• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.141-147
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• 2021

In this paper, in order to improve the utilization of hyperspectral large-capacity data feature information by reducing complex computations by dimension reduction of neural network inputs in embedded systems, the band selection algorithm is applied in each subset. Among feature extraction and feature selection techniques, the feature selection aim to improve the optimal number of bands suitable for datasets, regardless of wavelength range, and the time and performance, more than others algorithms. Through this experiment, although the time required was reduced by 1/3 to 1/9 times compared to the others band selection technique, meaningful results were improved by more than 4% in terms of performance through the K-neighbor classifier. Although it is difficult to utilize real-time hyperspectral data analysis now, it has confirmed the possibility of improvement.

• Convergence Security Journal
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• v.24 no.3
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• pp.41-49
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• 2024

A tokenizer, which is a key component of the Transformer model, lacks the ability to effectively comprehend numerical data. Therefore, to develop a Transformer-based intrusion detection model that can operate within a real-world network environment by training packet payloads as sentences, it is necessary to convert the hexadecimal packet payloads into a character-based format. In this study, we applied three character encoding methods to convert packet payloads into numeric or character format and analyzed how detection performance changes when training them on transformer architecture. The experimental dataset was generated by extracting packet payloads from PCAP files included in the UNSW-NB15 dataset, and the RoBERTa was used as the training model. The experimental results demonstrate that the ISO-8859-1 encoding scheme achieves the highest performance in both binary and multi-class classification. In addition, when the number of tokens is set to 512 and the maximum number of epochs is set to 15, the multi-class classification accuracy is improved to 88.77%.

• Journal of KIISE:Information Networking
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• v.31 no.6
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• pp.556-565
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• 2004

A Sandbox has been widely used to prevent damages caused by running of unknown malicious codes. It prevents damages by containing running environment of a program. There is a trade-off in using sandbox, between configurability and ease-of-use. MAPbox, an instance system of sandbox, had employed sandbox classification technique to satisfy both configurability and ease-of-use [1]. However, the configurability of MAPbox can be improved further. In this paper, we introduce a technique to attach dynamic class facility to MAPbox and implement MAPbox-advanced one. Newly generated class in our system has an access control with proper privileges. We show an example for improvements which denote our system have increased the configurability of MAPbox. It was determined as abnormal by MAPbox although is not. Our system could determine it as normal. We also show our techniques to overcome obstacles to implement the system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.435-442
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• 2015

In this paper, we propose a tracking and recognition of pedestrian/vehicle for intelligent multi-visual surveillance system. The intelligent multi-visual surveillance system consists of several fixed cameras and one calibrated PTZ camera, which automatically tracks and recognizes the detected moving objects. The fixed wide-angle cameras are used to monitor large open areas, but the moving objects on the images are too small to view in detail. But, the PTZ camera is capable of increasing the monitoring area and enhancing the image quality by tracking and zooming in on a target. The proposed system is able to determine whether the detected moving objects are pedestrian/vehicle or not using the SVM. In order to reduce the tracking error, an improved camera calibration algorithm between the fixed cameras and the PTZ camera is proposed. Various experimental results show the effectiveness of the proposed system.

• Journal of Chest Surgery
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• v.20 no.1
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• pp.112-127
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• 1987

