• Korean Institute of Interior Design Journal
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• no.32
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• pp.64-71
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• 2002

Today, term of ‘industrial society’means the totally changing society by new technical innovation through the Industrial Revolution begun in England at the mid-nineteenth century and it made social structure centering agricultural culture change with industrial culture with expansion of goods by the massive production. Information working area has to bo a space not just for improving business efficiency in industrial society but for developing efficiency in working in harmony with the information and structural aspects based on computer and communication technology. There are two kinds of environmental elements of working space : the tangible ones are the area and the structure of working space, lights, network, layout and information machines in office, the intangible ones are a feeling of satisfaction of management and arrangement of office information machines capability of information delivery, common ownership of documents and their files, the security and connection between office workers, and harmony. According to the wave of information begun from the late twentieth century, structural layout of working area has become various by the working type through network of new developed information communication machines. But it is hard to apply them in actually.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.30-44
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• 2013

Traffic congestion has become a serious problem with the recent exponential increase in the number of vehicles. In urban areas, almost all traffic congestion occurs at intersections. One of the ways to solve this problem is road expansion, but it is difficult to realize in urban areas because of the high cost and long construction period. In such cases, traffic signal control is a reasonable method for reducing traffic jams. In an actual situation, the traffic flow changes randomly and its randomness makes the control of traffic signals difficult. A prediction of traffic jams is, therefore, necessary and effective for reducing traffic jams. In addition, an autonomous distributed (stand-alone) point control of each traffic light individually is better than the wide and/or line control of traffic lights from the perspective of real-time control. This paper describes a stochastic optimum control of crossroads and multi-way traffic signals. First, a stochastic model of traffic flows and traffic jams is constructed by using a Bayesian network. Secondly, the probabilistic distributions of the traffic flows are estimated by using a cellular automaton, and then the probabilistic distributions of traffic jams are predicted. Thirdly, optimum traffic signals of crossroads and multi-way intersection are searched by using a modified particle swarm optimization algorithm to realize real-time traffic control. Finally, simulations are carried out to confirm the effectiveness of the real-time stochastic optimum control of traffic signals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.485-487
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• 2017

The whirlpool hash function adopted as an ISO / IEC standard 10118-3 by the international standardization organization is an algorithm that provides message integrity based on an SPN (Substitution Permutation Network) structure similar to AES block cipher. In this paper, we describe the hardware implementation of the Whirlpool hash function. The round block is designed with a 64-bit data path and encryption is performed over 10 rounds. To minimize area, key expansion and encryption algorithms use the same hardware. The Whirlpool hash function was modeled using Verilog HDL, and simulation was performed with ModelSim to verify normal operation.

• Journal of Electrochemical Science and Technology
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• v.10 no.2
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• pp.131-138
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• 2019

To improve the performance of Si anodes in advanced Li-ion batteries, the design of the electrode plays a critical role, especially due to the large volumetric expansion in the Si anode during Li insertion. In our study, we used a simple fabrication method to prepare Si-based electrodes by grafting polyacrylic acid (PAA) to a carboxymethyl cellulose (CMC) binder (CMC-g-PAA). The procedure consists of first mixing nano-sized Si and the binders (CMC and PAA), and then coating the slurry on a Cu foil. The carbon network was formed via carbonization of the binders i.e., by a simple heat treatment of the electrode. The carbon network in the electrode is mechanically and electrically robust, which leads to higher electrical conductivity and better mechanical property. This explains its long cycle performance without the addition of a conducting agent (for example, carbon). Therefore, the partially carbonized CMC-g-PAA binder presented in this study represents a new feasible approach to produce Si anodes for use in advanced Li-ion batteries.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.239-246
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• 2021

This article substantiates the scientific provisions for modelling the level of Ukraine's public financial security taking into account the impact of budget policy, in the process of which identified indicators of budget policy that significantly affect the public financial security and the factors of budget policy based on regression analysis do not interact closely with each other. A seven-factor regression equation is constructed, which is statistically significant, reliable, economically logical, and devoid of autocorrelation. The objective function of maximizing the level of public financial security is constructed and strategic guidelines of budget policy in the context of Ukraine's public financial security are developed, in particular: optimization of the structure of budget revenues through the expansion of the resource base; reduction of the budget deficit while ensuring faster growth rates of state and local budget revenues compared to their expenditures; optimization of debt serviced from the budget through raising funds from the sale of domestic government bonds, mainly on a long-term basis; minimization of budgetary risks and existing threats to the public financial security by ensuring long-term stability of budgets etc.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.5
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• pp.35-46
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• 2006

The current Internet Key Exchange protocol(IKE) which has been used for key exchange of security system was pointed out the faults of scalability, speed, efficiency and stability. In this research, we tried to resolve those faults, and implemented the newly designed IKEv2 protocol in the IPsec test bed system. In the trend of network expansion, the current Internet Key Exchange protocol has a limitation of network scalability, so we implemented the new Internet Key Exchange protocol as a recommendation of RFC proposal, so as to resolve the fault of the key exchange complexity and the speed of authentication process. We improved the key exchange speed as a result of simplification of complex key exchange phase, and increased efficiency with using the preexistence state value in negotiation phase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.79-83
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• 2021

This paper proposes a novel image classification method based on few-shot learning, which is mainly used to solve model overfitting and non-convergence in image classification tasks of small datasets and improve the accuracy of classification. This method uses model structure optimization to extend the basic convolutional neural network (CNN) model and extracts more image features by adding convolutional layers, thereby improving the classification accuracy. We incorporated certain measures to improve the performance of the model. First, we used general methods such as setting a lower learning rate and shuffling to promote the rapid convergence of the model. Second, we used the data expansion technology to preprocess small datasets to increase the number of training data sets and suppress over-fitting. We applied the model to 10 monkey species and achieved outstanding performances. Experiments indicated that our proposed method achieved an accuracy of 87.92%, which is 26.1% higher than that of the traditional CNN method and 1.1% higher than that of the deep convolutional neural network ResNet50.