• Journal of the Korean Institute of Gas
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• v.14 no.3
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• pp.14-20
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• 2010

This paper was studied on the explosion hazard by spark discharge of the lithium-ion battery. The experimental samples were chosen lithium-ion battery(general, notebook) which were used for source of portable equipment. The IEC(International Electrotechnical Commission) type spark ignition test apparatus and experimental gases such as methane, propane, ethylene or hydrogen were used for explosiveness test. It was confirmed through the experiment that the explosion hazard by spark discharge. Also, it was used thermal imager for confirm that spontaneous ignition possibility by short-circuit. As the result, this paper verified that lithium-ion battery should be used and designed by special attention safety in the hazardous zone which is existed explosiveness gas.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.241-246
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• 2020

Hadoop is a framework to process large data sets in a distributed way across clusters of nodes. It has been a popular platform to process big data, but in recent years, other platforms became competitive ones depending on the characteristics of the application. Spark is one of distributed platforms to enable real-time data processing and improve overall processing performance over Hadoop by introducing in-memory processing instead of disk I/O. Whereas Hadoop is designed to work on Java and data analysis is processed using Java API, Spark provides a variety of APIs with Scala, Python, Java and R. In this paper, the goal is to find out whether the APIs of different programming languages af ect the performances in Spark. We chose two popular APIs: Python and Scala. Python is easy to learn and is used in AI domain in a wide range. Scala is a programming language with advantages of parallelism. Our experiment shows much faster processing with Scala API than Python API. For the performance issues on AI-based analysis, further study is needed.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.32-42
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• 2016

Under part load condition of spark-ignition engine, pumping loss had great effect on engine efficiency. To reduce pumping loss, the study designed spark-ignited engines to make direct spray of gasoline to combustion chamber. In spark-ignited direct-injection engines, ignition probability is important for successful combustion and flame propagation characteristics are also different from pre-mixed combustion. This study designed a visualization testing device to study ignition probability of spark-ignited direct-injection LPG fuel and combustion flame characteristics. This visualization device consists of combustion chamber, fuel supply system, air supply system, electronic control system and data acquisition system. Ambient pressure, ambient temperature and ambient air flow velocity are important parameters on ignition probability of LPG-air mixture and flame propagation characteristics, and the study also found that sprayed LPG fuel can be directly ignited by spark-plug under proper ambient conditions. To all successful cases of ignition, the study recorded flame propagation image in digital method through ICCD camera and its flame propagation characteristics were analyzed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.371-374
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• 2001

This paper describes a technique that can predict electric fires by detecting a spark signal generated from operation of electric facilities. An electric fire lead a loss of life as well as huge property, therefore it is very Important to predict an electric fire and eliminate the causes of it. Electrical spark which is ranked as majority causes of electric fires has a characterized frequency bandwidthdistinguishedfrompowerfrequenry. In the experiment, various spark signals are simulated in a condition such as short circuit, flashover and surface discharge. The results showed that the monitoring of spark signals can predict electric fires.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.4
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• pp.32-40
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• 2005

The purpose of this study is to investigate variation of spark plug protrusion and PDA valve on the exhaust emission in a gasoline engine. Swirl is one of the important parameters that affects the characteristics of combustion. PDA valve has been developed to satisfy requirements of sufficient swirl generation for improving the combustion and reducing of emission level. Also, especially, the variation of spark plug protrusion have an important effect to the early flame propagative process. This is largely due to the high flame speed by short of flame propagation distance. So, this is forced that injection timing, spark timing and intake air motion govern the stable combustion. As a result, using two combustion chamber, without charge of engine specification and the variable spark plug location and PDA valve could be reduced exhaust gas at a part load engine conditions(1500rpm imep 3.9bar, 2000rpm imep 3.2bar, 2400rpm imep 3.9bar).

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.19-24
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• 2021

In this paper, we study performance factor of machine learning in the distributed environment using Apache Spark and presents an efficient distributed processing method through experiments. This work firstly presents performance factor when performing machine learning in a distributed cluster by classifying cluster performance, data size, and configuration of spark engine. In addition, performance study of regression analysis using Spark MLlib running on the Hadoop cluster is performed while changing the configuration of the node and the Spark Executor. As a result of the experiment, it was confirmed that the effective number of executors was affected by the number of data blocks, but depending on the cluster size, the maximum and minimum values were limited by the number of cores and the number of worker nodes, respectively.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.79-84
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• 2023

Apache Spark, which provides the fastest processing speed among recent distributed and parallel processing technologies, provides real-time functions and machine learning functions. Although official documentation guides for these functions are provided, a method for fusion of functions to predict a specific value in real time is not provided. Therefore, in this paper, we conducted a study to predict the value of data in real time by fusion of these functions. The overall configuration is collected by downloading stock price data provided by the Python programming language. And it creates a model of regression analysis through the machine learning function, and predicts the adjusted closing price among the stock price data in real time by fusing the real-time streaming function with the machine learning function.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.127-136
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• 2024

This study explores the correlation between system anomalies and large-scale logs within the Spark cluster environment. While research on anomaly detection using logs is growing, there remains a limitation in adequately leveraging logs from various components of the cluster and considering the relationship between anomalies and the system. Therefore, this paper analyzes the distribution of normal and abnormal logs and explores the potential for anomaly detection based on the occurrence of log templates. By employing Hadoop and Spark, normal and abnormal log data are generated, and through t-SNE and K-means clustering, templates of abnormal logs in anomalous situations are identified to comprehend anomalies. Ultimately, unique log templates occurring only during abnormal situations are identified, thereby presenting the potential for anomaly detection.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.48-59
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• 2018

Recently, network technology has been used in various fields due to development of network technology. However, there has been an increase in the number of attacks targeting public institutions and companies by exploiting the evolving network technology. Meanwhile, the existing network intrusion detection system takes much time to process logs as the amount of network log increases. Therefore, in this paper, we propose a Spark-based network log analysis system that detects unstructured network attack pattern. by using Snort. The proposed system extracts and analyzes the elements required for network attack pattern detection from large amount of network log data. For the analysis, we propose a rule to detect network attack patterns for Port Scanning, Host Scanning, DDoS, and worm activity, and can detect real attack pattern well by applying it to real log data. Finally, we show from our performance evaluation that the proposed Spark-based log analysis system is more than two times better on log data processing performance than the Hadoop-based system.