• Title, Summary, Keyword: 가속도센서

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Enhanced Energy Harvester Based on Vibration Analysis of Bicycle Riding (자전거 주행의 진동 분석에 기반한 에너지 수확 증진 기술 개발)

  • Yeo, Jung-Jin;Ryu, Mun-Ho;Kim, Jung-Ja;Yang, Yoon-Seok
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.49 no.1
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    • pp.47-56
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    • 2012
  • Bicycle has a large amount of kinetic energy available for energy harvesting technology in its speedy and balanced riding movement. Systematic and realistic analysis of its dynamic property is essential to improve the efficiency of energy harvester. However, there has not been enough researches about precise measurement or analysis of bicycle dynamics on real roads. This study aims to investigate the characteristics of vibrational movement of bicycle using MEMS-based accelerometer and to develop a prototype of electromagnetic energy harvester with nonlinear behavior which is proper to the random vibrations accompanied in bicycle riding. The vibrational components have average magnitude of 1 g and turn out to be independent of riding speed. The developed prototype of energy harvester was installed on a front port of a bicycle to use this ambient vibration and generated an average electrical power of 1.5 mW which is enough to support power for most of portable sensors and short range radio-frequency communication. Further study about isolation of vibration from a rider and conversion efficiency is ongoing. The developed energy harvester is expected to be a platform technology for sustainable portable power supply for various smart IT devices and applications.

Study of the Long-Term Behavior Characteristics of Roadbed on Concrete Track of High-speed Railway (고속철도 콘크리트 궤도상 토공노반의 장기거동 특성 연구)

  • Choi, Chan-Yong;Jung, Jae-Hyun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.4
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    • pp.8-16
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    • 2018
  • This study examined the long-term behavior of a roadbed using high-speed railway concrete track and roadbed measurement data and evaluated the long-term performance of the track and roadbed. Recently, high-speed railway track type has been adopted as a concrete slab. On the other hand, the concrete track is vulnerable to roadbed settlement. In the case of gravel tracks, it is easy to restore the original state by maintenance even if the roadbed settles. On the other hand, in the case of the concrete track, if excessive settlement of the roadbed occurs, cracks are generated continuously on the slabs and sleepers, resulting in greatly reduced usability. For this reason, it is difficult to restore the original state only by partial maintenance. In this paper, a long-term performance evaluation was carried out on a concrete track during operation by monitoring the measurement data of sensors buried from the beginning of construction for approximately 3 years after the high-speed railway opened. Performance evaluation methods include a performance evaluation of track/roadbed when the train passes, long-term track and roadbed performance evaluation, analysis of the track/roadbed effect on long-term settlement and analysis of the factors influencing long-term settlement. The trail response of KTX-Sancheon was greatest in the track/roadbed performance evaluation by train. The results of the long-term track and roadbed performance evaluation were measured within the standard values. The track and roadbed performance impact assessment with long-term settlement was strongly related to TCL settlement. The influences of the water content and groundwater level were verified by analyzing the external factors of long-term settlement. Through such a method, the stability of a track/roadbed can be secured.

A Statistical Analysis of External Force on Electric Pole due to Meteorological Conditions (기상현상에 의한 전주 외력의 통계적 분석)

  • Park, Chul Young;Shin, Chang Sun;Cho, Yong Yun;Kim, Young Hyun;Park, Jang Woo
    • KIPS Transactions on Computer and Communication Systems
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    • v.6 no.11
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    • pp.437-444
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    • 2017
  • Electric Pole is a supporting beam used for power transmission/distribution which is sensitive to external force change of environmental factors. Therefore, power facilities have many difficulties in terms of maintenance/conservation from external environmental changes and natural disasters that cause a great economic impact. The aerial wire cause elasticity due to the influence of temperature, or factors such as wind speed and wind direction, that weakens the electric pole. The situation may lead to many safety risk in day-to-day life. But, the safety assessment of the pole is carried out at the design stage, and aftermath is not considered. For the safety and maintenance purposes, it is very important to analyze the influence of weather factors on external forces periodically. In this paper, we analyze the acceleration data of the sensor nodes installed in electric pole for maintenance/safety purpose and use Kalman filter as noise compensation method. Fast Fourier Transform (FFT) is performed to analyze the influence of each meteorological factor, along with the meteorological factors on frequency components. The result of the analysis shows that the temperature, humidity, solar radiation, hour of daylight, air pressure, wind direction and wind speed were influential factors. In this paper, the influences of meteorological factors on frequency components are different, and it is thought that it can be an important factor in achieving the purpose of safety and maintenance.

