• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.101-108
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• 2021

In this paper, we analyze Shin's proposed dynamic ID-based user authentication scheme for TMIS(Telecare Medicine Information System), and Shin's authentication scheme is vulnerable to smart card loss attacks, allowing attackers to acquire user IDs, which enables user impersonation attack. In 2019, Shin's proposed authentication scheme attempted to generate a strong random number using ECC, claiming that it is safe to lose a smart card because it is impossible to calculate random number r'i due to the difficulty of the ECC algorithm without knowing random number ri. However, after analyzing Shin's authentication scheme in this paper, the use of transmission messages and smart cards makes it easy to calculate random numbers r'i, which also enables attackers to generate session keys. In addition, Shin's authentication scheme were analyzed to have significantly greater overhead than other authentication scheme, including vulnerabilities to safety analysis, the lack of a way to pass the server's ID to users, and the lack of biometric characteristics with slightly different templates.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.218-224
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• 2016

This paper proposes a computer-based practical automatic scoring system for Korean short answers through student answer analysis and natural language processing techniques. The proposed system reduces the overall scoring time and budget, while improving the ease-of-use to write answer templates from student answers as well as the accuracy and reliability of automatic scoring system. To evaluate the application of the automatic scoring system and compare to the human scoring process, we performed an experiment using the student answers of social science subject in 2014 National Assessment of Educational Achievement.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.49-57
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• 2012

In P2P streaming management, peer's churning and finding efficient topology architecture optimization algorithm that reduces streaming delay is important. This paper studies a topology optimization algorithm based on the P2P streaming using peer's link information. The proposed algorithm is based on the estimation of peer's upload bandwidth using peer's link information on mesh-network. The existing algorithm that uses the information of connected links is efficient to reduce message overload in the point of resource management. But it has a risk of making unreliable topology not considering upload bandwidth. And when some network error occurs in a server-closer-peer, it may make the topology worse. In this paper we propose an algorithm that makes up for the weak point of the existing algorithm. We compare the existing algorithm with the proposed algorithm using test data and analyze each simulation result.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2143-2149
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• 2007

Visual Cortex Stimulator is among artificial retina prosthesis for blind man, is the method that stimulate the brain cell directly without processing the information from retina to visual cortex. In this paper, we propose image construction and recognition model that is similar to human visual processing by recognizing the feature data with orientation information, that is, the characteristics of visual cortex. Back propagation algorithm based on Delta-bar delta is used to recognize after extracting image feature by Kirsh edge detector. Various numerical patterns are used to analyze the performance of proposed method. In experiment, the proposed recognition model to extract image characteristics with the orientation of information from retinal cells to visual cortex makes a little difference in a recognition rate but shows that it is not sensitive in a variety of learning rates similar to human vision system.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.347-354
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• 2022

Since the era of the 4th industrial revolution has been ripe, the use of artificial intelligence(AI) based on massive data is beginning to be actively applied in various fields. However, as the process of analyzing forest species is carried out manually, many errors are occurring. Therefore, in this paper, about 60,000 pieces of AI learning data were automatically analyzed for pine, larch, conifer, and broadleaf trees of aerial photographs and pseudo images in the metropolitan area, and an AI model was developed to distinguish tree species. Through this, it is expected to increase in work efficiency by using the tree species division image as basic data when producing forest change detection and forest field topics.

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.732-742
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• 2001

The concept of an agent has become important in computer science and has been applied to the number of application domains such electronic commerce and information retrieval. But, no one has proposed yet in software test. The test agent system applied the concept of an agent to software test is new test tool. It consists of the User Interface Agent. the Test Case Selection & Testing Agent and the Regression Test Agent. Each of these agents, with their intelligent rules, carry out the tests autonomously by empolying the object-oriented test processes. This system has 2 advantages. Firstly since the tests are carried our autonomously, it minimizes tester interference and secondly, since redundant-free and consistent effective test cases are intellectually selected, the testing time is reduced while the fault detection effectiveness improves. In this paper, by actually showing the testing process being carried out autonomously by the 3 agents that form the TAS, we show that the TAS minimizes tester interference. By also carrying out the 4 different types of experiments on the RE-Rule, CTS-Rule, overall TAS experiment, and the fault-detection effectiveness experiment on the RE-Rule, we show the cut-down on the testing time and improvement in the fault detection effectivity.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.97-102
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• 2019

In this paper, we propose a method to remotely intuitively identify faults found in storage devices in soft-RAID used in a server system composed of Linux. To do this, we analyze the principle and problem of fault reporting method in the soft-RAID system of Linux OS and suggest the state of storage devices in remote Internet Home-page. The proposed method consists of a method of displaying images on the Internet home-page, so that it can be arranged freely when creating a home-page, and the image data is composed of external files, so it is bery convenient to edit and replace images In order to verify the effectiveness of the proposed method, we have confirmed that the state of each storage device can be confirmed at a long distance without any major addition to the Home-page configuration.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.1
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• pp.179-187
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• 2019

With the recent development of small computing devices, IoT sensor network can be widely deployed and is now readily available with sensing, calculation and communi-cation functions at low cost. Sensor data management is a major component of the Internet of Things environment. The huge volume of data produced and transmitted from sensing devices can provide a lot of useful information but is often considered the next big data for businesses. New column-wise compression technology is mounted to the large data server because of its superior space efficiency. Since sensor nodes have narrow bandwidth and fault-prone wireless channels, sensor-based storage systems are subject to incomplete data services. In this study, we will bring forth a short overview through providing an analysis on IoT sensor networks, and will propose a new storage management scheme for IoT data. Our management scheme is based on RAID storage model using column-wise segmentation and compression to improve space efficiency without sacrificing I/O performance. We conclude that proposed storage control scheme outperforms the previous RAID control by computer performance simulation.