• Title/Summary/Keyword: Information Search Model

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Legal search method using S-BERT

  • Park, Gil-sik;Kim, Jun-tae
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.11
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    • pp.57-66
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    • 2022
  • In this paper, we propose a legal document search method that uses the Sentence-BERT model. The general public who wants to use the legal search service has difficulty searching for relevant precedents due to a lack of understanding of legal terms and structures. In addition, the existing keyword and text mining-based legal search methods have their limits in yielding quality search results for two reasons: they lack information on the context of the judgment, and they fail to discern homonyms and polysemies. As a result, the accuracy of the legal document search results is often unsatisfactory or skeptical. To this end, This paper aims to improve the efficacy of the general public's legal search in the Supreme Court precedent and Legal Aid Counseling case database. The Sentence-BERT model embeds contextual information on precedents and counseling data, which better preserves the integrity of relevant meaning in phrases or sentences. Our initial research has shown that the Sentence-BERT search method yields higher accuracy than the Doc2Vec or TF-IDF search methods.

ACA: Automatic search strategy for radioactive source

  • Jianwen Huo;Xulin Hu;Junling Wang;Li Hu
    • Nuclear Engineering and Technology
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    • v.55 no.8
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    • pp.3030-3038
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    • 2023
  • Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

Design and Implementation of Interactive Search Service based on Deep Learning and Morpheme Analysis in NTIS System (NTIS 시스템에서 딥러닝과 형태소 분석 기반의 대화형 검색 서비스 설계 및 구현)

  • Lee, Jong-Won;Kim, Tae-Hyun;Choi, Kwang-Nam
    • Journal of Convergence for Information Technology
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    • v.10 no.12
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    • pp.9-14
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    • 2020
  • Currently, NTIS (National Technology Information Service) is building an interactive search service based on artificial intelligence technology. In order to understand users' search intentions and provide R&D information, an interactive search service is built based on deep learning models and morpheme analyzers. The deep learning model learns based on the log data loaded when using NTIS and interactive search services and understands the user's search intention. And it provides task information through step-by-step search. Understanding the search intent makes exception handling easier, and step-by-step search makes it easier and faster to obtain the desired information than integrated search. For future research, it is necessary to expand the range of information provided to users.

Brand Equity and Purchase Intention in Fashion Products: A Cross-Cultural Study in Asia and Europe (상표자산과 구매의도와의 관계에 관한 국제비교연구 - 아시아와 유럽의 의류시장을 중심으로 -)

