• Journal of Korea Multimedia Society
• /
• v.18 no.3
• /
• pp.287-297
• /
• 2015

A railway security detection system is very important. There are many safety factors that directly affect the safe operation of trains. Security detection technology can be divided into passive and active approaches. In this paper, we will first survey the railway security systems and compare them. We will also propose a subway security detection system with computer vision technology, which can detect three kinds of problems: the spark problem, the obstacle problem, and the lost screw problem. The spark and obstacle detection methods are unique in our system. In our experiment using about 900 input test images, we obtained about a 99.8% performance in F- measure for the spark detection problem, and about 94.7% for the obstacle detection problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.665-666
• /
• 2022

Differential evolution is an efficient algorithm for continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems quickly degrades performance and exponentially increases runtime. To overcome this problem, a new cooperative coevolution differential evolution based on Spark (referred to as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• Journal of information and communication convergence engineering
• /
• v.19 no.3
• /
• pp.155-160
• /
• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.1
• /
• pp.161-166
• /
• 2022

In this paper, we analyze the problems that occur when performing the big data analysis using R as a data analysis tool, and present the usefulness of the data analysis with SparkR which connects R and Spark to support distributed processing of big data effectively. First, we study the memory allocation problem of R which occurs when loading large amounts of data and performing operations, and the characteristics and programming environment of SparkR. And then, we perform the comparison analysis of the execution performance when linear regression analysis is performed in each environment. As a result of the analysis, it was shown that R can be used for data analysis through SparkR without additional language learning, and the code written in R can be effectively processed distributedly according to the increase in the number of nodes in the cluster.

• International Journal of Contents
• /
• v.11 no.1
• /
• pp.69-72
• /
• 2015

Generally, existing parallel main-memory spatial index structures to avoid the trade-off between query freshness and CPU cost uses light-weight locking techniques. However, still, the lock based methods have some limits such as thrashing which is a well-known problem in lock based methods. In this paper, we propose a distributed index structure for moving objects exploiting the parallelism in multiple machines. The proposed index is a lock free multi-version concurrency technique based on the D-Stream model of Spark Stream. The proposed method exploits the multiversion nature of D-Stream of Spark Streaming.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.51-59
• /
• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.11
• /
• pp.1925-1929
• /
• 2007

This paper is studied on a novel Electric Safety Protection Device (ESPD) of high speed for incapable operation of Earth Leakage Circuit Breaker (ELB) and Molded_case Circuit Breaker (MCCB) using the low voltage distribution line. 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 ELB and MCCB, 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 novel ESPD of high speed to trip of distribution line on electric arc or spark due to electrical fire. Some experimental results of the proposed ESPD are confirmed to the validity of the analytical results.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.16 no.1
• /
• pp.38-42
• /
• 2006

Spark plasma sintering processes have been rapidly introduced recently to improve the quality and productivity of ceramic products and to solve the problem of environmental pollutions. Sintering temperatures and pressing pressures in the spark plasma sintering process are known to be the important factors highly affecting the quality of the ceramics. In this research, in order to see the effects of sintering temperatures and pressing pressures on the grain growth during the spark plasma sintering process of $Al_2O_3$ the grain growth processes associated with sintering temperatures and pressing pressures are simulated by the Monte Carlo method (MCM) and the finite element method (FEM). In this Part 1, the formulations for the simulation, which is the theoretical background of Part 2, are introduced.

• Journal of information and communication convergence engineering
• /
• v.16 no.2
• /
• pp.99-105
• /
• 2018

k-Nearest neighbor join (k-NN Join) is a computationally intensive algorithm that is designed to find k-nearest neighbors from a dataset S for every object in another dataset R. Most related studies on k-NN Join are based on single-computer operations. As the data dimensions and data volume increase, running the k-NN Join algorithm on a single computer cannot generate results quickly. To solve this scalability problem, we introduce the locality-sensitive hashing (LSH) k-NN Join algorithm implemented in Spark, an approach for high-dimensional big data. LSH is used to map similar data onto the same bucket, which can reduce the data search scope. In order to achieve parallel implementation of the algorithm on multiple computers, the Spark framework is used to accelerate the computation of distances between objects in a cluster. Results show that our proposed approach is fast and accurate for high-dimensional and big data.

• Proceedings of the KIPE Conference
• /
• 2014.07a
• /
• pp.5-6
• /
• 2014

According to 2013 fire statistical yearbook in the National Emergency Management Agency, the main cause of an electrical fire are classified to short circuit fault, overload fault, electric leakage, partial disconnection, and joint/contact badness. The occurrence factor of fire is electric arc or spark accompanied with electrical faults. Residual Current Protective Devices(RCDs) of high sensitivity type used at single phase (220V) cut off earth leakage and overload but the RCDs can not cut off electric arc or spark to be a main cause of electrical fire. To be improved on such problem, this thesis is proposed to a auxiliary control device for RCD cut off electric arc or spark.