• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.541-548
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• 2006

Sensor network is a network for realizing the ubiquitous computing circumstances, which aggregates data by means of observation or detection deployed at the inaccessible places with the capacities of sensing and communication. To realize this circumstance, data which sensor nodes gathered from sensor networks are delivered to users, in which it is required to encrypt the data for the guarantee of secure communications. Therefore, it is needed to design key management scheme for encoding appropriate to the sensor nodes which feature continual data transfer, limited capacity of computation and storage and battery usage. We propose a key management scheme which is appropriate to sensor networks organizing hierarchical architecture. Because sensor nodes send data to their parent node, we can reduce routing energy. We assume that sensor nodes have different security levels by their levels in hierarchy. Our key management scheme provides different key establishment protocols according to the security levels of the sensor nodes. We reduce the number of sensor nodes which share the same key for encryption so that we reduce the damage by key exposure. Also, we propose key update protocols which take different terms for each level to update established keys efficiently for secure data encoding.

• Journal of Korean Neurosurgical Society
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• v.29 no.11
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• pp.1415-1420
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• 2000

Essential tremor(ET) is the most common movement disorder however there has been little agreement in the neurologic literature regarding diagnostic criteria for ET. Familial ET is an autosomal dominant disorder presenting as an isolated postural tremor. The main feature of ET is postural tremor of the arms with later involvement of the head, voice, or legs. In previous studies, it was reported that ET susceptibility was inherited in an autosomal dominant inheritance. As with previous results, it would suggest that ET might be associated with defect of mitochondrial or nuclear DNA. Recent studies are focusing molecular genetic detection of movement disorders, such as essential tremor and restless legs syndrome. Parkinson's disease(PD) is a neurodegenerative disease involving mainly the loss of dopaminergic neurons in substantia nigra by several factors. The cause of dopaminergic cell death is unknown. Recently, it has been suggested that Parkinson's disease many result from mitochondrial dysfunction. The authors have analysed mitochondrial DNA(mtDNA) from the blood cell of PD and ET patients via long and accurate polymerase chain reaction(LA PCR). Blood samples were collected from 9 PD and 9 ET patients. Total DNA was extracted twice with phenol followed by chloroform : isoamylalcohol. For the analysis of mtDNA, LA PCR was performed by mitochondrial specific primers. With LA PCR, 1/3 16s rRNA~1/3 ATPase 6/8 and COI~3/4 ND5 regions were observed in different patterns. But, in the COI~1/3 ATPase 6/8 region, the data of PCR were observed in same pattern. This study supports the data that ET and PD are genentic disorders with deficiency of mitochondrial DNA multicomplexes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.507-515
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• 2015

This paper suggests 6 cases of TFA parameters algorithm(Mean, VA, RS, SKEW, UN, EN) to search for the detection of recognition rates regarding breast disease using CAD on ultrasound images. Of the patients who visited a university hospital in Busan city from August 2013 to January 2014, 90 cases of breast ultrasound images based on the findings in breast US and pathology were selected. $50{\times}50$ pixel size ROI was selected from the breast US images. After pre-processing histogram equalization of the acquired test images(negative, benign, malignancy), we calculated results of TFA algorithm using MATLAB. As a result, in the TFA parameters suggested, the disease recognition rates for negative and malignancy was as high as 100%, and negative and benign was approximately 83~96% for the Mean, SKEW, UN, and EN. Therefore, there is the possibility of auto diagnosis as a pre-processing step for a screening test on breast disease. A additional study of the suggested algorithm and the responsibility and reproducibility for various clinical cases will determine the practical CAD and it might be possible to apply this technique to range of ultrasound images.