Myasthenia gravis is a neuromuscular transmission function disorder characterized by fatigue and weakness of voluntary muscles. This muscular weakness is intensified by activity and stress, and improved by the use of anticholinesterase compounds. It was initially described by Erb in 1879 and later named myasthenia gravis by Jolly in 1895. Although the pathogenesis is Known to be an autoimmune related reduction in the number of available acetylcholine receptors at neuromuscular junctions, the role of thymus in myasthenia gravis is still unclear and under investigation. Thymectomy in the management of myasthenia gravis has become increasingly important since Dr. Blalock observed in 1939 that some patients with thymic tumors and myasthenia gravis improved following thymectomy. A clinical study of 102 cases of myasthenia gravis was performed at Yonsei University College of Medicine. Seoul, Korea from Jan. 1976 to Jun. 1986. In order to determine which factors are of prognostic significance, attention is focused upon pre-operative patient evaluation, problems in operative and post-operative care, and long-term follow-up observations. The results were as follows: 1. The sex distribution was 67 females and 35 males, the mean age of onset was 28.95*1.69 years, and the maximal incidence occurred between 21 and 40 years of age [56 cases: 54.9%]. 2. Clinical manifestations of ocular symptoms were seen to 70 patients [68.6%] extremities weakness in 33 [32.3%], bulbar weakness in 29 [28.4%], and dyspnea in 13 [12.7%]. 3. Study cases more than two thirds were classified as mild types [MG 1 and MG 11A] and 6 cases as grave [MG 1V] based on the modified Osserman`s classification system, 4. Thymectomy was performed in 19 cases which presented in severe myasthenia symptoms and showed no improvement with cholinergic drugs. Histologic examination of the excised thymus glands revealed no abnormalities in 4 cases, thymic hyperplasia in 5, benign thymoma in 5, and malignant thymoma in 5. 5. Immediate post-operative complications included 2 cases of pneumothorax which were treated by tube thoracostomies, there was no operative mortality. 6. The response to cholinergic drugs in 36 cases younger than 20 years old and in 27 cases older than 40 years was relatively poor, while that in 35 cases between the ages of 21 and 40 years old was good. 7. Thirty of 39 cases in groups IIB, III & IV improved markedly with medical or surgical management while only 16 of 59 cases in the mild groups [I and IIA] improved, almost all surgical cases improved in all categories. 8. There were 5 deaths. occurring between 7 months and 3 years 3 months of treatment of myasthenia gravis. The causes of death were myasthenic crisis in 2 cases, respiratory failure due to candidiasis & radiation pneumonitis in one case, cerebral hemorrhage due to high blood pressure in two case.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.997-1004
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• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• Industry Promotion Research
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• v.2 no.2
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• pp.45-52
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• 2017

this study, the problem of the quality is analyzed by questionnaire analysis of the current quality managers, and the German meister system, the Japanese name deduction system, the functional manager, and the quality manager system. First, we set up a quality guide selection classification guide model. Secondly, we set up a model for combining experience and expertise with theory. Third, we set up a quality brand application model to enhance competitiveness of SMEs. Fourth, The basic model is presented. The expected effects of these models are that, in the knowledge-based economy, in terms of identifying, fostering, and utilizing superior talents, quality experts combine academic theories and experience in each field to provide core expertise and knowledge, As a transformative leader and expert, we will become a leader of innovation activities by enhancing corporate competitiveness, developing younger leaders, and cooperating with suppliers. In order to strengthen competitiveness of SMEs, It will be possible to scale and cultivate the technology of SMEs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2595-2602
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• 2010

This paper introduces a mobile spam filtering system that considers the style of short text messages sent to mobile phones for detecting spam. The proposed system not only relies on the occurrence of content words as previously suggested but additionally leverages the style information to reduce critical cases in which legitimate messages containing spam words are mis-classified as spam. Moreover, the accuracy of spam classification is improved by normalizing the messages through the correction of word spacing and spelling errors. Experiment results using real world Korean text messages show that the proposed system is effective for Korean mobile spam filtering.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.35-40
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• 2019

Traditional Malware Detection is susceptible for detecting malware which is modified by polymorphism or obfuscation technology. By learning patterns that are embedded in malware code, machine learning algorithms can detect similar behaviors and replace the current detection methods. Data must collected continuously in order to learn malicious code patterns that change over time. However, the process of storing and processing a large amount of malware files is accompanied by high space and time complexity. In this paper, an HDFS-based distributed processing system is designed to reduce space complexity and accelerate feature extraction time. Using a distributed processing system, we extract two API features based on filtering basis, 2-gram feature and APICFG feature and the generalization performance of ensemble learning models is compared. In experiments, the time complexity of the feature extraction was improved about 3.75 times faster than the processing time of a single computer, and the space complexity was about 5 times more efficient. The 2-gram feature was the best when comparing the classification performance by feature, but the learning time was long due to high dimensionality.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.161-166
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• 2021

Errors in the semiconductor process are generated by a change in the state of the equipment, and errors usually arise when the state of the equipment changes or when parts that make up the equipment have flaws. In this investigation, we anticipated that aging of the mass flow controller in the plasma enhanced chemical vapor deposition SiO2 thin film deposition method caused a minute flow rate shift. In seven cases, fourier transformation infrared film quality analysis of the deposited thin film was used to characterize normal and pathological processes. The plasma condition was monitored using optical emission spectrometry data as the flow rate changed during the procedure. Preprocessing was used to apply the collected OES data to the artificial immune system algorithm, which was then used to process diagnosis. Through comparisons between datasets, the learning algorithm compared classification accuracy and improved the method. It has been confirmed that data characterized as a normal process and abnormal processes with differing flow rates may be discriminated by themselves using the artificial immune system data mining method.