• 한국전자거래학회지
• /
• 제4권1호
• /
• pp.21-36
• /
• 1999

Using user monitoring techniques on web, marketing decision makers in cyber shopping malls can gather customer behavior data as well as sales transaction data and customer profiles. In this paper, we present a marketing rule extraction technique for customer behavior analysis in cyber shopping malls, The technique is an application of market basket analysis which is a representative data mining technique for extracting association rules. The market basket analysis technique is applied on a customer behavior log table, which provide association rules about web pages in a cyber shopping mall. The extracted association rules can be used for mall layout design, product packaging, web page link design, and product recommendation. A prototype cyber shopping mall with customer monitoring features and a customer behavior analysis algorithm is implemented using Java Web Server, Servlet, JDBC(Java Database Connectivity), and relational database on windows NT.

• Ocean Systems Engineering
• /
• 제7권4호
• /
• pp.319-328
• /
• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• 한국통신학회논문지
• /
• 제17권9호
• /
• pp.1001-1009
• /
• 1992

본 논문에서는 경면수정기술을 이용하여 INTELSAT 위성을 위한 표준 A형 지구국 안테나를 설계하고 제작했다. 설계된 안테나는 18m 직경의 C 밴드 경면수정 카세 그레인 안테나로 CCIR의 규정사항인 사이드로브 포락선 29-25 log\ulcorner를 만족했으며, 모든 복사특서잉 설계목표치를 충분히 만족하는 것으로 나타났다.

• 대한전기학회논문지
• /
• 제45권2호
• /
• pp.283-288
• /
• 1996

This paper presents the development technology of standard shielded resistor divider for full lightning impulse voltage. The ability of large-capacity power apparatus to withstand lighting stroke is usually evaluated by means of full lightning impulse voltage. Lightning impulse voltage test has been essential to evaluate the insulation performance of electrical power apparatus. Recently international standard (IEC 60) on high voltage measurement techniques is being revised and requests a formal traceability of high voltage measurements. Therefore, general interest for this area has grown considerably during last years, and several international intercomparisons have already completed worldwide, i.e. Europe, Japan, America etc., In this viewpoint, we have also investigated the step response of the standard shielded resistor divider, which satisfies the IEC recommendation.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2001년도 추계학술발표논문집 (상)
• /
• pp.601-604
• /
• 2001

도서 정보 추천 시스템에서 기존 사용자들의 정보를 이용하여 마이닝 기법중 군집 분석을 적용하여 사이트에 처음으로 접속하는 사용자와 접속률이 낮아 피드백 정보가 많이 없고 적절한 추천을 하지 못하는 사용자에게 비슷한 군집의 사용자들의 정보를 이용하여 적절한 정보를 추천한다. 본 논문에서는 기존의 멀티에이전트 추천 시스템에 데이터 마이닝 에이전트와 패턴 분석 에이전트를 접목하여 더 나은 추천 정보를 제공하기 위한 시스템을 제안한다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2018년도 춘계학술발표대회
• /
• pp.420-423
• /
• 2018

본 논문에서는 웹 및 문자 공지문을 자동으로 분류하고 추천함으로써 사용자가 원하는 공지문만을 볼 수 있도록 하는 애플리케이션을 개발한다. 본 애플리케이션은 공지문을 여러 카테고리로 자동 분류하여 사용자가 원하는 카테고리에 속한 공지문만을 볼 수 있도록 하며, 사용자가 선호할 만한 공지문을 추천하는 기능을 제공한다. 공지문 분류를 위해 다층 신경망 모델과 Naive Bayes 분류기를 사용하였으며, 공지문 추천을 위해 키워드 기반 자체 알고리즘을 사용하였다. 그 밖에 Word2Vec 을 활용한 검색어 추천 등 부가 기능을 제공하여 사용자가 쉽게 공지문을 찾을 수 있도록 하였다. 본 애플리케이션을 통해 사용자는 수많은 공지문 중 관심 있는 공지문만을 효율적으로 확인할 수 있다.

• Environmental Engineering Research
• /
• 제24권2호
• /
• pp.191-201
• /
• 2019

Petroleum industry produces one of the popular hazardous waste known as Petroleum Sludge. The treatment and disposal of petroleum sludge has created a major challenge in recent years. This review provides insights into various approaches involved in the treatment, and disposal of petroleum sludge. Various methods used in the treatment and disposal of petroleum sludge such as incineration, stabilization/solidification, oxidation, and bio-degradation are explained fully and other techniques utilized in oil recovery from petroleum sludge such as solvent extraction, centrifugation, surfactant EOR, freeze/thaw, pyrolysis, microwave irradiation, electro-kinetic method, ultrasonic irradiation and froth flotation were discussed. The pros and cons of these methods were critically considered and a recommendation for economically useful alternatives to disposal of this unfriendly material was presented.

• 전자통신동향분석
• /
• 제34권6호
• /
• pp.100-107
• /
• 2019

Recently, reinforcement learning (RL) has expanded from the research phase of the virtual simulation environment to a wide range of applications, such as autonomous driving, natural language processing, recommendation systems, and disease diagnosis. However, RL is less likely to be used in these complex real-world environments. In contrast, inverse reinforcement learning (IRL) can obtain optimal policies in various situations; furthermore, it can use expert demonstration data to achieve its target task. In particular, IRL is expected to be a key technology for artificial general intelligence research that can successfully perform human intellectual tasks. In this report, we briefly summarize various IRL techniques and research directions.

• International Journal of Computer Science & Network Security
• /
• 제22권6호
• /
• pp.163-171
• /
• 2022

Online services are widely spread, and their use increases day by day. As COVID-19 spread and people spent much time online, fraud scams have risen unexpectedly. Manipulation techniques have become more effective at swindling those lacking basic technological knowledge. Unfortunately, a user needs a quorum. The interest in preventing scammers from obtaining effective quality service has become the most significant obstacle, increasing the variety of daily Internet platforms. This paper is concerned with analyzing purchase data and extracting provided results. In addition, after examining relevant documents presenting research discussing them, the recommendation was made that future work avoids them; this would save a lot of effort, money, and time. This research highlights many problems a person may face in dealing with online institutions and possible solutions to the epidemic through theft operations on the Internet.

• Asia pacific journal of information systems
• /
• 제27권2호
• /
• pp.126-138
• /
• 2017

The proliferation of items increased the difficulty of customers in finding the specific items they want to purchase. To solve this problem, companies adopted recommender systems, such as collaborative filtering systems, to provide personalization services. However, companies use only meaningful and essential data given the explosive growth of data. Some customers are concerned that their private information may be exposed because CF systems necessarily deal with personal information. Based on these concerns, we analyze the effects of the amount of information on recommendation performance. We assume that a customer could choose to provide overall information or partial information. Experimental results indicate that customers who provided overall information generally demonstrated high performance, but differences exist according to the characteristics of products. Our study can provide companies with insights concerning the efficient utilization of data.