• Journal of the Korean Institute of Gas
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• v.16 no.3
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• pp.22-28
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• 2012

For a methanol separation column of the BPA (Bisphenol A) plant, HAZOP (hazard and operability) assessment was performed and damage ranges were predicted from the accident scenarios for the fire and the explosion. As a result, the damage range of the jet fire was 20 m in the case of rupture of the discharge pipe (50 mm diameter) of safety valve, and that of the flash fire was 267 m in the case of catastrophic rupture. Also, the damage ranges of the unconfined vapor cloud explosion (UVCE) for the rupture of the discharge pipe and for the catastrophic rupture were 22 m and 542 m, respectively. For the worst case of release scenarios, safety measures were suggested as follows: the pressure instruments, which can detect abnormal rise of the internal pressure in the methanol separation column, should be installed by the 2 out of 3 voting method in the top section of the column. Through the detection, the instruments should simultaneously shut down the control and the emergency shut-off valves.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.9 s.174
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• pp.77-84
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• 2005

In most vision applications, we are frequently confronted with determining the position of object continuously. Generally, intertwined processes ire needed for target tracking, composed with tracking and control process. Each of these processes can be studied independently. In case of actual implementation we must consider the interaction between them to achieve robust performance. In this paper, the robust real time visual tracking in complex background is considered. A common approach to increase robustness of a tracking system is to use known geometric models (CAD model etc.) or to attach the marker. In case an object has arbitrary shape or it is difficult to attach the marker to object, we present a method to track the target easily as we set up the color and shape for a part of object previously. Robust detection can be achieved by integrating voting-based visual cues. Kalman filter is used to estimate the motion of moving object in 3D space, and this algorithm is tested in a pan/tilt robot system. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.465-470
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• 2004

Web Information Predictive Systems have the restriction such as they need users profiles and visible feedback information for obtaining the necessary information. For overcoming this restrict, this study designed and implemented Dynamic Web Information Predictive System using Ensemble Support Vector Machine to be able to predict the web information and provide the relevant information every user needs most by click stream data and user feedback information, which have some clues based on the data. The result of performance test using Dynamic Web Information Predictive System using Ensemble Support Vector Machine against the existing Web Information Predictive System has preyed that this study s method is an excellence solution.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.247-259
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• 1992

In this paper, the types of the data exchange between a cell site and a cobile phonefor the call processing on the AMPS(Advanced Mobile Phone Service) are investigated, and the circuit for processing the wideband data stream according to the data types is designed and implemented. The circuit for detecting the Busy / Idle bit which is needed for determining the channel access, the circuit for detecting the word sync and the circuit for transmitting and receiving the wideband data is designed. The 3-out-of-5 majority vote of the 5received data is performed to reduce error and an algorithm requiring a small buffer size for real time processing of voting process is proposed. The method to overcome the computational complexity and the real time constraint of the conventional BCH decoding is proposed.

• Korean Journal of Health Education and Promotion
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• v.33 no.5
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• pp.107-119
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• 2016

Objectives: This study aims at examining the association of sense of community with community participation for health promotion in urban areas of Korea. Methods: We analyzed data from 'Community Capacity for Healthy Gangdong Communites' Survey' in 2007. The survey was based on self-reported questionnaires, which were distributed to 1,800 community residents over the age of nineteen in five administrative communities of Gangdong-gu, Seoul, in Korea by using proportionate probability sampling method. We measured 'Sense of community' with four indicators including 'Good neighborhoods', 'Perceived possibility of cooperation', 'Pride of community' and 'Possibility of development' by using 5-point Likert scales. Community participation was measured with the experience rate or the extent of participation by 5-point Likert scales in seven community actions or activities including voting, community program planning, social actions, etc. We examined the association of sense of community with community participation by using regression analyses. Results: This study has shown that sense of community was associated with and made positive impacts on community participation in diverse community actions or activities in urban communities. Conclusions: For promoting community health in urban areas, we can increase community participation more effectively with the efforts of enhancing sense of community.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.579-586
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• 2003

In this paper, we propose a novel approach to employ emotional features to document retrieval systems. Fine emotional features, such as HAPPY, SAD, ANGRY, FEAR, and DISGUST, have been used to represent Korean document. Users are allowed to use these features for retrieving their documents. Next, retrieved documents are learned by classification methods like cohesion factor, naive Bayesian, and, k-nearest neighbor approaches. In order to combine various approaches, voting method has been used. In addition, k-means clustering has been used for our experimentation. The performance of our approach proved to be better in accuracy than other methods, and be better in short texts rather than large documents.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.443-458
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• 2014

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.

• Journal of Information Processing Systems
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• v.8 no.1
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• pp.175-190
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• 2012

Shuffling is an effective method to build a publicly verifiable mix network to implement verifiable anonymous channels that can be used for important cryptographic applications like electronic voting and electronic cash. One shuffling scheme by Groth is claimed to be secure and efficient. However, its soundness has not been formally proven. An attack against the soundness of this shuffling scheme is presented in this paper. Such an attack compromises the soundness of the mix network based on it. Two new shuffling protocols are designed on the basis of Groth's shuffling and batch verification techniques. The first new protocol is not completely sound, but is formally analyzed in regards to soundness, so it can be applied to build a mix network with formally proven soundness. The second new protocol is completely sound, so is more convenient to apply. Formal analysis in this paper guarantees that both new shuffling protocols can be employed to build mix networks with formally provable soundness. Both protocols prevent the attack against soundness in Groth's scheme. Both new shuffling protocols are very efficient as batch-verification-based efficiency-improving mechanisms have been adopted. The second protocol is even simpler and more elegant than the first one as it is based on a novel batch cryptographic technique.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.226-228
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• 2005

Predicting the cellular location of an unknown protein gives valuable information for inferring the possible function of the protein. For more accurate Prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting . The overall prediction accuracy evaluated by the 5-fold cross-validation reached $88.53\%$ for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful forpredicting subcellular localization of proteins.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.