• 한국정보시스템학회지:정보시스템연구
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• 제26권4호
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• pp.17-37
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• 2017

Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.178-192
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• 2021

File system forensics typically focus on the contents or timestamps of a file, and it is common to work around file/directory centers. But to recover a deleted file on the disk or use a carving technique to find and connect partial missing content, the evidence must be analyzed using cluster-centered analysis. Forensics tools such as EnCase, TSK, and X-ways, provide a basic ability to get information about disk clusters, but these are not the core functions of the tools. Alternatively, Sysinternals' DiskView tool provides a more intuitive visualization function, which makes it easier to obtain information around disk clusters. In addition, most current tools are for Windows. There are very few forensic analysis tools for MacOS, and furthermore, cluster analysis tools are very rare. In this paper, we developed a tool named FACT (Forensic Analyzer based Cluster Information Tool) for analyzing the state of clusters in a HFS+ file system, for digital forensics. The FACT consists of three features, a Cluster based analysis, B-tree based analysis, and Directory based analysis. The Cluster based analysis is the main feature, and was basically developed for cluster analysis. The FACT tool's cluster visualization feature plays a central role. The FACT tool was programmed in two programming languages, C/C++ and Python. The core part for analyzing the HFS+ filesystem was programmed in C/C++ and the visualization part is implemented using the Python Tkinter library. The features in this study will evolve into key forensics tools for use in MacOS, and by providing additional GUI capabilities can be very important for cluster-centric forensics analysis.

• 한국컴퓨터정보학회논문지
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• 제14권10호
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• pp.73-82
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• 2009

본 논문은 자연이미지에 포함된 텍스트 영역을 찾기 위한 방법으로서 기존에 제안한 색 분산 특징을 이용한 방법에서 분산이 제대로 추출되지 않는 문자 획들에 대한 복원 방법을 제안한다. 이전의 색 분산 특징을 이용한 추출방법에서는 고정된 크기의 수평 및 수직 분간 추출 윈도우를 사용함으로서 문자 획이 두껍거나 긴 경우에는 색 분산이 제대로 추출되지 않는 단점이 있었다. 따라서 본 논문에서는 미 추출된 색 분산을 연결요소 외곽사각형의 기하학적인 정보와 경험적인(Heuristic) 지식을 함께 이용하여 복원하는 방법을 제안한다. 제안한 방법은 다양한 종류의 디지털 카메라와 휴대폰 카메라를 이용해서 취득한 문서 유형의 이미지와 간판, 거리 표지판 등의 자연이미지를 사용하여 테스트 하였으며, 특히 큰 글자를 포함하는 자연이미지에 대해서도 텍스트 추출의 정확성이 향상된 것을 확인할 수 있었다.

• Smart Structures and Systems
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• 제32권1호
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• pp.23-35
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• 2023

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

• 대한간호
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• 제35권2호
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• pp.52-70
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• 1996

This historical research was conducted to analyze and categorize the titles which were presented in the journal, The Korean Nurse, from August 1962 to October 1995. Titles which were published with the purpose of educating graduate nurses and to update 0 their nursing knowledge to improve professional practice were included. There were 348 articles published from the beginning of publication in August, 1962 to October, 1995. All of the journals were reviewed except nine missing journals which were not available in any library. According to the characteristics of the articles in the periodical, the articles were divided into three periods. In each of the three peroids there were five categories: Subject, Clinical Practice, Fundamentals of Nursing Science, Nursing Administration, Others. These categories were adopted from Kim's(1994) division system which was developed to analyze nurse's insurance education program. The special feature peroid was from August, 1962 to December, 1974. In this period the articles were presented in an unorganized manner in the area of special feature or main issue. The largest area was the subject category(44%). The second largest area wes the fundamental of nursing science category(31%). From May, 1975 to December, 1977, the articles with the educational purposes were published in a designated area called continuing education. This period was labelled as the continuing education period. Among the published articles in this period, 45% focused on the subject category and 45% on the fundamentals of nursing science category. In this period the articles were focusing on nurses 'work in specific areas such as industry, nurses' aid schools, and nursing administration, articles on physical assessment first started to appear. The written continuing education period was from January 1978 to October, 1995. All the educational articles published in this area were analyzed and categorized into five areas as for the other periods. 48% of the articles focused on the subject category. In the mid-eighties, the term nurse specialist first.appeared and ten years later in 1990, the titles were subdivided into more specific titles, such as, home nursing, industrial nursing, emergency nursing, 23% were in the fundamental nursing science category and they dealt with nursing process, nursing theories, theory development. For the content analysis, three articles, one from each period, dealing with cardiovscular diseases were selected for comparsion. First, the special feature period, the title of the article was on diet therapy for cardiovascular disease patients, but instead the content was about rest, hygiene, medication, observation. They recommended rings to prevent bed sores, which is now considered as a cause for bed sores. In the continuing education period, the content was more focused on rehabilitation rather than general nursing .care. It became more specific, systematic, and organized compared with the special feature period. In the written continuing education period, the focus was on rehabilitation, not broadly, but very specifically on exercise. The further research on the content analysis is recommended along with a comparison of the trends in the Journal of Nurses Academic Society.

