A new algorithm has been propose to detect the reflected light region as disturbances in a real-time vision system. There have been several attempts to detect existing reflected light region. The conventional mathematical approach requires a lot of complex processes so that it is not suitable for a real-time vision system. On the other hand, when a simple detection process has been applied, the reflected light region can not be detected accurately. Therefore, in order to detect reflected light region for a real-time vision system, the detection process requires a new algorithm that is as simple and accurate as possible. In order to extract the reflected light, the proposed algorithm has been adopted several filter equations and clustering processes in the HSI (Hue Saturation Intensity) color space. Also the proposed algorithm used the pre-defined reflected light data generated through the clustering processes to make the algorithm simple. To demonstrate the effectiveness of the proposed algorithm, several images with the reflected region have been used and the reflected regions are detected successfully.

딥러닝 모델은 주어진 학습용 데이터에서 탐지하고자 하는 물체의 특징을 추출하기 때문에, 딥러닝 모델 학습을 위한 학습용 데이터 구축은 매우 중요하다. 본 연구에서는 균열을 탐지하는 딥러닝 모델의 성능을 향상시키기 위해, 실제 콘크리트 구조물이나 아스팔트 도로 표면에서 자주 발견될 수 있는 나뭇가지, 거미줄, 전선 등을 학습 데이터에 자동으로 포함시키고, negative 영역으로 분류하는 알고리즘을 개발하였다. 제안된 알고리즘을 사용하여 학습된 딥러닝 모델을 실제 도로 표면에 발생한 균열 탐지에 적용하여 실제 균열 탐지에 사용될 수 있음을 보였다.

In this study, we adapted very low resolution (240x320 = 76,800 pixels) images by which it is difficult to detect cracks. An automatic crack detection technique has been studied using digital image processing technology for low resolution images of sewage pipeline. Authors have developed two algorithms to detect cracks. The third step covers an algorithm developed to find optimal threshold value, and the sixth step deals with algorithm to determine cracks. As the result, in spite of very low-resolution images, the performance of crack detection turned out to be excellent.

As drones gain more popularity these days, drone detection becomes more important part of the drone systems for safety, privacy, crime prevention and etc. However, existing drone detection systems are expensive and heavy so that they are only suitable for industrial or military purpose. This paper proposes a novel approach for training Convolutional Neural Networks to detect drones from images that can be used in embedded systems. Unlike previous works that consider the class probability of the image areas where the class object exists, the proposed approach takes account of all areas in the image for robust classification and object detection. Moreover, a novel loss function is proposed for the CNN to learn more effectively from limited amount of training data. The experimental results with various drone images show that the proposed approach performs efficiently in real drone detection scenarios.

본 연구에서는 상수관망의 최적 수압모니터링 지점선정 알고리즘과 역추적 계산법을 이용한 누수탐지 알고리즘을 개발하였고 파일럿 플랜트에서 실험을 통해 검증하였다. 먼저 누수탐지를 위한 최적의 모니터링 지점선정을 선정하고 압력계를 설치하여 누수로 인한 압력변위를 측정하였다. 누수탐지를 위한 역추적계산법의 검증을 위하여 누수탐지를 수행하였고 파일럿플랜트에서 실제 재현된 누수의 지점과 누수량을 정확하게 산정하였다. 누수량을 0.0005 m3/s로부터 0.0018 m3/s까지 변화시키며 누수를 재현하였고 누수지점에 근접한 장소를 찾는데 성공하였다. 본 연구에 서는 실험을 통해서 누수탐지기법으로 사용된 역추적계산법이 유효함을 검증할 수 있었다.

Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

All living organisms use memory and oblivion algorithms considering the estimated lifetime and the changes in the ambient environment. Because of the expected lifetime of a bridge is similar to the human’s one, if a bridge uses the same algorithm of human memory, the abnormal responses of the structure can be easily detected. This paper introduces unfamiliarity index (UFI) that calculated from the FFT results of the short term timeline acceleration responses. If this algorithm, which can detect an abnormal behavior from the maximum constant signal, is used to the terminal sensors of an structure, more accurate safety control criteria will be prepared efficiently.

