• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.83-90
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• 2015

Almost of current Automatic Incident Detection(AID) algorithms involve the vulnerability that detects the traffic accident in open road or in tunnel as the traffic jam not as the traffic accident. This paper proposes the improved accident detection algorithm to enhance the detection probability based on accident detection algorithms applied in open roads. The improved accident detection algorithm provides the preliminary judgment of potential accident by detecting the stopped object by Gaussian Mixture Model. Afterwards, it measures the detection area is divided into blocks so that the occupancy rate can be determined for each block. All experimental results of applying the new algorithm on a real incident was detected image without error.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.509-517
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• 2020

Geographic information system (GIS) sewer network data are a fundamental input material for urban inundation modeling, which is important to reduce the increasing damages from urban inundation due to climate change. However, the essential attributes of the data built by a local government are often missing because the purpose of building the data is the maintenance of the sewer system. Inconsistent simplification and supplementation of the sewer network data made by individual researchers may increase the uncertainty of flood simulations and influence the inundation analysis results. Therefore, it is necessary to develop a basic algorithm to convert the GIS-based sewage network data into input data that can be used for inundation simulations in consistent way. In this study, the format of GIS-based sewer network data for a watershed near the Sadang Station in Seoul and the Oncheon River Basin in Busan was investigated, and a missing data supplementing algorithm was developed. The missing data such as diameter, location, elevation of pipes and manholes were assumed following a consistent rule, which was developed referring to government documents, previous studies, and average data. The developed algorithm will contribute to minimizing the uncertainty of sewer network data in an urban inundation analysis by excluding the subjective judgment of individual researchers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.449-459
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• 2021

More than 80 % of agricultural reservoirs are old facilities over 50 years old, and safety and function declines occur. As a result, safety accidents such as the collapse of the reservoir have occurred. Precise safety diagnosis is conducted in advance to prevent accidents such as reservoir collapse, and Water resources facility improvement project are implemented based on priority. However, the priority of the business is selected based on the subjective judgment of the facility manager. In this study, we set 80 hypotheses based on the results of precision safety diagnosis and decision-making examples of existing Water resources facility improvement project and selected 45 variables using correlation analysis and significance test. Using logistic regression analysis, the final 21 variables were selected and a decision support model was presented, and the classification accuracy of the model was 86.8 %. In this research, the part that presented the quantitative index for decision support when selecting the Water resources facility improvement project has important significance.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.741-747
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• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4491-4509
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• 2017

The present paper proposes a novel dynamic system for hand gesture recognition. The approach involved is comprised of three main steps: detection, tracking and recognition. First, the gesture contour captured by a 2D-camera is detected by combining the three-frame difference method and skin-color elliptic boundary model. Then, the trajectory of the hand gesture is extracted via a gesture-tracking algorithm based on an occlusion-direction oriented linear extrapolation predictor, where the gesture coordinate in next frame is predicted by the judgment of current occlusion direction. Finally, to overcome the interference of insignificant trajectory segments, the longest common subsequence (LCS) is employed with the aid of velocity information. Besides, to tackle the subgesture problem, i.e., some gestures may also be a part of others, the most probable gesture category is identified through comparison of the relative LCS length of each gesture, i.e., the proportion between the LCS length and the total length of each template, rather than the length of LCS for each gesture. The gesture dataset for system performance test contains digits ranged from 0 to 9, and experimental results demonstrate the robustness and effectiveness of the proposed approach.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.9-18
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• 2005

Flicker is the 'variation in brightness or he perceived won stimulation by intermittent or temporally non uniform light'. This phenomenon is blown as the cause of eye strain and headaches. Many researchers are dedicated to reducing this phenomenon. The flicker phenomenon also exists in PDP as some other display types, and is a critical problem in 50 Hz PDP. However, it is difficult to define flicker by more than one subjective judgment. So, an objective measurement of flicker is necessary and convenient for research on displays. In this paper, a computational prediction model is proposed which is used to predict luminance flicker (not chromatic flicker) by giving a quantitative output that describes the probability of occurrence of flicker. Through this work, we expected to provide a practical tool for flicker-free design in PDP.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.35-42
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• 2022

