• Journal of Digital Convergence
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• v.12 no.1
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• pp.415-421
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• 2014

In this paper, we proposed problem and improvement of halftoning using random space filling curve. Random space filling curve is developed as a solution for shortcoming which space filling curve has self-similarity. It is used to reduce regular pattern can be occurred in constant brightness area in order that randomness apply to scanning path. But there is a problem that some area along scanning path can show too bright result in halftoning using random space filling curve. In this paper, we analyzed cause of problem and proposed single pixel error diffusion as a solution method. This method can avoid over-accumulated error and show better result in halftoning.

• Journal of fish pathology
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• v.16 no.1
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• pp.51-59
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• 2003

Streptococcal infection is one of the most serious disease of cultured olive flounder, Paralychthys olivaceus in Korea and caused by more than one species. However, there has been considerable confusions about the taxonomic position of the fish pathogenic streptococci. In this study, We performed the randomly amplified polymorphic DNA(RAPD) pattern analysis to evaluate the possible classification in 8 streptococci isolated from diseased olive flounder and reference strains based on their DNA structure. RAPD PCR with DNA solution prepared by simple boiling and 10-mer random primer was appeared to be a good tool for discrimination of different streptococcal strains. Phenotypical characters by simple biological test and API 20 Strep corresponded well to the specific profiles of RAPD in streptococcal isolates of this study. Therefore, the RAPD profile was considered as one of differential characters to discriminate the streptococcal isolates from diseased olive flounder.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.742-746
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• 2010

In this paper, we propose new randomness testing methods based on approximate periods in order to improve the previous randomness testing method using exact pattern matching. Finding approximate periods of random sequences enables us to search similarly repeated parts, but it has disadvantages since it takes long time. In this paper we propose randomness testing methods whose time complexity is O($n^2$) by reducing the time complexity of computing approximate periods from O($n^3$) to O($n^2$). Moreover, we perform some experiments to compare pseudo random number generated by AES cryptographic algorithms and true random number.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.481-492
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• 1997

This paper presents an algorithm for recognizing surface mount device(SMD) IC pattern based on the error back propoagation(EBP) neural network and discrete cosine transform(DCT). In this approach, we chose such parameters as frequency, angle, translation and amplitude for the shape informantion of SMD IC, which are calculated from the coefficient matrix of DCT. These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Learning of EBP neural network is carried out until maximum error of the output layer is less then 0.020 and consequently, after the learning of forty thousand times, the maximum error have got to this value. Experimental results show that the rate of recognition is 100% in case of the random pattern taken at a similar circumstance as well as normalized training pattern. It also show that proposed method is not only relatively relatively simple compare with the traditional space domain method in extracting the feature parameter but also able to re recognize the pattern's class, position, and existence.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• ETRI Journal
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• v.16 no.1
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• pp.35-43
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• 1994

In this paper, the robustness of the artificial neural networks to noise is demonstrated with a multilayer perceptron, and the reason of robustness is due to the statistical orthogonality among hidden nodes and its hierarchical information extraction capability. Also, the misclassification probability of a well-trained multilayer perceptron is derived without any linear approximations when the inputs are contaminated with random noises. The misclassification probability for a noisy pattern is shown to be a function of the input pattern, noise variances, the weight matrices, and the nonlinear transformations. The result is verified with a handwritten digit recognition problem, which shows better result than that using linear approximations.

• Journal of Korean Society of Forest Science
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• v.99 no.3
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• pp.259-266
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• 2010

Forest fire occurrence in Korea is highly related to human activities and its spatial distribution shows a strong spatial dependency with cluster pattern. In this study, we analyzed spatial distribution pattern of forest fire with point pattern analysis considering spatial dependency. Distributional pattern was derived from Ripley's K-function according to causes and distances. Spatially clustered intensity was found out using Kernel intensity estimation. As a result, forest fires in Korea show clustered pattern, although the degrees of clustering for each cause are different. Furthermore, spatial clustering pattern can be classified into two groups in terms of degrees of clustering and distance. The first group shows the national-wide cluster pattern related to the human activity near forests, such as human-induced accidental fire in mountain and field incineration. Another group shows localized cluster pattern which is clustered within a short distance. It is associated with the smoker fire, arson, accidental by children. The range of localized clustering was 30 km. Beyond of this range, the patterns of forest fire became random distribution gradually. Kernel intensity analysis showed that the latter group, which have localized cluster pattern, was occurred in near Seoul with high densed population.

• Proceedings of the Korean Aquaculture Society Conference
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• 2003.10a
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• pp.34-34
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• 2003

한국산 선발계통 및 일본산 양식계통과 이들 두 계통간 잡종 참돔 집단의 유전적 특성을 분석하기 위하여, RAPD (Random Amplified Polymorphic DNA) marker를 탐색하였다. 10개의 염기로 이루어진 200개의 random primer 분석을 통하여 polymorphic pattern을 나타내는 23개의 random primer를 선발하였으며, 각 primer의 재현성을 확인하였다. 이들 중 OPA-11 primer는 크기가 각각 600 bp, 650 bp 및 750 bp 인 3개의 DNA 단편에 의하여 4개의 genotype을 나타냈으며, 각 genotype의 빈도는 집단간차이를 보였고, 한국산 선발계통 집단에서는 4개의 genotype이 모두 발견되는 반면, 일본산 양식계통 및 일본산 양식계통을 포함한 교배집단에서는 특정 genotype만 발견되었다. OPA-11 primer 유래의 polymorphic DNA 단편을 cloning하고 염기서열을 결정하였으며, SCAR (Sequence Characterized Amplified Region) primer를 제작하고 분석하였다. 본 연구는 참돔집단의 유전적 특성 파악 및 집단 구별에 RAPD marker를 활용하였으며, 참돔 육종시 형질 및 기능관련 DNA marker 탐색에 적용하기 위하여, 이후의 연구에서는 SCAR과 RFLP 분석에 RAPD marker를 이용하여 100% 정확도를 갖는 RFLP maker를 찾고, MAS (Marker-Assisted Selection)에 적용하고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.595-598
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• 2003

A new classifier is constructed by using a generalized regression neural network (GRNN) in conjunction with a random generator (RC). The RG played a role of generating a number of sets of random spreads given a range for gaussian functions in the pattern layer, The range experimentally varied from 0.4 to 1.4. The DNA sequences consisted 4 types of promoters. The performance of classifier is examined in terms of total classification sensitivity (TCS), and individual classification sensitivity (ICS). for comparisons, another GRNN classifier was constructed and optimized in conventional way. Compared GRNN, the RG-GRNN demonstrated much improved TCS along with better ICS on average.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.91_92
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• 2009

As the increasing integrity of VLSI, the BIST(Built-In Self Test) is used as an effective method to test chips. Generally the pseudo-random test pattern generation is used for BIST. But it requires too many test patterns when there exist random resistant faults. Therefore we propose a mixed test scheme which applies to the circuit under test, a deterministic test sequence followed by a pseudo-random one. This scheme allows the maximum fault coverage detection to be achieved, furthermore the silicon area overhead of the mixed hardware generator can be reduced.