• 지능정보연구
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• 제20권2호
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• pp.73-92
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• 2014

기존 협업 필터링 기법은 사용자들의 아이템에 대한 선호도를 기반으로 유사 아이템 집합 또는 유사 사용자 집합을 구성하고, 이를 이용해 예측된 사용자의 특정 아이템에 대한 선호도를 기반으로 추천을 수행한다. 이로 인해, 사용자 선호도 정보가 부족하게 되면, 유사 아이템 사용자 집합의 신뢰도가 낮아지고, 추천 서비스의 신뢰도 또한 따라서 낮아진다. 또한, 서비스의 규모가 커질수록, 유사 아이템, 사용자 집합의 생성에 걸리는 시간은 기하급수적으로 증가하고 추천서비스의 응답시간 또한 그에 따라 증가하게 된다. 위와 같은 문제점을 해결하기 위해 본 논문에서는 적응형 군집화 기법을 제안하고 이를 적용한 협업 필터링 기법을 제안하고 있다. 이 기법은 크게 네 가지 방법으로 이루어진다. 첫째, 사용자와 아이템의 특성 벡터를 기반으로 사용자와 아이템 각각을 군집화 하여, 기존 협업 필터링 기법에서 유사 아이템, 사용자 집합을 생성하는데 소요되는 시간을 절약하며, 사용자 선호도 정보만을 이용한 부분 집합 생성보다 추천의 신뢰도를 높이고, 초기 평가 문제와 초기 이용자 문제를 일부 해소한다. 둘째, 미리 구성된 사용자와 아이템의 군집을 기반으로 군집간의 선호도를 이용해 추천을 수행한다. 사용자가 속한 군집의 선호도가 높은 순서대로 아이템 군집을 조회하여 사용자에게 제공할 아이템 목록을 구성하여, 추천 시스템의 부하 대부분을 모델 생성 단계에서 부담하고 실제 수행 시 부하를 최소화한다. 셋째, 누락된 사용자 선호도 정보를 사용자와 아이템 군집을 이용하여 예측함으로써 협업 필터링 추천 기법의 사용자 선호도 정보 희박성으로 인한 문제를 해소한다. 넷째, 사용자와 아이템의 특성 벡터를 사용자의 피드백에 따라 학습시켜 아이템과 사용자의 정성적 특성 정량화의 어려움을 해결한다. 본 연구의 검증은 기존에 제안되었던 하이브리드 필터링 기법들과의 성능 비교를 통해 이루어졌으며, 평가 방법으로는 평균 절대 오차와 응답 시간을 이용하였다.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• 한국학교수학회논문집
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• 제12권4호
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• pp.453-472
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• 2009

본 연구는 예비교사들이 어떻게 공변추론(covariational reasoning)의 개념을 이해하는지를 정성연구방법을 통하여 연구하였다. Geometer's Sketchpad를 이용해 만들어진, 문제상황들을 위한 모의실험을 통해 두 학생들은 공변 추론의 단계가 '방향'수준에서 '순간비율' 수준으로 발전하였음이 연구분석 결과로 나타났다. 하지만, 이 연구를 통해 함수 학습을 위한 중요한 개념 중 하나인 '인과성'이 공변추론 양식틀에서 빠져있음을 알 수 있었고, 따라서 앞으로 학생들의 함수개념 발달의 연구를 위해서 공변추론과 인과성이 서로 연계되어 이루어 져야 할 필요성이 제시되었다.

• 한국통신학회논문지
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• 제32권10C호
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• pp.1000-1009
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• 2007

본 논문에서는 공간적인 디인터레이싱을 위한 컨텐츠 기반 적응적 보간 기법을 제안한다. 제안하는 알고리즘은 전처리와 컨텐츠 분석, 컨텐츠에 따른 적응적 보간의 3 단계로 구성된다. 또한 적응적 보간 방식으로써 변형된 에지기반 라인 평균 방식과 그레디언트 기반 방향성 보간, 윈도우 매칭 방식의 세 가지 보간 방식을 제안한다. 각각의 보간 방식은 공간적인 영상 특징에 따라 다양한 성능을 나타낸다. 따라서 각각의 보간할 픽셀 영역은 그레디언트 검출을 통해 영역 특징을 분석하고 네 가지 카테고리로 분류된다. 이러한 분류 결과를 기반으로 각각에 적합한 디인터레이싱 방법을 사용함으로써 최적의 성능을 구현할 수 있다. 다양한 영상에 대한 실험을 통해 제안한 방식이 기존의 방식에 비해 가장 좋은 성능을 보임을 확인하였다.

• 인터넷정보학회논문지
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• 제18권5호
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• pp.39-46
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• 2017

본 논문은 도심을 촬영한 실내, 실외의 영상은 대부분 직육면체를 이룬다는 "Manhattan World" 가정을 기반으로 한 소실점의 직교성을 이용한 구조적인 소실점 검출 방법을 제안한다. 소실점들이 서로 직교하는 특징은 3개의 소실점 중 검출되지 않은 소실점을 추론하는데 매우 유용하게 사용될 수 있으며 소실점이 근접하여 검출되는 경우를 방지할 수 있다. 본 논문에서는 통계적인 접근을 통해 수직 소실점을 검출하고 구조적인 방법으로 수평, 전방 소실점을 검출하였다. 실험결과에서는 제안된 방법이 기존 방법과 비교하여 소실점 검출 정확도가 향상됨을 보인다.