• Journal of IKEEE
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• v.18 no.2
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• pp.282-290
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• 2014

This paper presents the design of embedded electrical power control unit for Personal Electrical Vehicle(PEV). The embedded unit is designed using PIC18F8720 processor, 16Mb flash ROM, 32Mb SDRAM and signal condition circuits. The proposed PEV consists of 4KW in-wheel Brushless DC Motor(BLDCM), 3 phase voltage source inverter with the $180^{\circ}$ conduction space vector PWM method, PID speed controller and the embedded control unit. The PEV has mechanical manufacture of inverse 3 wheel system, which is applied by the in-wheel BLDCM and steering mechanism with tilting function. Also, the performances of the proposed embedded electrical power control unit are verified through the lab experiment and road driving test of PEV.

• Management & Information Systems Review
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• v.4
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• pp.257-284
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• 2000

According as the drivers increase who have their cars, the comprehensive studies on the automobile for the traffic safety have been raised as the important problems. Visual Recognition System for radio-controled driving is a part of the sensor processor of Unmanned Autonomous Vehicle System. When a driver drives his car on an unknown highway or general road, it produces a model from the successively inputted road traffic information. The suggested Recognition System of the Road Traffic Safety Information Board is to recognize and distinguish automatically a Road Traffic Safety Information Board as one of road traffic information. The whole processes of Recognition System of the Road Traffic Safety Information Board suggested in this study are as follows. We took the photographs of Road Traffic Safety Information Board with a digital camera in order to get an image and normalize bitmap image file with a size of $200{\times}200$ byte with Photo Shop 5.0. The existing True Color is made up the color data of sixteen million kinds. We changed it with 256 Color, because it has large capacity, and spend much time on calculating. We have practiced works of 30 times with erosion and dilation algorithm to remove unnecessary images. We drawing out original image with the Region Splitting Technique as a kind of segmentation. We made three kinds of grouping(Attention Information Board, Prohibit Information Board, and Introduction Information Board) by RYB( Red, Yellow, Blue) color segmentation. We minimized the image size of board, direction, and the influence of rounding. We also minimized the Influence according to position. and the brightness of light and darkness with Eigen Vector and Eigen Value. The data sampling this feature value appeared after building the learning Code Book Database. The suggested Recognition System of the Road Traffic Safety Information Board firstly distinguished three kinds of groups in the database of learning Code Book, and suggested in order to recognize after comparing and judging the board want to recognize within the same group with Nearest Neighborhood Decision Making.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1478-1485
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• 2007

In this paper, a meteorological system including a wind speed & direction module and the DSP(Digital Signal Processor) sensor interface circuit board are proposed. This DSP system accepts and process the informations from a wind speed & direction module, the atmospheric pressure sensor, the ambient air temperature sensor and transfers it to the PC monitoring system. Especially, a wind speed & direction module and a DSP hardware are directly designed and applied. A wind speed & direction module have a construction that it have four film type RID(Resistive Temperature Detectors) resistive sensor adhered around the circular metal body heated constantly by heating coil for obtaining vector informations about wind. By this structure, the module is enabled precise measurement having a robustness about vibration, humidity, corrosion. A sensor signal processing circuit is using TMS320F2812 TI(Texas Instrument) Corporation high speed DSP. An economical meteorological system could be constructed through the data from wind speed & direction module and by the fast processing of DSP interface circuit board.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.889-898
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• 2022

In this paper, parallel computing method on the three-dimensional electromagnetic field is proposed. The present electromagnetic scattering analysis is conducted based on the time-harmonic vector wave equation and the finite element method. The edge-based element and 2^nd -order absorbing boundary condition are used. Parallelization of the elemental numerical integration and the matrix assemblage is accomplished by allocating the partitioned finite element subdomain for each processor. The graph partitioning library, METIS, is employed for the subdomain generation. The large sparse matrix computation is conducted by MUMPS, which is the parallel computing library based on the multi-frontal method. The accuracy of the present program is validated by the comparison against the Mie-series analytical solution and the results by ANSYS HFSS. In addition, the scalability is verified by measuring the speed-up in terms of the number of processors used. The present electromagnetic scattering analysis is performed for a perfect electric conductor sphere, isotropic/anisotropic dielectric sphere, and the missile configuration. The algorithm of the present program will be applied to the finite element and tearing method, aiming for the further extended parallel computing performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.401-410
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• 2023

In this paper, we propose optimization methods for implementing SPARKLE, one of the NIST LWC finalists, on a 64-bit ARMv8 processor. The proposed methods consist of two approaches: an implementation using ARM A64 instructions and another using NEON ASIMD instructions. The A64-based implementation is optimized by performing register scheduling to efficiently utilize the available registers on the ARMv8 architecture. By utilizing the optimized A64-based implementation, we can achieve speeds that are 1.69 to 1.81 times faster than the C reference implementation on a Raspberry Pi 4B. The ASIMD-based implementation, on the other hand, optimizes data by parallelizing the ARX-boxes to perform more than three of them concurrently through a single vector instruction. While the general speed of the optimized ASIMD-based implementation is lower than that of the A64-based implementation, it only slows down by 1.2 times compared to the 2.1 times slowdown observed in the A64-based implementation as the block size increases from SPARKLE256 to SPARKLE512. This is an advantage of the ASIMD-based implementation. Therefore, the ASIMD-based implementation is more efficient for SPARKLE variant block cipher or permutation designs with larger block sizes than the original SPARKLE, making it a useful resource.