• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.9
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• pp.10-19
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• 1999

In this study , a simple method has been developed to detect the required spark voltage by using the primary spark voltage instead of the secondary spark voltage. Through engine motoring experiments, this method testified to be quite satisfactory. Though the required spark voltage is affected by many in-cylinder conditions, temperature is one of the most important factors. The temperature increases significantly by combustion and the required spark voltage also changes by the temperature during the expansion stroke. On the basis of this fact, misfire can be monitored by comparing the required spark voltage between compression stroke and expansion stroke. So, in this study, two step ignition method is introduced to monitor combustion at expansion stroke. The test result shows that this method can be used to detect complex misfire pattern.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.299-303
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• 2017

By piling up hidden layers in artificial neural networks, deep learning is delivering outstanding performances for high-level abstraction problems such as object/speech recognition and natural language processing. Alternatively, deep-learning users often struggle with the tremendous amounts of time and resources that are required to train deep neural networks. To alleviate this computational challenge, many approaches have been proposed in a diversity of areas. In this work, two of the existing Apache Spark-based acceleration frameworks for deep learning (SparkNet and DeepSpark) are compared and analyzed in terms of the training accuracy and the time demands. In the authors' experiments with the CIFAR-10 and CIFAR-100 benchmark datasets, SparkNet showed a more stable convergence behavior than DeepSpark; but in terms of the training accuracy, DeepSpark delivered a higher classification accuracy of approximately 15%. For some of the cases, DeepSpark also outperformed the sequential implementation running on a single machine in terms of both the accuracy and the running time.

• Journal of KIISE
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• v.43 no.4
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• pp.450-459
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• 2016

Every year, RDFS data tends further toward scalability; hence, the manner of SPARQL processing needs to be changed for fast query. The query processing method of SPARQL has been studied using a scalable distributed processing framework. Current studies indicate that the query engine based on the scalable distributed processing framework i.e., Hadoop(MapReduce) is not suitable for real-time processing because of the repetitive tasks; in addition, it is difficult to construct a query engine based on an In-memory Distributed Query engine, because distributed structure on the low-level is required to be considered. In this paper, we proposed a method to construct a query engine for improving the speed of the query process with the mass triple data. The query engine processes the query of SPARQL using the SparkSQL, which is an In-memory based, distributed query processing framework. SparkSQL is a high-level distributed query engine that facilitates existing SQL statement. In order to process the SPARQL query, after generating the Algebra Tree using Jena, the Algebra Tree is required to be translated to Spark Algebra Tree for application in the Spark system, and construction of the system that generated the SparkSQL query. Furthermore, we proposed the design of triple property table based on DataFrame for more efficient query processing in the Spark system. Finally, we verified the validity through comparative evaluation with the query engine, which is the existing distributed processing framework.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.1
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• pp.100-108
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• 2006

Time-resolved current and voltage measurements for an inductive automotive spark system were made. Also presented are measurements of the total energy delivered to the spark gap. The measurements were made in air for a range of pressures from 1-18atm, at ambient temperatures. The measured voltage and current characteristics were found to be a function of many ignition parameters; some of these include: spark gap distance, internal resistance of the spark plug and high tension wire, and pressure. The voltages presented were measured either at the top of the spark plug or at the spark gap. The measurements were made at different time resolutions to more accurately resolve the voltage and current behavior throughout the discharge process. This was necessary because the breakdown event occurs on a time scale much shorter than the arc and glow phases. The breakdown, are, and glow voltages were found to be functions of spark plug resistance, gas density, and spark plug gap as expected from the literature. Spark duration was found to decrease as either pressure or gap was increased. The transition from the arc to glow phase is usually distinguished by a sudden rise in the voltage across the gap. At pressures above about 7atm this transition was not observed suggesting that a glow phase was not present. Energy delivered to the gap increased with increasing pressure. The effective resistance of the spark gap during discharge was about twice as large for the glow phase as the arc phase.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.270-276
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• 2018

Apache Spark is one of the high performance in-memory computing frameworks for big-data processing. Recently, to improve the performance, general-purpose computing on graphics processing unit(GPGPU) is adapted to Apache Spark framework. Previous Spark-GPGPU frameworks focus on overcoming the difficulty of an implementation resulting from the difference between the computation environment of GPGPU and Spark framework. In this paper, we propose a Spark framework based on a heterogenous pipeline computing with OpenCL to further improve the performance. The proposed framework overlaps the Java-to-Native memory copies of CPU with CPU-GPU communications(DMA) and GPU kernel computations to hide the CPU idle time. Also, CPU-GPU communication buffers are implemented with switching dual buffers, which reduce the mapped memory region resulting in decreasing memory mapping overhead. Experimental results showed that the proposed Spark framework based on a heterogenous pipeline computing with OpenCL had up to 2.13 times faster than the previous Spark framework using OpenCL.

