• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2239-2244
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• 2013

Industrial machines have constraints on movement due to its wire for power supply. Recently, the research on wireless power supply for industrial machine which is required to move freely is receiving a lot of attention. In this paper, we suggest a magnetic induction system which can charge a equipment's battery with wireless at a close range. The system was designed to operate at 13.56 MHz and a distance of 20~30 mm between the transmitting and the receiving power module. From experiment, it was found that it takes about 135 minutes for charging the battery with about 15 V using the proposed system.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.173-178
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• 2007

This research was conducted to develop and analyze a wireless monitoring system for judging if sick or dead layers (SDL) exist in multi-tier layers battery (MLB) by machine vision, and to evaluate the performance between a wired monitoring system and it. This study used the AP (Access Point), the RS-285 to RS-232 converter, RS-232 to Ethernet converter, PICBASIC board and upgraded lump image processing method to change wired monitoring system into wireless monitoring system. The system was tested at a pilot farm and farm layer house. Results showed that monitoring judgement success rate at a pilot farm on normal cage (without SDL) was 82.3% and that on abnormal cage (with SDL) was 87.5%, respectively. And communication performance test results showed at farm layer house was $700{\sim}900$ kbps while equipments operated. There were dropped slightly than performance of wired monitoring system, however, the quantity was too small to make a significant difference of performance of the controling system developed for wireless communication.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2899-2902
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• 2003

Mobile communication is one of the fastest growing industries in the world and especially in Korea, where we have well over 30 million voice subscribers. Mobile communication now offers variety of data communication services such as wireless internet, multimedia messages, and color graphic displays. Handsets also have grown to accommodate such services, thus with functionalities such as programmable virtual machines that users can download and execute their own programs. In this paper, we have demonstrated a PCS handset monitoring system which can monitor biometry signals - in this case ECG over wireless internet and wireless data network (IS95C) based on programmable virtual machine architecture. The remote client handset receives data signals from a centralized server and processes and displays them in human friendly form in real time. Since every single handset on sales in Korea has programmable virtual machine and more than 10 million handsets are in distribution already, proper applications may have substantial impact on the related fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5425-5448
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• 2018

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.

• Tuberculosis and Respiratory Diseases
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• v.86 no.4
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.288-290
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• 2006

This paper presents the real-time water tank wireless control in a ship using duplex RF modules and PLC(Programmable logic Controller). The purpose of this paper is developing HMI(Human Machine Interface) for automation equipments. The system can low a cost because long wires are no more used. Analog signals of the water gauge and flow meter are changed to discrete signals by the micro-processor. The PLC checks the volume of water and runs On or Off of the valve and pump. Duplex RF modules send and receive data between the water tank and control room. Everywhere the Internet is used, operators are able to check the status of the system by the web-server.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1203-1212
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• 2017

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.196.1-196.1
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• 2005

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.36.3-36
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• 2002

$\textbullet$ Motion capture system $\textbullet$ Exoskeleton mechanism $\textbullet$ Kinematics analysis $\textbullet$ Man-machine Interface $\textbullet$ Wireless communication $\textbullet$ Control algorithm

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.121-124
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• 2002

The IEEE 802.lla PLCP H/W for Processing efficiently control message between MAC and PHY is designed. Slate machine and clock control according to rate is designed and timing diagram is verified on the FPGA simulation environment.