• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2927-2941
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• 2022

The primary task of machine fault diagnosis is to judge whether the current state is normal or damaged, so it is a typical binary classification problem with mutual exclusion. Mutually exclusive events and out-of-domain detection have one thing in common: there are two types of data and no intersection. We proposed a fusion model method to improve the accuracy of machine fault diagnosis, which is based on the mutual exclusivity of events and the commonality of out-of-distribution detection, and finally generalized to all binary classification problems. It is reported that the performance of a convolutional neural network (CNN) will decrease as the recognition type increases, so the variational auto-encoder (VAE) is used as the primary model. Two VAE models are used to train the machine's normal and fault sound data. Two reconstruction probabilities will be obtained during the test. The smaller value is transformed into a correction value of another value according to the mutually exclusive characteristics. Finally, the classification result is obtained according to the fusion algorithm. Filtering normal data features from fault data features is proposed, which shields the interference and makes the fault features more prominent. We confirm that good performance improvements have been achieved in the machine fault detection data set, and the results are better than most mainstream models.

• Nuclear Engineering and Technology
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• 제53권4호
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• pp.1304-1310
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• 2021

Two-phase flow, especially gas-liquid two-phase flow, has a wide application in industrial field. The diagnosis of two-phase flow parameters, which directly determine the flow and heat transfer characteristics, plays an important role in providing the design reference and ensuring the security of online operation of two-phase flow system. Computer tomography (CT) is a good way to diagnose such parameters with imaging method. This paper has proposed a novel image reconstruction method for thermal neutron CT of two-phase flow with improved simulated annealing (ISA) algorithm, which makes full use of the prior information of two-phase flow and the advantage of stochastic searching algorithm. The reconstruction results demonstrate that its reconstruction accuracy is much higher than that of the reconstruction algorithm based on weighted total difference minimization with soft-threshold filtering (WTDM-STF). The proposed method can also be applied to other types of two-phase flow CT modalities (such as X(𝛄)-ray, capacitance, resistance and ultrasound).

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• Human Ecology Research
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• 제61권3호
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• pp.375-387
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• 2023

In research on housing welfare policy, there has been little interest in middle-aged households compared with young or elderly households. The purpose of this study was to explore influences on the housing satisfaction of middle-aged renter households using microdata from the Korea Housing Survey 2021. A statistical analysis of data was performed on a total of 2,709,152 middle-aged (aged between 40 and 64 years) Jeonse (lumpsum housing lease) renters and monthly renters with deposits, living in private rental housing units. The major findings were as follows. Firstly, there were significant differences in housing unit satisfaction and residential environment satisfaction among renter groups by age and rental type. Early-middle-aged Jeonse renters displayed the highest satisfaction with both housing units and the residential environment, while semielderly monthly renters with deposits displayed the lowest satisfaction. Secondly, living in aged structures or in apartment units exerted the strongest influences on housing satisfaction, which implies the need for residential area regeneration programs that consider the situation of rental households. Thirdly, living in Incheon and Gyeonggi-do was found to have a negative influence on housing satisfaction. Fourthly, upward filtering on tenure types or lease renewal of the current house did not necessarily have a positive influence on the housing satisfaction of middle-aged renters. Based on the findings, suggestions were made to improve the housing situation of middle-aged renter households.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• 지능정보연구
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• 제23권2호
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• pp.19-38
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• 2017

본 연구는 사용자 평점 이외에 사용자 간 직접 간접적 신뢰 및 불신 관계 네트워크의 분석 결과를 추가로 반영한 새로운 하이브리드 협업필터링(Collaborative filtering, CF) 추천방법을 제안한다. 구체적으로 사용자 간의 유사도를 계산할 때 사용자 평가점수의 유사성만을 고려하는 기존의 CF와 다르게, 사용자 신뢰 및 불신 관계 데이터의 사회연결망분석 결과를 추가적으로 고려하여 보다 정교하게 사용자 간의 유사도를 산출하였다. 이 때, 사용자 간의 유사도를 재조정하는 접근법으로 특정 이웃 사용자가 신뢰 및 불신 관계 네트워크에서 높은 신뢰(또는 불신)를 받을 때, 추천 대상이 되는 사용자와 해당 이웃 간의 유사도를 확대(강화) 또는 축소(약화)하는 방안을 제안하고, 더 나아가 최적의 유사도 확대 또는 축소의 정도를 결정하기 위해 유전자 알고리즘(genetic algorithm, GA)을 적용하였다. 본 연구에서는 제안 알고리즘의 성능을 검증하기 위해, 특정 상품에 대한 사용자의 평가점수와 신뢰 및 불신 관계를 나타낸 실제 데이터에 추천 알고리즘을 적용하였으며 그 결과, 기존의 CF와 비교했을 때 통계적으로 유의한 수준의 예측 정확도 개선이 이루어짐을 확인할 수 있었다. 또한 신뢰 관계 정보보다는 불신 관계 정보를 반영했을 때 예측 정확도가 더 향상되는 것으로 나타났는데, 이는 사회적인 관계를 추적하고 관리하는 측면에서 사용자 간의 불신 관계에 대해 좀 더 주목해야 할 필요가 있음을 시사한다.