• Journal of KIISE
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• v.45 no.2
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• pp.99-105
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• 2018

One of the spatial statistical analysis, hotspot analysis is one of easy method of see spatial patterns. It is based on the concept that "Adjacent ones are more relevant than those that are far away". However, in hotspot analysis is spatial adjacency must be considered, Therefore, distributed processing is not easy. In this paper, we proposed a distributed algorithm design for hotspot spatial analysis. Its performance was compared to standalone system and Hadoop, Spark based processing. As a result, it is compare to standalone system, Performance improvement rate of Hadoop at 625.89% and Spark at 870.14%. Furthermore, performance improvement rate is high at Spark processing than Hadoop at as more large data set.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.3
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• pp.122-129
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• 1996

The spark timing is one of major parameters to the engine performance and emissions. The ECU controls the spark timing based on preset values, which are functions of load and speed, in most of today's automotive SI engine. In this system, the preset spark timing can be different from optimum value due to the deviations from mass production, aging effects and so on. In the present study, a control logic is investigated for real time adaptation of spark timing to optimal value. It has been found that crank angle of miximum cylinder pressure is one of the appropriate parameters to estimate the optimum spark timing throught experiment. It has also been observed for spark timing convergence by variation of engineering model factors. The simulation program including engineering model for cycle by cycle variation of combustion is developed for surveying spark timing control logic. It is also shown that simulation results reflect experiment outputs and reasonableness of spark timing control logic for crank angle of maximum cylinder pressure.

• Journal of ILASS-Korea
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• v.28 no.4
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• pp.198-206
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• 2023

In this study, visualization of in-cylinder spray behavior and spark channel stretching by air flow characteristics depending on engine operating conditions were investigated. For in-cylinder spray behavior, increase in engine rpm did not alter the counter-clockwise air flow direction and location of in-cylinder dominant air flow but increased average air flow velocity, which hindered spray propagation parallel to the piston surface. When injection timing was retarded, direction of in-cylinder dominant air flow was changed, and average air flow velocity was reduced resulting in an increase in spray penetration length and change in direction. For spark channel stretching, increase in air flow speed did not affect spark channel stretch direction but affected length due to increase in spark channel resistance and limitation of energy ignition coil can handle. Change in air flow direction affected spark channel stretch direction where the air flow was obstructed by ground electrode which caused spark channel direction to occur in the opposing direction of air flow. It also affected spark channel stretch length due to change in air flow speed around the spark plug electrode from the interaction between the air flow and ground electrode.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.1
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• pp.22-31
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• 1993

Spark-ignition engine knock is affected by engine operating conditions such as engine speed, spark timing and intake air temperature. In this study the effect of intake air temperature on knock characteristics was studied experimentally using a 4-cylinder carburetor spark-ignition engine. The cylinder pressure data at 2000rpm were taken for intake air temperature range of $30^{\circ}C$ to $80^{\circ}C$ with $10^{\circ}C$ interval. And 80 consecutive cycles were taken at each experimental condition. As the same spark timing, as the intake air temperature increased by $50^{\circ}C$, the mean knock intensity increased about 20kPa. This effect corresponds to that of spark timing advance of 3 crank angle degrees.

• Journal of IKEEE
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• v.15 no.4
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• pp.305-312
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• 2011

A primary interest of this work is to develop an efficient and powerful repetitive pulser system for the application of ultra wide band generation. The important component of the pulser system is a small-sized coaxial type spark gap with planar electrodes filled with SF6 gas. A repetitive switching action by the coaxial spark gap generates two consecutive pulses in less than a microsecond with rise times of a few hundred picoseconds (ps). A set of several parameters for the repetitive switching of the spark gap is required to be optimized in charging and discharging systems of the pulser. The parameters in the charging system include a circuit scheme, circuit elements, the applied voltage and current ratings from power supplies. The parameters in the discharging system include the spark gap geometry, electrode gap distance, gas type, gas pressure and the load. The characteristics of the spark gap discharge, such as breakdown voltage, output current pulse and recovery rate are too dynamic to control by switching continuously at a high pulse repetition rate (PRR). This leads to a low charging efficiency of the spark gap system. The breakthrough of the low charging efficiency is achieved by a parallel operation of two spark gaps system. The operational behavior of the two spark gaps system is presented in this paper. The work has focused on improvement of the charging efficiency by scaling the PRR of each spark gap in the two spark gaps system.