• Journal of Computational Design and Engineering
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• 제1권2호
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• pp.103-115
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• 2014

The engineering design process is a creative process, and the designers must repeatedly apply Undo/Redo operations to modify CAD models to explore new solutions. Undo/Redo has become one of most important functions in interactive graphics and CAD systems. Undo/Redo in a collaborative CAD system is also very helpful for collaborative awareness among a group of cooperative designers to eliminate misunderstanding and to recover from design error. However, Undo/Redo in a collaborative CAD system is much more complicated. This is because a single erroneous operation is propagated to other remote sites, and operations are interleaved at different sites. This paper presents a multi-user selective Undo/Redo approach in full distributed collaborative CAD systems. We use site ID and State Vectors to locate the Undo/Redo target at each site. By analyzing the composition of the complex CAD model, a tree-like structure called Feature Combination Hierarchy is presented to describe the decomposition of a CAD model. Based on this structure, the dependency relationship among features is clarified. B-Rep re-evaluation is simplified with the assistance of the Feature Combination Hierarchy. It can be proven that the proposed Undo/Redo approach satisfies the intention preservation and consistency maintenance correctness criteria for collaborative systems.

• Journal of Information Technology Applications and Management
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• 제24권4호
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• pp.1-12
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• 2017

Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer's purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer's purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.

• 로봇학회논문지
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• 제14권4호
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• pp.311-317
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• 2019

Direct teaching is an essential function for collaborative robots for easy use by non-experts. For most robots, direct teaching is implemented only in joint space because the realization of Cartesian space direct teaching, in which the orientation of the end-effector is fixed while teaching, requires a measurement of the end-effector force. Thus, it is limited to the robots that are equipped with an expensive force/torque sensor. This study presents a Cartesian space direct teaching method for torque-controlled collaborative robots without either a force/torque sensor or joint torque sensors. The force exerted to the end-effector is obtained from the external torque which is estimated by the disturbance observer-based approach with the friction model. The friction model and the estimated end-effector force were experimentally verified using the robot equipped with joint torque sensors in order to compare the proposed sensorless approach with the method using torque sensors.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 춘계학술대회
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• pp.232-239
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• 2005

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. This paper discuss an approach to collaborative filtering based on the Simple Bayesian and apply this model to two variants of the collaborative filtering. One is user-based collaborative filtering, which makes predictions based on the users' similarities. The other is item-based collaborative filtering which makes predictions based on the items' similarities. To evaluate the proposed algorithms, this paper used a database of movie recommendations. Empirical results show that the proposed Bayesian approaches outperform typical correlation-based collaborative filtering algorithms.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.

• 공학교육연구
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• 제17권1호
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• pp.26-41
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• 2014

This paper recommends to teach technical writing as a part of the curriculum of engineering design courses. Some features of both engineering design and keystone design course as well as capstone design course for engineering students are studied before the relationship of those features with written communication are investigated. After the characteristics of collaborative writing are reviewed, some aspects of integration of teaching technical writing into engineering design courses are evaluated. Technical writing for engineering students is best taught by collaborative writing approach in engineering design courses.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
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• pp.385-386
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• 2020

In this paper, we propose a collaborative filtering recommendation algorithm based on the LDA2Vec topic model. By extracting and analyzing the article's content, calculate their semantic similarity then combine the traditional collaborative filtering algorithm to recommend. This approach may promote the system's recommend accuracy.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권9호
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• pp.669-675
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• 2002

최근 전자상거래에서 대부분의 개인화 된 추천 시스템들은 고객의 취향에 맞는 적절한 상품을 추천하기 위하여 협동적 필터링 기술을 적용하고 있다. 사용자 기반 협동적 필터링은 특정 고객의 선호도와 가장 유사한 선호도를 가지는 고객 그룹의 선호도를 바탕으로 그 고객의 특정 상품에 대한 선호도를 예측하는 기법이다. 그러나 이 방법은 두 고객이 모두 평가를 한 상품이 있어야 하고 오직 두 고객 사이에서만 상관 관계를 구할 수 있으므로 예측의 정확성이 떨어질 가능성이 있다. 아이템 기반 협동적 필터링은 고객이 선호도를 입력한 기존의 상품들과 예측하고자 하는 상품의 상관 관계를 계산하여 선호도를 예측한다. 이 방법에서는 상품들간의 유사도를 계산하기 위하여 두 상품에 대해 선호도를 입력한 고객들의 정보를 사용한다. 그러나 고객들간의 유사도가 전혀 고려되지 않기 때문에 만약 특정 고객과 전혀 선호도가 비슷하지 않은 사용자들의 평가를 기반으로 한다면, 상품들간의 유사도가 정확 하지 않고 아울러 추천 시스템의 예측 능력과 추천 능력이 저하되는 문제점이 있다. 본 논문에서는 기존의 아이템 기반 협동적 필터링 기술의 문제점을 보완하고 추천 시스템의 예측 능력을 향상시키기 위하여 유사한 선호도를 가지는 고객들의 평가에 근거하여 상품들간의 유사도를 구하여 특정 상품에 대한 고객의 선호도를 예측하여 추천해 주는 기법을 제안한다. 본 논문에서 제안한 방법의 성능을 기존의 여러 다른 협동적 필터링 방법들과의 비교실험을 통해 평가하였다. 실험 결과 본 논문에서 제안한 방법이 기존의 다른 방법들보다 우수함을 확인할 수 있었다.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2007년도 International Conference
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• pp.576-581
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• 2007

The purpose of this paper is to strengthen trust on the automated collaborative filtering system. Automated collaborative filtering system is quickly becoming a popular technique for recommendation system. This elaborative methodology contributes for reducing information overload and the result becomes index of users' preference. In addition, it can be applied to various industries in various fields. After it collaborative filtering system was developed, many researches are executed to enhance credibility and to apply in various fields. Among these diverse systems, collaborative filtering system which uses Pearson correlation coefficient is most common in many researches. In this paper, we proposed new process diagram of collaborative filtering algorithm and new factors which should improve the credibility of system. In addition, the effects and relationships are also tested.

• Advances in environmental research
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• 제3권1호
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• pp.87-106
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• 2014

The concern for the global environment ensues researchers from various disciplines to work in collaboration to tackle with the issues of sustainability and environmental conservation for well-being of the people. In this study, we have selected and focused on few basic environment-effecting factors such as temperature, humidity, carbon dioxide and oxygen concentration level and referred them as meteorological data. In this paper, we present the development of our own customized hardware setup, environmental monitoring device (EMD) to obtain the data. Utilizing the relationship among these basic parameters, represented in the form of formulas and equations, we tried to encode them using Matlab programming. Data visualization is achieved by plotting the graphs of basic parameters obtained from EMD as well for the derivatives using Matlab programs.