• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.4
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• pp.41-49
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• 2010

This paper proposes a new high speed SHA-1 architecture using multiple unfolding and pre-computation techniques. We unfolds iterative hash operations to 2 continuos hash stage and reschedules computation timing. Then, the part of critical path is computed at the previous hash operation round and the rest is performed in the present round. These techniques reduce 3 additions to 2 additions on the critical path. It makes the maximum clock frequency of 118 MHz which provides throughput rate of 5.9 Gbps. The proposed architecture shows 26% higher throughput with a 32% smaller hardware size compared to other counterparts. This paper also introduces a analytical model of multiple SHA-1 architecture at the system level that maps a large input data on SHA-1 block in parallel. The model gives us the required number of SHA-1 blocks for a large multimedia data processing that it helps to make decision hardware configuration. The hs fospeed SHA-1 is useful to generate a condensed message and may strengthen the security of mobile communication and internet service.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.63-79
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• 2008

With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.689-699
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• 2016

As the demand for LCD increases, the importance of inspection equipment for improving the efficiency of LCD production is continuously emphasized. The pattern inspection apparatus is one that detects minute defects of pattern quickly using optical equipment such as line scan camera. This pattern inspection apparatus makes a decision on whether a pixel is a defect or not using a single threshold value in order to meet constraint of real time inspection. However, a method that uses an adaptive thresholding scheme with different threshold values according to characteristics of each region in a pattern can greatly improve the performance of defect detection. To apply this adaptive thresholding scheme it has to be known that a certain pixel to be inspected belongs to which region. Therefore, this paper proposes a region matching algorithm that recognizes the region of each pixel to be inspected. The proposed algorithm is based on the pattern matching scheme with the consideration of real time constraint of machine vision and implemented through GPGPU in order to be applied to a practical system. Simulation results show that the proposed method not only satisfies the requirement for processing time of practical system but also improves the performance of defect detection.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.281-285
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• 2008

Wireless sensor networks are closely related with the geometric environment in which they are deployed. We consider the probable case when a routing protocol runs on an environment with many complex obstacles like downtown surroundings. In addition, there are no unrealistic assumptions in order to increase practicality of the protocol. Our goal is to find a routing protocol for maximizing network lifetime by using only connectivity information in the complex sensor network environment. We propose a topology-based routing algorithm that accomplishes good performance in terms of network lifetime and routing complexity as measures. Our routing algorithm makes routing decision based on a weighted graph as topological abstraction of the complex network. The graph conduces to lifetime enhancement by giving alternative paths, distributing the skewed burden. An energy estimation method is used so as to maintain routing information without any additional cost. We show how our approach can be used to maximize network lifetime and by extensive simulation we prove that out approach gives good results in terms of both measures-network lifetime and routing complexity.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.15 no.3
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• pp.101-109
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• 2015

Classic galactosemia (OMIM #230400) is an autosomal recessive inherited metaboic disorder caused by a deficiency of the galactose-1-phosphate uridyltransferase (GALT, EC2.7.7.12) due to mutations in the GALT gene. If untreated, classic galactosemia is a potentially lethal disease presenting with poor feeding, vomiting, jaundice, liver failure, increased bleeding tendency, and septicemia leading to death within a few days after birth. Since 2006, expansion of newborn screening has been enabled the early diagnosis and early intervention of classic galactosemia in Korea. However, newborn screening, followup testing for confirmatory diagnosis and intervention for galactosemia continue to present challenges. In Korea, the prevalence of the classic galactosemia is considered relatively low compared to that of western countries. And the genotype is also clearly different from those of other population. Therefore, our own guideline for confirmatory diagnosis and intervention is needed. Here, the diagnostic algorithm for galactosemia after positive newborn screening result in Korea has been proposed. Considering the low prevalence and different mutation spectrum in Koreans, the early mutation analysis of GALT gene could be a useful tool for the accurate diagnosis and making any treatment decision.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.649-656
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• 2015

