• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.30-37
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• 2011

This paper proposes a language set of hand motions for enhancing the performance of vision-based hand signal recognizer. Based on the statistical analysis of the angular tendency of hand movements in sign language and the hand motions in practical use, we construct four motion primitives as building blocks for basic hand motions. By combining these motion primitives, we design a discernable 'fundamental hand motion set' toward increasing the hand signal recognition. To demonstrate the validity of proposed designing method, we develop a 'fundamental hand motion set' recognizer based on hidden Markov model (HMM). The recognition system showed 99.01% recognition rate on the proposed language set. This result validates that the proposed language set enhances discernaility among the hand motions such that the performance of hand signal recognizer is improved.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1251-1255
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• 2015

The purpose of this study is to measurement the cardiac output using hand to hand impedance method to provide convenience to user when measuring SV(stroke volume) with the use of ICG(Impedance Cardiography). We suggest the optimized modified formula, which can be applied when using impedance with the use of hand to hand Impedance. To verify this formula, a SV from transthoracic approach and hand to hand approach are compared for the 36 subjects, respectively. The acquired data was analyzed by using LabVIEW 8.6, analysis was implemented by SPSS 12.0. Fine difference was shown by individual. We showed that as a result of analyzing the ICG measurement value on thoracic and hand to hand, the correlation with SV was r=0.716, thereby having indicated the results of regression model in relatively high correlation.

• Journal of the Korean Applied Science and Technology
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• v.35 no.3
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• pp.587-594
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• 2018

The purpose of this study was to evaluate the correlation between hand washing practice and hand washing on the health belief model among dental practitioners. A self-reported questionnaire was completed by each 140 dental practitioners in dental clinics, general and university dental hospitals. The data were analyzed statistically with t-test, 1-way ANOVA, Pearson's correlation coefficient test(${\alpha}=.05$). The hand washing practice showed a quantitative linear relationship(p<0.01) of r=0.285 with the hand washing on health belief. In order to prevent hospital infection of dental practitioners working in dental clinics or general and university dental hospitals, health beliefs about hand washing consisting of perceived susceptibility, perceived seriousness, perceived benefit, perceived barriers, and cues to action in infection control education should be considered.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.467-473
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• 2007

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.16 no.4
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• pp.398-412
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• 2006

The purpose of this study is to investigate the relationship between the acceleration of vibration by the powered hand tools used in the shipbuilding industry, and to develop the prospective prevalence model for the hand-arm vibration syndrome among the shipbuilding workers.The acceleration levels and frequencies of six types of grinder were measured using the ISO5349 method along with the time of exposure to the vibration from the powered hand tools. Medical examination for 114 workers were performed using the cold provocation test. Comparisons were made between the estimated prevalence of hand-arm vibration syndrome from ISO5349 and the observed values from the medical examinations. By multiple regression, we developed the prospective prevalence model of hand-arm vibration syndrome produced by the hand tools used in the shipbuilding industry. 4 hour-energy-equivalent frequency-weighted accelerations were $6.23m/s^2$ in the grinding job done after welding, and $13.39m/s^2$ in the grinding job done before painting. The mean exposure time while holding powered hand tools was 4.64 hours. Prevalence rates of Raynaud's Phenomenon were 12.04% in the grinding after soldering, and 42.9% in the grinding before painting measured using the ISO5349 method. After exposure to vibration for 10.79 years, about a half of the workers in the grinding after welding could developed Raynaud's Phenomenon. For the workers in the grinding before painting, the latency was 5.02 years. The ISO equation for dose response relationship was not significantly correlated with observed recovery rates of finger skin temperatures, blood flows and amplitudes of nerve conduction velocities. A multiple regression model for dose-response relationship was proposed from the results. Recovery rate of the skin temperatures = -0.668+ 0.337 ${\times}$ 4 hour energy equivalent frequency-weighted accelerations + 0.767 ${\times}$ duration of vibration exposure(years) The validity was proved by multiple regression analysis after correlation transformation and regression results based on model-building data and validation data.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.121-125
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• 2004

In the most of recognizing systems of hand-written numbers. extraction of feature shape by using character elements shapes and a method of morphological analysis by using then extraction of feature shapes were usually used. In this paper, however, peculiar chain-code is used, and differential code which gets minimal value by differentiating the chain-code which is generated by the peculiar chain-code is made. We found this differential code is very successful in discriminating hand-written numbers according to the result of applying to most of the hand-written numbers. Testing recognition of hand-written numbers by HMM network. From the results, we can recognize of 96.1 percentage hand-written numbers but can not recognize extremely distorted hand-written numbers.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.395-398
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• 2003

This study presents wrong hand calculations for fault analysis at field on the real AC railway system. Hence, we correctly revised the hand calculations. We propose AC railway system model by using EMTDC. Fault studies are performed. We also compare revised hand calculations with EMTDC simulation to verify the proposed model made by EMTDC. So, we can confirm that the model made by using EMTDC is correctly designed.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.177-186
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• 2012

In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.265-279
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• 2008

The idea of using hand gestures for human-computer interaction is not new and has been studied intensively during the last dorado with a significant amount of qualitative progress that, however, has been short of our expectations. This paper describes a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on the image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of skin extraction and modeling, and motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to a model. In an experiment with ten isolated gestures, we obtained the recognition rate upwards of 99.59% with cross validation. The proposed model and the related approach are believed to have a strong potential for successful applications to other related problems such as sign languages.