• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2927-2941
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• 2022

The primary task of machine fault diagnosis is to judge whether the current state is normal or damaged, so it is a typical binary classification problem with mutual exclusion. Mutually exclusive events and out-of-domain detection have one thing in common: there are two types of data and no intersection. We proposed a fusion model method to improve the accuracy of machine fault diagnosis, which is based on the mutual exclusivity of events and the commonality of out-of-distribution detection, and finally generalized to all binary classification problems. It is reported that the performance of a convolutional neural network (CNN) will decrease as the recognition type increases, so the variational auto-encoder (VAE) is used as the primary model. Two VAE models are used to train the machine's normal and fault sound data. Two reconstruction probabilities will be obtained during the test. The smaller value is transformed into a correction value of another value according to the mutually exclusive characteristics. Finally, the classification result is obtained according to the fusion algorithm. Filtering normal data features from fault data features is proposed, which shields the interference and makes the fault features more prominent. We confirm that good performance improvements have been achieved in the machine fault detection data set, and the results are better than most mainstream models.

• 한국인공지능학회지
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• 제11권3호
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2010년도 하계학술대회 논문집
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• pp.161-161
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• 2010

In order to achieve low power consumption and the uniform power spectrum of LED BLU (Back Light Unit) system, new circuits with a 2 stage L-C (Inductor-Capacitor) coupler have been proposed. From the simulation results based on our proposed model, the ripple power of the L-C regulation-embedded BLU circuit shows a dramatic reduction by more than 89.3% as compared to the normal BLU (without L-C circuits). This indicates that the proposed circuit is very promising for the realization of high-efficiency BLU circuits.

• 한국군사과학기술학회지
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• 제8권4호
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• pp.24-31
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• 2005

This paper presents an implementation of a real-time multi-sensor data fusion algorithm for Flight Test Computer. The sensor data consist of positional information of the target from a radar, a GPS receiver and an INS. The data fusion algorithm is designed by the 21st order distributed Kalman Filter which is based on the PVA model with sensor bias states. A fault detection and correction logics are included in the algorithm for bad measurements and sensor faults. The statistical parameters for the states are obtained from Monte Carlo simulations and covariance analysis using test tracking data. The designed filter is verified by using real data both in post processing and real-time processing.

• International Journal of Aeronautical and Space Sciences
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• 제11권1호
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• pp.31-40
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• 2010

For weapon cueing and Head-Mounted Display (HMD), it is essential to continuously estimate the motion of the helmet. The problem of estimating and predicting the position and orientation of the helmet is approached by fusing measurements from inertial sensors and stereo vision system. The sensor fusion approach in this paper is based on nonlinear filtering, especially expended Kalman filter(EKF). To reduce the computation time and improve the performance in vision processing, we separate the structure estimation and motion estimation. The structure estimation tracks the features which are the part of helmet model structure in the scene and the motion estimation filter estimates the position and orientation of the helmet. This algorithm is tested with using synthetic and real data. And the results show that the result of sensor fusion is successful.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2016년도 추계학술대회
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• pp.184-186
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• 2016

기술의 발전으로 방송환경이 다양화되면서 전문가가 아니어도 단순한 제작 환경에서 직접 콘텐츠를 제공할 수 있는 개인 방송 산업 규모가 점차 증가하고 있다. 그로 인해 개인 방송 제작자들은 필요에 의해 선택적으로 최적의 시스템을 구성하기 위한 기술들을 요구하고, 개인 방송 시청자들은 PC뿐만 아니라 모바일 등의 선호하는 디스플레이 장치를 선택하여 언제 어디서나 시청하는 것을 원한다. 이를 위해서는 제작자와 시청자를 고려한 개인방송 서비스 모델 및 기술을 제공하는 것이 필요하다. 이에 본 논문에서는 IP 네트워크 및 클라우드 컴퓨팅 환경을 활용하여 제작에서부터 전송 및 표출에 대한 전체적인 시스템 기술과 서비스 모델에 대해 논한다.

• 센서학회지
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• 제25권6호
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• pp.418-422
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• 2016

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권3호
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• pp.245-249
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• 2012

In this paper, we propose a navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using an ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a command fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process.

• 한국기계가공학회지
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• 제17권5호
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• pp.111-116
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• 2018

Thermal fusion bonding is a method to enclose open microchannels fabricated on polymer chips for use in lab-on-a-chip (LOC) devices. Polymethyl methacrylate (PMMA) is utilized in various biomedical-microelectromechanical systems (bio-MEMS) applications, such as medical diagnostic kits, biosensors, and drug delivery systems. These applications utilize PMMAs biochemical compatibility, optical transparency, and mold characteristics. In this paper, we elucidate both the conformational entanglement of PMMA molecules at the contact interfacial regime, and the qualitative nature of the thermal fusion bonding phenomena through systematic molecular dynamics simulations.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.78-80
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• 2011

In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.