When an accident occurs, the associated human activity is typically regarded as a "human error," or a temporal deviation. On the other hand, if the accident results in a serious loss or if it evokes a social issue, the person determined to be responsible may be punished with a "violation" of related laws or regulations. However, as Heinrich stated, it is neither appropriate nor reasonable in terms of probability theory and cognitive science to distinguish whether it is a "human error" or a "violation" with a criterion of resultant accident severity. Nonetheless, some in society get on the social climate to strengthen regulations on workers who have caused accidents, especially violations. This response can present a social issue due to the lack of systematic judgment procedure which distinguishes violations from human errors. The purpose of this study was to develop an objective and systematic procedure to assess whether workers' activities which induced industrial accidents should be categorized as violations rather than human errors. Various analysis techniques for the determination of violation procedure were investigated and compared using an analysis approach method. An appropriate technique was not found, however, for judging the culpability of intentional violations. As an alternative, this study developed the process of creating violations, based on cognitive procedure, as well as the criteria to determine and categorize an activity as a violation. In addition, the developed procedure was applied to cases of industrial accidents and nuclear power plant issues to test its practical applicability. The study demonstrated that the proposed model could be used to determine the existence of a violation even in the case of multiple workers who work simultaneously.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.67-78
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• 2018

The stability of cutting slope influences the safety of railway operation, and how to identify the stability of the slope quickly and determine the rational monitoring plan is a pressing problem at present. In this study, the attribute recognition model of risk assessment for high cutting slope stability in the heavy haul railway is established based on attribute mathematics theory, followed by the consequent monitoring scheme design. Firstly, based on comprehensive analysis on the risk factors of heavy haul railway loess slope, collapsibility, tectonic feature, slope shape, rainfall, vegetation conditions, train speed are selected as the indexes of the risk assessment, and the grading criteria of each index is established. Meanwhile, the weights of the assessment indexes are determined by AHP judgment matrix. Secondly, The attribute measurement functions are given to compute attribute measurement of single index and synthetic attribute, and the attribute recognition model was used to assess the risk of a typical heavy haul railway loess slope, Finally, according to the risk assessment results, the monitoring content and method of this loess slope were determined to avoid geological disasters and ensure the security of the railway infrastructure. This attribute identification- risk assessment- monitoring design mode could provide an effective way for the risk assessment and control of heavy haul railway in the loess plateau.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1626-1637
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• 2020

This work covers an important point of the benchmark released by the expert group on Uncertainty Analysis in Modeling of Light Water Reactors. This ambitious benchmark aims to determine the uncertainty in light water reactors systems and processes in all stages of calculation, with emphasis on multi-physics (coupled) and multi-scale simulations. The Gesellschaft für Anlagen und Reaktorsicherheit methodology is used to propagate the thermal-hydraulic uncertainty of macroscopic parameters through TRACE5.0p3/PARCSv3.0 coupled code. The main innovative points achieved in this work are i) a new thermal-hydraulic model is developed with a highly-accurate 3D core discretization plus an iterative process is presented to adjust the 3D bypass flow, ii) a control rod insertion occurrence -which data is obtained from a real PWR test- is used as a transient simulation, iii) two approaches are used for the propagation process: maximum response where the uncertainty and sensitivity analysis is performed for the maximum absolute response and index dependent where the uncertainty and sensitivity analysis is performed at each time step, and iv) RESTING MATLAB code is developed to automate the model generation process and, then, propagate the thermal-hydraulic uncertainty. The input uncertainty information is found in related literature or, if not found, defined based on expert judgment. This paper, first, presents the Gesellschaft für Anlagen und Reaktorsicherheit methodology to propagate the uncertainty in thermal-hydraulic macroscopic parameters and, then, shows the results when the methodology is applied to a PWR reactor.