtain a more reliable and comprehensive emitters-ship assignment, and then uses information entropy method to identify the type of the target ship on the basis of this association and assign the credibility. The simulation results show that this algorithm can effectively solve the problem of target ship type identification using the information of multi-passive sensors.

Text Rank for Domain Specific Using Field Association Words

Text Rank is a popular tool for obtaining words or phrases that are important for many Natural Language Processing (NLP) tasks. This paper presents a practical approach for Text Rank domain specific using Field Association (FA) words. We present the keyphrase separation technique not for a single document, although for a particular domain. The former builds a specific domain field. The second collects a list of ideal FA terms and compounds FA terms from the specific domain that are considered to be contender keyword phrases. Therefore, we combine two-word node weights and field tree relationships into a new approach to generate keyphrases from a particular domain. Studies using the changed approach to extract key phrases demonstrate that the latest techniques including FA terms are stronger than the others that use normal words and its precise words reach 90%.

Query Through Heterogeneous Ontologies Using Association Matrix

This paper introduces the definition and calculation of the association matrix between ontologies. It uses the association matrix to describe the relations between concepts in different ontologies and uses concept vectors to represent queries; then computes the vectors with the association matrix in order to rewrite queries. This paper proposes a simple method of querying through heterogeneous Ontology using association matrix. This method is based on the correctness of approximate information filtering theory; and it is simple to be implemented and expected to run quite fast. Key words semantic Web - information retrieval - ontology - query - association matrix CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024), National Grand Fundamental Research 973 Program of China (2002CB312000) and National Research Foundation for the Doctoral Program of Higher Education of China (20020286004)Biography: KANG Da-zhou (1980-), male, Master candidate, research direction: Semantic Web, knowledge representation on the Web.

Functions and Developmental Countermeasures of Rural Professional Economic Association

On the basis of defining rural professional economic association, the advantages of it in terms of non-profitability, low operation cost and good communication skills are pointed out. Functions of rural professional economic association are expounded. It can display the advantages of information; intensify the connections among governments, agriculture and rural areas; display the advantages of organization and strengthen the gaming capability of rural households; play the advantages of association and promote the improvement of quality of agricultural products; play the advantage of association and help government to transfer to limited government;play association and information advantages and intensify international competitiveness. It is pointed out that the defects caused by imperfect non-profit association restrict the function of rural professional economic association. In order to well display the functions of rural professional economic association, the countermeasures are put forward:establishing perfect laws and regulations to let rural economic association have the features of non-profit association;the government establishes new relations with rural economic association to support their development from multiple channels;fully displaying the service functions of rural economic association and finishing self-development; perfecting incentive and restraint mechanisms for rural economic association; intensifying supervision management of rural economic association.

Research on Industrial Relevance Degree and Industrial Financial Situation Risk Matrix Construction——Based on Chinese Information Technology Industry

Based on Input-Output Table in 2010 issued by National Bureau of Statistics of China, with the help of input-output model and with the calculation of indexes of industrial relevance degree in Chinese information technology industry, the paper reveals the industrial relevance in Chinese information technology industry. The paper also selects the relevant industries which are highly associated with the development of Chinese information technology industry based on industrial relevance degree to analyze the influences of these industries on the financial situation risk fluctuation in information technology industry and to design the matrix of financial situation risk in information technology industry. Then, the paper offers countermeasures and suggestions for the development of our information technology industry.

A New Approach of Time Series Variation Based on Power Links and Field Association Words

This paper has proposed a new methodology extracting stability classes of field association words depending on automatically power link analysis to enhance the precision of decision tree. In this paper, we have studied the effects of the time variation based on the frequencies of specific words called field association words that connected to documents using power link in a specific period. The stability classes have referred to the popularity of field association words based on the change of time in a given period. The new approach has evaluated by conducting experiments simulating results of 1575 files (about 5.16 MB). Based on these experiments, it has turned out that, the F-measure for ascending, stable and descending classes have achieved 93.6%, 99.8% and 75.7%, respectively. These results mean that F-measure was increasing by 12%, 4% and 34% than traditional methods because of the power link analysis.

