• Journal of Sensor Science and Technology
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• v.16 no.6
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• pp.449-456
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• 2007

The work described in this study concerns the development of a disposable amperometric sensor for the electrochemical detection of a well-known aqueous pollutant, free available chlorine (FAC). The FAC sensor developed used screen printed carbon electrodes (SPCEs) coupled with immobilised syringaldazine, commonly used as an indicator in photometric FAC detection, which was directly immobilised on the surface of SPCEs using a photopolymer PVA-SbQ. To enable in-field analysis of FAC, a prototype hand-held electrochemical analyzer has been developed to withstand the environment with its rugged design and environmentally sealed connections; it operates from two PP3 (9 volt) batteries and is comparable in accuracy and sensitivity to commercial bench top systems. The sensitivity of the FAC sensor developed was $3.5{\;}nA{\mu}M^{-1}cm^{-2}$ and the detection limit for FAC was found to be $2.0{\;}{\mu}M$.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.3
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• pp.130-138
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• 1993

A mechanism for collision detection in general animation system is necessary to prevent the interpenetration among multiple objects. On the other hand, a dynamic simulation system which is a part of animation system simulates realistic motions using dynamics after the collision, which is called collision response. In this paper, a method for reducing the CPU time for collision detection by removing redundant calculations and object sorting is proposed. A dynamic simulation system including collision detection and response function was implemented to demonstrate the proposed methods, where the input data as elasticity, friction, gravity, object shape, external force and external torque are given by the user. The system simulates motions of multiple objects using dynamics, and generates the wireframe display.

• Journal of IKEEE
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• v.22 no.4
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• pp.1222-1225
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• 2018

This paper proposes an image detection algorithm to implement gesture. By using a camera sensor, the performance of the extracted image algorithm based on the gesture pattern was verified through experiments. In addition, through the experiments, we confirmed the proposed method's possibility of the implementation. For efficient image detection, we applied a segmentation technique based on image transition which divides into small units. To improve gesture recognition, the proposed method not only has high recognition rate and low false acceptance rate in real gesture environment, but also designed an algorithm that efficiently finds optimal thresholds that can be applied.

• ETRI Journal
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• v.41 no.4
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.22-28
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• 2019

This paper presents a method for detection of an emergency signal expressed by arm gestures based on motion segmentation and face area detection in the surveillance system. The important indicators of emergency can be arm gestures and voice. We define an emergency signal as the 'Help Me' arm gestures in a rectangle around the face. The 'Help Me' arm gestures are detected by tracking changes in the direction of the horizontal motion vectors of left and right arms. The experimental results show that the proposed method successfully detects 'Help Me' emergency signal for a single person and distinguishes it from other similar arm gestures such as hand waving for 'Bye' and stretching. The proposed method can be used effectively in situations where people can't speak, and there is a language or voice disability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1098-1101
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• 2022

With the development of computer music notation programs, when drawing sheet music, it is often drawn using a computer. However, there are still many use of hand-written notations for educational purposes or to quickly draw sheet music such as listening and dictating. In previous studies, OMR focused on recognizing the printed music sheet made by music notation program. the result of handwritten OMR with camera is poor because different people have different writing methods, and lens distortion. In this study, as a pre-processing process for recognizing handwritten music sheet, we propose a method for recognizing a staff using linear regression and a method for recognizing a bar using CNN. F₁ scores of staff recognition and barline detection are 99.09% and 95.48%, respectively. This methodologies are expected to contribute to improving the accuracy of handwriting.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.65-75
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• 2013

This paper proposes a method of tracking the hand region and detecting the fingertip using only depth images. In order to eliminate the influence of lighting conditions and obtain information quickly and stably, this paper proposes a tracking method that relies only on depth information, as well as a method of using region growing to identify errors that can occur during the tracking process and a method of detecting the fingertip that can be applied for the recognition of various gestures. First, the closest point of approach is identified through the process of transferring the center point in order to locate the tracking point, and the region is grown from that point to detect the hand region and boundary line. Next, the ratio of the invalid boundary, obtained by means of region growing, is used to calculate the validity of the tracking region and thereby judge whether the tracking is normal. If tracking is normal, the contour line is extracted from the detected hand region and the curvature and RANSAC and Convex-Hull are used to detect the fingertip. Lastly, quantitative and qualitative analyses are performed to verify the performance in various situations and prove the efficiency of the proposed algorithm for tracking and detecting the fingertip.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.323-335
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• 2012

In this paper, we propose an AR(augmented reality) system using user's bare hand based on computer vision. It is important for registering a virtual object on the real input image to detect and track correct feature points. The AR systems with markers are stable but they can not register the virtual object on an acquired image when the marker goes out of a range of the camera. There is a tendency to give users inconvenient environment which is limited to control a virtual object. On the other hand, our system detects fingertips as fiducial features using adaptive ellipse fitting method considering the geometric characteristics of hand. It registers the virtual object stably by getting movement of fingertips with determining the shortest distance from a palm center. We verified that the accuracy of fingertip detection over 82.0% and fingertip ordering and tracking have just 1.8% and 2.0% errors for each step. We proved that this system can replace the marker system by tacking a camera projection matrix effectively in the view of stable augmentation of virtual object.

• Environmental Analysis Health and Toxicology
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• v.26
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• pp.8.1-8.9
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• 2011

Objectives: This study assessed the health risks for children exposed to phthalate through several pathways including house dust, surface wipes and hand wipes in child facilities and indoor playgrounds. Methods: The indoor samples were collected from various children's facilities (40 playrooms, 42 daycare centers, 44 kindergartens, and 42 indoor-playgrounds) in both summer (Jul-Sep, 2007) and winter (Jan-Feb, 2008). Hazard index (HI) was estimated for the non-carcinogens and the examined phthalates were diethylhexyl phthalate (DEHP), diethyl phthalate (DEP), dibutyl-n-butyl phthalate (DnBP), and butylbenzyl phthalate (BBzP). The present study examined these four kinds of samples, i.e., indoor dust, surface wipes of product and hand wipes. Results: Among the phthalates, the detection rates of DEHP were 98% in dust samples, 100% in surface wipe samples, and 95% in hand wipe samples. In this study, phthalate levels obtained from floor dust, product surface and children's hand wipe samples were similar to or slightly less compared to previous studies. The $50^{th}$ and $95^{th}$ percentile value of child-sensitive materials did not exceed 1 (HI) for all subjects in all facilities. Conclusions: For DEHP, DnBP and BBzP their detection rates through multi-routes were high and their risk based on health risk assessment was also observed to be acceptable. This study suggested that ingestion and dermal exposure could be the most important pathway of phthalates besides digestion through food.