• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.1
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• pp.23-33
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• 2018

In the general manual feeder of an LED lighting assembly system, many workers are needed to supply parts to the main conveyor. The automatic feeder for modern automation lighting assembly systems consists of a completely automated feeding system and a magazine system that supplies the parts automatically. A standardized LED panel and diffusion cover is stacked in the cartridge of the magazine system. The structural safety of the automatic feeding system with regard to handling the load from the panels and covers stored in the cartridge should be guaranteed. LED convergency lighting modules are assembled using two LED panels and one diffusion cover in an automatic feeder. In this study, the structural safety and fatigue life of the automatic feeder and magazine were analyzed by considering the load generated in the automatically assembled LED convergency lighting system. In addition, the dynamic behavior of each auto-feeding system and magazine delivery system was visualized, and the working process was evaluated via dynamic simulation using a virtual engineering method. A tack time table for automatic feeding systems was derived by developing a virtual prototype.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2835-2844
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• 1998

Most data parallel languages provide the block-cyclic distribution (cyclic(k)) that is one of the most general regular distributions. In order to generate local addresses for an array section A(l:h:s) with block-cyclic distribution, efficient compiling methods or run-time methods are required. In this paper, two local address generation methods for the block-cyclic distribution are presented. One is a simple scan method that is modified from the virtual-block scheme. The other is a linear-time ${\Delta}M$ table that contains the local memory access information construction method. This method is simpler than other algorithms for generating a ${\Delta}M$ table. Experimental results show that a simple that a simple scan method has poor performance but a linear-time ${\Delta}M$ table generation method is faster than other algorithms in ${\Delta}M$ table generation time and access time for 10,000 array elements.

• Journal of Korea Game Society
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• v.12 no.5
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• pp.67-78
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• 2012

Summed area table allows filtering of arbitrary-width box regions for every pixel in constant time per pixel. This characteristic makes it beneficial in image processing applications where the sum or average of the surrounding pixel intensity is required. Although calculating the summed area table of an image data is primarily a memory bound job consisting of row or column-wise summation, previous works had to endure excessive access to the high latency global memory in order to exploit data parallelism. In this paper, we propose an efficient algorithm for generating the summed area table in the GPGPU environment where the input is decomposed into square sub-images with intermediate data that are propagated between them. By doing so, the global memory access is almost halved compared to the previous methods making an efficient use of the available memory bandwidth. The results show a substantial increase in performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.395-400
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• 2017

The paper discussed the air-to-fuel ratio control of automotive fuel-injection systems using the cerebellar model articulation controller(CMAC) neural network. Because of the internal combustion engines and fuel-injection's dynamics is extremely nonlinear, it leads to the discontinuous of the fuel-injection and the traditional method of control based on table look up has the question of control accuracy low. The advantages about CMAC neural network are distributed storage information, parallel processing information, self-organizing and self-educated function. The unique structure of CMAC neural network and the processing method lets it have extensive application. In addition, by analyzing the output characteristics of oxygen sensor, calculating the rate of fuel-injection to maintain the air-to-fuel ratio. The CMAC may easily compensate for time delay. Experimental results proved that the way is more good than traditional for petrol control and the CMAC fuel-injection controller can keep ideal mixing ratio (A/F) for engine at any working conditions. The performance of power and economy is evidently improved.

• 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.

• Journal of KIISE
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• v.42 no.8
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• pp.998-1009
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• 2015

In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.