• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.89-98
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• 2009

As data stream is entered into system continuously and the memory space is limited, the data exceeding the memory size cannot be processed. In order to solve the problem, load shedding methods which drop a part of data to prevent exceeding the storage space have been researched. Generally, a traditional load shedding method uses random sampling with optimized rate according to data deviation. The method samples data not to distinguish those used in spatial query because the method uses only a random sampling with optimized rate according to data deviation. Therefore, the accuracy of query was reduced in u-GIS environment including spatial query. In this paper, we researched a new load shedding method improving accuracy of the query in u-GIS environment which runs spatial query and aspatial query simultaneously. The method uses a new sampling method that samples data having low probability used in query. Therefore proposed method improves spatial query accuracy and query processing speed as applying spatial filtering operation to sampling operator.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• The korean journal of orthodontics
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• v.23 no.4 s.43
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• pp.725-734
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• 1993

Nitinol wires are now widely used in the orthodontic field because of their unique shape memory effect and superelasticity. But sometimes Nitinol wires are deformed in clinical use. Fatigue load is possible cause of Nitinol deformation. To determine the effect of fatigue load to the mechanical properties of Nitinol, various fatigue cycle$(1\times10^4,\;2\times10^4,\;3\times10^4,\;4\times10^4,\;5\times10^4,\;1\times10^5)$ were applied to $0.017\times0.025$ inch Nitinol. The results obtained were as follows ; 1. Applied load increased as fatigue cycle increased in three point bending test. 2. Maximum tensile strength and elongation decreased as fatigue cycle increased. 3. Tn SEM, brittle fracture pattern was increased when fatigue cycle increased.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

• ETRI Journal
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• v.45 no.3
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• pp.433-447
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• 2023

Critical issues such as connection congestion, long transmission delay, and packet loss become even worse during epidemic, disaster, and so on. In this study, a link load balancing method is proposed to address these issues on the data plane, a plane of the software-defined network (SDN) architecture. These problems are NP-complete, so a meta-heuristic approach, discrete particle swarm optimization, is used with a novel hybrid cost function. The superiority of the proposed method over existing methods in the literature is that it provides link and switch load balancing simultaneously. The goal is to choose a path that minimizes the connection load between the source and destination in multipath SDNs. Furthermore, the proposed work is dynamic, so selected paths are regularly updated. Simulation results prove that with the proposed method, streams reach the target with minimum time, no loss, low power consumption, and low memory usage.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1035-1045
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• 2015

Recent studies in Big Data Analysis are showing promising results, utilizing the main memory for rapid data processing. In-memory computing technology can be highly advantageous when used with high-performing servers having tens of gigabytes of RAM with multi-core processors. The constraint in network in these infrastructure can be lessen by combining in-memory technology with distributed parallel processing. This paper discusses the research in the aforementioned concept applying to a test taxi hailing application without disregard to its underlying RDBMS structure. The application of IMDG technology in the application's backend API without restructuring the database schema yields 6 to 9 times increase in performance in data processing and throughput. Specifically, the change in throughput is very small even with increase in data load processing.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3077-3085
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• 1999

Conventional caching system doesn't support Real-Time attributes and load balance. To solve these problems, this paper describes the design and implementation of the RIOP(Real-Time Inter-ORB Protocol) to provide QoS guarantees mechanism integrating RSVP and TAO ORB. Futhermore, it provides fast XCSLS(Extended Caching System for Load Balance) implementing main memory cache in Primary Server using locality of objects. In this paper, a key feature is presented : QoS enforcement, PS(Primary Server) and RS(Replicated Server)

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1101-1105
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• 2011

Radiation particles can introduce temporary errors in memory systems. To protect against these errors, so-called soft errors, error detection and correcting codes are used. In addition, scrubbing is applied which is a fundamental technique to avoid the accumulation of soft errors. This paper introduces an optimal scrubbing scheme, which is suitable for a system with auto error detection and correction logic. An auto error detection and correction logic can correct soft errors without CPU's writing operation. The proposed scrubbing scheme leads to maximum reliability by considering both allowable scrubbing load and the periodic accesses to memory by the tasks running in the system.

• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.17-24
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• 2017

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.