• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.123-140
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• 2009

Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.183-200
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• 2009

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content-based filtering. Content-based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well.known department stores in Korea, is used.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.113-123
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• 2012

Recently, various attempts have been made to apply Intelligent Transportation System under various environments and conditions. Consequently, an accurate license plate recognition regardless of vehicle location and viewing angle is required. In this paper, we propose a novel license plate recognition system which exploits a) the format of license plates to remove false candidates of license plates and to extract characters in license plates and b) the characteristics of Hangul for accurate character recognition. In order to eliminate false candidates of license plates, the proposed method first aligns the candidates of license plates horizontally, and compares the position and the shape of objects in each candidate with the prior information of license plates provided by Korean Ministry of Construction & Transportation. The prior information such as aspect ratio, background color, projection image is also used to extract characters in license plates accurately applying an improved local binarization considering luminance variation of license plates. In case of recognizing Hangul in license plates, they are initially grouped according to their shape similarity. Then a super-class method, a hierarchical analysis based on key feature points is applied to recognize Hangul accurately. The proposed method was verified with high recognition rate regardless of background image, which eventually proves that the proposed LPR system has high performance regardless of the vehicle location or viewing angle.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.17-26
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• 1998

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But for the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. The time-of-flight(TOF) method generally used for distance measurement can not distinguish small object patterns of plane, corner or edge. To resolve the problem, an increased number of the sensors in the forms of a linear array or 2-dimensional array of the sensors has been used. Also better resolution has been obtained by shifting the array in several steps using mechanical actuators. Also simple patterns are classified based on analyzing signal reflections. In this paper we propose a method of a sensor array system with improved capability in pattern distinction using electronic circuits accompanying the sensor array, and intelligent algorithm based on neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. A set of different return signals from neighborhood sensors is manipulated to provide enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

• Smart Media Journal
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• v.9 no.2
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• pp.51-62
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• 2020

Starting with the "Smart Grid National Road Map" in 2010, the Smart Grid 2030 was introduced through the basic plan and implementation plan of the intelligent power grid with the goal of building the world's first national smart grid. In this paper, we intend to build a power domain ontology based on the power business platform based on the upper and lower conceptual models of the "Smart Grid Interoperability Standard Framework and Roadmap", the standard of implementation plan. Ontology is suitable for expressing and utilizing the smart grid conceptual model because it considers hierarchical structure as knowledge defines the properties of entities and relationships between entities, but there is no research related to them. Therefore, in this paper, the upper ontology was defined as a major category for smart grid-related fields, and the lower ontology was defined as detailed systems and functions for the upper ontology to construct the ontology. In addition, scenarios in various situations that could occur in the power system were constructed and significant inference results were derived through inference engines and queries.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.165-178
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• 2011

A gigantic ship is constructed by assembling various types of ship blocks, each block being made by cutting and piecing the steel-plates together. The steel-plate piling process as the initial stage of ship construction sorts and manages the steel-plates according to the ship blocks that the steel-plates are used to make. The steel-plate piling process poses some problems such as process delay due to piling errors, safety vulnerability due to the handling of extra heavy-weight objects, and the uncertainty of work plan due to lack of information management in the pile spaces. We constructed a steel-plate piling process system based on the context-aware computing to resolve such problems. We built simulation system that can simulate the piling process and then established a smart space within the system by using tags, sensors and a real-time location system in order to collect context information. Workers receive an appropriate or intelligent service from the system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1247-1258
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• 2004

The LBS(Location-Based Service), which is based on individual's mobility, is required increasingly as mobile telecommunication and various infrastructures have developed rapidly. The technologies for LBS are location determination technology, service platform technology, contents provider technology and moving object database technology generally. Among these, service platform must be interoperable with location gate-way server and provide common function of billing, authentification, protect location information, privacy control, location trigger and intelligent acquisition and so on. The TTA(Telecommunications Technology Association) published specification that defines a standard protocol for safe and simple interface between LBS client and LBS platform and the OpenLS(Open Location Service) in OGC (Open GIS Consortium) released implementation specifications for providing Location based core services. In this paper, we implemented service platform for LBS which is able to interoperable with location gateway server and contents provider and is caracterized as follows. First, it could require and response location information from different types of location gateway server with same interface. Second, it complies with the standard interfaces with OpenLS 4 contents providers for core LBS. Third, it could provide location of wired phone as well as wireless mobile terminal compling with the standard protocol. Last, it could provide trajectorH information based past location as well as current location, because it is able to interoperable with moving object DBMS. This paper contributes to the construction and practical use of LBS by providing the method of implementation of service platform for LBS.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.397-406
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• 2000

