• 센서학회지
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• 제15권6호
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• pp.433-441
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• 2006

Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.222-230
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• 2023

The purpose of this study is to provide basic data on sports entertainment programs by collecting data on unstructured data generated by Naver and Google for SBS entertainment program 'Women Who Score Goal', which began regular broadcast in June 2021, and analyzing public perceptions through data mining, semantic matrix, and CONCOR analysis. Data collection was conducted using Textom, and 27,911 cases of data accumulated for 16 months from June 16, 2021 to October 15, 2022. For the collected data, 80 key keywords related to 'Kick a Goal' were derived through simple frequency and TF-IDF analysis through data mining. Semantic network analysis was conducted to analyze the relationship between the top 80 keywords analyzed through this process. The centrality was derived through the UCINET 6.0 program using NetDraw of UCINET 6.0, understanding the characteristics of the network, and visualizing the connection relationship between keywords to express it clearly. CONCOR analysis was conducted to derive a cluster of words with similar characteristics based on the semantic network. As a result of the analysis, it was analyzed as a 'program' cluster related to the broadcast content of 'Kick a Goal' and a 'Soccer' cluster, a sports event of 'Kick a Goal'. In addition to the scenes about the game of the cast, it was analyzed as an 'Everyday Life' cluster about training and daily life, and a cluster about 'Broadcast Manipulation' that disappointed viewers with manipulation of the game content.

• Journal of Information Processing Systems
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• 제2권2호
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• pp.82-87
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• 2006

In spite of the rapid development of the public network, the variety of network-based developments currently raises numerous risks factors regarding copyright violation, the prohibition and distribution of digital media utilization, safe communication, and network security. Among these problems, multimedia data tend to increase in the distributed network environment. Hence, most image information has been transmitted in the form of digitalization. Therefore, the need for multimedia contents protection must be addressed. This paper is focused on possible solutions for multimedia contents security in the public network in order to prevent data modification by non-owners and to ensure safe communication in the distributed network environment. Accordingly, the Orthogonal Forward Wavelet Transform-based Scalable Digital Watermarking technique is proposed in this paper.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.602-605
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• 2001

This paper focus on a method to use Ethernet Network for Industrial Communication Network. Ethernet use the CSMA/CD MAC(Medium Access Control) Protocol at the Data-Link Layer, Which isn't suit for Industrial Communication Network requiring Real-Time Communication, periodic data processing, critical data processing characteristics. In this paper we proposed the Virtual Polling Algorithm at the Application Layer will be solution of using the Ethernet Network for the Industrial Communication Network, Proposed Algorithm terminate the Collision in the network thus Delay Time is reduced and Real-Time Communication will be implemented.

• Journal of information and communication convergence engineering
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• 제4권2호
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• pp.67-70
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• 2006

A distributed healthcare monitoring system prototype for clinical and trauma patients was developed, using wireless sensor network node. The proposed system aimed to measure various vital physiological health parameters like ECG and body temperature of patients and elderly persons, and transfer his/her health status wirelessly in Ad-hoc network to remote base station which was connected to doctor's PDA/PC or to a hospital's main Server using wireless sensor node. The system also aims to save the cost of healthcare facility for patients and the operating power of the system because sensor network is deployed widely and the distance from sensor to base station was shorter than in general centralized system. The wireless data communication will follow IEEE 802.15.4 frequency communication with ad-hoc routing thus enabling every motes attached to patients, to form a wireless data network to send data to base-station, providing mobility and convenience to the users in home environment.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 추계학술대회 논문집
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• pp.1253-1258
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• 2004

A general idea of implementing and managing real-time control on the VDN for rail transit vehicles is presented. In particular, the virtual device network considered in this paper is composed of Ethernet as the data network and LonWorks network as the device (control) network. A LonWorks/IP web server was used as a gateway to realize peer to peer data communication on the virtual device networks. Experimental results are given to validate the suggested architecture.

• 정보보호학회논문지
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• 제23권4호
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• pp.721-728
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• 2013

빅데이터는 오늘날 가장 각광받고 있는 데이터 수집 및 분석기술의 경향으로, 대량의 비정형 데이터 분석을 요구하는 다양한 분야에 접목되어 효용성을 인정받고 있다. 네트워크 트래픽 분석 역시 대량의 비정형 데이터를 다루는 분야로, 빅데이터 접목시 그 효과가 극대화될 수 있다. 따라서 본 논문에서는 고도의 보안이 요구되는 군 C4I망과 같은 내부망 환경의 침해사고 및 이상행위를 실시간으로 탐지하기 위한 빅데이터 기반의 네트워크 트래픽 분석 플랫폼(RENTAP)을 소개한다. 빅데이터 분석 지원을 위해 최근 각광받고 있는 오픈소스 솔루션들을 대상으로 비교 분석을 수행하였으며, 선정된 솔루션을 기반으로 고안된 최종 설계에 대해서 설명한다.

• 한국컴퓨터정보학회논문지
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• 제7권4호
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• pp.188-198
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• 2002

본 논문에서는 이동 애드혹 네트워크에서 신뢰성 향상을 위한 액티브 네트워크를 제안하였다. 제안된 알고리즘에서는 DSR을 기본 프로토콜로서 액티브네트워크를 구성하였고, 송수신 노드를 액티브 노드로서 이용하였다. 신뢰성 향상을 위해 송신노드는 최근 보낸 데이터를 저장할 임시 버퍼를 생성한 후 수신노드의 요구에 의해 데이터 재전송을 수행하며 수신노드 당 흐름관리를 할 수 있는 기능을 추가하여 송신 액티브노드로 기능을 변경하였다. 수신노드는 손실된 데이터에 대한 재 전송요구와 흐름제어 기능을 추가하는 수신 액티브노드로 기능을 변경하였다. 그 결과로서, 제안된 액티브 노드는 움직임이 적은 이동 애드혹 네트워크에서는 100［%］에 근접하는 신뢰성을 보였고, 움직임이 많은 이동 애드혹 네트워크에서 보다 평균 3.5737% 증가한 95%의 데이터 수신율을 보장하였다.

• Journal of Platform Technology
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• 제6권1호
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• pp.3-8
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• 2018

This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.