• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.675-679
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• 2001

In this paper, we present a new competitive learning algorithm called Dynamic Competitive Learning (DCL). DCL is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. It introduces a new parameter called LOG (Limit of Grade) to decide whether an output neuron is created or not. If the class of at least one among the LOG number of nearest output neurons is the same as the class of the present training pattern, then DCL adjusts the weight vector associated with the output neuron to learn the pattern. If the classes of all the nearest output neurons are different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the selected neuron for learning is not limited only to the winner and the output neurons are dynamically generated during the learning process. In addition, the proposed algorithm has a small number of parameters, which are easy to be determined and applied to real-world problems. Experimental results for pattern recognition of remote sensing data and handwritten numeral data indicate the superiority of DCL in comparison to the conventional competitive learning methods.

• Genomics & Informatics
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• v.4 no.4
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• pp.173-181
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• 2006

Pattern finding is one of the important tasks in a protein or DNA sequence analysis. Alignment is the widely used technique for finding patterns in sequence analysis. BLAST (Basic Local Alignment Search Tool) is one of the most popularly used tools in bio-informatics to explore available DNA or protein sequence databases. BLAST may generate a huge output for a large sequence data that contains various sequence patterns. However, BLAST does not provide a tool to summarize and analyze the patterns or matched alignments in the BLAST output file. BLAST lacks of general and robust parsing tools to extract the essential information out from its output. This paper presents a pattern summary system which is a powerful and comprehensive tool for discovering pattern structures in huge amount of sequence data in the BLAST. The pattern summary system can identify clusters of patterns, extract the cluster pattern sequences from the subject database of BLAST, and display the clusters graphically to show the distribution of clusters in the subject database.

• New & Renewable Energy
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• v.18 no.4
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• pp.64-69
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• 2022

Double-sided photovoltaic (PV) modules have received significant attention in recent years as a technology that can achieve higher annual energy production rates than single-sided modules. The shingled technology is a promising method for manufacturing high-density and high-power modules. These modules are divided by laser and joined with electrically conductive adhesives. The output efficiency of the divided cells depends on the division pattern and the electrode pattern, making it important to understand the output characteristics. In this study, the output characteristics of large-area double-sided light-receiving shingled cells with different split patterns and electrode patterns were investigated. The M6 size, with 6 divisions in the electrode pattern, had the highest efficiency when using 142 front fingers and 146 rear fingers. The M10 size, with 7 divisions, had the highest output when using 150 fingers equally in the front and rear. The M12 size, also with 7 divisions, showed the highest output characteristics when using 192 front fingers and 208 rear fingers.

• Korean Journal of Optics and Photonics
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• v.19 no.1
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• pp.43-47
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• 2008

Nowadays, the pattern size of BLU (Back Light Unit) adopted in TFT-LCD (Thin Film Transistor Liquid Crystal Display) is typically a few tens of micrometers. However, recently, researches on the TFT-LCD output characteristics depending on various types of BLU patterns are being performed in order to improve the output and uniformity. In this study, we analyzed the influence of pattern size, distribution, and areal ratio on the output characteristics.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.63-70
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• 1994

In this paper, we propose a fault simulation method based on primary output in combinational circuit. In the deterministic test pattern generation, each test pattern is genterated incrementally. The test pattern is applied to the primary inputs of circuit under test to simulate faults. We detect the faults with respect to each primary output. The fault detection with resptect to each primary output is reflected by the corresponding bit in the detection words, and efficient fault detection for the reconvergent fan-out stem is achieved with dynamic fault propagation. As an experimental result of the fault simulation with our method for the several bench mark circuits, we illustrated the good performance showing that the number of gates to be activated is much reduced as compared with other method which is not based on primary output.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3080-3086
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• 2009

We have analyzed the output characteristics of a LGP with pyramid shaped pattern by using a 3-D simulation tool. The influences on the LGP output of various parameters such as the pattern shape, pattern occupation ratio, pattern size, and etching angle were investigated. Comparing the pyramid shaped pattern with hemispherical patterns, little difference was observed. And, it was proved that the pattern occupation ratio and etching angle have relatively large effects on LGP output characteristics, while the pattern size has no effect. Therefore, we can improve the LGP characteristics by optimizing pattern structure and distribution.

• Solar Energy
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• v.19 no.4
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• pp.63-69
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• 1999

The conventional grid connected PV(Photovoltaic) system has a unstable output pattern due to its dependence on the weather condition, although solar-cell array averagely has a regular output characteristics to have a peak output nearly at noon. Therefore assuming the high density grid connection in the future, this unstable output pattern can be one of the main reasons to generate power disturbance such as voltage variation, frequency variation and harmonic voltage generation in low voltage distribution line. However general grid connected solar-cell system do not have functions to cope with these disturbances. Therefore this study proposed a advanced type grid connected PV system with functions to suppress output power fluctuation due to solar-cell array output variation and showed the levelling effect of fluctuation due to instantaneous array output variation.

• Korean Journal of Optics and Photonics
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• v.21 no.3
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• pp.97-102
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• 2010

A pattern density function using simulation analysis for controlling LGP output distribution was proposed. The pattern density function was found as [Pexp(-y/70)+Qexp(+y/25)]R. We analyzed the LGP output distribution of a hemi-sphere pattern using the function and then found that its output distribution was clearly improved as compared with that of the equi-distance pattern. We found that the density function works well for the pyramid pattern case as well as.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.407-408
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• 2007

In this paper, we present a new method for classifying object using LVQ (Learning Vector Quantization) with flexible output layer. The proposed LVQ is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. If the classes of the nearest output neuron is different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the output neurons are dynamically generated during the learning process.

• Journal of Bio-Environment Control
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• v.20 no.4
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• pp.277-282
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• 2011

We carried out shape recognition. We found out cucumber's feature shape by means of neural network and back propagation algorithm. We developed an algorithm which finds object position and shape in real image and we gained following conclusion as a result. It was processed for feature shape extraction of cucumber to detect automatic. The output pattern rates of the miss-detected objects was 0.1~4.2% in the output pattern which was recognized as cucumber. We were gained output pattern according to image resolution $445{\times}363$, $501{\times}391$, $450{\times}271$, $297{\times}421$. It was appeared that no change was detected. When learning pattern was increased to 25, miss-detection ratio was 16.02%, and when learning pattern had 2 pattern, it didn't detect 8 cucumber in 40 images.