• Annual Conference of KIPS
• /
• 2016.10a
• /
• pp.50-53
• /
• 2016

Image processing techniques play an increasingly important role in many aspects of our daily life. For example, it has been shown to improve agricultural productivity in a number of ways such as plant pest detecting or fruit grading. However, massive quantities of images generated in real-time through multi-devices such as remote sensors during monitoring plant growth lead to the challenges of big data. Meanwhile, most current image processing systems are designed for small-scale and local computation, and they do not scale well to handle big data problems with their large requirements for computational resources and storage. In this paper, we have proposed an IPABigData (Image Processing Algorithm BigData) platform which provides algorithms to support large-scale image processing in agriculture based on Hadoop framework. Hadoop provides a parallel computation model MapReduce and Hadoop distributed file system (HDFS) module. It can also handle parallel pipelines, which are frequently used in image processing. In our experiment, we show that our platform outperforms traditional system in a scenario of image segmentation.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.3
• /
• pp.234-240
• /
• 1999

This paper presents a new approach for large-scale generator maintenance scheduling optimizations. The generator preventive maintenance scheduling problems are typical discrete dynamic n-dimensional vector optimization ones with several inequality constraints. The considered objective function to be minimized a subset of{{{{ { R}^{n } }}}} space is the variance (i.g., second-order momentum) of operating reserve margin to levelize risk or reliability during a year. By its nature of the objective function, the optimal solution can only be obtained by enumerating all combinatorial states of each variable, a task which leads to computational explosion in real-world maintenance scheduling problems. This paper proposes a new priority search mechanism based on each generator's discrete sensitivity value which was analytically developed in this study. Unlike the conventional capacity-based priority search, it can prevent the local optimal trap to some extents since it changes dynamically the search tree in each iteration. The proposed method have been applied to two test systems (i.g., one is a sample system with 10 generators and the other is a real-world lage scale power system with 280 generators), and the results anre compared with those of the conventional capacith-based search method and combinatorial optimization method to show the efficiency and effectiveness of the algorithm.

• Journal of the Korea Safety Management & Science
• /
• v.18 no.4
• /
• pp.47-55
• /
• 2016

Recently, industrial accidents rate has been gradually decreasing due to the development of safety management methods, but until now, the accident rate in the construction sector is higher than other industries. Large-scale construction sites are operating systematic safety systems to reduce industrial accidents. However, small and medium sized construction sites do not have systematic safety system and lack safety management ability, so that disaster is not reduced compared with large scale construction site. As a result, disaster prevention technology instruction system has been implemented to reduce the disasters of small and medium scale construction sites. However, in the case of a small construction site less than 2 billion won, there is little decrease in the accident rate, and in some cases, the accident rate increases. After the technical guidance system has been implemented, it is necessary to identify the performance and problems of implementation and to improve its effectiveness. In this study, we suggest the improvement plan to improve the efficiency of the technical guidance system by analyzing the problems and actual conditions of technical guidance operation in small and medium sized construction work sites.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3923-3942
• /
• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.11
• /
• pp.117-126
• /
• 2003

GA(genetic algorithm) has a powerful searching ability and is comparatively easy to use and to apply as well. By that reason, GA is in the spotlight these days as an optimization skill for mechanical systems.$^1$However, GA has a low efficiency caused by a huge amount of repetitive computation and an inefficiency that GA meanders near the optimum. It also can be shown a phenomenon such as genetic drifting which converges to a wrong solution.$^{8}$ These defects are the reasons why GA is not widdy applied to real world problems. However, the low efficiency problem and the meandering problem of GA can be overcomed by introducing parallel computation$^{7}$ and gray code$^4$, respectively. Standard GA(SGA)$^{9}$ works fine on small to medium scale problems. However, SGA done not work well for large-scale problems. Large-scale problems with more than 500-bit of sere's have never been tested and published in papers. In the result of using the SGA, the powerful searching ability of SGA doesn't have no effect on optimizing the problem that has 96 design valuables and 1536 bits of gene's length. So it converges to a solution which is not considered as a global optimum. Therefore, this study proposes ExpGA(experience GA) which is a new genetic algorithm made by applying a new probability parameter called by the experience value. Furthermore, this study finds the solution throughout the whole field searching, with applying ExpGA which is a optimization technique for the structure having genetic drifting by the standard GA and not making a optimization close to the best fitted value. In addition to them, this study also makes a research about the possibility of GA as a optimization technique of large-scale design variable problems.

