• Asia pacific journal of information systems
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• 제21권2호
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• pp.43-58
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• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

• Corrosion Science and Technology
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• 제3권4호
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• pp.166-171
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• 2004

Recent developments in Bridge Management Systems (BMS) and in Life-Cycle Cost (LCC) of bridges, have raised the need for evaluation procedure of future condition (Deterioration) of a bridge. Predicting future deterioration is not an easy task due to limited past data to extrapolate from and also due to difficulty in measuring actual deterioration such as section loss of steel on an actual steel bridge. Also, increase in live load and reduction of resistance are random variables, thus a probabilistic approach should be adopted for determining the future deterioration. Due to difficulties in evaluation of future deterioration on steel bridges, accepting uncertainties within a reasonable error, a deterministic procedure using bridge condition rating can be a useful tool for projection of future condition of bridges to identify repair and maintenance needs. The object of this paper is to determine applicability of evaluating deterioration of steel bridge components based on Bridge condition ratings. Bridge condition ratings of bridge components show wide variation for bridges of same age and does not directly correlate well with the age of the bridge and/or deterioration of the bridge. High uncertainty can be reduced by breaking down the rating and by sensitivity analysis. From refined condition rating data, generalized deterioration profile of structures based on age can be derived. Examples are shown for sample bridges in USA. Approximately, 3,000 short to medium span steel bridges were listed in the inventory database. Results show wide variation of rating factors but by subdividing the Bridge condition ratings for various categories general deterioration profiles of steel bridges can be determined.

• 경영과정보연구
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• 제36권2호
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• pp.93-111
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• 2017

본 연구는 4차 산업혁명을 맞이하여 ICT기반 미래 신성장동력 산업의 경제적 파급효과를 알아보고, 이를 위한 신성장 동력의 성공요인은 무엇인지 분석하고자 하였다. 이를 위해, 기술보증기금에서 제시하는 기술평가모형 자료를 활용하였다. 구체적으로, ICT기반 신성장동력 산업의 경제적 효과를 경제성과 공공성으로 나누어 효과를 검증하고, 기술평가모형의 요인들 즉, CEO역량, 기술성, 사업성이 기업의 경제성 및 공공성에 미치는 영향을 조사하였다. 분석결과, 미래성장동력 산업에 해당하는 기업의 경제성 및 공공성에 대한 파급효과는 긍정적으로 나타났으며, 기업의 사업화 역량은 경제성과 공공성 효과를 증가시킬 성공요인으로 나타났다. 이러한 연구결과를 토대로 미래 신성장동력 산업의 기술혁신 산업정책의 관리 및 지원방안을 제시하고자 한다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.510-514
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• 2004

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation methodology based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of original CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than original collaborative filtering methodology.

• Smart Structures and Systems
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• 제12권6호
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• pp.661-678
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• 2013

In this paper, a wireless sensing system for structural field evaluation and rating of bridges is presented. The system uses a wireless platform integrated with traditional analogue sensors including strain gages and accelerometers along with the operating software. A wireless vehicle position indicator is developed using a tri-axial accelerometer node that is mounted on the test vehicle, and was used for identifying the moving truck position during load testing. The developed software is capable of calculating the theoretical bridge rating factors based on AASHTO Load and Resistance Factor Rating specifications, and automatically produces the field adjustment factor through load testing data. The sensing system along with its application in bridge deck rating was successfully demonstrated on the Evansville Bridge in West Virginia. A finite element model was conducted for the test bridge, and was used to calculate the load distribution factors of the bridge deck after verifying its results using field data. A confirmation field test was conducted on the same bridge and its results varied by only 3% from the first test. The proposed wireless sensing system proved to be a reliable tool that overcomes multiple drawbacks of conventional wired sensing platforms designed for structural load evaluation of bridges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• 한국정보기술학회논문지
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• 제16권12호
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• pp.109-114
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• 2018

협업 필터링은 사회적 추천 방식으로서 뛰어난 성능을 제공하는 대표적인 추천 시스템 알고리즘으로 폭넓게 사용되어 오고 있다. 협업 필터링은 구조적으로 아이템 평가 데이터에 의존하고 있기 때문에 평가 행렬의 희박도는 추천 성능에 직접적으로 영향을 미친다. 평가 행렬의 희박성 문제 개선을 위해 협업 필터링과 내용 기반 방법을 결합하는 복합형 추천 방법에 대한 연구는 꾸준하게 이루어져 왔으며, 본 연구에서는 협업 필터링의 희소 평가 행렬(sparse rating matrix) 문제 개선 방안의 하나로 공통 평가 아이템이 누락되어 유사도 측정이 불가능한 상황에 대처하기 위한 방법을 제안한다. 이를 위하여 사용자간 이행적 관계 그래프에 기반하는 유사도 평가 모델을 설계하고 오픈 데이터셋인 Movielens에 적용하여 추천 정확도를 측정 비교하였다.

• 한국초전도학회:학술대회논문집
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• 한국초전도학회 2006년도 High Temperature Superconductivity Vol.XVI
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• pp.84-84
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• 2006

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.386-398
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• 2019

The transmission capacity of transmission lines is affected by environmental parameters such as ambient temperature, wind speed, wind direction and so on. The environmental parameters can be measured by the installed measuring devices. However, it is impossible to install the environmental measuring devices throughout the line, especially considering economic cost of power grid. Taking into account the limited number of measuring devices and the distribution characteristics of environment parameters and transmission lines, this paper first studies the environmental parameter estimating method of inverse distance weighted interpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEE standard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect the load capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line is realized by using the data obtained from China meteorological data network. The cost of the environmental measurement device is reduced, and the accuracy of dynamic rating is improved.