Human-robot co-operation becomes increasingly frequent due to the widespread use of service robots. However, during such co-operation, robots have a high chance of colliding with humans, which may result in serious injury. Thus, many solutions were proposed to ensure collision safety, and among them, collision detection algorithms are regarded as one of the most practical solutions. They allow a robot to quickly detect a collision so that the robot can perform a proper reaction to minimize the impact. However, conventional collision detection algorithms required the precise model of a robot, which is difficult to obtain and is subjected to change. Also, expensive sensors, such as torque sensors, are often required. In this study, we propose a novel collision detection algorithm which only requires motor encoders. It detects collisions by monitoring the high-pass filtered version of the velocity error. The proposed algorithm can be easily implemented to any robots, and its performance was verified through various tests.

The road icing forecast and snow detection system using state evaluation algorithm of multi sensor optimizes snow melting system to control spread time and amount of chemical spread application This system operates integrated of contact/non-contact sensor and infrared camera. The state evaluation algorithm of the system evaluates road freezing danger condition and snowfall condition (snowfall intensity also) using acquired data such as temperature/humidity, moisture detection and result of image signal processing from field video footage. In the field experiment, it proved excellent and reliable evaluated result of snowfall state detection rate of 89% and wet state detection rate of 94%.

충돌 처리는 게임의 물리 법칙을 구현하기 위하여 매우 중요한 요소 중의 하나이며, 게임을 생동감 있게 하기 위한 과정이다. 충돌 처리는 매우 많은 연산이 필요하기 때문에 게임 성능에 중대한 영향을 미친다. 이를 해결하기 위해서 연산량을 줄이는 방식의 연구가 많이 진행되었고, 대표적으로 SAP 알고리즘이 많이 사용되고 있으나 반복되는 연산이 있어 효율성이 떨어진다. 본 논문에서는 충돌 처리의 연산량을 줄이고 SAP 알고리즘의 문제점을 해결하기 위하여, 거리기반 SAP 알고리즘을 새롭게 제안한다. 본 논문에서 제안한 알고리즘으로 만든 시뮬레이션 프로그램을 이용하여 FPS를 측정하는 실험을 진행한 결과, 제안한 알고리즘을 사용하는 경우가 사용하지 않는 경우에 비해서 FPS가 약 2~33배 높았기 때문에 충돌 처리의 효율이 향상되었다고 판단할 수 있었다.

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.

Radio waves including GPS signals, various TV communications, and radio broadcasting can be disturbed by a strong solar storm, which may occur due to solar flares and produce an ionospheric delay anomaly in the ionosphere according to the change of total electron content. Electron density irregularities can cause deep signal fading, frequently known as ionospheric scintillation, which can result in the positioning error using GPS signal. This paper proposes a detection algorithm for the ionosphere delay anomaly during a solar storm by using multi-reference stations. Different TEC grid which has irregular electron density was applied above one reference station. Then the ionospheric delay in zenith direction applied different TEC will show comparatively large ionospheric zenith delay due to the electron irregularity. The ionospheric slant delay applied an elevation angle at reference station was analyzed to detect the ionospheric delay anomaly that can result in positioning error. A simulation test was implemented and a proposed detection algorithm using data logged by four reference stations was applied to detect the ionospheric delay anomaly compared to a criterion.

This paper presents a robust lane detection algorithm based on RGB color and shape information during autonomous car control in realtime. For realtime control, our algorithm increases its processing speed by employing minimal elements. Our algorithm extracts yellow and white pixels by computing the average and standard deviation values calculated from specific regions, and constructs elements based on the extracted pixels. By clustering elements, our algorithm finds the yellow center and white stop lanes on the road. Our algorithm is insensitive to the environment change and its processing speed is realtime-executable. Experimental results demonstrate the feasibility of our algorithm.