• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.93-101
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• 2021

In this paper, a vehicle license plate detection scheme is proposed that uses the spatial attention areas to detect accurately the license plates in various real-road situations. First, the previous WPOD-NET was analyzed, and its detection accuracy is evaluated as lower due to the unnecessary noises in the wide detection candidate areas. To resolve this problem, a vehicle license plate detection model is proposed that uses the candidate area of the license plate as a spatial attention areas. And we compared its performance to that of the WPOD-NET, together with the case of using the optimal spatial attention areas using the ground truth data. The experimental results show that the proposed model has about 20% higher detection accuracy than the original WPOD-NET since the proposed scheme uses tight detection candidate areas.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.79-88
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• 2002

As a part of NERI:;, Truck Shipment Safety Information is divided into Optimal Route Guidance system and Emergency Response system. This study which is for developing of Truck Shipment Incident Emergency Response System intends to prevent or early response damage caused by incidents through realtime monitoring about the position and the state of Hazard material transport truck. For this, we divide it into three scenarios; realtime monitoring, management of incidents, information provision to related organizations and present functional requirements and architecture coming with each scenario. As a result of the first step among total three steps, it would able to not only realtime management of trucks but also guide for auto-enforcement or management about illegal act like dumping scrapped material. It is now under examination about position of detection and technology of communication to application. From now on, it is expect to test in the range of Metropolitan after selecting appropriate technology.

• Tribology and Lubricants
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• v.28 no.3
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• pp.130-135
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• 2012

This study presents a new methodology for realtime tool breakage detection by sensor fusion concept of two hall sensor and an acoustic emission (AE) sensor. Spindle induction motor torque of CNC Lathe during machining is estimated by two hall sensor. Estimated motor torque instead of a tool dynamometer was used to measure the cutting torque and tool breakage detection. A burst of AE signal was used as a triggering signal to inspect the cutting torque. A significant drop of cutting torque was utilized to detect tool breakage. The algorithm was implemented on a NI DAQ (Data Acquisition) board for in-process tool breakage detection. The result of experiment showed an excellent monitoring capability of the proposed tool breakage detection system. This system is available tool breakage monitoring through internet also provides this system's user with current cutting torque of induction motor.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• Journal of the Korea Computer Industry Society
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• v.5 no.3
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• pp.337-344
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• 2004

ITS(Intelligent transportation system) provides users with realtime traffic information based on technologies such as advanced information ＆ telecommunication, electronic control and transportation engineering. To operate efficient ITS, it is necessary to quickly identify and take actions for incidents(accidents, broken vehicles, public functions, traffic control, etc.). However, there have been few reliable incident detection algorithms developed so far. The algorithm presented in this study greatly resolved the problems in the existing incident detection algorithms, which determine incidents according to the input of constant values, by defining ranges based on the concept of pseudo level of service. With this improvement, operators can determine the incident detection parameters more accurately.

• Journal of Microbiology and Biotechnology
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• v.20 no.3
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• pp.569-573
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• 2010

A quantitative detection method for Salmonella in seafood was developed using a SYBR Green-based real-time PCR assay. The assay was developed using pure Salmonella DNA at different dilution levels [i.e., 1,000 to 2 genome equivalents (GE)]. The sensitivity of the real-time assay for Salmonella in seeded seafood samples was determined, and the minimum detection level was 20 CFU/g, whereas a detection level of 2 CFU/ml was obtained for pure culture in water with an efficiency of ${\geq}85%$. The real-time assay was evaluated in repeated experiments with seeded seafood samples and the regression coefficient ($R^2$) values were calculated. The performance of the real-time assay was further assessed with naturally contaminated seafood samples, where 4 out of 9 seafood samples tested positive for Salmonella and harbored cells <100 GE/g, which were not detected by direct plating on Salmonella Chromagar media. Thus, the method developed here will be useful for the rapid quantification of Salmonella in seafood, as the assay can be completed within 2-3 h. In addition, with the ability to detect a low number of Salmonella cells in seafood, this proposed method can be used to generate quantitative data on Salmonella in seafood, facilitating the implementation of control measures for Salmonella contamination in seafood at harvest and post-harvest levels.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1671-1677
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• 2018

In this research, the collision detection system based on insect's visual interneuron has been designed. The lobula giant movement detector (LGMD) corresponds to the movement value that increases in direct collision process. If the collision is detected by the LGMD only, it could generate a crash warning even in a non-collision situation, resulting in a lot of false alarms. Directionally sensitive movement detectors (DSMD) are directionally sensitive algorithm based on the elementary movement detectors (EMD) in four directions (up, down, left, and right). In this paper, we propose an efficient VLSI architecture for a realtime collision detection system that is robust to the surrounding environment while improving accuracy. The proposed architecture is synthesized with Dongbu Hightech 110nm standard cell library and shows 333MHz of maximum operating frequency and requires 8400 gates with about 16.5KB of internal memories.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.125-132
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• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.272-279
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• 2008

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see realtime operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST company. Thus, our algerian in the paper can be useful in PMP(portable multimedia player) or other portable players.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.159-170
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• 2009

It is very important to extract the expression data and capture a face image from a video for online-based 3D face animation. In recently, there are many researches on vision-based approach that captures the expression of an actor in a video and applies them to 3D face model. In this paper, we propose an automatic data extraction system, which extracts and traces a face and expression data from realtime video inputs. The procedures of our system consist of three steps: face detection, face feature extraction, and face tracing. In face detection, we detect skin pixels using YCbCr skin color model and verifies the face area using Haar-based classifier. We use the brightness and color information for extracting the eyes and lips data related facial expression. We extract 10 feature points from eyes and lips area considering FAP defined in MPEG-4. Then, we trace the displacement of the extracted features from continuous frames using color probabilistic distribution model. The experiments showed that our system could trace the expression data to about 8fps.