• 대한임베디드공학회논문지
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• 제8권3호
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• pp.137-143
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• 2013

This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.

• 한국인공지능학회지
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• 제11권4호
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.77-86
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• 2022

The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.42-49
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• 2021

As cyber attacks become more intelligent, there is difficulty in detecting advanced attacks in various fields such as industry, defense, and medical care. IPS (Intrusion Prevention System), etc., but the need for centralized integrated management of each security system is increasing. In this paper, we collect big data for intrusion detection and build an intrusion detection platform using deep learning and CNN (Convolutional Neural Networks). In this paper, we design an intelligent big data platform that collects data by observing and analyzing user visit logs and linking with big data. We want to collect big data for intrusion detection and build an intrusion detection platform based on CNN model. In this study, we evaluated the performance of the Intrusion Detection System (IDS) using the KDD99 dataset developed by DARPA in 1998, and the actual attack categories were tested with KDD99's DoS, U2R, and R2L using four probing methods.

• 로봇학회논문지
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• 제15권3호
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• pp.221-232
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• 2020

In a four-wheel independent drive platform, four wheels and motors are connected directly, and the rotation of the motors generates the power of the platform. It uses a skid steering system that steers based on the difference in rotational power between wheel motors. The platform can control the speed of each wheel individually and has excellent mobility on dirt roads. However, the difficulty of the straight-running is caused due to torque distribution variation in each wheel's motor, and the direction of rotation of the wheel, and moving direction of the platform, and the difference of the platform's target direction. This paper describes an algorithm to detect the slip generated on each wheel when a four-wheel independent drive platform is traveling in a harsh environment. When the slip is detected, a compensation control algorithm is activated to compensate the torque of the motor mounted on the platform to improve the trajectory tracking performance of the platform. The four-wheel independent drive platform developed for this study verified the algorithm. The wheel slip detection and the compensation control algorithm of the platform are expected to improve the stability of trajectory tracking.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.553-558
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• 2008

Advancement in information technology have enabled applying vision sensor to railway, such as CCTV. CCTV has been widely used in railway application, however the CCTV is a passive system that provide limited capability to maintain safety from boarding platform. The station employee should monitor continuously CCTV monitors. Therefore immediate recognition and response to the situation is difficultin emergency situation. Recently, urban transit operators are pursuing applying an unattended station operation system for their cost reduction. Therefore, an intelligent monitoring system is need for passenger's safety in railway. The paper proposes a vision based monitoring system and object detection algorithm for passenger's safety in railway platform. The proposed system automatically detects accident in platform and analyzes level of danger using image processing technology. The system uses stereo vision technology with multi-sensors for minimizing detection error in various railway platform conditions.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.141-143
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• 2013

현재 나와 있는 휴대용 방사선 검출장치들은 기능적으로 제약이 크다. 이러한 단점을 해결하고자 안드로이드 플랫폼 기반의 휴대용 방사선 검출장치에 관한 연구가 이루어졌다. 연구초기 단계인 만큼 방사선 측정은 가능하지만, 정확도가 나와 있는 제품들만큼 높지 못하다. 이러한 안드로이드 플랫폼 기반의 휴대용 방사선 검출장치는 온도에 따라 오차가 발생하게 된다. 이와 같은 온도에 따른 오차를 제거하기 위해 온도에 따른 보상 알고리즘을 구현하여 정확도를 향상하고자 한다.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• 한국컴퓨터정보학회논문지
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• 제9권1호
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• pp.55-62
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• 2004

컴퓨터네트워크의 확대와 인터넷 이용자의 증가에 따른 부작용으로 컴퓨터 보안 문제가 중요하게 대두되고 있다. 따라서 침입자들로부터 위험을 줄이기 위한 침입 탐지 시스템에 관한 연구가 활발하다. 이동에이전트를 이용하는 제안된 컴퓨팅 패러다임의 잇점은 네트워크의 지연시간 극복. 네트워크 부하 감소, 비동기적이고 자율적인 실행, 동적인 적합성과 이기종 환경에서의 운영이다. 많은 정보 보호 모델들은 agent-to-agent, agent-to-platform, 그리고 platform-to-agent 위험한 요소들을 완화하기 위하여 제안되었다. 본 논문에서는 침입 탐지 시스템의 개발을 통해서 이동 에이전트의 성능을 분석하여 관리함으로써 데이터가 최상의 환경을 유지하도록 하였다.

• 한국산학기술학회논문지
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• 제13권12호
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• pp.6098-6104
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• 2012

현재 철도 역사의 승객 위험영역과 보안영역 등의 감시를 위해 수십대의 CCTV를 널리 이용하고 있다. 그중에서 가장 빈번한 사고가 발생되는 곳은 승객의 열차 승하차가 이루어지는 승강장 영역이다. 하지만 사고 예방과 신속한 대응을 위해 역무원이 여러 대의 CCTV를 항시 모니터링 하기는 불가능하다. 따라서 위험상황을 자동으로 인지할 수 있는 영상처리 기술을 이용한 승강장 모니터링 시스템이 요구되며, 이를 위해서는 우선적으로 승강장에서의 정확한 열차상태 판단이 필요하다. 본 논문은 비전기반 승강장 모니터링 시스템을 위한 승강장에서의 열차상태에 대한 검지방법을 제안한다. 제안된 검지 방법은 승강장에서의 열차 점유영역을 분석하여 열차의 진입(IN), 진출(OUT), 정지(ON), 없음(OFF)의 4가지 생태를 판별한다. 제안된 검지방법의 성능 평가을 위해 서울메트로 4호선 동작역과 남태령역을 대상으로 열차상태 검지결과를 제시하였다.