• Journal of KIISE:Computer Systems and Theory
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• v.30 no.9
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• pp.512-522
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• 2003

For a time-multiplexed FPGA, a circuit is partitioned into several subcircuits, so that they temporally share the same physical FPGA device by hardware reconfiguration. In these architectures, all the hardware reconfiguration information called contexts are generated and downloaded into the chip, and then the pre-scheduled context switches occur properly and timely. Typically, the size of the chip required to implement the circuit depends on both the maximum number of the LUT blocks required to implement the function of each subcircuit and the maximum number of micro-registers to store results over context switches in the same time. Therefore, many partitioning or synthesis methods try to minimize these two factors. In this paper, we present a new estimation technique to find the lower bound on the number of micro-registers which can be obtained by any synthesis methods, respectively, without performing any actual synthesis and/or design space exploration. The lower bound estimation is very important in sense that it greatly helps to evaluate the results of the previous work and even the future work. If the estimated lower bound exactly matches the actual number in the actual design result, we can say that the result is guaranteed to be optimal. In contrast, if they do not match, the following two cases are expected: we might estimate a better (more exact) lower bound or we find a new synthesis result better than those of the previous work. Our experimental results show that there are some differences between the numbers of micro-registers and our estimated lower bounds. One reason for these differences seems that our estimation tries to estimate the result with the minimum micro-registers among all the possible candidates, regardless of usage of other resources such as LUTs, while the previous work takes into account both LUTs and micro-registers. In addition, it implies that our method may have some limitation on exact estimation due to the complexity of the problem itself in sense that it is much more complicated than LUT estimation and thus needs more improvement, and/or there may exist some other synthesis results better than those of the previous work.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.163-174
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• 2020

Particulate matter levels are rapidly increasing daily, and this can affect human health. Therefore, air pollutant emissions from sea vessels require management. This study evaluates the status of air pollutants, focusing on air pollutant emissions from the vessels of the Korea Coast Guard (KCG), and proposes national management measures to reduce emissions. According to a report recently released (2018) by the National Institute of Environmental Research (NIER), emissions from vessels constituted 6.4 % of the total domestic emissions, including 13.1 % NO_x, 10.9 % SO_x, and 9.6 % particulate matter (PM10/PM2.5). Among the rates of pollutant emission from vessels, the emission rates of domestic and overseas cargo vessels were the highest (50.6 %); the ratio of fishing boats was 42.6 %. With respect to jurisdictional sea area, 44.1 % of the emissions are from the south sea, including the Busan and Ulsan ports, and 24.8 % of the emissions are from the west sea, including the Gwangyang and Yeosu ports. The KCG inspects boarding lines to manage emission conditions and regulate air pollutant emissions, but it takes time and effort to operate various discharge devices and measure fuel oil standards. In addition, owing to busy ship schedules, inspection documents are limited in terms of management. Therefore, to reduce the air pollutant emissions of such vessels, regulations will be strengthened to check for air pollutants, and a monitoring system based on actual field data using KCG patrol ships will be established, for each sea area, to manage the emissions of such vessels. Furthermore, there is a need for technological development and institutional support for the introduction of environmentally friendly vessels.