• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4467-4488
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• 2014

The bibliometric analysis is widely used for understanding research domains, trends, and knowledge structures in a particular field. The analysis has majorly been used in the field of information science, and it is currently applied to other academic fields. This paper describes the analysis of academic literatures for classifying research domains and for suggesting empty research areas in the telecommunications policy. The application software is developed for retrieving Thomson Reuters' Web of Knowledge (WoK) data via web services. It also used for conducting text mining analysis from contents and citations of publications. We used three text mining techniques: the Keyword Extraction Algorithm (KEA) analysis, the co-occurrence analysis, and the citation analysis. Also, R software is used for visualizing the term frequencies and the co-occurrence network among publications. We found that policies related to social communication services, the distribution of telecommunications infrastructures, and more practical and data-driven analysis researches are conducted in a recent decade. The citation analysis results presented that the publications are generally received citations, but most of them did not receive high citations in the telecommunications policy. However, although recent publications did not receive high citations, the productivity of papers in terms of citations was increased in recent ten years compared to the researches before 2004. Also, the distribution methods of infrastructures, and the inequity and gap appeared as topics in important references. We proposed the necessity of new research domains since the analysis results implies that the decrease of political approaches for technical problems is an issue in past researches. Also, insufficient researches on policies for new technologies exist in the field of telecommunications. This research is significant in regard to the first bibliometric analysis with abstracts and citation data in telecommunications as well as the development of software which has functions of web services and text mining techniques. Further research will be conducted with Big Data techniques and more text mining techniques.

• 지능정보연구
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• 제10권3호
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• pp.59-72
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• 2004

인터넷의 활용범위는 정보의 검색 및 수집을 넘어서 여러 범위로 확대되고 있고 정보의 양 또한 방대해졌다. 그러나 필요한 정보를 찾기는 더욱 어려워지고 있고, 그에 따라 개인에게 맞는 정보를 제공해주는 시스템이 절실해지고 있다. 본 연구에서는 웹 서비스 기반위에 추론엔진을 사용하여 사용자에게 가장 적합한 상품을 검색하여 추천해주는 추천 시스템의 모델을 제시하고 있다. 현재의 웹 애플리케이션이 사용자에게 필요한 서비스를 제공하는데 비하여 애플리케이션마다 상이한 플랫폼의 구조와 분산된 환경에서 객체간의 통신을 쉽게 하고 통일된 개발을 위해 표준이 필요하게 되었다. 웹 서비스는 프로그램 언어에 독립적이고 상호 운용적 환경을 제공하기 위한 것으로 네트워크를 통해 기술하고 배포하여 실행시킬 수 있는 모듈화된 애플리케이션을 의미한다. 본 논문은 웹 서비스 기반위에 시스템을 구축함으로써 표준 웹 서비스의 실현 가능성을 가늠하고, 추론엔진과 결합하여 사용자의 정보와 변화하는 성향을 토대로 필요한 정보를 예측하여 추천하는 추천시스템 개발에 중점을 둔다.

• Journal of Platform Technology
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• 제11권4호
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• pp.3-12
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• 2023

포스트-코로나 시대에는 방역 조치의 중요성이 크게 강조되고 있으며, 이에 맞춰 딥러닝을 이용한 마스크 착용 상태 검출 및 다른 전염병 예방에 관련된 연구가 진행되고 있다. 그러나 질병 확산 방지를 위한 문화시설 관람객 탐지 및 추적 연구도 마찬가지로 중요하므로 이에 대한 연구가 진행되어야 한다. 본 논문에서는 사전 수집된 데이터 셋을 이용하여 컨볼루션 신경망 기반 객체 탐지 모델을 전이 학습시키고, 학습된 탐지 모델의 가중치를 다중 객체 추적 모델에 적용하여 방문객을 모니터링 한다. 방문객 탐지 모델은 Precision 96.3%, Recall 85.2% F1-Score 90.4%의 결과를 보여주었다. 추적 모델의 정량적 결과로 MOTA 65.6%, IDF1 68.3%. HOTA 57.2%의 결과를 보여주었으며, 본 논문의 모델과 다른 다중 객체 추적 모델 간의 정성적 비교에서 우수한 결과를 보여주었다. 본 논문의 연구는 포스트-코로나 시대의 문화시설 내 방역 시스템에 적용될 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

• 한국통신학회논문지
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• 제28권11A호
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• pp.859-866
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• 2003

본 논문에서는 Ethernet PON에서 서비스 우선 순위를 고려한 상향 채널 대역 할당 기법을 설계하였다. 제안된 대역 할당 기법의 목적은 광 라인 장치인 OLT(Optical Line Termination) 또는 광 단말 장치인 ONU(Optical Network Unit)에서 전송 제어시 서비스의 우선 순위를 고려하도록 하여 차별화 된 전송 품질을 제공하는데 있다. 이를 위하여 OLT 측에 이중 스케줄링 기법을 적용하여 전송 지연에 민감한 서비스에 대해서는 고정 대역 할당기법을, 전송 지연에 덜 민감한 best-effort 서비스에 대해서는 동적 대역 할당 기법을 동시에 제공할 수 있도록 하고, 동적 대역 할당에 의해 허락된 전송 지속 시간 동안 ONU에서 트래픽 특성별로 계층화된 우선 순위를 적용하여 전송을 제어하도록 하였다. 따라서 제안된 전송 제어 알고리즘에 의해 T$_1$, E$_1$등의 고정 대역 서비스와 전용선 서비스, best-effort 서비스 내에서 실시간과 비 실시간 서비스 등을 서비스 특성에 따라 차별화 된 전송 품질로 제공할 수 있다. 본 논문에서는 OPNET을 이용한 모델링과 시뮬레이션 결과를 통해 기존 대역 할당 기법과 제안된 대역 할당 기법의 성능을 비교하였다.

• Imaging Science in Dentistry
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• 제43권3호
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• 한국정보과학회논문지:정보통신
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• 제29권5호
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• pp.553-561
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• 2002

이 논문에서는 자원을 공유하는 여러 개의 QoS(Quality of Service) 큐(queue)를 서비스하기 위한 새로운 서비스 규칙 - 선형 서비스 규칙을 제안하고, 그 특징을 분석하였다. 제안하는 선형 서버는 각각의 큐에 대한 출력 트래픽(traffic) 및 고객 수 과정을 입력 트래픽의 선형 함수로 만든다 특히 입력 트래픽이 가우스 분포를 갖는 경우에는 큐 길이의 분포와 출력 트래픽 분포가 모두 가우스 분포를 갖게 하며, 그 분포의 평균과 분산이 입력 트래픽의 평균과 전력 스펙트럼(power Spectrum)의 함수로 나타나게 한다. 중요한 QoS 척도인 버퍼 넘침 확률 및 지연 분포 역시 입력 트래픽의 평균과 전력 스펙트럼의 함수로 나타나게 된다. 이 연구는 네트워크의 각 노드를 하나의 선형 필터로 볼 수 있게 하므로, 선형 시스템 이론에 기초한 네트워크 전반에 걸친 트래픽 관리 기술의 새로운 방향을 제시하였다.

• Structural Engineering and Mechanics
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• 제57권4호
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• pp.657-680
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• 2016

Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.

• 대한인간공학회지
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• 제29권4호
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.