• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.47-61
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• 2011

Recently, the importance of digital forensic has been magnified because of the dramatic increase of cyber crimes and the increasing complexity of the investigation of target systems such as PCs, servers, and database systems. Moreover, some systems have to be investigated with live forensic techniques. However, even though live forensic techniques have been improved, they are still vulnerable to anti-forensic activities when the target systems are remotely accessible by criminals or their accomplices. To solve this problem, we first suggest a layer-based model and the anti-forensic scenarios which can actually be applicable to each layer. Our suggested model, the Anti-Forensic Activites layer-based model, has 5 layers - the physical layer, network layer, OS layer, database application layer and data layer. Each layer has possible anti-forensic scenarios with detailed commands. Second, we propose a fuzzy expert system for effectively detecting anti-forensic activities. Some anti-forensic activities are hardly distinguished from normal activities. So, we use fuzzy logic for handling ambiguous data. We make rule sets with extracted commands and their arguments from pre-defined scenarios and the fuzzy expert system learns the rule sets. With this system, we can detect anti-forensic activities in real time when performing live forensic.

• Journal of Korea Game Society
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• v.6 no.4
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• pp.47-53
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• 2006

In the game development differently with general software development, game planers, programers, and graphic designers, the specialists of the various fields, accomplished one team and they are advanced all to their goal. So it is very difficult for the development participants to communicate each other accurately and efficiently. For successful game development, all development participants should understand accurately the contents of the game design document. Specially the game mechanics as a major part of game design, requires the no-error contents, the no-error expression, and the no-error readings to all development participants because it contains almost game-play logic. It becomes more difficult for the development participants to understand accurately the game mechanics which becomes larger and complicated as the size of game development becomes larger. And configuration management of the game mechanics becomes more complicated and inefficient. In this paper, we propose a new representation method of game mechanics using UML notations for solving this problem. The proposed method satisfies the visual expression and the logical expression simultaneous for the requirements of the game mechanics because of UML notations. And the proposed method could be an efficient management of configuration because the management is based on the UML model management. The proposed representation of game mechanics of "Capture The Dude" game, shows good visual expression and good logical expression.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.131-138
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• 2019

The world's electricity production ratio is 40% for coal, 20% for natural gas, 16% for hydroelectric power, 15% for nuclear power and 6% for petroleum. Fossil fuels also cause serious problems in terms of price and supply because of the high concentration of resources on the earth. Solar energy is attracting attention as a next-generation eco-friendly energy that will replace fossil fuels with these problems. In this study, we test the charge-operation plan and the discharge operation plan for peak-cut operation by applying the maximum power demand reduction simulation. To do this, we selected the electricity usage from November to February, which has the largest amount of power usage, and applied charge / discharge logic. Simulation results show that the contract power decreases as the peak demand power after the ESS Peak-cut service is reduced to 50% of the peak-target power. As a result, the contract power reduction can reduce the basic power value of the customer and not only the economic superiority can be expected, but also contribute to the improvement of the electric quality and stabilization of the power supply system.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1
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• pp.21-29
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• 2006

In this Paper a split bandwidth wideband speech coder and its highband coding method are Proposed. The coder uses a split-band approach. where the wideband input speech signal is split into two equal frequency bands from 0-4kHz and 4-8kHz. The lowband and the highband are coded respectively by the 11.8kb/s G.729 Annex E and the proposed coding method. After the LPC analysis, the highband is divided by two modes according to the properties of signals. In stationary mode. the highband signals are compressed by the mixture excitation model; CELP algorithm and W (Matching Pursuit) algorithm. The others are coded by the only CELP algorithm. We compare the performance of the new wideband speech coder with that of G.722 48kbps SB-ADPCM and G.722.2 12.85kbps in a subjective method. The simulation results show that the Performance of the proposed wideband speech coder has better than that of 48kbps G.722 and no better than that of 12.85kbps G.722.2.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.