• Journal of Positioning, Navigation, and Timing
• /
• v.13 no.3
• /
• pp.289-299
• /
• 2024

In the Pedestrian Dead Reckoning (PDR)/Global Positioning System (GPS)/Wi-Fi-integrated navigation system for indoor/outdoor continuous positioning of pedestrians, the process of detecting outliers in measurements is very important. When accurate location information from measurements is used, reliable correction data can be generated during the fusion filtering process. However, abnormal measurements may occur in certain situations, such as indoor/outdoor transitions, which can degrade filter performance and lead to significant errors in the estimated position. To address this issue, this paper proposes a method for detecting outliers in measurements based on Rényi Divergence (RD). When the deviation of the RD value is large, the measurements are considered outliers, and positioning is performed using only pure PDR. Based on experiments conducted with real data, it was confirmed that outliers were effectively detected for abnormal measurements, leading to an improvement in the performance of pedestrian navigation.

• Journal of the Institute of Convergence Signal Processing
• /
• v.18 no.1
• /
• pp.6-12
• /
• 2017

In this paper, we proposed IoT based Smart Health Service using Motion Recognition for Human UX/UI. Until now, sensor networks using M2M-based u-healthcare are using non-IP protocol instead of TCP / IP protocol. However, in order to increase the service utilization and facilitate the management of the IoT-based sensor network, many sensors are required to be connected to the Internet. Therefore, IoT-based smart health service is designed considering network mobility because it is necessary to communicate not only the data measured by sensors but also the Internet. In addition, IoT-based smart health service developed smart health service for motion detection as well as bio information unlike existing healthcare platform. WBAN communications used in u-healthcare typically consist of many networked devices and gateways. The method proposed in this paper can easily cope with dynamic changes even in a wireless environment by using a technology supporting mobility between WBAN sensor nodes, and systematic management is performed through detection of a user's motion.

• Tuberculosis and Respiratory Diseases
• /
• v.45 no.6
• /
• pp.1178-1187
• /
• 1998

Background : Recently the incidence of tuberculosis is increasing in many countries and control of the disease is further threatened by the emergence of multi-drug resistant tuberculosis. So rapid detection of drug resistance is very important. Pyrazinamide (PZA) is a first-line chemotherapeutic agent for tuberculosis. Now in Korea, we perform PZase activity test instead of actual pyrazinamide susceptibility test for the detection of PZA resistant M. tuberculosis. Recently the pncA gene, encoding the PZase of M. tuberculosis, was completely sequenced. And it was reported that the mutation of pncA gene would be associated with PZA resistance of M. tuberculosis. Therefore we performed this study to evaluate the possibility for the rapid detection of PZA resistant M. tuberculosis using PCR-SSCP of pncA gene. Method : 44 cultured clinical isolates of M. tuberculosis, BCG Tokyo strain. BCG French strain, and one M. bovis isolate were studied. We used H37Rv as the reference strain, The PZase activity test was done at the reference laboratory of Korean Tuberculosis Institute. DNA was extracted by bead-beater method and 561 bp fragment including pncA gene was amplified by PCR. The PCR product were digested by BstB I enzyme. SSCP was done using MDE gel. Of the 44 strains of M. tuberculosis, 22 strains were PZase-positive and other 22 strains were PZase negative. Results : Of the 22 PZase positive strains, 18 strains(82%) showed the same mobility compared with that of H37Rv and 4(18%) showed different mobility. Of the 22 PZase-negative strains, 19(86%) strains showed the same mobility pattern compared with that of H37Rv and 3(14%) showed different mobility. Naturally PZA-resistant BeG-French strain, BCG-Tokyo strain, and one M. bovis isolate showed the same band pattern each other, but their mobility were different from that of H37Rv. The results of PZase activity test and PCR-SSCP of pncA of M. tuberculosis were statistically significantly correlated each other (p<0.01). Conclusion : The PCR-SSCP after BstB I restriction of pncA gene of M. tuberculosis may be a useful method for the rapid detection of PZA-resistant M. tuberculosis.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.3
• /
• pp.302-309
• /
• 2020