  • Kim, Kyung-Hoon;Ko, Eun-Ju;Graham, Hooley;Lee, Nick;Lee, Dong-Hae;Jung, Hong-Seob;Jeon, Byung-Joo;Moon, Hak-Il
    • Journal of Global Scholars of Marketing Science
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    • v.18 no.4
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    • pp.245-276
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    • 2008
  • Brand equity is one of the most important concepts in business practice as well as in academic research. Successful brands can allow marketers to gain competitive advantage (Lassar et al.,1995), including the opportunity for successful extensions, resilience against competitors' promotional pressures, and the ability to create barriers to competitive entry (Farquhar, 1989). Branding plays a special role in service firms because strong brands increase trust in intangible products (Berry, 2000), enabling customers to better visualize and understand them. They reduce customers' perceived monetary, social, and safety risks in buying services, which are obstacles to evaluating a service correctly before purchase. Also, a high level of brand equity increases consumer satisfaction, repurchasing intent, and degree of loyalty. Brand equity can be considered as a mixture that includes both financial assets and relationships. Actually, brand equity can be viewed as the value added to the product (Keller, 1993), or the perceived value of the product in consumers' minds. Mahajan et al. (1990) claim that customer-based brand equity can be measured by the level of consumers' perceptions. Several researchers discuss brand equity based on two dimensions: consumer perception and consumer behavior. Aaker (1991) suggests measuring brand equity through price premium, loyalty, perceived quality, and brand associations. Viewing brand equity as the consumer's behavior toward a brand, Keller (1993) proposes similar dimensions: brand awareness and brand knowledge. Thus, past studies tend to identify brand equity as a multidimensional construct consisted of brand loyalty, brand awareness, brand knowledge, customer satisfaction, perceived equity, brand associations, and other proprietary assets (Aaker, 1991, 1996; Blackston, 1995; Cobb-Walgren et al., 1995; Na, 1995). Other studies tend to regard brand equity and other brand assets, such as brand knowledge, brand awareness, brand image, brand loyalty, perceived quality, and so on, as independent but related constructs (Keller, 1993; Kirmani and Zeithaml, 1993). Walters(1978) defined information search as, "A psychological or physical action a consumer takes in order to acquire information about a product or store." But, each consumer has different methods for informationsearch. There are two methods of information search, internal and external search. Internal search is, "Search of information already saved in the memory of the individual consumer"(Engel, Blackwell, 1982) which is, "memory of a previous purchase experience or information from a previous search."(Beales, Mazis, Salop, and Staelin, 1981). External search is "A completely voluntary decision made in order to obtain new information"(Engel & Blackwell, 1982) which is, "Actions of a consumer to acquire necessary information by such methods as intentionally exposing oneself to advertisements, taking to friends or family or visiting a store."(Beales, Mazis, Salop, and Staelin, 1981). There are many sources for consumers' information search including advertisement sources such as the internet, radio, television, newspapers and magazines, information supplied by businesses such as sales people, packaging and in-store information, consumer sources such as family, friends and colleagues, and mass media sources such as consumer protection agencies, government agencies and mass media sources. Understanding consumers' purchasing behavior is a key factor of a firm to attract and retain customers and improving the firm's prospects for survival and growth, and enhancing shareholder's value. Therefore, marketers should understand consumer as individual and market segment. One theory of consumer behavior supports the belief that individuals are rational. Individuals think and move through stages when making a purchase decision. This means that rational thinkers have led to the identification of a consumer buying decision process. This decision process with its different levels of involvement and influencing factors has been widely accepted and is fundamental to the understanding purchase intention represent to what consumers think they will buy. Brand equity is not only companies but also very important asset more than product itself. This paper studies brand equity model and influencing factors including information process such as information searching and information resources in the fashion market in Asia and Europe. Information searching and information resources are influencing brand knowledge that influences consumers purchase decision. Nine research hypotheses are drawn to test the relationships among antecedents of brand equity and purchase intention and relationships among brand knowledge, brand value, brand attitude, and brand loyalty. H1. Information searching influences brand knowledge positively. H2. Information sources influence brand knowledge positively. H3. Brand knowledge influences brand attitude. H4. Brand knowledge influences brand value. H5. Brand attitude influences brand loyalty. H6. Brand attitude influences brand value. H7. Brand loyalty influences purchase intention. H8. Brand value influence purchase intention. H9. There will be the same research model in Asia and Europe. We performed structural equation model analysis in order to test hypotheses suggested in this study. The model fitting index of the research model in Asia was $X^2$=195.19(p=0.0), NFI=0.90, NNFI=0.87, CFI=0.90, GFI=0.90, RMR=0.083, AGFI=0.85, which means the model fitting of the model is good enough. In Europe, it was $X^2$=133.25(p=0.0), NFI=0.81, NNFI=0.85, CFI=0.89, GFI=0.90, RMR=0.073, AGFI=0.85, which means the model fitting of the model is good enough. From the test results, hypotheses were accepted. All of these hypotheses except one are supported. In Europe, information search is not an antecedent of brand knowledge. This means that sales of global fashion brands like jeans in Europe are not expanding as rapidly as in Asian markets such as China, Japan, and South Korea. Young consumers in European countries are not more brand and fashion conscious than their counter partners in Asia. The results have theoretical, practical meaning and contributions. In the fashion jeans industry, relatively few studies examining the viability of cross-national brand equity has been studied. This study provides insight on building global brand equity and suggests information process elements like information search and information resources are working differently in Asia and Europe for fashion jean market.

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A Study on Ontology Modeling for Weapon Parts Development Information (무기체계 부품국산화 정보의 온톨로지 구축방안 연구)

  • Jang, Woo Hyuk
    • Journal of Korea Multimedia Society
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    • v.18 no.7
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    • pp.873-885
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    • 2015
  • Today, It is difficult to search the various and numerous information efficiently. For this reason, Semantic Web emerged to provide searching services more easily through the structuring of a variety of unstructured format data and the definition of meaningful relationships between information. Especially, definition of relationship and meaning among resources is significant to share and infer related information. Ontology modeling plays just that role. Weapon parts development information is unstructured and dispersed all over. There are many difficulties in finding desired information, leading to getting improper outcomes. In this paper, we present an intuitive ontology model with weapon parts development information including the multi-dimensional information analysis and expansion of the relevant information. This study build up a ontology model through creating class and hierarchy about parts information and defining the properties of classes with Ontology Development 101[1] procedures using Protégé tools. The ontology model provides users with a platform on which search of needed information can be easy and efficient.

