• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2453-2467
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• 2017

Wireless communication is a telecommunication technology, which enables wireless transmission between the portable devices to provide wireless access in all types of environments. In this research, the measurements and various empirical models are analysed and compared in order to find out a suitable propagation model to provide guidelines for cell planning of wireless communication systems. The measured data was taken in urban region with low vegetation and some trees at 900 MHz frequency band. Path loss models are useful planning tools, which permit the designers of cellular communication to obtain optimal levels for the base station deployment and meeting the expected service level requirements. Outcomes show that these empirical models tend to overestimate the propagation loss. As one of the key outputs, it was observed that the calculations of Weissberger model fit with the measured data in urban environment.

• 한국컴퓨터정보학회논문지
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• 제4권4호
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• pp.226-235
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• 1999

본 연구에서는 전자상거래시스템 공급자의 평가와 선정을 위한 모형을 구축하였다. 기업의 의사결정과 정은 여러 상충하는 목적들을 동시에 고려하는 경우가 대부분이기 때문에 이러한 상황에 적합한 다목표 지향적인 수리모형 구축의 필요성이 제시되었다. 또한 제공되는 데이터의 불명확성과 여러 목적들을 동시에 고려할 경우 발생할 수 있는 의사결정자의 열망수준과 그 만족정도를 반영하기 위해 본 연구에서는 3유형의 다목적계획모형을 제시하였다. 최소연산자 모형, 가중치 다목적계획모형 및 선제우선순위 다목적계획모형의 구축 후. 가상기업에 대한 설례를 통해 그 적용가능성을 알아보았다.

• 산업경영시스템학회지
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• 제33권2호
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• pp.63-71
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• 2010

The existing reliability evaluation models which have already developed by the corporations are so various because of using Maximum Likelihood Method. The existing models are very complicated owing to using system designing methods. Therefore, it is very difficult to utilize the existing models in business fields of many corporations. The purposes of this paper are as follows: The first purpose is to study the simple estimated Parameter to be easily utilized in the business fields of the corporations. The second purpose is to testify the simplification of the developed Parameter of estimated method by comparing the developed reliability evaluation model with the existing reliability evaluation models which are used in the business fields of the corporations.

• 한국컴퓨터정보학회논문지
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• 제23권4호
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• pp.17-24
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• 2018

This paper proposes data mining models relevant to the serial publication periods and mobile gamification likelihood of webtoon contents which were either serialized or completed in platform. The size of the cartoon industry including webtoon takes merely 1% of the total entertainment contents industry in Korea. However, the significance of webtoon business is rapidly growing because its intellectual property can be easily used as an effective OSMU (One Source Multi-Use) vehicle for multiple types of contents such as movie, drama, game, and character-related merchandising. We suggested a set of data mining classifiers that are deemed suitable to provide prediction models for serial publication periods and mobile gamification likelihood for the sake of webtoon contents. As a result, the balanced accuracies are respectively recorded as 85.0% and 59.0%, from the two models.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.449-452
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• 2023

The rapid development of the Internet has led to a significant increase in multimedia content in social networks. How to better analyze and improve video classification models has become an important task. Deep learning models have typical "black box" characteristics. The model requires explainable analysis. This article uses two classification models: ConvLSTM and VGG16+LSTM models. And combined with the explainable method of LRP, generate visualized explainable results. Finally, based on the experimental results, the accuracy of the classification model is: ConvLSTM: 75.94%, VGG16+LSTM: 92.50%. We conducted explainable analysis on the VGG16+LSTM model combined with the LRP method. We found VGG16+LSTM classification model tends to use the frames biased towards the latter half of the video and the last frame as the basis for classification.

• IEIE Transactions on Smart Processing and Computing
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• 제6권4호
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• pp.237-245
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• 2017

This paper presents a fire detection algorithm using color and motion models from video sequences. The proposed method detects change in color and motion of overall regions for detecting fire, and thus, it can be implemented in both fixed and pan/tilt/zoom (PTZ) cameras. The proposed algorithm consists of three parts. The first part exploits color models of flames and smoke. The candidate regions in the video frames are extracted with the hue-saturation-value (HSV) color model. The second part models the motion information of flames and smoke. Optical flow in the fire candidate region is estimated, and the spatial-temporal distribution of optical flow vectors is analyzed. The final part accumulates the probability of fire in successive video frames, which reduces false-positive errors when fire-like color objects appear. Experimental results from 100 fire videos are shown, where various types of smoke and flames appear in indoor and outdoor environments. According to the experiments and the comparison, the proposed fire detection algorithm works well in various situations, and outperforms the conventional algorithms.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.90-104
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• 2020

It is difficult for non-experts to build machine learning (ML) models at the level that satisfies their needs. Deep learning models are even more challenging because it is unclear how to improve the model, and a trial-and-error approach is not feasible since training these models are time-consuming. To assist these novice users, we examined how interactive and explainable feedback while training a deep learning network can contribute to model performance and users' satisfaction, focusing on the data preparation process. We conducted a user study with 31 participants without expertise, where they were asked to improve the accuracy of a deep learning model, varying feedback conditions. While no significant performance gain was observed, we identified potential barriers during the process and found that interactive and explainable feedback provide complementary benefits for improving users' understanding of ML. We conclude with implications for designing an interface for building ML models for novice users.

• 한국시뮬레이션학회논문지
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• 제25권4호
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• pp.117-126
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• 2016

전투지휘훈련의 목적은 컴퓨터 기반의 시뮬레이션 모델을 활용하여 지휘관과 참모들의 지휘통제 및 의사결정 능력을 향상시키는데 있다. 본 연구는 전투지휘훈련을 위한 시뮬레이션 모델에 지휘통제 모델을 포함시키는 방법을 제시한다. 지휘통제 모델을 포함시킴으로써 모델 상에서 모의되는 개별 전투개체 또는 단위부대는 게이머를 통하지 않고 전술적 통제수단으로 구성된 상급부대의 명령을 직접 입력받아 처리할 수 있다. 본 연구에서는 그러한 지휘통제 및 의사결정 과정을 이산사건시스템명세 (DEVS) 형식론을 활용하여 모델로 명세하였다. 또한 본 연구에서는 개체단위 기반의 지휘통제 및 의사결정 과정 모델과 현재 연대 및 대대급에서 활용 중인 전투21모델에서의 모의 결과를 비교하여 사례를 제시하였다. 본 연구에서는 지휘통제 및 의사결정 과정 모델이 갖는 장점 및 기대효과 등을 제시한다.

• ETRI Journal
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• 제25권6호
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• pp.423-436
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• 2003

This paper describes our design of a contents distribution framework that supports transparent distribution of digital contents on the Internet as well as copyright protection of participants in the contents distribution value chain. Copyright protection must ensure that participants in the distribution channel get the royalties due to them and that purchasers use the contents according to usage rules. It must also prevent illegal draining of digital contents. To design a contents distribution framework satisfying the above requirements, we first present four digital contents distribution models. On the basis of the suggested distribution models, we designed a contract system for distribution of royalties among participants in the contents distribution channel, a license mechanism for enforcement of contents usage to purchasers, and both a packaging mechanism and a secure client system for prevention of illegal draining of digital contents.