• 정보보호학회논문지
• /
• 제6권4호
• /
• pp.53-66
• /
• 1996

World Wide Web is a total solution for multi-media data transmission on Internet. Because of its characteristics like ease of use, support for multi-media data and smart graphic user interface, WWW has extended to cover all kinds of applications. The Secure Information Center(SIC) is a data transmission system using conventional cryptography between client and server on WWW. It's main function is to support the encryption of sending data. For encryption of data IDEA(International Data Encryption Algorithm) is used and for authentication mechanism MD5 hash function is used. Since Secure Information Center is used by many users, conventional cryptosystem is efficient in managing their secure interactions. However, there are some restrictions on sharing of same key and data transmission between client and server, for example the risk of key exposure and the difficulty of key sharing mechanisms. To solve these problems, the Secure Information Center provides encryption mechanisms and key management policies.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권7호
• /
• pp.2304-2320
• /
• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2013년도 추계학술발표대회
• /
• pp.486-488
• /
• 2013

In the earth more than half of the space filled with water. In that water most of the part is in the form of oceans. The ocean atmosphere determines climate on the land. Combining the Underwater Acoustic Sensor Network (UWASN) system with Internet Of Things (IoT) is called Internet of Underwater Things (IoUT). Using IoUT we can find the changes in the ocean environment. Underwater sensor nodes are used in UWASN. Underwater sensor nodes are constructive in offshore investigation, disaster anticipation, data gathering, assisted navigation, pollution checking and strategic inspection. By using IoT components such as Database, Server and Internet, ocean data can be broadcasted. This paper introduces IoUT architecture and and explains fish forming application scenario with this IoUT architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권3호
• /
• pp.974-992
• /
• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• International Journal of Internet, Broadcasting and Communication
• /
• 제10권3호
• /
• pp.59-64
• /
• 2018

A cooperative spectrum sensing has been proposed to improve the sensing performance in cognitive radio (CR) network. However, cooperative sensing causes additional overhead for reporting the result of local sensing to the fusion center. In this paper, we propose a technique to reduce the overhead of data transmission of cooperative sensing for applying the quantum data fusion technique in cognitive radio networks by omitting the lowest quantized in the local sensed results. If a CR node senses the lowest quantized level, it will not send its local sensing data in the corresponding sensing period. The fusion center can implcitly know that a spectific CR node sensed lowest level if there is no report from that CR node. The goal of proposed sensing policy is to reduce the overhead of quantized data fusion scheme for cooperative sensing. Also, our scheme can be adapted to all quantized data fusion schemes because it only deal with the form of the quantized data report. The experimental results show that the proposed scheme improves performance in terms of reporting overhead.

• Journal of Power Electronics
• /
• 제12권1호
• /
• pp.157-163
• /
• 2012

Internet Data Centers (IDCs), which are essential facilities in the modern IT industry, typically have scores of MW of concentrated electric loads. The provision of an Uninterruptible Power Supply (UPS) is necessary for the power feed system of IDCs owing to the need for stable power. Thus, conventional IDC AC power feed systems have three cascaded power conversion stages, (AC-DC), (DC-AC), and (AC-DC), resulting in a very low conversion efficiency. In comparison, DC power feed systems require only a single power conversion stage (AC-DC) to supply AC main power to DC server loads, resulting in comparatively high conversion efficiency and reliability [4-11]. This paper compares the efficiencies of a 220V AC power feed system with those of a 300V DC power feed system under equal load conditions, as established by the Mok-Dong IDC of Korea Telecom Co. Ltd. (KT). Experimental results show that the total operation efficiency of the 300V DC power feed system is approximately 15% higher than that of the 220V AC power feed system.

• 대한설비공학회:학술대회논문집
• /
• 대한설비공학회 2006년도 하계학술발표대회 논문집
• /
• pp.938-943
• /
• 2006

The primary purpose of a computer room of data center and associated infrastructure is to support the operation of critical IT equipment. Traditionally, most owners of large critical data centers have been more than willing to accept a reasonable amount of computer room worker discomfort if necessary to support critical IT systems. All electrical equipment produces heat, which must be removed to prevent the equipment temperature from rising to an unacceptable level. Most information technology equipment and other equipment found in a data center or network room is air-cooled. Sizing a cooling system requires an understanding of the amount of heat produced by the equipment contained in the enclosed space, along with the heat produced by the other heat sources typically encountered.

• 한국컴퓨터정보학회논문지
• /
• 제24권9호
• /
• pp.83-95
• /
• 2019

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

• 인터넷정보학회논문지
• /
• 제18권1호
• /
• pp.121-129
• /
• 2017

수리부속 수요예측은 장비가동률 향상과 국방 운영 예산 효율화 제고를 위한 국방 군수 분야의 핵심 과제 중 하나이다. 현재 우리군은 수리부속 소요 데이터를 활용한 시계열 기법으로 과거 데이터 분석을 통해 수리부속 수요예측에 활용하고 있으나 정확도 제고에 지속적인 노력이 요구되고 있는 실정이다. 이에 본 연구에서는 지난 5개년의 수리부속 18,476개 품목의 수요데이터를 수집하고 데이터마이닝 기법을 활용한 수리부속 수요예측 모델을 제안하였다. 제안한 모델에 따른 실험 결과는 기존 시계열 기법에 비해 개선된 수요예측 정확도를 보였다.

• 인터넷정보학회논문지
• /
• 제17권5호
• /
• pp.43-49
• /
• 2016

This paper presents a workflow validation method for data-intensive graphical workflow models using real-time workflow tracing mode on data-intensive workflow designer. In order to model and validate workflows, we try to divide as modes have editable mode and tracing mode on data-intensive workflow designer. We could design data-intensive workflow using drag and drop in editable-mode, otherwise we could not design but view and trace workflow model in tracing mode. We would like to focus on tracing-mode for workflow validation, and describe how to use workflow tracing on data-intensive workflow model designer. Especially, it is support data centered operation about control logics and exchange variables on workflow runtime for workflow tracing.