대부분의 국방정보시스템의 소프트웨어는 높은 가용성, 신뢰성, 신속성, 정확성 등을 요구하는 대규모이면서 복잡한 실시간 시스템이다. 이러한 국방정보시스템의 소프트웨어 개발사업에 있어서 저비용 고효율의 미개국방경영 건설을 위하고 강한 전투력을 육성하기 위해서는 국방정보시스템의 효율적인 소프트웨어 개발사법이 요구된다. 따라서, 국방정보시스템의 소프트웨어 사업관리자가 개발사업을 관리하고 감독하는데 있어서 개발자와 사용자간의 조정 및 통제 기능을 수행하고 해당 국방정보시스템의 특성을 파악하여 성공적인 사업수행을 할 수 있도록 기술적인 사업관리 측면에서 구체적이고 상세화된 방안/지침을 제공하기 위한 전문가시스템의 지식베이스 도메인 지식개발에 관한 연구이다. 기존의 국방정보시스템의 사업관리자가 경험을 동해 축적해 온 기술, 정책, 아이디어, 노하우 등에 대한 지식을 습득하고 사업 관련자료에서 제시한 소프트웨어 생명주기 단계별 방안이나 지침 등을 바탕으로 하여 식별된 사실이나 내용을 지식베이스로 구축하여 국방정보시스템의 사업관리자가 필요로 할 때 설명모듈을 거쳐 임무 및 세부활동사항을 게시하여 줌으로써 사업관리 경험이 부족하거나 사업관리자가 교체되었을 때 사업관리자들이 업무를 지속적으로 연계시켜 임무수행이 가능하도록 기초/기반 여건을 제공하고자 한다. 본 논문은 국방정보시스템의 소프트웨어 개발사업에서 소프트웨어 생명주기 단계별 사업관리자의 임무 및 세부활동사항 지원용 전문가시스템을 개발할 때 이용할 수 있도록 도메인 지식을 개발하는 것이며 논문의 결과를 활용시 기대되는 효과는 본문을 참고 바란다.의 장점을 취합하여 설계되었다. 본 시스템은 기존의 UN/EDIFACT표준을 사용하고 있는 EDI환경과 기존 VAN 방식의 EDI 중계 시스템과 연동되며, 향후 관세청의 XML/EDI 표준 시행을 미리 대비하는 선도연구로서 자리매김이 된다. 본 연구에서는 개발된 XML/EDI 통관시스템은 향후, 서비스의 최대 걸림돌이 되어왔던 값비싼 EDI 사용료의 부담에서 벗어날 수 있게 할 것이며, 저렴한 EDI구축/운영 비용으로 전자문서교환의 활성화와 XML이 인터넷 기반의 문서유통 표준으로 자리매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.643-654
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• 2018

In order to understand and manage traffic flows in urban areas in the past, a variety of traffic engineering theoretical indicators such as intersection lag and highway speed have been applied. However, these theories and indicators have been developed under the constraints of traffic engineering research before the construction of intelligent transportation system. Since the ATIS system currently exists, it is necessary to introduce a separate traffic engineering technology that utilizes the data. In this paper, it is aimed to confirm whether it is applicable to intermittent flow (approach road, intersection, control group, main road axis) by using 'congestion intensity' which is already used in traffic engineering field. The results of this study are as follows: (1) The traffic signal improvement effect of urban road access road, intersection road, control group, Two verification studies were performed to verify the derived congestion intensity index. (1) verification of congestion intensity threshold value analysis and (2) crossing improvement using the congestion intensity. Through verification, it was confirmed that it is possible to apply the congestion intensity in the inter - city intermittent flow using the 5 - minute unit speed data so as to be able to escape from the existing traffic signal operation management which is past passive and manpower limit.