• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.361-367
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• 2016

The intruder detection system using digital PIR sensor has the problem that it can't recognize human correctly. In this paper, we suggest a new intruder detection system based on analog PIR sensor to get around the drawbacks of the digital PIR sensor. The analog type PIR sensor emits the voltage output at various levels whereas the output of the digitial PIR sensor is binary. The signal captured using analog PIR sensor is sampled, and its frequency feature is extracted using FFT or MFCC. The extracted features are used for the input of neural networks. After neural network is trained using various human and pet's intrusion data, it is used for classifying human and pet in the intrusion situation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.515-516
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• 2018

본 논문에서는 수동 적외선 (PIR: passive infrared) 센서를 탑재한 센싱 시스템과 딥러닝 알고리즘을 활용하여 실내 환경에서 사용자의 이동 방향을 인식하기 위한 방법을 제안한다. PIR 센싱 소자는 사람의 이동 방향에 따라 구별이 가능한 신호를 발생시키는데, 4개의 PIR 센서를 $45^{\circ}$씩 증가하도록 배치한 센싱 시스템을 개발하여 실내 천장에 설치한 뒤 6명의 사용자로부터 인식 범위 내에서 움직이는 동안 PIR 센서 신호를 수집하였다. 수집한 원시 데이터와 특징 데이터를 추출하여 딥러닝 알고리즘을 적용한 인식률을 실험하였으며, 제안한 센싱 시스템과 딥러닝 알고리즘이 사용자의 움직임을 99.2%%로 인식할 수 있음을 보였다. 또한 한 개의 센서만을 이용하였을때에도 98.4%의 정확도로 사용자의 움직임 방향을 탐지하였다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.11-17
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• 2019

In this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by $45^{\circ}$ increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users' moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.

• 방재와보험
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• s.112
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• pp.42-45
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• 2006

단열 샌드위치 패널은 안전하지 못한다는 인식이 있었지만, PIR 내장 패널은 불연건물에서 사용할 수 있도록 LPCB의 인증을 획득한 것으로 화재 시나리오 결과에 영향이 거의 없는 것으로 나타났다. 실제 화재 상황에서 입증된 성능으로 화재안전솔루션을 제공하는 있는 PIR 패널에 대해 알아본다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.106-107
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• 2021

This paper implements a door lock using PIR sensor based on Arduino. When the PIR sensor detects movement, the door lock device is activated and RFID tags can be used. This door rock can prevent lonely death by detecting whether the resident is in or out, and it also has hygienic advantage because it unlocks without contact by using RFID. In addition, as door locks are frequently used in everyday life, the above-mentioned implementation can also increase participation in hands-on lectures.

• ETRI Journal
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• v.35 no.1
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• pp.80-88
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• 2013

A passive infrared or pyroelectric infrared (PIR) sensor is mainly used to sense the existence of moving objects in an indoor environment. However, in an outdoor environment, there are often outbreaks of false alarms from environmental changes and other sources. Therefore, it is difficult to provide reliable detection outdoors. In this paper, two algorithms are proposed to reduce false alarms and provide trustworthy quality to surveillance systems. We gather PIR signals outdoors, analyze the collected data, and extract the target features defined as window energy and alarm duration. Using these features, we model target and false alarms, from which we propose two target decision algorithms: window energy detection and alarm duration detection. Simulation results using real PIR signals show the performance of the proposed algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.129-136
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• 2016

A novel sensing system for standing and moving human body using PIR(pyroelectric infrared) sensor was development. The system consists of power supply, interface circuit of PIR sensor, small stepping motor, and digital control. The detecting principle for stop human body is detecting the human body when the stepping motor sticking the PIR sensor and the fresnel lens has rotated by 180 degree at six second and has stopped the motor for no detecting signal of human body. We developed control algorism for proposed the detection system. The experimentation shows that the detector system had detected length and angle were 6m and 30 degree against as standing and moving human body with $37^{\circ}C$.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.774-784
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• 2017

Occupancy sensors installed in buildings and households turn off the light if the space is vacant. Currently PIR (pyroelectric infra-red) motion sensors have been utilized. Recently, the researches using camera sensors have been carried out in order to overcome the demerit of PIR that can not detect static people. If the tradeoff of cost and performance is satisfied, the camera sensors are expected to replace the current PIRs. In this paper, we propose vision sensor-based occupancy detection being composed of tracking, recognition and detection. Our softeware is designed to meet the real-time processing. In experiments, 14.5fps is achieved at 15fps USB input. Also, the detection accuracy reached 82.0%.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.1
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• pp.173-181
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• 2021

Purpose : The purpose of this study is to compare muscle activity after applying two muscle energy techniques (MET) to subjects with forward head posture to see if the post isometric relaxation (PIR) technique is more effective than the reciprocal inhibition (RI) technique. Methods : The muscle activity was measured using EMG after applying the PIR and RI techniques to 30 adults at K College. Subjects were selected for forward head posture whose ear center was 2.5 ㎝ front of the center of the shoulder. EMG equipment was used to measure muscle activity, and the measurement sites were measured in cervical flexor and extensor muscles. The experiment period was performed once a week for a total of two weeks, and after the pre-measurement was performed for 5 minutes PIR and RI exercise. In the PIR technique, the head is tilted back in a sitting position, and the experimenter applies resistance with the same force for 7~10 seconds and repeats 3-5 times after rest. In the RI technique, in a sitting position, the subject gives the force to bend the head forward, and the experimenter applies resistance with the same force for 7 to 10 seconds, and repeats 3 to 5 times after rest. Results : The result is same as the following. In the comparison of muscle activity, there was a significant decrease in both PIR and RI at 1 and 2 weeks. And there was a greater decrease in muscle activity in PIR. There was no difference in the comparison of decrease in muscle activity at 1 week and 2 week. Conclusion : Both PRI and RI can be said to be effective in improving the function of the forward head posture in the neck muscles. Therefore, the selection of the two techniques in clinical practice should be appropriately performed under the judgment of experts according to the patient's situation.

• Journal of the Regional Association of Architectural Institute of Korea
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• v.20 no.6
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• pp.113-119
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• 2018

Occupancy-based heating control is effective in reducing heating energy by preventing unnecessary heating during unoccupied period. Various technologies on detecting human occupancy have been developed using complicated machine learning algorithm and stochastic methodologies. This study aims at deriving low-cost and simple algorithm of occupancy inference that can be implemented to residential buildings. The core concept of the algorithm is to combine the occupancy probabilities based on indoor CO2 concentration and PIR(passive infrared) signals. The probability was estimated by applying different levels of decrement ratio depending on CO2 concentration change rate and aggregated PIR signals. The developed algorithm was validated by comparing the inference results with the occupancy schedule in a real residential building. The results showed that the inference algorithm can achieve the accuracy of 75~99%, which would be successfully implemented to the control of residential heating systems.