• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.167-173
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• 2015

Extraction of Finger-end points is one of the most process for user multi-commands in the Hand-Gesture interface technology. However, most of previous works use the geometric and morphological method for extracting a finger-end points. Therefore, this paper proposes the method of user finger-end points extraction that is motivated a ultrared diffused illumination, which is used for the user commands in the multi-touch display device. Proposed air-mouse is worked by the quantity state and moving direction of extracted finger-end points. Also, our system includes a basic mouse event, as well as the continuous command function for expending a user multi-gesture. In order to evaluate the performance of the our proposed method, after applying to the web browser application as a command device. As a result, the proposed method showed the average 90% success-rate for the various user-commands.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.1
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• pp.73-80
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• 2015

In this paper, we propose the robotic arm control method based on upper extremity electromyogram for lower upper extremity amputation patient. The muscle activity of the forearm flexor, forearm extensor and biceps was analyzed to utilize distribution of muscle activity to a specific position in order to the control input. This control input is converted into a control command for controlling the robotic arm through the algorithm. For the experiment and verify the proposed method, 5DoF robotic arm control system was constructed with 1 channel EMG Module and PC applications through the interworking with each module to perform a three-channel EMG analysis. For accuracy and performance evaluation of control, Experiments were performed with robotic arms moving objects. As a result of experiments which after training for 10 hours by middle 20's man, Validity of the proposed method was evaluated based an average accuracy of 92.5%.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.206-211
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• 2009

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.101-107
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• 2014

This study is proposed the novel PPG sensor device and the signal processing method to replace the acceleration sensor that is used to reject motion artifacts contained in photoplethysmography(PPG). The proposed method is to reject motion artifacts by an adaptive filter based on the estimated motion artifact by using a blue LED light. To evaluate the performance of the proposed method experimentally, We did design a novel sensor consisted of blue/red LEDs and photo-sensor and implemented, and then rejected the motion artifacts by using an adaptive filter and the implemented sensor. In the results of the experiments, it is shown that the proposed sensor device and signal processing can reconstruct the PPG signal despite the occurrence of motion artifacts, and also that the SNR was 4.5 times of moving average filter. According to the experimental results, the proposed method can be applied to design a low-cost device.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.53-61
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• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.19-26
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• 2007

In the sigma filter, there is a specialized neighbours distribution scheme in which the sigma value is computed from local statistics. It is designed to modify a standard average filter to preserve edges. However this filter is vulnerable to details-enhancement and conventional sigma approaches have been focused on denoising, not enhancing the characteristic area. This paper proposes an adaptive image enhancement algorithm using local statistics and functional synthesis which are utilized for adaptive realization of the enhancement, so that not only image noise may be smoothed but also details may be enhanced. For the local adaptation, parameters are estimated and weighted at each moving window that satisfy the criteria. The experimental results illuminates the effectiveness of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.95-99
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect. locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal. Rising time. Half period. and Global time, they are used as the inputs to neural network . Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising clime. Half Period amplitude. The result shored that the neural network would be applied to LPMS. Also, applying the neural network to thin practical false alarm data during startup and impact test signal at nuclear power plant, the false alarms are reduced effectively.

• Journal of Environmental Health Sciences
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• v.38 no.5
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• pp.393-397
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• 2012

Objectives: The objectives of this study were to measure passengers' exposure to ultrafine particles (UFP) and to determine effects of fuel, operating condition and position of seat. Method: UFP exposures in front and back seats of the Elephant vehicle in Seoul Grand Park were simultaneously measured by a condensation particle counter (P-Trak model 8525, TSI). The measurements were conducted 7 times with diesel-powered vehicle and 3 times with electricity vehicle in one day. The vehicle stopped at 3 locations along with 2.2 km of driving route. Results: UFP concentration in diesel-powered vehicle was significantly higher than electricity vehicle. At front seat of diesel-powered vehicle, average UFP exposure during stopping was significantly higher than during moving. When diesel-powered vehicle moved, UFP exposure in back seat was significantly higher than in front seat. Conclusions: Passengers in the diesel-powered Elephant vehicle could be exposed to high level of UFP. The UFP exposure was associated with operation condition and position of seat.

• Journal of Environmental Science International
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• v.13 no.7
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• pp.703-710
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• 2004

Petroleum refineries have been considered as an important emission source for atmospheric volatile hazardous air pollutants(HAPs). The emission source includes petroleum refinery processes and process equipment. The control strategy for volatile HAPs requires emission estimations of these pollutants. However, systematic methods of volatile HAPs emission from petroleum refineries have not yet been established. Accordingly, present study surveyed the estimation method of volatile HAPs emitted from the petroleum refinery processes and process equipment. The emission estimation methods for the petroleum refinery processes are applied for 11 petroleum refining facilities: fluidized catalytic cracking, thermal cracking, moving bed catalytic cracking, compressed engine, blowdown system, vacuum distilled column condensator, natural gas or distilled boiler, natural gas or distilled heater, oil boiler, oil heater and flare. Four emission estimation methods applied for the petroleum refinery process equipment are as follows: average emission factor approach, screening ranges approach, EPA correlation approach and unit-specific correlation approach. The process equipment for which emission factors are available are valves, pump seals, connectors, flanges and open-ended lines.

• Journal of KIISE
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• v.42 no.6
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• pp.769-780
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• 2015

Smoothing a digital curve by averaging its connected points is widely employed to minimize sharp changes of the curve that are generally introduced by noise. An appropriate degree of smoothing is critical since the area or features of the original shape can be distorted at a higher degree while the noise is insufficiently removed at a lower degree. In this paper, we provide a mathematical relationship between the parameters, such as the number of iterations, average distance between neighboring points, weighting factors for averaging and the moving distance of the point on the curve after smoothing. Based on these findings, we propose to control the smoothed curve such that its deviation is bounded particular error level as well as to significantly expedite smoothing for a pixel-based digital curve.