• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.4
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• pp.18-29
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• 2019

A dynamic adaptive streaming server consumes high processor power because it handles a large amount of transcoding operations at a time. For this purpose, multi-processor architecture is mandatory for which effective transcoding task distribution strategies are essential. In this paper, we present the design and implementation details of the transcoding workload distribution schemes at a 2-tier (frontend node and backend node) transcoding server. For this, we implemented four schemes: 1) allocation of transcoding tasks to appropriate back-end nodes, 2) task scheduling in the back-end node and 3) the communication between front-end and back-end nodes. Experiments were conducted to compare the estimated and the actual power consumption in a real testbed to verify the efficacy of the system. It also proved that the system can reduce the load on each node to optimize the power and time used for transcoding.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.47-53
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• 2021

In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.285-293
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• 2022

The COVID-19 outbreak, which took longer than expected, caused considerable damage to students' basic academic ability in mathematics. In this paper, a multiplication table practice application that can help students improve their basic multiplication arithmetic skills has been developed based on a cloud-service. The performance of the application was improved by integrating the Flutter framework, Google Cloud, and Google Sheets. As a result of applying this application to 72 6th graders in elementary schools located in K Metropolitan City, for one week. students' spending time required for solving multiplication table problems was reduced by more than 28% compared to the initial period, while students' learning data was able to be accurately collected without errors. It is hoped that the development case conducted through the Flutter framework in this study can lead to the development of other educational learning applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.459-461
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• 2022

One of the big differences between Network-on-Chip (NoC) and the existing parallel processing system based on an off-chip network is that data packet routing is performed using a centralized control scheme. In such an environment, the best-effort packet routing problem becomes a real-time assignment problem in which data packet arriving time and processing time is the cost. In this paper, the Hungarian algorithm, a representative computational complexity reduction algorithm for the linear algebraic equation of the allocation problem, is implemented in the form of a hardware accelerator. As a result of logic synthesis using the TSMC 0.18um standard cell library, the area of the circuit designed through case analysis for the cost distribution is reduced by about 16% and the propagation delay of it is reduced by about 52%, compared to the circuit implementing the original operation sequence of the Hungarian algorithm.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.463-471
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• 2019

This paper proposes a method for reducing the complexity of LIC (Local Illuminance Compensation) for bi-directional inter prediction. The LIC performs local illumination compensation using neighboring reconstruction samples of the current block and the reference block to improve the accuracy of the inter prediction. Since the weight and offset required for local illumination compensation are calculated at both sides of the encoder and decoder using the reconstructed samples, there is an advantage that the coding efficiency is improved without signaling any information. Since the weight and the offset are obtained in the encoding prediction step and the decoding step, encoder and decoder complexity are increased. This paper proposes two methods for low complexity LIC. The first method is a method of applying illumination compensation with offset only in bi-directional prediction, and the second is a method of applying LIC after weighted average step of reference block obtained by bidirectional prediction. To evaluate the performance of the proposed method, BD-rate is compared with BMS-2.0.1 using B, C, and D classes of MPEG standard experimental image under RA (Random Access) condition. Experimental results show that the proposed method reduces the average of 0.29%, 0.23%, 0.04% for Y, U, and V in terms of BD-rate performance compared to BMS-2.0.1 and encoding/decoding time is almost same. Although the BD-rate was lost, the calculation complexity of the LIC was greatly reduced as the multiplication operation was removed and the addition operation was halved in the LIC parameter derivation process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.15-20
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• 2023

Until now, Pollard's Rho algorithm has been known as the most efficient way for discrete algebraic problems to decrypt symmetric keys. However, the algorithm is being studied on how to further reduce the complexity of O(${\sqrt{p}}$) performance, along with the disadvantage of having to store the giant stride m=⌈${\sqrt{p}}$⌉ data. This paper proposes an array method for cycle detection in discrete logarithms. The proposed method reduces the number of updates of stack memory by at least 73%. This is done by only updating the array when (x_i<0.5x_i-1)∩(x_i<0.5(p-1)). The proposed array method undergoes the same number of modular calculation as stack method, but significantly reduces the number of updates and the execution time for array through the use of a binary search method.

• Environmental and Resource Economics Review
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• v.16 no.1
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• pp.27-63
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• 2007

Using the CGE model, this paper investigates economic impacts of a shortage in crude oil resulting from voluntary export restraints, OPEC's agreement of a cut in oil production, and/or a storing on speculation. Unlike most previous studies considering oil price as the unpredictable variable, this study constructs the model to determine the oil price endogenously under the condition of an insufficient supply of crude oil. According to IEA's extraordinary steps for a shortage of crude oil, we investigate an economic impact of 7~12% shortage below the level of business as usual. The results show that oil price soars by 17.3~33.5%, the rate of economic growth falls by 0.52~0.96%p, and the consumer price index(CPI) rises by 0.8~1.51%p. These results imply that increasing in 1%p of oil price results in decreasing in 0.03%p of economic growth and increasing in 0.045%p of consumer price index. The production of electricity declines because of the increase in production cost. A shortage of crude oil has an effect on sources of electricity. Most reduction in electricity generation occurs from the reduction in the thermal power generation which is highly dependent on crude oil. The shortage of crude oil causes demand for petroleum to significantly decline but demand for coal and heat to increase because of the substitution effect with petroleum. Demand for gas rise in the first year but falls from the second year.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.277-289
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• 2005

To overcome the long design time/high computational effort/low computational performance of phylogenetic learning featuring selection and reproduction, this paper proposes a genetic representation based on XML. Since genetic programs (GP) and genetic operations of this representation are maintained by the invocation of the built-in off-the-shelf XML parser's API, the proposed approach features significant reduced time consumption of GP design process. Handling only semantically correct GPs with standard XML schema can reduce search space and computational effort. Furthermore, computational performance can be improved by the parallelism of GP caused by the utilization of XML, which is a feasible system and wire format for migration of genetic programs in heterogeneous distributed computer environments. To verify the proposed approach, it is applied to the evolution of social behaviors of multiple agents modeling the predator-prey pursuit problem. The results show that the approach can be applied for fast development and time efficiency of GPs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.126-136
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• 2007

This paper presents an enhanced algorithm for compensating the visual quality in back-light image. Current cameras do not represent all details of scene into human's eye. Saturation and underexposure are common problems in back-light image. Retinex algorithm, derived from Land's theory on human visual perception is known to be effective in enhancing the contrast. However, its weaknesses are long processing time and low contrast of bright area in back-light scene because of compensating the details of dark area. In this paper, K-Retinex algorithm is proposed to reduce the processing time and enhance the contrast in both dark and bright area. To show the superiority of proposed algorithm, we compare the processing time, local standard deviation and contrast per pixel of each area above.