• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12A
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• pp.1339-1345
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• 2007

An important drawback in Lattice Reduction (LR) aided detectors has been investigated. For the solution, an improved LR aided detection with ignorable complexity overhead is proposed for MIMO system, where the additional correction operation is performed for the case of unreliable symbol decision. We found that LR aided detection errors mainly occur when the lattice points after the inverse lattice transform in the final step fall outside the constellation point set. In the proposed scheme, we check whether or not the lattice point obtained through LR detection is out of constellation. Only for the case of out of constellation, we additionally perform ML search with reduced search region restricted to the neighboring points near to the obtained lattice points. Using this approach, we can effectively and significantly improve the detection performance with just a slight complexity overhead which is negligible compared to full searched ML scheme. Simulation results show that the proposed scheme achieves the detection performance near to that of the ML detection with a lower computational complexity.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.126-136
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• 2014

In the Advanced Driver Assistance Systems (ADAS) of smart vehicle and Intelligent Transportation System (ITS) for to detect the boundary of lane is being studied a lot of Hough Transform. This method detects correctly recognition the lane. But recognition rate can fall due to detecting straight lines outside of the lane. In order to solve this problems, this paper proposed an algorithm to recognize the lane boundaries and the accumulator cells in Hough space. Based on proposed algorithm, we develop application for Android was developed by H/W verification. Users of smart phone devices could use lane detection and lane departure warning systems for driver's safety whenever and wherever. Software verification using the OpenCV showed efficiency recognition correct rate of 93.8% and hardware real-time verification for an application development in the Android phone showed recognition correct rate of 70%.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.63-69
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• 2017

This study is about the development of a smart bed control system to be able to detect the human position and body signal on bed. The main control board in the bed control system consists of the human sensing part, motor driving part and MCU. Here, to increase the credibility to check the human presence on bed, the human sensing part is combined with the human position part by membrane sensor and the body-signal detecting part of EMFI sensor. Also, remotely connecting the two detected signal to the application program of the app mode makes it possible to monitor human information on bed. In this paper, the remote function monitoring of the on-bed human information by bluetooth communication will be abe to make it applicable to the technical prevention method of the bed fall and absence accident in hospital and care facilities.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.214-217
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• 2010

DDoS(distributed denial of service)공격은 1990년 중반에 처음 나타나기 시작하여 1,2세대 네트워크 자체에 대한 트래픽 폭주형태의 공격에서부터 3세대 봇넷을 이용하여 특정 서버와 특정서비스를 마비시키기 위한 공격을 거쳐 4세대의 분산 형식의 C&C를 이용하는 공격의 유형으로 발전 하고 있다. DDoS공격은 점점 지능화 되고 있으며 기존의 IDS(Intrusion Detection System) 시스템을 이용한 탐지방법으로 공격을 탐지하기에는 어려움이 존재한다. 본 논문은 IDS시스템을 보다 더 지능화시키기 위한 논문으로 IDS는 내부시스템으로부터 쿼리를 넘겨받아 업데이트를 수행하고 업데이트를 수행함과 동시에 라우터에게 C&C서버로부터 나오는 패킷을 차단하도록 알려 준다. 즉, IDS에서 일어나는 False Negative문제를 줄여줌으로써 DDoS 공격에 대하여 Zombie시스템을 생성하지 못하도록 하고자 하는데 그 목적이 있으며 점점 지능화되어 가고 있는 DDoS공격에 대하여 차단을 하고자 하는 방향성을 제시하고 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.77-83
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• 2019

Major causes of industrial accidents include falls and gas leak. The existing safety helmet and smart device combination products are focused on convenience, so the functions to prevent such accidents are insufficient. We developed a smart helmet focusing on fall accident detection and gas leak detection. We also developed management system to manage workers efficiently. Its core function is to detect dangerous conditions of employees, to communicate with managers and to confirm the situations of workers. The effectiveness of the combustible gas measurement capability was verified through experiments. However, since a significant amount of power consumption is founded due to continuous operation of the board and the sensor, countermeasures such as replacing with a large capacity battery are required.

• Atmosphere
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• v.24 no.2
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• pp.235-243
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• 2014

Sources of aviation turbulence vary through the seasons, especially in the East Asia including Korean peninsula, associated primarily with the changes in the jet/front system and convective activities. For this reason, a seasonal Korean aviation Turbulence Guidance (KTG) system (seasonal-KTG) is developed in the present study by using pilot reports (PIREPs) and analysis data of the operational Unified Model (UM) of the Korea Meteorological Administration (KMA) for two years between June 2011 and May 2013. Twenty best diagnostics of aviation turbulence in each season are selected by the method of probability of detection (POD) using the PIREPs and UM data. After calculating a weighting value of each selected diagnostics using their area under curve (AUC), the 20 best diagnostics are combined with the weighting scores into a single ensemble-averaged index by season. Compared with the current operational-KTG system that is based on the diagnostics applying all seasons, the performances of the seasonal-KTG system are better in all seasons, except in fall.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.8
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• pp.671-680
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• 2021

This paper deals with the contents of designing and producing reconnaissance hardware and software, and verifying the functions after being installed on the CanSat platform and ground stations. The main reconnaissance mission is largely composed of two things: terrain search that renders the surrounding terrain in 3D using radar, GPS, and IMU sensors, and real-time detection of major objects through optical camera image analysis. In addition, data analysis efficiency was improved through GUI software to enhance the completeness of the CanSat system. Specifically, software that can check terrain information and object detection information in real time at the ground station was produced, and mission failure was prevented through abnormal packet exception processing and system initialization functions. Communication through LTE and AWS server was used as the main channel, and ZigBee was used as the auxiliary channel. The completed CanSat was tested for air fall using a rocket launch method and a drone mount method. In experimental results, the terrain search and object detection performance was excellent, and all the results were processed in real-time and then successfully displayed on the ground station software.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.91-98
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• 2019

Accidents of worker that occur in isolated places are difficult to rescue, unlike general construction accidents. There is a problem of communication limitation when an accident occurs in an isolated place. Also, it is difficult to search the accident place due to the absence of CCTV. In order to solve these problems, this paper proposes a device that combines IoT technology with a safety helmet, which must be worn in the workplace. The proposed device additionally designs and implements a real-time PPG(Photoplethysmography) sensor, body temperature sensor, accelerometer sensor and a camera sensor on the helmet. The proposed helmet system allows the user and the control center to monitor the state of the worker. In addition, when an abnormal biological signal or fall occurs to the worker, the image is transmitted to the control center. By using the proposed system, it is possible to check the status of the worker in real time, so that it has an advantage that it can cope with the accident quickly.