• 한국산학기술학회논문지
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• 제11권6호
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• pp.2214-2218
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• 2010

대용량 데이터에서 의미있고 유용한 지식을 추출하는 기법 중의 하나인 연관규칙은 데이터베이스에 존재하는 속성들 사이에 유사성 또는 패턴을 기술하여 사용자에게 데이터에 관한 유용한 정보를 줄 수 있다. 기존에 연구되어 온 연관규칙은 이진(boolean) 데이터베이스에 존재하는 유무에 대한 규칙으로 발견하는 것에 대해서 주로 연구되어왔다. 본 논문에서는 정량적 속성의 데이터를 기호적 속성 값으로 바꾼 후 연관규칙을 추출함으로써, 퍼지개념을 사용한 퍼지 연관규칙을 이용한 지능적 질의 처리 시스템을 제안하고자 한다.

• 한국농촌계획학회:학술대회논문집
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• 한국농촌계획학회 1998년도 정기총회 및 춘계 학술논문발표회
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• pp.15-16
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• 1998

It was studied Horticultural field by ARC/lNFO software, applicated on GIS- management information system, which dealt with graphic and attribute data together. This result obtained was summarized as follow : 1. It would be to output about position, attribute and photographic data of this horticultural farm through AML. 2. It would be easy to use with internal interface which is composed in basic function menu and application menu. 3. It would be good to plant Water melon and Tomato in this Horticultural farm. 4. The method which applicated overlay and analysis would be need to manage farming data in this horticultural farm and to develop a dynamic decision support system interfaced with GIS.

• IEIE Transactions on Smart Processing and Computing
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• 제5권5호
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• pp.358-365
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• 2016

We propose a novel application of random forest, a machine learning-based general classification algorithm, to analyze the influence of design attributes on the silicon-to-SPICE (S2S) gap. To improve modeling accuracy, we introduce magnification of learning data as well as randomization for the counting of design attributes to be used for each tree in the forest. From the automatically generated decision trees, we can extract the so-called importance and impact indices, which identify the most significant design attributes determining the S2S gap. We apply the proposed method to actual silicon data, and observe that the identified design attributes show a clear trend in the S2S gap. We finally unveil 10nm key fin-shaped field effect transistor (FinFET) structures that result in a large S2S gap using the measurement data from 10nm test vehicles specialized for model-hardware correlation.

• 한국정보처리학회논문지
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• 제7권4호
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• pp.1050-1061
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• 2000

This paper presents a new object model, LOM(Lightweight Object Model) and an implementation method for the distributed trader in heterogeneous distributed computing environment including mobile network. Trader is third party object that enables clients to find suitable servers, which provide the most appropriate services to client in distributed environment including dynamic reconfiguration of services and servers. Trading service requires simpler and more specific object model than genetic object models which provide richer multimedia data types and semantic characteristics with complex data structures. LOM supports a new reference attribute type instead of the relationship, inheritance and composite attribute types of the general object oriented models and so LOM has simple data structures. Also in LOM, the modelling step includes specifying of the information about users and the access right to objects for security in the mobile environment and development of the distributed storage for trading service. Also, we propose and implementation method of the distributed trader, which integrates the LOM-information object model and the OMG (object Management Group) computational object model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권1호
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• pp.1-12
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• 2016

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

• 한국조경학회지
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• 제23권3호
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• pp.94-105
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• 1995

Purposes of this study were to apply and examine that the importance-performance analysis(IPA) could be a useful park management tool. The IPA is an easily-applied technique developed in marketing field to measure attribute importance and performance. It displays them simultaneously on a 4-quardrant action grid; therefore, park managers can upgrade current services and address marketing strategies for various user segments. The children's grand park was selected as a study are. Two different types of questionaires were utilized for data analyses. They were collected from one respondent by on-site distribution or by mail survey, because attribute importance and performance should be rated before and after visiting a park, respectively. Then, total data was segmented systematically by 2 variables-age and benefit sought. Action grids from order to meet the needs of each segment. Several comments on both data collection methods and segment variables selection were described to improve the IPA in recreation settings for future researches.

• ETRI Journal
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• 제41권4호
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• pp.465-472
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• 2019

The major challenge in wireless body area networks (WBAN) is setting up a protected communication between data consumers and a body area network controller while meeting the security and privacy requirements. This paper proposes efficient and secure data communication in WBANs using a Twofish symmetric algorithm and ciphertext-policy attribute-based encryption with constant size ciphertext; in addition, the proposed scheme incorporates policy updating to update access policies. To the best of the author's knowledge, policy updating in WBAN has not been studied in earlier works. The proposed scheme is evaluated in terms of message size, energy consumption, and computation cost, and the results are compared with those of existing schemes. The result shows that the proposed method can achieve higher efficiency than conventional methods.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.89-95
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• 2023

Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제12권7호
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• pp.295-302
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• 2023

자연어로 작성되는 소프트웨어 요구사항은 이해관계자가 바라보는 관점에 따라 의미가 달라질 수 있다. 품질 속성 기반으로 아키텍처 설계시에 품질 속성별로 적합한 설계 전술(Tactic)을 선택해야 효율적인 설계가 가능해 품질 속성 요구사항의 정확한 분류가 필요하다. 이에 따라 고비용 작업인 요구사항 분류에 관한 자연어처리 모델이 많이 연구되고 있지만, 품질 속성 데이터셋(dataset)의 불균형을 처리해 분류 성능을 개선하는 주제는 많이 다루고 있지 않다. 본 연구에서는 먼저 실험을 통해 분류 모델이 한국어 요구사항 데이터셋을 자동으로 분류할 수 있음을 보인다. 이 결과를 바탕으로 EDA(Easy Data Augmentation) 기법을 통한 데이터 증강과 언더샘플링(undersampling) 전략으로 품질 속성 데이터셋의 불균형을 개선할 수 있음을 설명하고 요구사항의 카테고리 분류에 효과가 있음을 보인다. 실험 결과 F1 점수(F1-Score) 기준으로 최대 5.24%p 향상되어 불균형 데이터 처리 기법이 분류 모델의 한국어 요구사항 분류에 도움이 됨을 확인할 수 있다. 또한, EDA의 세부 실험을 통해 분류 성능 개선에 도움이 되는 데이터 증강 연산에 관해 설명한다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1170-1178
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• 2022

Indoor POI data, an essential component of indoor spatial data, has attribute information of a specific place in the room and is the most critical information necessary for the user. Currently, indoor POI data is manually updated by direct investigation, which is expensive and time-consuming. Recently, research on updating POI using the attribute information of indoor photographs has been advanced to overcome these problems. However, the range of use, such as using only photographs with text information, is limited. Therefore, in this study, and to improvement this, I proposed a new method to update indoor POI data using a smartphone camera. In the proposed method, the POI name is obtained by classifying the indoor scene's photograph into artificial intelligence technology CNN and matching the location criteria to indoor spatial data through AR technology. As a result of creating and experimenting with a prototype application to evaluate the proposed method, it was possible to update POI data that reflects the real world with high accuracy. Therefore, the results of this study can be used as a complement or substitute for the existing methodologies that have been advanced mainly by direct research.