• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2023년도 춘계학술대회
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• pp.256-257
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• 2023

This paper aims to propose optimal method to assess and cumulate the daily profit for liner shipping to support the shipping lines in making optimal decision with the highest average daily profit. This paper not only explains the actual calculated results align with decision-makers' behavior from concepts indicated in cumulative prospect theory but also contributes to an easy-to-apply method for liner shipping network predictability in and provides optimal decision-making is helpful for shipping managers for the best effective selection of the most appropriate alternative under uncertainties.

• Steel and Composite Structures
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• 제49권6호
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• pp.633-643
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• 2023

In this study, the natural frequencies of Functional Graded Materials (FGM) plates are predicted using Artificial Neural Network (ANN). A model based on Third-order Shear Deformation Theory (TSDT) and FEM is used to train the ANN model. Different training methods are tested to simulate input and output dependency. As this is a parametric model, several architectures and optimization algorithms were tested. The proposed model allows us to minimize the CPU time to evaluate candidate material properties for FGM plate material selection and demonstrate their influence on dynamic behavior. Consequently, the time required for the FGM design process (candidate materials for material selection) and the geometric optimization of the FGM structure would remain reasonable. The ANN model can help industries to produce FGM plates with good mechanical properties of the selected materials. I addition, this model can be used to directly predict vibration behavior by testing a large number of FGM plates, representing all possible combinations of metals and ceramics in today's industry, without having to solve any eigenvalue problems.

• 한국정보과학회논문지:정보통신
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• 제33권2호
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• pp.183-192
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• 2006

이 논문은 QoS (Quality of Service) 네트워크 서비스를 위한 우선순위 방식의 스케줄링에서, 각 서비스 레벨의 가격을 정하는 문제를 분석한다. 특히 본 논문에서는 균형 분석 (equilibrium analysis)에 근거한 가격정책의 문제점을 파악하는 것에 중점을 둔다. 균형분석은 다음과 같은 두 가정하에 이루어진다. 첫째, 각각의 사용자들이 시스템 전체에 미치는 영향은 극히 미약하여 무시할 수 있다. 둘째, 사용자들은 전체 시스템 상태에 대한 최신의 정보를 알고 있다. 그러나 이러한 가정들은 실제 상황을 정확하게 반영하지 못하는 경우가 종종 있고, 따라서 균형분석에 의한 가격의 결정은 문제점이 있다고 본다. 본 논문에서는 시스템 작동상황을 분석하기 위해 '동적모델'을 개발하고, 이를 이용하여 현존하는 균형분석 방법의 정확성을 평가하였다. 연구결과에 의하면 균형분석은 실제적인 환경에서의 시스템의 작동 현황을 정확하게 반영하지 못하는 경우가 많은 것으로 나타났다.

• KNOM Review
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• 제21권2호
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• pp.18-25
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• 2018

사토시 나타모토에 의해 블록체인 기술이 개발되고 비트코인이 새로운 암호화폐 시장을 개척한 이후 여러 암호 화폐들이 등장하고 그 수와 규모는 나날이 증가하고 있다. 또한 블록체인 기술의 익명성과 여러 취약점을 이용한 범죄들이 발생하고 있으며 이에 취약점 개선과 범죄 예방을 위한 많은 연구들이 진행되고 있으나 범죄를 저지르는 사용자들을 탐지해내기엔 역부족이다. 따라서 네트워크 내 자금 세탁, 자금 탈취 등 이상 행위를 탐지 하는 것은 매우 중요하며 이에 본 논문에서는 비트코인 네트워크의 트랜잭션 및 유저 그래프의 특징들을 수집하고 이로부터 통계정보를 추출한 후 이를 로그 스케일 상에서 플롯으로 나타낸다. 시각화된 플롯을 Densification Power Law와 Power Degree Law에 따라 분석하고 결과적으로 비트코인 네트워크 내 비정상 트랜잭션 및 비정상 유저를 포함하는 이상 탐지에 적절한 특징들을 제시한다.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• 전자공학회논문지SC
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• 제46권4호
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• pp.7-16
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• 2009

로봇이 지능적이고 합리적으로 임무를 수행하기 위해서는 다양한 솜씨(skill)가 필요하다. 우리는 솜씨를 생성하기 위해 우선 행동유발성(affordance)을 학습한다. 행동유발성은 행동을 유발하게 하는 물체 또는 환경의 성질로써 솜씨를 생성하는데 유용하게 사용될 수 있다. 로봇이 수행하는 대부분의 임무는 순차적이고 목표 지향적인 행동을 필요로 한다. 그러나 행동유발성만을 이용하여 이러한 임무를 수행하는 것은 쉽지 않다. 이를 위해 우리는 행동유발성과 목표 지향적 요소를 반영하기 위한 소프트 행동동기 스위치(soft behavioral motivation switch)를 이용하여 솜씨를 생성한다. 솜씨는 현재 인지된 정보와 목표 지향적 요소를 결합하여 행동동기를 생성한다. 여기서 행동동기는 목표 지향적인 행동을 활성화시키기 위한 내부 상태를 말한다. 또한, 로봇은 임무 수행을 위해 순차적인 행동 선택을 필요로 한다. 우리는 목표 지향적이고 순차적인 행동 선택이 가능하도록 솜씨를 이용하여 솜씨 네트워크(skill network)를 생성한다. 로봇은 솜씨 네트워크를 이용하여 목표 지향적이고 순차적인 행동을 선택할 수 있다. 본 논문에서는 베이지안 네트워크를 이용한 행동유발성 모델링 및 학습 방법, 행동유발성과 소프트 행동동기 스위치를 이용한 솜씨 및 솜씨 네트워크 생성 방법, 마지막으로 솜씨 네트워크를 이용한 목표 지향적 행동 선택 방법을 제안한다. 우리의 방법을 증명하기 위해 제니보(애완 로봇)를 이용한 교시 기반 학습 방법을 통해 "물체 찾기", "물체에 접근하기", "물체의 냄새 맡기", 그리고 "물체를 발로 차기" 행동유발성들을 학습하였다. 또한, 이들을 이용하여 솜씨 및 솜씨 네트워크를 생성하여 제니보에 적용하고 실험하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.240-242
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• 2007

The game theoretic analysis in wireless networks can be classified into the jamming game of the physical layer, the multiple access game of the medium access layer, the forwarder's dilemma and joint packet forwarding game of the network layer, and etc. In this paper, the game theoretic analysis about the multiple access game that selfish nodes exist in the IEEE 802.11 DCF(Distributed Coordination Function) wireless networks is addressed. In this' wireless networks, the modeling of the CSMA/CA protocol based DCF, the utility or payoff function calculation of the game, the system optimization (using optimization theory or convex optimization), and selection of Pareto-optimality and Nash Equilibrium in game strategies are the important elements for analyzing how nodes are operated in the steady state of system. Finally, the main issues about the game theory in the wireless network are introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• 여성건강간호학회지
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• 제29권2호
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.