• Geomechanics and Engineering
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• v.8 no.5
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• pp.649-661
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• 2015

This research provides insight on the laboratory investigation of the engineering properties of a lateritic soil modified with the mucilage of Opuntia ficus-indica cladodes (MOFIC), which has a history of being used as an earthen plaster. The soil is classified, according to AASHTO classification system, as A-2-6(1). The Atterberg limits, compaction, permeability, California bearing ratio (CBR) and unconfined compressive strength of the soil were determined for each of 0, 4, 8 and 12% addition of the MOFIC, by dry weight of the soil. The plasticity index, optimum moisture content, swell potential, unconfined compressive strength and permeability decreased while the soaked and unsoaked CBR increased, with increasing MOFIC contents. The engineering properties of the natural soil, which only satisfies standard requirements for use as subgrade material, became improved by the application of MOFIC such that it meets the standard requirements for use as sub-base material for road construction. The effects of MOFIC on the engineering properties of the soil resulted from bioclogging and biocementation processes. MOFIC is recommended for use as a modifier of the engineering properties of soils, especially those with similar characteristics to that of the soil used in this study, to be used as a pavement layer material. It is more economical and environment-friendly than conventional soil stabilizers or modifiers.

• Smart Structures and Systems
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• v.22 no.2
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• 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 Korean society of Dental Hygiene
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• v.20 no.5
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• pp.645-653
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• 2020

Objectives: The purpose of this study is to identify the difference between the awareness and reuser rate of infection control t for disposable dental care supplies (DDCS) according to general characteristics and infection management-related characteristics. Methods: A questionnaire was used for 277 dental hygienists to check the general characteristics, infection management-related characteristics, awareness of infection control disposable dental care products, syringe needle, prophylaxis cup, prophylaxis brush, plastic saliva ejector, orthodontic bracket, and gloves reuse rate. Results: The awareness of infection control for DDCS differed according to 'hospital type', 'average number of patients per day', 'presence or absence of infection control guidelines', and 'experience in infection management training in the last two years' (p<0.05). Reuser rates of disposable dental care products differed according to 'hospital type', 'average number of patients per day', 'presence or absence of infection control guidelines', and 'experience in infection management training in the last two years' (p<0.05). Conclusions: In order to manage infection of DDCS, the level of infection control system in the workplace is improved and support for related education is needed. In addition, guidelines and regulations on prohibition of reuse and classification criteria for various DDCS should be prepared.

• Korean Journal of Computational Design and Engineering
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• v.19 no.3
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• pp.214-223
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• 2014

This paper presents a bottleneck detection framework using simulation approach in a wafer FAB (Fabrication). In a semiconductor manufacturing industry, wafer FAB facility contains various equipment and dozens kinds of wafer products. The wafer FAB has many characteristics, such as re-entrant processing flow, batch tools. The performance of a complex manufacturing system (i.e. semiconductor wafer FAB) is mainly decided by a bottleneck. This paper defines the problem of a bottleneck process and propose a simulation based framework for bottleneck detection. The bottleneck is not the viewpoint of a machine, but the viewpoint of a step with the highest WIP in its upstream buffer and severe fluctuation. In this paper, focus on the classification of bottleneck steps and then verify the steps are not in a starvation state in last, regardless of dispatching rules. By the proposed framework of this paper, the performance of a wafer FAB is improved in on-time delivery and the mean of minimum of cycle time.

• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.634-638
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• 2015

Four major social safety indexes including industrial accident, traffic accident, fire, and violent crime were selected, and transition of those values by time series data analysis since 2003 was presented. Comparing with the 2003 figure, the index of industrial accident was reduced by 27.8%, which was the most improved safety index. The indicators describing the traffic accident and violent crime rate were reduced by approximately 12%. However, the fire safety index showed an increase of 40% compared with the base year because national fire classification system was changed so that minor fire is also included in the counting since 2006.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.62-69
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• 2009

For the fault diagnosis of power transformer, we develop a diagnosis algorithm based on support vector machine. The proposed fault diagnosis system consists of data acquisition, fault/normal diagnosis, and identification of fault. In data acquisition part, concentrated gases are extracted from transformer for data gas analysis. In fault/normal diagnosis part, KEPCO based decision rule is performed to separate normal state from fault types. The determination of fault type is executed by multi-class SVM in identification part. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.5
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• pp.225-233
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• 2001

SIME(SIngle Machine Equivalent) method has been recognized as a useful tool to determine transient stability of power systems. In this paper, SIME method is used to develop the KEPCO transient stability assessment (TSA) tool. A new screening algorithm that can be implemented in SIME method is proposed. The salient feature of the proposed screening algorithm is as follows. First, critical generators are identified by a new index in the early stage of the time domain simulation. Thus, computational time required to find OMIB(One Machine Infinite Bus) can be reduced significantly. Second, clustering critical machines can be performed even in very stable cases. It enables to be avoid extra calculation of time trajectory that is needed in SIME for classifying the stable cases. Finally, using power-angle trajectory and subdividing contingency classification have improved the screening capability. This algorithm is applied to the fast TSA of the KEPCO system.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.841-850
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• 2004

This paper proposes a GA and GDM-based method for removing unnecessary rules and generating relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. When the fine fuzzy partition is used with conventional methods, the number of fuzzy rules has been enormous and the performance of fuzzy inference system became low. This paper presents the application of GA as a means of finding optimal solutions over fuzzy partitions. In each rule, the antecedent part is made up the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. It is shown that the proposed method has improved than the performance of conventional method in formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules.