• Journal of Physiology & Pathology in Korean Medicine
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• v.31 no.4
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• pp.201-212
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• 2017

This study draws pattern differentiations of headache disorders on the ground of modern clinical applications and Korean medical literature. Categorization and symptoms of headache disorders are based on International Classification of Headache Disorders 3rd edition(beta version). And clinical papers are searched in China Academic Journals(CAJ) of China National Knowledge Infrastructure(CNKI). In the aspect of eight principle pattern identification, primary headache occurs due to lots of yang qi and has more inner pattern rather than exterior pattern, heat pattern rather than cold pattern, excess pattern rather than deficiency pattern. And primary headache is related with liver in the aspect of visceral pattern identification and blood stasis, wind and phlegm are relevant mechanisms. Migraine without aura is associated with ascendant hyperactivity of liver yang, phlegm turbidity, sunken spleen qi, wind-heat, blood deficiency or yin deficiency. Migraine with aura is mainly related with wind and it's major mechanisms are ascendant hyperactivity of liver yang, liver fire, yin deficiency of liver and kidney, blood deficiency or liver depression and qi stagnation. High repetition rate of tension-type headache can be identified as heat pattern or excess pattern. And trigeminal autonomic cephalalgias can also be accepted as heat pattern or excess pattern when the occurrence frequency is high and is relevant to combined pattern with excess pattern of external contraction and deficiency pattern of internal damage based on facial symptoms by external contraction and nervous and anxious status by liver deficiency. This study can be expected to be Korean medical basis of clinical practice guidelines on headache by proposing pattern identifications corresponding to the western classifications of headache disorders.

• The Journal of Korea Robotics Society
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• v.7 no.1
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• pp.45-56
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• 2012

This paper proposes how to improve the performance of CSS-based indoor localization system. CSS based localization utilizes signal flight time between anchors and tag to estimate distance. From the distances, the 3-dimensional position is calculated through trilateration. However the error in distance caused from multi-path effect transfers to the position error especially in indoor environment. This paper handles a problem of reducing error in raw distance information. And, we propose the new localization method by pattern matching instead of the conventional localization method based on trilateration that is affected heavily on multi-path error. The pattern matching method estimates the position by using the fact that the measured data of near positions possesses a high similarity. In order to gain better performance of localization, we use EKF(Extended Kalman Filter) to fuse the result of CSS based localization and robot model.

• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.125-128
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• 2001

In this paper, we propose a low power Scan-based Built-ln Self Test based on circuit partitioning and pattern suppression using modified test control unit. To partition a CUT(Circuit Under Testing), the MHPA(Multilevel Hypergraph Partition Algorithm) is used. As a result of circuit partition, we can reduce the total length of test pattern, so that power consumptions are decreased in test mode. Also, proposed Scan-based BIST architecture suppresses a redundant test pattern by inserting an additional decoder in BIST control unit. A decoder detects test pattern with high fault coverage, and applies it to partitioned circuits. Experimental result on the ISCAS benchmark circuits shows the efficiency of proposed low power BIST architecture.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.592-600
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• 2006

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.110-115
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• 2019

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.

• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1B
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• pp.47-55
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• 2009

In recent network intrusion detection systems, regular expressions are used to represent malicious packets. In order to process incoming packets through high speed networks in real time, we should perform hardware-based pattern matching using the configurable device such as FPGAs. However, operating speed of FPGAs is slower than giga-bit speed network and so, multi-byte processing per clock cycle may be needed. In this paper, we propose a hardware architecture of multi-byte based regular expression pattern matching and implement the pattern matching circuit generator. The throughput improvements in four-byte based pattern matching circuit synthesized in FPGA for several Snort rules are $2.62{\sim}3.4$ times.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.14 no.1
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• pp.70-78
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• 2010

Objectives : To qualify or enhance the diagnostic ability of students in Oriental Medicine, so called standardized patients are ideal modality, but because it's a man-based method, more convenient tools are required. Computer-based diagnostic ability test program gives effective way for the very purpose. So we made a pilot software evaluating Pattern Identification ability in Oriental Medicine. Methods and Materials : The pilot software was coded with Microsoft's EXCEL VBA. 87 names of Zheng (Symptom Pattern) and 674 names of symptom (including some signs) are adopted from the former standardization works conducted by Korean Institute of Oriental Medicine (KIOM) in 1996. Results : Compared with some manned modalities to test Pattern Identification ability, the test by this software shows superiority in convenience and objectivity. Conclusion : This software is world's first program to perform computer-based evaluation of Pattern Identification in Oriental Medicine, and it gives effective way to complement both written test and manned clinical performance test (CPX).

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.