• Asia pacific journal of information systems
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• 제24권4호
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• pp.493-513
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• 2014

With the development of related technologies, Location-Based Services (LBS) are growing fast and being used in many ways. Past LBS studies have focused on adoption of LBS because of the fact that LBS users have privacy concerns regarding revealing their location information. Meanwhile, the number of LBS users and revenues from LBS are growing rapidly because users can get some benefits by revealing their location information. Little research has been done on how LBS affects consumers' information search behavior in product purchase. The purpose of this paper is examining the effect of LBS information filtering on buyers' uncertainty and their information search behavior. When consumers purchase a product, they try to reduce uncertainty by searching information. Generally, there are two types of uncertainties - knowledge uncertainty and choice uncertainty. Knowledge uncertainty refers to the lack of information on what kinds of alternatives are available in the market and/or their important attributes. Therefore, consumers having knowledge uncertainty will have difficulties in identifying what alternatives exist in the market to fulfil their needs. Choice uncertainty refers to the lack of information about consumers' own preferences and which alternative will fit in their needs. Therefore, consumers with choice uncertainty have difficulties selecting best product among available alternatives.. According to economics of information theory, consumers narrow the scope of information search when knowledge uncertainty is high. It is because consumers' information search cost is high when their knowledge uncertainty is high. If people do not know available alternatives and their attributes, it takes time and cognitive efforts for them to acquire information about available alternatives. Therefore, they will reduce search breadth. For people with high knowledge uncertainty, the information about products and their attributes is new and of high value for them. Therefore, they will conduct searches more in-depth because they have incentive to acquire more information. When people have high choice uncertainty, people tend to search information about more alternatives. It is because increased search breadth will improve their chances to find better alternative for them. On the other hand, since human's cognitive capacity is limited, the increased search breadth (more alternatives) will reduce the depth of information search for each alternative. Consumers with high choice uncertainty will spend less time and effort for each alternative because considering more alternatives will increase their utility. LBS provides users with the capability to screen alternatives based on the distance from them, which reduces information search costs. Therefore, it is expected that LBS will help users consider more alternatives even when they have high knowledge uncertainty. LBS provides distance information, which helps users choose alternatives appropriate for them. Therefore, users will perceive lower choice uncertainty when they use LBS. In order to test the hypotheses, we selected 80 students and assigned them to one of the two experiment groups. One group was asked to use LBS to search surrounding restaurants and the other group was asked to not use LBS to search nearby restaurants. The experimental tasks and measures items were validated in a pilot experiment. The final measurement items are shown in Appendix A. Each subject was asked to read one of the two scenarios - with or without LBS - and use a smartphone application to pick a restaurant. All behaviors on smartphone were recorded using a recording application. Search breadth was measured by the number of restaurants clicked by each subject. Search depths was measured by two metrics - the average number of sub-level pages each subject visited and the average time spent on each restaurant. The hypotheses were tested using SPSS and PLS. The results show that knowledge uncertainty reduces search breadth (H1a). However, there was no significant correlation between knowledge uncertainty and search depth (H1b). Choice uncertainty significantly reduces search depth (H2b), but no significant relationship was found between choice uncertainty and search breadth (H2a). LBS information filtering significantly reduces the buyers' choice uncertainty (H4) and reduces the negative relationship between knowledge uncertainty and search breadth (H3). This research provides some important implications for service providers. Service providers should use different strategies based on their service properties. For those service providers who are not well-known to consumers (high knowledge uncertainty) should encourage their customers to use LBS. This is because LBS would increase buyers' consideration sets when the knowledge uncertainty is high. Therefore, less known services have chances to be included in consumers' consideration sets with LBS. On the other hand, LBS information filtering decrease choice uncertainty and the near service providers are more likely to be selected than without LBS. Hence, service providers should analyze geographically approximate competitors' strength and try to reduce the gap so that they can have chances to be included in the consideration set.

