• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.57-64
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• 2024

In this paper, we propose a Line-of-Sight (LoS)/Non-Line-of-Sight (NLoS) identification- based Human Activity Recognition (HAR) system using Channel State Information (CSI) to improve the accuracy of HAR, which dynamically changes depending on the reception environment. to consider the reception environment of HAR system, the proposed system includes three operational phases: Preprocessing phase, Classification phase, and Activity recognition phase. In the preprocessing phase, amplitude is extracted from CSI raw data, and noise in the extracted amplitude is removed. In the Classification phase, the reception environment is categorized into LoS and NLoS. Then, based on the categorized reception environment, the HAR model is determined based on the result of the reception environment categorization. Finally, in the activity recognition phase, human actions are classified into sitting, walking, standing, and absent using the determined HAR model. To demonstrate the superiority of the proposed system, an experimental implementation was performed and the accuracy of the proposed system was compared with that of the existing HAR system. The results showed that the proposed system achieved 16.25% higher accuracy than the existing system.

• Smart Media Journal
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• v.9 no.4
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• pp.97-101
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• 2020

Using passive electric field sensor which operates in non-contact mode, we can measure the electric potential induced from the change of electric charges on a sensor caused by the movement of human body or hands. In this study, we propose a new method, which utilizes PLN induced to the sensor around the moving object, to detect one's hand movement and extract gesture frames from the detected signals. Signals from the EPS sensors include a large amount of power line noise usually existing in the places such as rooms or buildings. Using the fact that the PLN is shielded in part by human access to the sensor, signals caused by motion or hand movement are detected. PLN consists mainly of signals with frequency of 60 Hz and its harmonics. In our proposed method, signals only 120 Hz component in frequency domain are chosen selectively and exclusively utilized for detection of hand movement. We use FFT to measure a spectral-separated frequency signal. The signals obtained from sensors in this way are continued to be compared with the threshold preset in advance. Once motion signals are detected passing throng the threshold, we determine the motion frame based on period between the first threshold passing time and the last one. The motion detection rate of our proposed method was about 90% while the correct frame extraction rate was about 85%. The method like our method, which use PLN signal in order to extract useful data about motion movement from non-contact mode EPS sensors, has been rarely reported or published in recent. This research results can be expected to be useful especially in circumstance of having surrounding PLN.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.213-220
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• 2020

The power consumption and performance of hardware-based mobile and IoT embedded systems that require high specifications are one of the important issues of these systems. In particular, the problem of excessive power consumption is because it causes a problem of increasing heat generation and shortening the life of the device. In addition, in the same environment, software also needs to perform stable operation in limited power and memory, thereby increasing power consumption of the device. In order to solve these issues, we propose a Low level power improvement via identifying performance dissipation. The proposed method identifies complex modules (especially Cyclomatic complexity, Coupling & Cohesion) through code visualization, and helps to simplify low power code patterning and performance code. Therefore, through this method, it is possible to optimize the quality of the code by reducing power consumption and improving performance.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.617-622
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• 2005

Lately, the MANET research has focused on providing routing services with security and previous schemes have been proposed for detecting malicious nodes in a MANET. However, they still include some problems which malicious nodes cannot be found when they falsely report other nodes as malicious. Accordingly, we propose a novel and efficient scheme for detecting malicious nodes using report messages and a report table which is consisted of node ID both for suspecting and reporting when the malicious nodes behave normally during the route discovery, but the other hand they drop and modify packets, or falsely report other nodes as malicious during the data transmission. Our proposed scheme is applicable to not only DSR but aiso AODV. And we provide some simulation results of our proposed scheme by comparing general AODV with our proposed scheme. Simulation results show that our Proposed scheme outperforms general AODV in terms of average packet loss ratio and packet delivery ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.90-96
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• 2019

Storage cabinet in a lab in these days measures various environmental factors in real-time with IoT sensors. Preexisting system collects sensor data, analyze a risk and then command other equipment. Such centralized control system tends to have an issue with of speed slowing down. It's because when there are more storage cabinets, there are more data to process. In order to solve this issue, this report addresses decentralized IoT sensor based lab safety control system. It can analyze internal state of storage cabinet to identify any hazardous situations and effectively control them. Such decentralized control system using sensor modules for internal environment of the cabinet storage and automated control algorithm based on administrator's log history can manage any hazardous situations by automated control of environment factors of inside a lab. It would allow users to deal with a hazard if it happens. Even better, it can prevent it to happen from the beginning.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.663-668
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• 2023

This paper describes a mobile application that can recognize and identify cats residing on a university campus using the Google's machine learning platform, 'Teachable Machine'. Machine learning, one of the core technologies of the Fourth Industrial Revolution, performs an efficient task of finding optimal results through data learning. Therefore, the model is learned and generated using the platform based on machine learning, and then implemented as an application for smartphones, so that cats can be identified simply and efficiently. In this application, if you take a picture of a cat directly on the spot or call it from the gallery, the cat is identified and information about the cat is provided. Though this system was developed for a specific university campus, it is expected that it can be extended to other campuses and other species of animals.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.407-413
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• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1609-1610
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• 2006

이 논문은 인위적으로 전자파를 발생시켜 이 전자파에 의한 반도체 소자의 피해 효과를 조사한 것이다. 동작주파수가 2.45 GHz인 마그네트론으로부터 발생되는 전자파는 끝단이 개방되어있는 도파관을 통해 자유공간으로 전파되고, 도파관 끝단으로부터 $30\;cm\;{\sim}\;50\;cm$인 지점에 반도체 소자들을 위치시켜 동작상태를 확인하였다. 시험에 사용된 피시험체인 반도채 소자로는 TTL과 CMOS 기반기술의 반도체를 사용하였고, LED 구동회로를 구성하여 LED의 점등 여부로 오동작 및 파괴 여부를 육안 식별하였다. 또한 시험 전후의 반도체 소자 표면을 제거 후 칩 상태를 SEM 분석하였다. 시험 결과 도파관 끝단으로부터 50 cm, 40 cm 떨어진 지점에 반도체 소자를 위치시키고 도파관 끝단에서 발생되는 전자파에 의한 반도체 소자의 피해는 전혀 없었다. 그러나 30 cm 떨어진 지점에서 오동작 및 파괴가 일어났다. 오동작 및 파괴가 일어난 시료의 칩 상태를 SEM 분석한 결과 칩 내부의 onchipwire의 용융으로 인한 파괴와 bondingwire의 완전파괴를 확인할 수 있었다. 위의 시험 결과는 인위적인 전자파 환경에서 반도체 소자의 결합 기구를 해석하는 기초 자료로 활용되며, 전자 장비들의 전자파 장해에 대한 이해에 도움이 되는 자료로 활용될 수 있을 것이다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2A
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• pp.177-183
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• 2009

In this paper, we propose an improved anti-collision algorithm. We have designed an anti-collision unit using this algorithm for the 18000-6 Type C Class 1 Generation 2 standard (Gen2). The Gen2 standard uses a Q-algorithm for incremental method on the Dynamic Slot-Aloha algorithm. It has basically enhanced performance over the Slot-Aloha algorithm. Unfortunately, there are several non-clarified parts: initial $Q_{fp}$ value, weighted C, and the ending point of the algorithm. If an incorrect value is selected, it causes degradation in performance. Thus we propose an improved anti-collision algorithm by clearly defining the vague parts of the existing algorithm. Simulation results showed an improved performance of up to 34.8% using an optimized value of C and the initial $Q_{fp}$ value. With the ending condition, performance is 34.7%. The anti-collision unit is designed using the Verilog HDL. The module was synthesized using Synopsys' Design Compiler and the TSMC $0.2{\mu}m$ standard cell library. The synthesized result yielded 3,847 gates, and was guaranteed under the proposed working frequency of 19.2MHz.

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.396-402
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• 2010

The tracking of current location of residents is an essential requirement for context-aware service of smart houses. This paper presents a wireless sensor network system which could detect location transition such as entrance and exit to a room and also identify the user who passed the room, without duty of wearing any sort of tag. We designed new sensor node to solve the problem of short operation lifetime of previous work[1] which has two pyroelectric infrared (PIR) sensors and an ultrasonic sensor, as well as a 2.4 GHz radio frequency wireless transceiver. The proposed user identification method is to discriminate a person based on his/her height by using an ultrasonic sensor. The detection idea of entering/exiting behavior is based on order of triggering of two PIR sensors. The topology of the developed wireless sensor network system is simple star structure in which each sensor node is connected to one sink node directly. We evaluated the proposed sensing system with a set of experiments for three subjects in a model house. The experimental result shows that the averaged recognition rate of user identification is 81.3% for three persons. and perfect entering/exiting behavior detection performance.