Research on the Problem of Market Allocation Under the Condition of Asymmetric Information

In contemporary society, the problem of information asymmetry in talent markets has been becoming more prominent. On one hand, the company and candidates fight against each other based on the information available, so both of them could make fraud that will make the market level lower and lower. On the other hand, former scholars have studied from enterprises＇ perspective and put forward methods to solve it based on the aspect of improving the technology and standard mechanism, which could not solve the problem of information asymmetry thoroughly. Consequently, this research put up with the idea that the market can reduce information asymmetry through the establishing personnel information database and related platforms, which has a great practical significance on realizing the optimal allocation of the market and saving cost. At the same time, this study discussed the problems of information asymmetry fundamentally, which was of great importance to enrich the related theory research. Specific models were constructed through two perspectives from the enterprise and the candidates. And then two models would be eventually integrated into a large system. Finally, this research put all related information into a system, which was beneficial to the optimal allocation of human resources with constraints of the market environment.

IMM/MHT FUSING FEATURE INFORMATION IN VISUAL TRACKING

In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.

众源地理信息支持下的建成环境与户外慢跑关联交互

【目的】户外健身慢跑已成为城市居民提升健康水平的重要方式。探析城市建成环境与户外慢跑行为的关联交互,旨在为建立友好型人居环境提供科学依据。【方法】利用慢跑GPS轨迹、街景图片等众源地理大数据,在城市尺度测度户外慢跑行为和城市建成环境特征。采用地理探测器模型探究成都市主城区建成环境与慢跑行为的关联交互影响及其时空差异性。【结果】人口密度、土地利用混合度、公交站密度是户外慢跑空间分布的核心作用因子。建成环境因子两两交互后对慢跑流量的解释力均大于单因子,其中,人口密度和土地利用混合度二者的交互在50%程度上解释了慢跑活动的空间分异。部分环境因子对慢跑流量的交互影响会随着时间和土地利用类型的不同而变化。人口密度和土地利用混合度与其他建成环境因子呈现协同增强的交互影响,且核心交互因子对慢跑的解释力大小具有时空变化性。【结论】众源地理信息为大范围、低成本的感知测度人类行为和城市环境提供了新手段。系统解析建成环境对户外慢跑的关联交互影响,可为健康导向的城市设计提供决策建议。

组态视角下医联体成员信息共享行为影响因素研究

目的探究影响医联体成员信息共享行为的因素,为促进信息共享工作提供理论参考。方法采用问卷调查对医联体成员医院工作人员信息共享行为进行调研。以问卷所得的数据为样本,基于TOE理论构建技术、组织、环境3个维度共8个影响因素的组态效应理论框架,通过模糊集定性比较分析方法对影响医联体成员信息共享行为的因素进行组态分析。结果得出3条促进医联体诊疗信息共享行为的组态路径,识别出政府支持、公民需求为必要条件,行业环境为核心条件。结论政府体制支持对医联体成员信息共享决策至关重要,民众医疗数据共享服务需求是医联体成员信息共享行为的重要动力,医务人员的认同与配合是医联体成员信息共享行为的内在保障。

有所作为:将信息研究转化为实践、政策和行动——2023年ASIS&T年会综述

[目的/意义]探究第86届ASIS&T年会成果特点,有利于把握图书情报与信息科学领域2023年研究进展,展示其最新研究动态。[研究设计/方法]通过描述性统计分析、可视化图谱和文本分析法,对2023年ASIS&T年会收录成果进行文献综述,并对近10年ASIS&T年会论文内容特征的演变与发展进行分析、总结。[结论/发现] 2023年ASIS&T年会成果集中在信息技术、信息与社会、信息服务与用户行为、理论与方法论四大主题,ChatGPT、大语言模型、GAI等主题成为年度新兴热点;近10年ASIS&T年会持续关注社会需求、坚守“图书馆”传统阵地、围绕“用户中心”的技术与应用持续发展、密切关注人工智能、数据科学、数字人文等新领域和交叉领域,彰显了本学科的跨学科性。[创新/价值]揭示近年来图书情报与信息科学领域的研究热点,为学科研究与发展提供风向标。