• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.292-303
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• 2010

On-chip cache memories play an important role in both performance and energy consumption points of view in resource-constrained embedded systems by filtering many off-chip memory accesses. We propose a 2-level data cache architecture with a low energy-delay product tailored for the embedded systems. The L1 data cache is small and direct-mapped, and employs a write-through policy. In contrast, the L2 data cache is set-associative and adopts a write-back policy. Consequently, the L1 data cache is accessed in one cycle and is able to provide high cache bandwidth while the L2 data cache is effective in reducing global miss rate. To reduce the penalty of high miss rate caused by the small L1 cache and power consumption of address generation, we propose an ECP(Early Cache hit Predictor) scheme. The ECP predicts if the L1 cache has the requested data using both fast address generation and L1 cache hit prediction. To reduce high energy cost of accessing the L2 data cache due to heavy write-through traffic from the write buffer laid between the two cache levels, we propose a one-way write scheme. From our simulation-based experiments using a cycle-accurate simulator and embedded benchmarks, the proposed 2-level data cache architecture shows average 3.6% and 50% improvements in overall system performance and the data cache energy consumption.

• Proceedings of KOSOMES biannual meeting
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• 2007.05a
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• pp.203-210
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• 2007

This paper treats optimal route safety assessment system at seaway based on weather forecasting data through INMARSAT. Since early times, captain have been sailing to select the optimum route considering the weather, ship loading status condition and operational scheduling empirically. However, it is rare to find digitalized onboard route support system whereas weather facsimile or wave and swell chart are utilized for the officer, based on captain's experience. In this paper, optimal route safety assessment system which is composed of voyage efficiency and safety component is introduced. Optimum route minimized ETA(estimated time of arrival) and fuel consumption that shipping company. and captain are requiring to evaluate for efficient voyage considering speed loss and power increase based on wave added resistance of ship. In the view point of safety, seakeeping prediction is performed based on 3 dimensional panel method Basically, the weather forecast is assumed to be prepared previously in order to operate this system.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.5
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• pp.58-65
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• 2007

As multimedia portable devices become popular, the amount of computation for processing data including video compression has significantly increased. Various researches for low power consumption of the mobile devices and real time processing have been reported. Motion Estimation is responsible for 67% of H.264 encoder complexity. In this research, a new circuit is designed for motion estimation. The new circuit uses motion prediction based on approximate SAD, Alternative Row Scan (ARS), DAU, and FDVS algorithms. Our new method can reduce the amount of computation by 75% when compared to multi-frame motion estimation suggested in JM8.2. Furthermore, optimal number and size of reference frame blocks are determined to reduce computation without affecting the PSNR. The proposed Motion Estimation method has been verified by using the hardware and software Co-Simulation with iPROVE. It can process 30 CIF frames/sec at 50MHz.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.3
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• pp.345-352
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• 1989

Upsetting of non-circular blocks is characterized by the three-dimensional deformation with lateral sidewise spread as well as axial bulging along thickness. A kinematically admissible velocity field for the upsetting of prismatic or non-prismatic blocks is proposed which considers the different frictional conditions at the top and bottom surfaces of a billet. From the proposed velocity field the upper-bound load and the deformed configuration are determined by minimizing the total power consumption with respect to some chosen parameters. Experiments are carried out with annealed SM 15C steel billets at room temperature for different billet shapes and frictional conditions. The theoretical predictions both in the forging load and the deformed configurations are shown to be in good agreement with the experimental observations. Therefore, the velocity field proposed in this work can be used for the prediction of forging load and deformation in upsetting of prismatic or non-prismatic blocks, considering the different frictional conditions at two flat dies.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.9-15
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• 2022

NAND flash memory is used as a medium for various storage devices due to its high data processing speed with low power consumption. However, since the read processing speed of data is about 10 times faster than the write processing speed, various studies are being conducted to improve the speed difference. In particular, flash dedicated buffer management policies have been studied to improve write speed. However, SSD(solid state disks), which has recently been used for various purposes, is more vulnerable to read performance than write performance. In this paper, we find out why read performance is slower than write performance in SSD composed of NAND flash memory and study buffer management policies to improve it. The buffer management policy proposed in this paper proposes a method of improving the speed of a flash-based storage device by analyzing the pattern of read data and applying a policy of pre-reading data to be requested in the future from NAND flash memory. It also proves the effectiveness of the read-ahead policy through simulation.

• Journal of Advanced Marine Engineering and Technology
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• v.8 no.1
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• pp.17-36
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• 1984

Since 1973, the competition on the development of fuel saving type internal combustion engines has become severe by the two times oil shock, and new type engines are reported every several months. Whenever these new type engines are developed, new designs are required and they will be offered in the market after performing the endurance test for a long time. But the engine market is faced with a heavy burden of finance, as the developing of a new engine requires tremendous expenses. For this reason, the computer simulation method has been lately developed to cope with it. The computer simulation method can be available to perform the reasonable research works by the theoretical analysis before carrying out practical experiments. With these processes, the developing expenses are cut down and the period of development is curtailed. The object of this study is the development of simulation computer program for the small naturally aspirated four-stroke diesel engine which is intended to product by the original design of our country. The process of simulation is firstly investigated for the ideal engine cycle, and secondly for the real engine cycle. In the ideal engine cycle, each step of the cycle is simulated by the energy balance according to the first law of thermodynamics, and then the engine performance is calculated. In the real cycle imulation program, the injection rate, the preparation rate and the combustion rate of fuel and the heat transfer through the wall of combustion chamber are considered. In this case, the injection rate is supposed as constant through the crank angle interval of injection and the combustion rate is calculated by the Whitehouse-Way equation and the heat transfer is calculated by the Annand's equation. The simulated values are compared with measured values of the YANMAR NS90(C) engine and Mitsubishi 4D30 engine, and the following conclusions are drawn. 1. The heat loss by the exhaust gas is well agree with each other in the lower load, but the measured value is greater than the calculated value in the higher load. The maximum error rate is about 15% in the full load. 2. The calculated quantity of heat transfer to the cooling water is greater than the measured value. The maximum error rate is about 11.8%. 3. The mean effective pressure, the fuel consumption, the power and the torque are well agree with each other. The maximum error is occurred in the fuel consumption, and its error rate is about 7%. From the above remarks, it may be concluded that the prediction of the engine performance is possibly by using the developed program, although the program needs to reform by adding the simulation of intake and exhaust process and assumping more reliable mechanical efficiency, volumetric efficiency, preparation rate and combustion rate.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.153-164
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• 2010

A program consumes energy by executing its instructions. The amount of cosumed power is mainly proportional to algorithm complexity and it can be calculated by using complexity information. Generally, the complexity of a S/W is estimated by the microprocessor simulator. But, the simulation takes long time why the simulator is a software modeled the hardware and it only provides the information about computational complexity quantitatively. In this paper, we propose a complexity estimation method of analysis of S/W on source code level and produce the complexity metric mathematically. The function-wise complexity metrics give the detailed information about the calculation-concentrated location in function. The performance of the proposed method is compared with the result of the gate-level microprocessor simulator 'SimpleScalar'. The used softwares for performance test are $4{\times}4$ integer transform, intra-prediction and motion estimation in the latest video codec, H.264/AVC. The number of executed instructions are used to estimate quantitatively and it appears about 11.6%, 9.6% and 3.5% of error respectively in contradistinction to the result of SimpleScalar.