타부탐색(Tabu Search)의 확장모델을 이용한 '외판원 문제(Traveling Salesman Problem)' 풀기

  • 고일상
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1996.10a
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    • pp.135-138
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    • 1996
  • In solving the Travel Salesman Problem(TSP), we easily reach local optimal solutions with the existing methods such as TWO-OPT, THREE-OPT, and Lin-Kernighen. Tabu search, as a meta heuristic, is a good mechanism to get an optimal or a near optimal solution escaping from the local optimal. By utilizing AI concepts, tabu search continues to search for improved solutions. In this study, we focus on developing a new neighborhood structure that maintains the feasibility of the tours created by exchange operations in TSP. Intelligent methods are discussed, which keeps feasible tour routes even after exchanging several edges continuously. An extended tabu search model, performing cycle detection and diversification with memory structure, is applied to TSP. The model uses effectively the information gathered during the search process. Finally, the results of tabu search and simulated annealing are compared based on the TSP problems in the prior literatures.

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Content-search in Distributed Environment Using Standard Product Model (STEP) (분산환경에서 표준제품모델(STEP)을 이용한 내용검색)

  • 손정모;유상봉;김영호;이수홍
    • Korean Journal of Computational Design and Engineering
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    • v.4 no.4
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    • pp.285-294
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    • 1999
  • This paper presents a content-search system built on distributed environments using the open product model of STEP, The content-search system searches design data for given product descriptions such as part name and features. Distribute object interfaces has been defined by IDL and distributed product data are searched through CORBA protocols. Web interfaces are also provided for interactive user interfaces. Given a user request, a mediator interacts with distributed search servers and sends collected results back to the user. The mediator has such metadata as location, program name, and other information about product data stored on distributed system. The search servers use SDAI interfaces to search STEP files or databases. The content-search system promotes the reuse of previous design within a company and the outsourcing of part designs.

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The Study of Influence Factors on External Information Search Effort in Online Shopping Malls (온라인 쇼핑몰에서의 외부정보탐색노력에 대한 영향요인에 관한 연구)

  • Choi, Nak-Hwan;Lee, Chang-Won;Hwang, Yun-Yong
    • Journal of Global Scholars of Marketing Science
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    • v.15 no.3
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    • pp.93-116
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    • 2005
  • This study explored factors affecting consumer's external information search effort on online shopping malls through three approaches(cost-benefit approach, psychological motivation approach, and web-site theory approach) respectively, and designed the integrative model through searching the relationships among them. In this study search motivation are designed to be explained by search benefit and search cost that are explained by factors related to the web site and search efforts are designed to be explained by search benefit, search cost and search motivation. The results from our empirical research showed that search motivation, search benefits and prior knowledge on online shopping malls have a direct effect on consumer external information search effort. On the other hand, it was verified that the search motivation was affected by search benefits and purchase involvement, and the search benefits are directly influenced by purchase involvement, constructive simplicity, search convenience and trust. In addition, purchase involvement, time press, constructive simplicity and search convenience had direct effects on the search cost.

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A Video Information Management System for Supporting Caption- and Content-based Searches (주석 및 내용 기반 검색을 지원하는 동영상 정보 관리 시스템)

  • 전미경;김인홍;류시국;전용기;강현석
    • Journal of Korea Multimedia Society
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    • v.2 no.3
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    • pp.231-242
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    • 1999
  • Generally, either caption-based search method or content-based search methods is used to retrieve video information. However, each search method has its limitations. Caption-based search is apt to lose consistency as for user's subjects, and content-based search is hard to extract general means. To enhance efficiency and correctness as for complementing each other, we propose the Integrated Video Data Model(IVDM) which integrates the two search methods, to device the model, we analyze video data and construct the structure of video information hierarchically. IVDM supports caption-based search as assigning meta-data by analyzing thematic-unit in the higher level, and also supports content-based search as extracting feature data by analyzing the content of video data in the lower level. We design Object-Oriented database schema of news video, based-on the IVDM. And we provide 4-type of queries and query processing algorithm to retrieve news video information.

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Simultaneous outlier detection and variable selection via difference-based regression model and stochastic search variable selection

  • Park, Jong Suk;Park, Chun Gun;Lee, Kyeong Eun
    • Communications for Statistical Applications and Methods
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    • v.26 no.2
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    • pp.149-161
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    • 2019
  • In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.