• Fire Science and Engineering
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• v.20 no.1 s.61
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• pp.71-76
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• 2006

The major causes of electrical fire are classified to short circuit fault, overload fault, electric leakage and electric contact failure. The occurrence factor of the fire is electric arc or spark accompanied with electrical faults. Residual Current Protective Device(RCD) of high sensitivity type used at low voltage wiring cuts off earth leakage and overload, but the RCD can't cut off electric arc or spark to be a major factor of electrical fire. As the RCDs which are applied low voltage distribution panel are prescribed to rated breaking time about 30[ms](KS C 4613), the RCDs can't perceive to the periodic electric arc or spark of more short wavelength level. To be improved on such problem, this paper is proposed to a auxiliary control apparatus for RCD trip on electric arc or spark due to electrical fire. Some experimental results of the proposed apparatus is confirmed to the validity of the analytical results.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2102-2103
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• 2008

The major causes of electrical fire are classified to short circuit fault, overload fault, electric leakage and electric contact failure. The occurrence factor of the fire is electric arc or spark accompanied with electrical faults. Residual Current Protective Device(RCD) of high sensitivity type used at low voltage wiring cuts off earth leakage and overload, but the RCD can't cut off electric arc or spark to be a major factor of electrical fire. As the RCDs which are applied low voltage distribution panel are prescribed to rated breaking time about 30[ms](KS C 4613), the RCDs can't perceive to the periodic electric arc or spark of more short wavelength level. To be improved on such problem, this paper is proposed to a electrical fire disaster prevention device(EFDPD) for a RCD trip or a self circuit-breaking function on electric arc or spark due to electrical fire. Some experimental results of the proposed apparatus is confirmed to the validity of the analytical results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1649-1665
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• 2021

In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1953-1958
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• 2012

The major causes of electrical fire are classified to short circuit fault, overload fault, electric leakage and electric contact failure. The occurrence factor of the fire is electric arc or spark accompanied with such electric faults, specially short circuit faults. Earth Leakage Circuit Breaker (ELB) and Molded_case Circuit Breaker (MCCB), that is, Residual Current Protective Devices (RCDs) used on low voltage distribution lines cut off earth leakage and overload, but the RCD can not cut off electric arc or spark to be a major factor of electrical fire. As the RCDs which are applied in low voltage distribution panel are prescribed to rated breaking time about 30ms(KS C 4613), the RCDs can't perceive to the periodic electric arc or spark of more short wavelength level. To improve such problem, this paper proposes a prevention apparatus using the line voltage drop of neutral wire and some semiconductor switching devices. Some experimental tests of the proposed apparatus confirm the validity of the analytical results.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.140-149
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• 2018

Social data such as users' comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users' hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users' interests on different topics.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.371-372
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• 2007

The major causes of electrical fire are classified to short circuit fault, overload fault, electric leakage and electric contact failure. The occurrence factor of the fire is electric arc or spark accompanied with electrical faults. Residual Current Protective Device (RCD), that is Earth Leakage Circuit Breaker(ELB) and Molded_case Circuit Breaker (MCB), of high sensitivity type used at low voltage wiring cuts off earth leakage and overload, but the RCD can't cut off electric arc or spark to be a major factor of electrical fire. As the RCDs which are applied low voltage distribution panel are prescribed to rated breaking time about 30[ms] (KS C 4613), the RCDs can't perceive to the periodic electric arc or spark of more short wavelength level. To be improved on such problem, this research development is proposed to a auxiliary control apparatus for RCD trip on electric arc or spark due to electrical fire. Some experimental results of the proposed apparatus is confirmed to the validity of the analytical results.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.6
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• pp.42-48
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• 1999

Engine misfire causes of the negative effect on exhaust emission such as HC, CO, and NOX . Moreover, it causes damage to the three-way-catalyst(TWC) system permanently. The crankshaft velocity fluctuation(CVF) method has been applied for the real cars as misfire detection system usually, which utilizes the crank angle sensor input to calculate the variation of the crankshaft rotational speed. But this approach has the limit due to the fact that three could be problem under certain engine condition like as deceleration or high speed condition . Therefore the development of new methods are requested today. This study introduced the new method of misfire detection using breakdown voltage(BDV) characteristics between spark plug electrouds.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.672-674
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• 1997

In this paper, we study the idle speed control of the spark ignition engine. Engine idle speed control is a difficult problem because of troublesome characteristics such as severe process nonlinearities, variable time delays, time-varying dynamics and unobservable internal system states and disturbances. We investigate the intelligent control algorithms such as neural network controller and fuzzy controller for 4-cylinder 4-stroke engine.