• KIISE Transactions on Computing Practices
• /
• v.22 no.3
• /
• pp.151-156
• /
• 2016

Cloud computing systems require a huge number of storage servers due to the growing implications of power bills, carbon emissions, and logistics of data centers. These considerations have motivated researchers to improve the energy efficiency of storage servers. Most servers use a lot of power irrespective of the amount of computing that they are doing, and one important goal is to redesign servers to be power-proportional. However, Research on large-scale storage systems is hampered by their cost. It is therefore desirable to develop a scalable test-bed for evaluating the power consumption of large-scale storage systems. We are building on open-source projects to construct a test-bed which will contribute to the assessment of power consumption in tiered storage systems. Integrating the cloud application platform can easily extend the proposed testbed laying a foundation for the design and evaluation of low-power storage servers.

• Smart Structures and Systems
• /
• v.18 no.4
• /
• pp.733-754
• /
• 2016

Structural optimization involves a large number of structural analyses. When optimizing large structures, these analyses require a considerable amount of computational time and effort. However, there are specific types of structure for which the results of the analysis can be achieved in a much simpler and quicker way thanks to their special repetitive patterns. In this paper, frequency constraint optimization of cyclically repeated space trusses is considered. An efficient technique is used to decompose the large initial eigenproblem into several smaller ones and thus to decrease the required computational time significantly. Some examples are presented in order to illustrate the efficiency of the presented method.

• Journal of the Korean Ceramic Society
• /
• v.47 no.1
• /
• pp.1-7
• /
• 2010

Solid oxide fuel cells (SOFCs) have been under development for a variety of power generation applications. Power system sizes considered range from small watt-size units (e.g., 50-W portable devices) to very large multi-megawatt systems (e.g., 500-MW base load power plants). Because of the reversibility of its operation, the SOFC has also been developed to operate under reverse or electrolysis mode for hydrogen production from steam (In this case, the cell is referred to as solid oxide electrolysis cell or SOEC.). Potential applications for the SOEC include on-site and large-scale hydrogen production. One critical requirement for practical uses of these systems is long-term performance stability under specified operating conditions. Intrinsic material properties and operating environments can have significant effects on cell performance stability, thus performance degradation rate. This paper discusses potential applications of the SOFC/SOEC, technological status and current research and development (R&D) direction, and certain aspects of long-term performance degradation in the operation of SOFCs/SOECs for power generation/hydrogen production.

• Korean Management Science Review
• /
• v.21 no.2
• /
• pp.43-60
• /
• 2004

Every iteration of interior-point methods of large scale optimization requires computing at least one orthogonal projection. In the practice, symmetric variants of the Gaussian elimination such as Cholesky factorization are accepted as the most efficient and sufficiently stable method. In this paper several specific implementation issues of the symmetric factorization that can be applied for solving such equations are discussed. The code called McSML being the result of this work is shown to produce comparably sparse factors as another implementations in the $MATLAB^{***}$ environment. It has been used for computing projections in an efficient implementation of self-regular based interior-point methods, McIPM. Although primary aim of developing McSML was to embed it into an interior-point methods optimizer, the code may equally well be used to solve general large sparse systems arising in different applications.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.50 no.1
• /
• pp.28-37
• /
• 2001

This paper presents an efficient algorithm for the loss minimization of distribution system by automatic sectionalizing switch operation in large scale distribution systems. Simulated annealing is particularly well suited for large combinational optimization problem, but the use of this algorithm is also responsible for an excessive computation time requirement. Tabu search attempts to determine a better solution in the manner of a greatest-descent algorithm, but it can not give any guarantee for the convergence property. The hybrid algorithm of two methods with two tabu lists and the proposed perturbation mechanism is applied to improve the computation time and convergence property Numerical examples demonstrate the validity and effectiveness of the proposed methodology using a KEPCO's distribution system.