• Journal of Bio-Environment Control
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• v.20 no.2
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• pp.63-71
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• 2011

Pattern recognition of a cucumber were conducted to detect directly the binary images by using thresholding method, which have the threshold level at the optimum intensity value. By restricting conditions of learning pattern, output patterns could be extracted from the same and similar input patterns by the algorithm. The algorithm of pattern recognition was developed to determine the position of the cucumber from a real image within working condition. The algorithm, designed and developed for this project, learned two, three or four learning pattern, and each learning pattern applied it to twenty sample patterns. The restored success rate of output pattern to sample pattern form two, three or four learning pattern was 65.0%, 45.0%, 12.5% respectively. The more number of learning pattern had, the more number of different out pattern detected when it was conversed. Detection of feature pattern of cucumber was processed by using auto scanning with real image of 30 by 30 pixel. The computing times required to execute the processing time of cucumber recognition took 0.5 to 1 second. Also, five real images tested, false pattern to the learning pattern is found that it has an elimination rate which is range from 96 to 98%. Some output patterns was recognized as a cucumber by the algorithm with the conditions. the rate of false recognition was range from 0.1 to 4.2%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.249-255
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• 2017

The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.263-279
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• 2009

Shape features in a content-based image retrieval (CBIR) system are divided into two classes: contour-based and region-based. Contour-based shape features are simple but they are not as efficient as region-based shape features. Most systems using the region-based shape feature have to extract the region firs t. The prior works on region-based systems still have shortcomings. They are complex to implement, particularly with respect to region extraction, and do not sufficiently use the spatial relationship between regions in the distance model In this paper, a region extraction method that is the combination of an edge-based method and a region growing method is proposed to accurately extract regions inside an object. Edges inside an object are accurately detected based on the Canny edge detector and the Hough transform. And the modified Integrated Region Matching (IRM) scheme which includes the adjacency relationship of regions is also proposed. It is used to compute the distance between images for the similarity search using shape features. The experimental results show the effectiveness of our region extraction method as well as the modified IRM. In comparison with other works, it is shown that the new region extraction method outperforms others.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1449-1461
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• 2005

In this paper, we propose a vehicle recognition system based on the classification of vehicle identification mark and recognition of vehicle license plate. In the proposed algorithm, From the input vehicle image, we first simulate preprocessing procedures such as noise reduction, thinning etc., and detect vehicle identification mark and license plate region using the frequency distribution of intensity variation. And then, we classify extracted vehicle candidate region into identification mark, character and number of vehicle by using structural feature informations of vehicle. Lastly, we recognize vehicle informations with recognition of identification mark, character and number of vehicle using hybrid and vertical/horizontal pattern vector method. In the proposed algorithm, we used three properties of vehicle informations such as Independency property, discriminance property and frequency distribution of intensity variation property. In the vehicle images, identification mark is generally independent of the types of vehicle and vehicle identification mark. And also, the license plate region between character and background as well as horizontal/vertical intensity variations are more noticeable than other regions. To show the efficiency of the propofed algorithm, we tested it on 350 vehicle images and found that the propofed method shows good Performance regardless of irregular environment conditions as well as noise, size, and location of vehicles.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.315-324
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• 2011

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.127-134
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• 2014

In DRDoS(Distributed Reflective Denial of Service) attacks, the victim is bombarded by packets from legitimate reflector unlike DDoS(Distributed Denial of Service) attacks through zombie, which is more dangerous than DDoS attack because it is in stronger disguise. Therefore, the method of filtering packet method on router are useless. Moreover SCTP(Stream Control Transmission Protocol) multi-homing feature, such as with an improved transmission protocol allows detecting attacks is more difficult and the effect of the attack can be maximized. In this paper we propose a DRDoS detection mechanism based on DRDoS utilizing attention to the characteristics of stateful protocols. The proposed scheme is backed by stateful firewall, and detect DRDoS attacks through a rules table and perform a defense treatment against DRDoS attack. Rules table with a simple structure is possible to easily adapt for any kind of stateful protocol can used by DRDoS attack. The experimental result confirm that our proposed scheme well detect DRDoS attacks using SCTP, the next-generation transmission protocol which not known by victim, and reduce the attacking packets rapidly.