Early-detection and monitoring of toxic chemical gas cloud with chemical detector is essential for reducing the number of casualties. Conventional method for chemical detection and reconnaissance has the limitation in approaching to chemically contaminated site and prompt understanding for the situation. Stand-off detector can detect and identify the chemical gas at a long distance but it cannot know exact distance and position. Chemical detection UAV is an emerging platform for its high mobility and operation safety. In this study, we have conducted chemical gas cloud detection with the stand-off chemical detector and the chemical detection UAV. DMMP vapor was generated in the area where the cloud can be detected through the field of view(FOV) of stand-off chemical detector. Monitoring the vapor cloud with standoff detector, the chemical detection UAV moved back and forth at the area DMMP vapor being generated to detect the chemical contamination. The hybrid detection system with standoff cloud detection and point detection by chemical sensors with UAV seems to be very efficient as a new concept of chemical detection.

• The Journal of Korea Robotics Society
• /
• v.7 no.2
• /
• pp.113-119
• /
• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.3
• /
• pp.300-310
• /
• 2022

A vast amount of labeled data is required for deep neural network training. A typical strategy to improve the performance of a neural network given a training data set is to use data augmentation technique. The goal of this work is to offer a novel image augmentation method for improving object detection accuracy. An object in an image is removed, and a similar object from the training data set is placed in its area. An in-painting algorithm fills the space that is eliminated but not filled by a similar object. Our technique shows at most 2.32 percent improvements on mAP in our testing on a military vehicle dataset using the YOLOv4 object detector.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.39-40
• /
• 2022

최근 전동 킥보드, 전동휠, 전기 자전거 등 개인형 이동수단(Personal Mobility)의 보급이 늘면서 관련 인명 교통사고가 급증하고 있다. 본 논문에서는 개인형 이동수단의 사용위험 및 사고 감소를 목적으로, 딥러닝 객체탐지 기술을 적용하여 다양한 위험요소를 증강현실 기술을 기반으로 한 HMD(Helmet mounted display)에 표시하는 '딥러닝 객체 인식과 증강현실을 적용한 개인 이동장치를 위한 HMD(Helmet Mounted Display) 어플리케이션'을 설계한다. 제시하는 방법은 실시간으로 수집된 전방의 실시간 영상 정보를 객체 탐지 알고리즘을 통해 위험요소 및 안전한 주행을 보조하는 객체를 감지하고 증강현실을 적용해 사용자에게 적절한 운전 보조장치 및 기능을 제공한다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.11B
• /
• pp.900-910
• /
• 2004

Route failure is mainly caused by mobility of mobile host in ad hoc networks. Route failure, which may lead to sudden packet losses and delays, is losing the route from source to destination. In this situation, TCP assumes that congestion has occurred within the network and also initiates the congestion control procedures. Congestion control algorithm provides the means for the source to deal with lost packets. TCP performance in ad hoc environments will be degraded as TCP source cannot distinguish congestion from route failure. In this paper, we propose TCP-P as pre-detection approach to deal with route failure. TCP-P freezes TCP through pre-detection of route failure. Route failure information of the proposed mechanism is obtained not by routing protocol but by MAC protocol. The intermediated node, obtaining route failure information by its MAC layer, relays the information to TCP source and lets TCP source stop the congestion control algorithm. Results reveal that TCP-P responding with proactive manner outperforms other approaches in terms of communication throughput under the presence of node mobility.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.11
• /
• pp.2546-2553
• /
• 2015

Falling can happen to anyone, anywhere at anytime and especially it is one of the risk factor that can lead causes of death of persons aged 65 and over. Recently, the study of fall detection mechanisms as a smart healthcare service based on the IoT(Internet of Things) are being actively investigated. In this paper, we implement a fall detection system using arduino as a smart sensor communicates with a smart device. When transmitting the information of the acceleration on a sensor smart sensor with a BLE(Bluetooth Low Energy), the smart device processing and analyzing this information. and determines a fall situation. A fall detection system based on the Internet of Things which using smart sensor and smart device, has the advantage of being able to overcome the mobility and portability constraints.

• ETRI Journal
• /
• v.45 no.5
• /
• pp.836-846
• /
• 2023

Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian-rich spaces. This study proposes real-time, accurate, three-dimensional (3D) multi-pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected-component algorithm. The multi-pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.