Development of Exercise Analysis System Using Bioelectric Abdominal Signal (복부생체전기신호를 이용한 운동 분석 시스템 개발)

  • Gang, Gyeong Woo;Min, Chul Hong;Kim, Tae Seon
    • Journal of the Institute of Electronics and Information Engineers
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    • v.49 no.11
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    • pp.183-190
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    • 2012
  • Conventional physical activity monitoring systems, which use accelerometers, global positioning system (GPS), heartbeats, or body temperature information, showed limited performances due to their own restrictions on measurement environment and measurable activity types. To overcome these limitations, we developed a portable exercise analysis system that can analyze aerobic exercises as well as isotonic exercises. For bioelectric signal acquisition during exercise, waist belt with two body contact electrodes was used. For exercise analysis, the measured signals were firstly divided into two signal groups with different frequency ranges which can represent respiration related signal and muscular motion related signal, respectively. After then, power values, differential of power values, and median frequency values were selected for feature values. Selected features were used as inputs of support vector machine (SVM) to classify the exercise types. For verification of statistical significance, ANOVA and multiple comparison test were performed. The experimental results showed 100% accuracy for classification of aerobic exercise and isotonic resistance exercise. Also, classification of aerobic exercise, isotonic resistance exercise, and hybrid types of exercise revealed 92.7% of accuracy.

A Study on Tension for Cables of a Cable-stayed Bridge Damper is Attached (댐퍼가 부착된 사장교의 케이블 장력에 관한연구)

  • Park, Yeon Soo;Choi, Sun Min;Yang, Won Yeol;Hong, Hye Jin;Kim, Woon Hyung
    • Journal of Korean Society of Steel Construction
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    • v.20 no.5
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    • pp.609-616
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    • 2008
  • Recently, many ocean bridges that connect land to island or island to island have been constructed along with the improvement of the nation's economy. Long-span bridges can be categorized as suspension bridge, cable-stayed bridge, arch bridge and truss bridge. In this study, correction with respect to construction error can be presented on site through the monitoring of the cable tension change of real structure for four major construction stages so that construction accuracy, including the management of profiles, can be improved. A vibration method, the so-called indirect method that uses the cable's natural frequency changes from the acceleration sensor installed on the cable, is applied in measuring cable tension. In this study, the estimation formula for the effective length of cable with damper is presented by comparing and analyzing between actual measurement and analysis result for the change of the cable's effective length. By the way, it is known that the reliability of estimating cable tension by applying the former method that uses the net distance from damper to anchorage is low. Therefore, for future reference of the maintenance stage, the presented formula for estimating the effective length of cable can be used as a reference for the rational decision-making, such as the re-tensioning and replacement of cable.

Implementation on SVM based Step Detection Analyzer (SVM 기반의 걸음 검출 분석기의 구현)

  • An, Kyung Ho;Kim, En Tae;Ryu, Uk Jae;Chang, Yun Seok
    • Journal of Korea Multimedia Society
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    • v.16 no.10
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    • pp.1147-1155
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    • 2013
  • In this study, we designed and implemented a step detection analyzer that can compare and analyze the step detection rates and results among the step detection algorithms. The step detection analyzer converts 3-axes accelerometer data into continuous energy stream through SVM operation, shows the horizontal comparison among the step detection results for each step detection algorithms, and can make elemental detection analyses. For these processes, the step detection analyzer presents the continuous energy stream as energy waveform, checks the peak values and time location of the detected steps with step detection algorithms, and gives visual interface to get some possible causes in cases of step detection miss. It can also give the threshold graph for each algorithm to check the threshold value on missed cases directly and can help to get more appropriate threshold values or other adjustable parameters in step detection algorithm. This step detection analyzer can be applied efficiently on performance enhancement of step detection algorithm, on deciding an appropriate algorithm for a specific step counter system in the various step counter filed operations.

