• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.258-258
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• 2014

Spark discharge in water generates shockwaves which have been utilized to generate mechanical actuation for potential use in pumping application. Discharge pulses of several microseconds generate shockwaves and vapor bubbles which subsequently displace the water for a period of milliseconds. Through the use of a sealed discharge chamber and metal bellow spring, the fluid motion can be used create an oscillating linear actuator. Continuous actuation of the bellow has been demonstrated through the use of high frequency spark discharge. Discharge in water forms a region of high electric field around the electrode tip which leads to the creation of a thermal plasma channel. This process produces fast thermal expansion, vapor and bubble generation, and a subsequent shockwave in the water which creates physical displacement of the water [1]. Previous work was been conducted to utilize the shockwave effect of spark discharge in water for the inactivation of bacteria, removal of mineral fouling, and the formation of sheet metal [2-4]. Pulses ranging from 25 to 40 kV and 600 to 900 A are generated inside of the chamber and the bellow motion is captured using a slow motion video camera. The maximum displacements measured are from 0.7 to 1.2 mm and show that there is a correlation between discharge energy input to the water and the displacement that is generated. Subsequent oscillations of the bellow are created by the spring force of the bellow and vapor in the chamber. Using microsecond shutter speed ICCD imaging, the development of the discharge bubble and spark can be observed and measured.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.285-292
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• 2007

SoC which deploys software modules as well as hardware IPs on a single chip is a major revolution taking place in the implementation of a system design, and high-level synthesis is an important process of SoC design methodology. Recently, SPARK parallelizing high-level synthesis software tool has been developed. It takes a behavioral ANSI-C code as an input, schedules it using code motion and various code transformations, and then finally generates synthesizable RTL VHDL code. Although SPARK employs various loop transformation algorithms, the synthesis results generated by SPARK are not acceptable for basic signal and image processing algorithms with nested loop. In this paper we propose a SoC platform with an application-specific PLD targeting local operations which are feature of many loop algorithms used in signal and image processing, and demonstrate design process which maps behavioral specification with nested loops written in a high-level language (ANSI-C) onto 2D systolic array. Finally the derived systolic array is implemented on the proposed application-specific PLD of SoC platform.

• Journal of IKEEE
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• v.17 no.1
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• pp.10-15
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• 2013

This paper, we analyzed that the measured data of the radiated power spectrum of electromagnetic waves of the normal spark ignition system and the spark ignition system with feed through type ceramic condenser. The results show that the strength of power spectrum radiated from the system with feed through type ceramic condenser is weaker than the normal system, and the density of power spectrum radiated from the system with feed through type ceramic condenser is smaller than the normal system. From these results, the feed through type ceramic condenser can reduce the electromagnetic waves radiating from the spark ignition system which is the spurious emission, and it can be concluded that the ignition coil of the spark ignition system generating high voltage pulse is equivalent to the radio frequency oscillator which is oscillating high frequency from a electronic point of view.

• Journal of KIISE
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• v.42 no.9
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.35.1-35.1
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• 2005

• Journal of the korean Society of Automotive Engineers
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• v.6 no.3
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• pp.36-44
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• 1984

The prediction of the emission concentrations in 4-cycle 4-cylinder spark ignition engine is made by considering the model with the extended Zedovich mechanism. The predicted values for nitric oxide, carbon dioxide and carbon monoxide agree with the experimentally measured ones.

• 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.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.106-112
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• 1997

This study measured the ignition limits of methane-air, propane-air, ethylene-air, and hydrogen-air mixture gases by discharge spark of D.C. power resistive circuit. The used experimental device is the IEC type spark ignition test apparatus, it consists of explosion chamber and supply -exhaust system of mixture gas. Mixture gases (methane-air, propane-air, ethylene-air, and hydrogen-air) were put into explosion chamber of IEC type spark ignition test apparatus, then it was confirmed whether ignition was made by 3,200 times of discharge spark between tungsten electrode and cadmium electrode. The ignition limits were found by increasing or decreasing the value of current. For the exact experiment, the ignition sensitivity was calibrated before and after the experiment in each condition. The ignition limits were found by changing the value of concentration of each gas-air mixture in D.C. 24 [V] resistive circuit. As the result of experiment, it was found that the minimum ignition limit currents exist at the value of methane-air 8.3 [$Vol\%$], propane-air 5.25[$Vol\%$], ethylene-air 7.8 [$Vol\%$], and hydrogen-air 21[$Vol\%$] mixture gases. For each the minimum ignition concentration of gases, the relationships between voltage and minimum ignition current were found. The results are as follows. - The minimum ignition limits are decreasing in the order of methane, propane, ethylene, and hydrogen. - The value of ignition current is inversely proportional to the value of source voltage. - The minimum ignition limit currents increase sharply at more than 2 [A]. The reason is caused by overheating the electrode.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.774-779
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• 2015

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner using Apache Spark, an in-memory high speed cluster computing environment, which is effective for sequencing and iterating component reasoning jobs. The proposed reasoner can not only check the integrity of a large-scale spatial knowledge base representing topological and directional relationships between spatial objects, but also expand the given knowledge base by deriving new facts in highly efficient ways. In general, qualitative reasoning on topological and directional relationships between spatial objects includes a number of composition operations on every possible pair of disjunctive relations. The proposed reasoner enhances computational efficiency by determining the minimal set of disjunctive relations for spatial reasoning and then reducing the size of the composition table to include only that set. Additionally, in order to improve performance, the proposed reasoner is designed to minimize disk I/Os during distributed reasoning jobs, which are performed on a Hadoop cluster system. In experiments with both artificial and real spatial knowledge bases, the proposed Spark-based spatial reasoner showed higher performance than the existing MapReduce-based one.