• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1859-1864
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• 2004

An effective methodology is reported for the optimal design of multisite batch production/transportation and storage networks under uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, internally consumed, transported to or from other plant sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between plant sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of large-scale supply chain system.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2581-2595
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• 1997

In this paper, the real-time facility of the operating system for a VOD(Video On Demand) server have been analyzed and implemented. The requirements of the real-time scheduling have been gathered by analyzing the model of the video-data-transfer-path. Particularly, the influence of the bottleneck subsystem have been analyzed. Thus, we have implemented the real-time scheduler and primitives which is proper for processing the digital video. In performance measurements, the degree of the guarantee of the real-time scheduler have been experimented. The measured data show that the most time constraints of the process is satisfied. But, the network protocol processing by the interrupt is a major obstacle of the real-time scheduling. We also have compared the difference between the real-time scheduler and the non-real-time scheduler by measuring the inter-execution time. According to the measured results, the real-time scheduler should be used for efficient video service because the processor time allocated to the process can't be estimated when the non-real-time scheduler is used.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.683-691
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• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1081-1086
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• 2011

Last port logistics for the EDI(Electronic Data Interchange) document processing system using a rapid increase in the complaint is handled. However, the existing system in a way that the script processing EDI documents, but the complexity of the script writing and document processing efficiency, lower consumption due to increased demand for processing has not kept up. Therefore, we changed the script in a way how to handle the binary system was designed and implemented. Also used for port logistics has developed 12 types of EDI documents. Accordingly, the document processing speed compared to existing methods are improved twelvefold port logistics system for processing EDI documents are expected to be utilized.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.161-171
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• 1999

Query language has been widely used as a convenient tool to obtain information from a database. However, users demand more intelligent query processing systems that can understand the intent of an imprecise query and provide additional useful information as well as exact answers. This paper introduces a meta-database and presents a query processing mechanism that supports a variety of intelligent queries in a consistent and integrated way. The meta-database extracts data abstraction knowledge from an underlying database on the basis of a multilevel knowledge representation framework KAH. In cooperation with the underlying database, the meta-database supports four types of intelligent queries that provide approximately or conceptually equal answers as well as exact ones.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.161-171
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• 1999

Query language has been widely used as a convenient tool to obtain information from a database. However, users demand more intelligent query processing systems that can understand the intent of an imprecise query and provide additional useful information as well as exact answers. This paper introduces a meta-database and presents a query processing mechanism that supports a variety of intelligent queries in a consistent and integrated way. The meta-database extracts data abstraction knowledge form an underlying database on the basis of a multilevel knowledge representation framework KAH. In cooperation with the underlying database, the meta-database supports four types of intelligent queries that provide approximately or conceptually equal answers as well as exact ones.

• ETRI Journal
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• v.39 no.2
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• pp.202-212
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• 2017

With the advent of the Internet-of-Things paradigm, the amount of data production has grown exponentially and the user demand for responsive consumption of data has increased significantly. Herein, we present DART, a fast and lightweight stream processing framework for the IoT environment. Because the DART framework targets a geospatially distributed environment of heterogeneous devices, the framework provides (1) an end-user tool for device registration and application authoring, (2) automatic worker node monitoring and task allocations, and (3) runtime management of user applications with fault tolerance. To maximize performance, the DART framework adopts an actor model in which applications are segmented into microtasks and assigned to an actor following a single responsibility. To prove the feasibility of the proposed framework, we implemented the DART system. We also conducted experiments to show that the system can significantly reduce computing burdens and alleviate network load by utilizing the idle resources of intermediate edge devices.

• Korean journal of food and cookery science
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• v.4 no.1
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• pp.65-73
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• 1988

Food procurement practices were assessed in 31 general hospital's dietetics in terms of the level of processing for purchased food. The level of processing before purchase by the institution was noted by a numerical value. a Food Processing Index (FPI). Labor productivity measured in terms of labor minutes per meal served averaged 12.59 minutes. Responsible persons for procurement practices were mostly dietitians, but effective purchasing methods as well as inventory control techniques were not fully systematized. Dietetic directors anticipate the large increase in the use of convenience foods in the future mainly due to the improved labor productivity, and also a highly increasing demand for completely pre-prepared food items (FPI 3). The grand mean of FPI scores for purchased foods in surveyed hospitals was 3.34, which indicates partially pre-prepared to completely pre-prepared food items. Significant negative correlation was found between the grand mean of FPI scores and the number of beds in a hospital.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.3
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• pp.394-400
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• 2012

Due to the increasing demand for carious methodologies, the quality improvement of products were introduced. A brush, the most frequently used type of grinding process, is one of the deburring. In order to produce consistent burr shape, various machining conditions have been combined and applied to disk grinding process. By tool dynamometer, acoustic emission sensor and acceleration sensor depend on changes in processing conditions(depth of engagement, cutting speed, workpiece position, workpiece orientation, cutting time) signals were obtained for brush deburring grinding system. Root mean square obtained by processing the signal processing conditions by analyzing the characteristics of deburring is to derive the optimum conditions.