Human-Computer Interface using sEMG according to the Number of Electrodes (전극 개수에 따른 근전도 기반 휴먼-컴퓨터 인터페이스의 정확도에 대한 연구)

  • Lee, Seulbi;Chee, Youngjoon
    • Journal of the HCI Society of Korea
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    • v.10 no.2
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    • pp.21-26
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    • 2015
  • NUI (Natural User Interface) system interprets the user's natural movement or the signals from human body to the machine. sEMG (surface electromyogram) can be observed when there is any effort in muscle even without actual movement, which is impossible with camera and accelerometer based NUI system. In sEMG based movement recognition system, the minimal number of electrodes is preferred to minimize the inconvenience. We analyzed the decrease in recognition accuracy as decreasing the number of electrodes. For the four kinds of movement intention without movement, extension (up), flexion (down), abduction (right), and adduction (left), the multilayer perceptron classifier was used with the features of RMS (Root Mean Square) from sEMG. The classification accuracy was 91.9% in four channels, 87.0% in three channels, and 78.9% in two channels. To increase the accuracy in two channels of sEMG, RMSs from previous time epoch (50-200 ms) were used in addition. With the RMSs from 150 ms, the accuracy was increased from 78.9% to 83.6%. The decrease in accuracy with minimal number of electrodes could be compensated partly by utilizing more features in previous RMSs.

Trunk Stabilization Measurements Using the Nintendo Wii (닌텐도 위를 활용한 흉부 흔들림의 자세 안정성 측정)

  • Yang, Juyeong;Yoo, Jaeha;Kim, Dongyon;Park, Junmo;Kim, Soochan
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.7
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    • pp.239-247
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    • 2014
  • The CTSIB (Clinical Test of Sensory Integration on Balance) using high sensitive pressure sensors is normally used to assess the sense of balance in hospital. It takes an objective measurement of the amount of sway that occurs in the body trunk by observing the change in the center of pressure (CoP) on the foot at the upright posture. In this paper, we would like to propose method to easily measure trunk sway in home. Although the Wii balance board(WBB) is used for games, it can measures the center of pressure, which is highly correlated. The Wii remote controller(WRC) is inexpensive compared to the WBB, but it has problems with estimation of trunk sway because it can't measure pressure directly like WBB. We collected data from 10 normal subjects (5 males, 5 females) from two devices in order to compare the CoP from WBB and the center of mass (CoM) from WRC. The results of WRC and WBB was similar when the data were analyzed by the convex hull and ellipse area.

A Moving Control of an Automatic Guided Vehicle Based on the Recognition of Double Landmarks (이중 랜드마크 인식 기반 AGV 이동 제어)

  • Jeon, Hye-Gyeong;Hong, Youn-Sik
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37 no.8C
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    • pp.721-730
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    • 2012
  • In this paper the problem of a moving control of an automatic guided vehicle(AGV) which transports a dead body to a designated cinerator safely in a crematorium, an special indoor environment, will be discussed. Since a method of burying guided lines in the floor is not proper to such an environment, a method of moving control of an AGV based on infrared ray sensors is now proposed. With this approach, the AGV emits infrared ray to the landmarks adheres to the ceiling to find a moving direction and then moves that direction by recognizing them. One of the typical problems for this method is that dead zone and/or overlapping zone may exist when the landmarks are deployed. To resolve this problem, an algorithm of recognizing double landmarks at each time is applied to minimize occurrences of sensing error. In addition, at the turning area to entering the designated cinerator, to fit an AGV with the entrance of the designated cinerator, an algorithm of controlling the velocity of both the inner and outer wheel of it. The functional correctness of our proposed algorithm has been verified by using a prototype vehicle. Our real AGV system has been applied to a crematorium and it moves automatically within an allowable range of location error.

A Deep Learning Based Approach to Recognizing Accompanying Status of Smartphone Users Using Multimodal Data (스마트폰 다종 데이터를 활용한 딥러닝 기반의 사용자 동행 상태 인식)

  • Kim, Kilho;Choi, Sangwoo;Chae, Moon-jung;Park, Heewoong;Lee, Jaehong;Park, Jonghun
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.163-177
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    • 2019
  • As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.