The objective of this study was to analyze the optimal thinning regimes for timber or carbon managements in Cryptomeria japonica stands of Hannam Experimental Forest, Korea Forest Research Institute. In solving the problem, PATH algorithm, developed by Paderes and Brodie, was used as the decision-making tool and the individual-tree/distance-free stand growth simulator for the species, developed by Kwon et al., was used to predict the stand growth associated with density control by thinning regimes and mortality. The results of this study indicate that the timber management for maximum net present value (NPV) needs less number of but higher intensity thinnings than the carbon management for maximum carbon absorption does. In case of carbon management, the amount of carbon absorption is bigger than that of timber management by about 6% but NPV is reduced by about 3.2%. On the other hand, intensive forest managements with thinning regimes promotes net income and carbon absorption by about 60% compared with those of the do-nothing option.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.7-12
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• 2012

Recently the medical field to efficiently process the vast amounts of information to decision trees, neural networks, Bayesian Networks, including the application method of various data mining techniques are investigated. In addition, the basic personal information or patient history, family history, in addition to information such as MRI, HRCT images and additional information to collect and leverage in the diagnosis of disease, improved diagnostic accuracy is to promote a common status. But in real world situations that affect the results much because of the variable exists for a particular data mining techniques to obtain information through the enemy can be seen fairly limited. Medical images were taken as well as a minor can not give a positive impact on the diagnosis, but the proportion increased subjective judgments by the automated system is to deal with difficult issues. As a result of a complex reality, the situation is more advantageous to deal with the relative probability of the multivariate model based on Bayesian network, or TAN in the K2 search algorithm improves due to expansion model has been proposed. At this point, depending on the type of search algorithm applied significantly influenced the performance characteristics of the extended Bayesian network, the performance and suitability of each technique for evaluation of the facts is required. In this paper, we extend the Bayesian network for diagnosis of diseases using the same data were carried out, K2, TAN and changes in search algorithms such as classification accuracy was measured. In the 10-fold cross-validation experiment was performed to compare the performance evaluation based on the analysis and the onset of high-risk classification for patients with HRCT images could be possible to identify high-risk data.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

• Journal of IKEEE
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• v.24 no.1
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• pp.302-310
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• 2020

In this paper, we have implemented the living waste analysis system based on IoT and AI(Artificial Intelligence), and proposed effective waste process and management method. The Jeju location have the strong point to devise a stratagem and estimate waste quantization, rather than others. Especially, we can recognized the amount variation of waste to the residence people compare to the sightseer number, and the good example a specific waste duty. Thus this paper have developed the IoT device for interconnecting the existed CCTV camera, and use the AI algorithm to analysis the waste image. By using these decision of image analysis, we can inform their deal commend and a decided information to the map of the waste cars. In order to evaluate the performance of IoT, we have experimented the electromagnetic compatibility under a national official authorization KN-32, KN61000-4-2~6, and obtained the stable experimental results. In the further experimental results, we can applicable for an data structure for precise definition command by using the simulated several waste image with artificial intelligence algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.393-402
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• 2012

In this study, we propose a signal processing algorithm to measure the arousal level of a human subject using a PPG(Photoplethysmography) sensor. From the measured PPG signals, the arousal level is determined by PPI(Pulse to Pulse Interval) and discrete-time signal processing. We ran psychophysical experiments displaying visual stimuli on TV display while measuring PPG signal from a finger, where the nature landscape scenes were used for restorative effect, and the urban environments were used to stimulate the stress. However, the measured PPG signals may include noise due to subject movement and measurement error, which results in incorrect detections. In this paper, to mitigate the noise impact on stimulus detection, we propose a detecting algorithm using digital signal processing methods and statistics of measured signals. A filter is adopted to remove a high frequency noise and adaptively designed taking into account the statistics of the measured PPG signals. Moreover we employ a hysteresis method to reduce the distortion of PPI in decision of emotional. Via experiment, we show that the proposed scheme reduces signal noise and improves stimulus detection.