• 지능정보연구
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• 제14권2호
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• pp.179-192
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• 2008

본 논문은 금융기관을 이용한 자금세탁 및 불법적인 외화유출 방지를 목적으로 자금세탁 관련 혐의거래보고 등 금융정보를 수집하여 심사하는 금융정보분석원에 지식기반시스템을 도입한 사례연구이다. 한정된 심사인력으로 기하급수적으로 증가하는 협의거래보고에 효과적으로 대응하기 위하여 지식기반시스템의 도입은 필수적이라고 할 수 있다. 이렇게 구축된 지식기반시스템은 보고된 혐의거래를 여과(filtering)하여 자금세탁혐의가 인정된 정보만을 수사기관에 제공하는 심사 및 분석 업무의 효과성과 효율성을 극대화시킨다. 특히, 금융정보분석원은 여러 금융기관들로부터 보고된 혐의거래정보와 심사분석과정에서 유관기관으로부터 수집된 여러 종류의 정보가 집중되기 때문에 축적된 정보를 체계적으로 관리 및 활용할 수 있는 지식 베이스의 구축이 더욱 필요하다. 금융정보분석원은 많은 정보가 집중되는 만큼 축적된 데이터를 활용하여 자금세탁 관련 지식을 창출하는 업무를 수행해야만 하는 의무도 가지고 있기 때문이다. 이를 위하여 금융정보분석원의 심사분석시스템이 자금세탁방지를 위한 지식의 창출과 지식의 관리 측면까지 고려된 전체적인 프레임워크 하에서 지식기반시스템으로써의 토대를 마련하였다는 점에서 의의가 크다고 할 수 있다.

• 한국통신학회논문지
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• 제42권2호
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• pp.471-492
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• 2017

최근 데이터의 양 자체가 해결해야 할 문제의 일부분이 되는 빅데이터(Big Data) 분석에 대한 수요와 관심이 급증하고 있다. 빅데이터는 기존의 정형 데이터 뿐 아니라 이미지, 동영상, 로그 등 다양한 형태의 비정형 데이터 또한 포함하는 개념으로 사용되고 있으며, 다양한 유형의 데이터 중 특히 정보의 표현 및 전달을 위한 대표적 수단인 텍스트(Text) 분석에 대한 연구가 활발하게 이루어지고 있다. 텍스트 분석은 일반적으로 문서 수집, 파싱(Parsing) 및 필터링(Filtering), 구조화, 빈도 분석 및 유사도 분석의 순서로 수행되며, 분석의 결과는 워드 클라우드(Word Cloud), 워드 네트워크(Word Network), 토픽 모델링(Topic Modeling), 문서 분류, 감성 분석 등의 형태로 나타나게 된다. 특히 최근 다양한 소셜미디어(Social Media)를 통해 급증하고 있는 텍스트 데이터로부터 주요 토픽을 파악하기 위한 수요가 증가함에 따라, 방대한 양의 비정형 텍스트 문서로부터 주요 토픽을 추출하고 각 토픽별 해당 문서를 묶어서 제공하는 토픽 모델링에 대한 연구 및 적용 사례가 다양한 분야에서 생성되고 있다. 이에 본 논문에서는 텍스트 분석 관련 주요 기술 및 연구 동향을 살펴보고, 토픽 모델링을 활용하여 다양한 분야의 문제를 해결한 연구 사례를 소개한다.

• 대한토목학회논문집
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• 제36권3호
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• pp.521-528
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• 2016

고속도로의 주요 영업소 톨부스는 일반적으로 2개 차종(경차포함 승용차, 승용차 이외의 중차량)의 교통수요 변동에 따른 사전 대응방식으로 각 차종에 대하여 운영된다. 이러한 의미에서 2개 차종에 대한 정확한 교통량 예측은 영업소의 첨단 운영에 있어 주요 요소 중 하나이다. 유감스럽게도, 기존 연구로 보고된 현행의 일변량 단기 예측 기법들을 이용하여 2개 차종의 교통량을 동시에 예측하기는 용이하지 않다. 이러한 실용적 학술적 배경으로 인해 수용 가능한 정확도의 수준에서 2개 차종의 장래 교통량 예측은 ITS 예측 분야의 매력적인 연구 주제 중 하나이다. 따라서 본 연구에서는 기존의 일변량 단기 예측기법의 단점을 극복함과 더불어 2개 차종의 교통량을 동시에 예측하기 위한 다중 입출력(Multiple In-and-Out, MIO) 모형을 제시하도록 한다. 제안된 MIO 모형은 대용량 이력자료의 실시간 이용이 가능한 자료 환경에서 비모수 접근법을 기반으로 개발되었다. 실제 자료를 이용한 적용가능 실험에서, 개발모형은 다변량 예측 수준에도 불구하고 폭 넓게 이용되는 일변량 예측모형 중 하나인 Kalman filtering에 비하여 예측 정확도 측면에서 우수하게 나타났다.