• Journal of IKEEE
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• v.13 no.3
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• pp.39-46
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• 2009

Wireless sensor networks consist of small, autonomous devices with wireless networking capabilities. In order to further increase the applicability in real world applications, minimizing energy consumption is one of the most critical issues. Therefore, accurate energy model is required for the evaluation of wireless sensor networks. In this paper we analyze the power consumption for wireless sensor networks. To develop the power consumption model, we have measured the power characteristics of commercial Kmote node based on TelosB platforms running TinyOS. Based on our model, the estimated lifetime of a battery powered sensor node can use about 6.9 months for application of human detection using PIR sensors. This result indicates that sensor nodes can be used in a monitoring system for elderly living alone.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.23-28
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• 2021

In this paper, we studied a system that can efficiently build security management for single-person households using Arduino, ESP32-CAM and PIR sensors, and proposed an Android app with an internet connection. The ESP32-CAM is an Arduino compatible board that supports both Wi-Fi, Bluetooth, and cameras using an ESP32-based processor. The PCB on-board antenna may be used independently, and the sensitivity may be expanded by separately connecting the external antenna. This system has implemented an Arduino-based Unauthorized intrusion system that can significantly help prevent crimes in single-person households using the combination of PIR sensors, Arduino devices, and smartphones. unauthorized intrusion system, showing the connection between Arduino Uno and ESP32-CAM and with smartphone applications. Recently, if daily quarantine is underway around us and it is necessary to verify the identity of visitors, it is expected that it will help maintain a safety net if this system is applied for the purpose of facial recognition and restricting some access. This technology is widely used to verify that the characters in the two images entered into the system are the same or to determine who the characters in the images are most similar to among those previously stored in the internal database. There is an advantage that it may be implemented in a low-power, low-cost environment through image recognition, comparison, feature point extraction, and comparison.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2295-2296
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• 2008

본 논문에서는 에너지 절감을 위하여 POWER LED 구동회로를 PIR(Pyroelectric Infrared Ray) 센서를 이용하여 ON/OFF 제어를 하였다. POWER LED의 전류 특성을 설명하였으며 설명된 전류 특성을 개선하고자 정전류 유지 회로를 구성하였다. 그리고 인체 감지 센서에서 발생되는 ON/OFF 신호를 증폭시키는 구동 회로를 설계하여 정전류 유지 회로에 직접 결합하는 방식을 제안하였다. 실험한 결과는 POWER LED의 ON 상태시 4Watt, OFF 상태시 0.5Watt를 소비하였으며, 정전류 유지 회로에 의해 POWER LED 구동 전류의 리플이 줄어들어 안정적인 동작을 하는 것으로 나타났다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1070-1073
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• 2020

본 논문은 무선통신 중 하나인 블루투스를 이용하여 일반적인 센서로 이루어진 보안시스템, 보행, 주행 중 주변 감지 등 야간에 시야 확보가 잘 되지 않거나 예기치 못한 상황 속 사람을 인식하지 못해 야기되는 문제를 사용자에게 즉시 알려줌으로써 문제를 해결할 수 있고, 블루투스로 정해진 입력 값을 넣어주면서 시스템을 임의로 조작하여 해결하고자 PIR 센서로 사람 인식 후, 일정 거리 안으로 들어오면 초음파 센서로 인식해 버저가 울리며 무선통신 중 하나인 블루투스로 사용자에게 알려주고 시스템을 블루투스로 제어할 수 있는 강화된 디지털화 된 시스템으로 IT 관련 기술을 활용하였다.

• ETRI Journal
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• v.45 no.6
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• pp.1022-1034
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• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.

• Journal of IKEEE
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• v.14 no.1
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• pp.17-24
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• 2010

This paper presents calorie expenditure prediction model of daily activity of elderly living alone for LBS(Location Based Service) applications. The proposed method is to describe the daily activity patterns of older adult using PIR (Passive InfraRed) motion sensors and to examine the relationships between physical activity and calorie expenditure. The developed motion detecting system is composed of a sensing system and a server system. The motion detecting system is a set of wireless sensor nodes which has PIR sensor to detect a motion of elder. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to caregivers for more effective services. The experiment results show the adaptability and feasibility of the calorie expenditure model.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.274-282
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• 2018

Most occupancy sensors installed in buildings, households and so forth are pyroelectric infra-red (PIR) sensors. One of disadvantages is that PIR sensor can not detect the stationary person due to its functionality of detecting the variation of thermal temperature. In order to overcome this problem, the utilization of camera vision sensors has gained interests, where object tracking is used for detecting the stationary persons. However, the object tracking has an inherent problem such as tracking drift. Therefore, the recognition of humans in static trackers is an important task. In this paper, we propose a CNN-based human recognition to determine whether a static tracker contains humans. Experimental results validated that human and non-humans are classified with accuracy of about 88% and that the proposed method can be incorporated into practical vision occupancy sensors.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.161-167
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• 2015

This paper presented an occupancy-based energy saving system for appliance energy saving in smart house. The developed system is composed of a sensing system and a home gateway system. The sensing system is set of wireless sensor nodes which have pyroelectric infrared (PIR) sensor to detect a motion of human and set of smart plugs which measure the current using CT (current transformer) sensor and send the current to home gateway wirelessly. We measured current consumption of appliances in real time using smart plugs, and checked the occupation of residents using occupancy sensors installed on the door and room. The proposed system saves electric energy to switch off the supply power of unnecessary usages in the unoccupied spaces. Experiments conducted have shown that electric energy usage of appliances can be saved about 34% checked by using occupation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.7
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• pp.551-562
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• 2013

Current CCTV systems, which require continuous monitoring of the screens, have the limitation to detect and respond to the crime scenes in timely manner. Therefore, in recent years, the request for more intlligent surveillance system, with a ubiquitous sensor network, is increasing in order to behave more humanly fashions. Such systems require cllective data processing of the environments based on various sensors. In this article, we suggests a new paradigm based surveillance system which integrates PSD and dual PIR sensors. The proposed system evlves from a existing indoor intrusion detection system which can only identify the intrusion event to a better inteligent system with context awareness. We have conducted the various simulations in order to prove the effectiveness of the proposed system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.140-145
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• 2022

In an advanced modern society, among air pollutants caused by urban industrialization and public transportation, fine dust flows into indoors from the outdoors. The fine dust meter used indoors provides limited information and measures the pollution level differently, so there is a problem that users cannot monitor and monitor the data they want. To solve this problem, in this paper, indoor air quality data fine dust and ultra-fine dust (PM1.0, PM2.5, PM10), VOC (Volatile Organic Compounds) and PIR (Passive Infrared Sensor) are used to measure fine dust. and a monitoring system were designed and implemented. We propose a fine dust meter and monitoring system that is installed in a designated area to measure fine dust in real time, collects, stores, and visualizes data through App Engine of Google Cloud Platform and provides it to users.