• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.10
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• pp.39-50
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• 1998

Wide-band sharp-transition filters are widely used in applications such as wireless CODEC design or medical systems. Since these filters suffer from large sensitivity and roundoff noise, large word-length is required for the VLSI implementation, which increases the hardware size and the power consumption of the chip. In this paper, a low-power implementation technique for digital filters with wide-band sharp-transition characteristics is proposed using CPL (Complementary Pass-Transistor Logic), LWDF (Lattice Wave Digital Filter) and a modified DIFIR (Decomposed & Interpolated FIR) algorithm. To reduce the short-circuit current component in CPL circuits due to threshold voltage reduction through the pass transistor, three different approaches can be used: cross-coupled PMOS latch, PMOS body biasing and weak PMOS latch. Of the three, the cross-coupled PMOS latch approach is the most realistic solution when the noise margin as well as the energy-delay product is considered. To optimize CPL transistor size with insight, the empirical formulas for the delay and energy consumption in the basic structure of CPL circuits were derived from the simulation results. In addition, the filter coefficients are encoded using CSD (Canonic Signed Digit) format and optimized by a coefficient quantization program. The hardware cost is minimized further by a modified DIFIR algorithm. Simulation result shows that the proposed method can achieve about 38% reductions in power consumption compared with the conventional method.

• Fisheries and Aquatic Sciences
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• v.13 no.2
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• pp.167-181
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• 2010

To predict changes in the marine environment of the Beolgyo Stream Estuary in Jeonnam Province, South Korea, where cohesive tidal flats cover a broad area and a large bridge is under construction, this study conducted numerical simulations involving tidal flow and cohesive sediment transport. A wetting and drying (WAD) technique for tidal flats from the Princeton Ocean Model (POM) was applied to a large-scale-grid hydrodynamic module capable of evaluating the flow resistance of structures. Derivation of the eddy viscosity coefficient for wakes created by structures was accomplished through the explicit use of shear velocity and Chezy's average velocity. Furthermore, various field observations, including of tide, tidal flow, suspended sediment concentrations, bottom sediments, and water depth, were performed to verify the model and obtain input data for it. In particular, geologic parameters related to the evaluation of settling velocity and critical shear stresses for erosion and deposition were observed, and numerical tests for the representation of suspended sediment concentrations were performed to determine proper values for the empirical coefficients in the sediment transport module. According to the simulation results, the velocity variation was particularly prominent around the piers in the tidal channel. Erosion occurred mainly along the tidal channels near the piers, where bridge structures reduced the flow cross section, creating strong flow. In contrast, in the rear area of the structure, where the flow was relatively weak due to the formation of eddies, deposition and moderated erosion were predicted. In estuaries and coastal waters, changes in the flow environment caused by artificial structures can produce changes in the sedimentary environment, which in turn can affect the local marine ecosystem. The numerical model proposed in this study will enable systematic prediction of changes to flow and sedimentary environments caused by the construction of artificial structures.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Distribution Research
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• v.5 no.2
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• pp.49-68
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• 2001

In this chapter, we summarize the results on the optimal location selection and present limitation and direction of research. In order to reach the objective, this study selected and tested the interaction model which obtains the value of co-ordinates on location selection through the optimization technique. This study used the original variables in the model, but the results indicated that there is difference in reality. In order to overcome this difference, this study peformed market survey and found the new variables (first data such as price, quality and assortment of goods, and the second data such as aggregate area, and area of shop, and the number of cars in the parking lot). Then this study determined an optimal variable by empirical analysis which compares an actual value of market share in 1988 with the market share yielded in the model. However, this study found the market share in each variables does not reflect a reality due to an assumption of λ-value in the model. In order to improve this, this study performed a sensitivity analysis which adds the λ value from 1.0 to 2.9 marginally. The analyzed result indicated the highest significance with the market share ratio in 1998 at λ of 1.0. Applying the weighted value to a variable from each of the first data and second data yielded the results that more variables from the first data coincided with the realistic rank on sales. Although this study have some limits and improvements, if a marketer uses this extended model, more significant results will be produced.