• ETRI Journal
• /
• v.42 no.5
• /
• pp.748-760
• /
• 2020

MapReduce is a framework that can process huge datasets in parallel and distributed computing environments. However, a single machine failure during the runtime of MapReduce tasks can increase completion time by 50%. MapReduce handles task failures by restarting the failed task and re-computing all input data from scratch, regardless of how much data had already been processed. To solve this issue, we need the computed key-value pairs to persist in a storage system to avoid re-computing them during the restarting process. In this paper, the task failure resilience (TFR) technique is proposed, which allows the execution of a failed task to continue from the point it was interrupted without having to redo all the work. Amazon ElastiCache for Redis is used as a non-volatile cache for the key-value pairs. We measured the performance of TFR by running different Hadoop benchmarking suites. TFR was implemented using the Hadoop software framework, and the experimental results showed significant performance improvements when compared with the performance of the default Hadoop implementation.

• Journal of the Korea Safety Management & Science
• /
• v.20 no.3
• /
• pp.11-19
• /
• 2018

This study suggests a model of production information system that can reduce manufacturing lead time and uniformize quality by using DNC S/W as a part of constructing production information management system in the industrial field of the existing marine engine block manufacturing companies. Under the effect of development of this system, the NC machine interface device can be installed in the control computer to obtain the quality information of the workpiece in real time so that the time to inspect the process quality and verify the product defect information can be reduced by more than 70%. In addition, the reliability of quality information has been improved and the external credibility has been improved. It took 30 minutes for operator to obtain, analyze and manage the quality information when the existing USB memory is used, but the communication between the NC controller computer and the NC controller in real time was completed to analyze the workpiece within 10 seconds.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.11
• /
• pp.1249-1254
• /
• 2014

Big data based on numerous data made by the people are used in order to obtain useful information. We can obtain more useful information if it can apply machine learning techniques added deformation of human memory on the characteristics of the computer program. And big data is predicted by using these conclusions. Humans are used to remember similar data as an original data, so big data processing technology should reflect these human characteristics. In this study, this algorithm to provide the selectivity of information is proposed. This algorithm is the technology to reflect the above factors. This algorithm is selected the data with high selectivity to determine similar data based on the deformation characteristics of the data.

• Journal of Internet Computing and Services
• /
• v.5 no.2
• /
• pp.1-15
• /
• 2004

The current Internet space is evolving to the so called Ubiquitous space. Unlike the Internet space, the information in the Ubiquitous space is distributed evenly in the places like computer's memory, human's brain and physical machine. The 'hypertext', the connection model of the information, which is originally designed for the Internet space doesn't suit well to the Ubiquitous space. From this point of view, we proposed the CPM model in this paper. The CPM model is designed for comprising the such three computing mechanism as analog computing, digital computing and human computing. In this paper, we showed that the characteristics of the CPM model might answer the such purpose as the connection of information in the Ubiquitous space.

• The KIPS Transactions:PartD
• /
• v.16D no.1
• /
• pp.139-144
• /
• 2009

Neural network is a very exciting and generic framework to develop almost all ranges of machine learning technologies and its potential is far beyond its current capabilities. Among other characteristics, neural network acts as associative memory obtained from the values structurally stored in synaptic inherent structure. Due to innate complexity of neural networks system, in its practical implementation and maintenance, multifaceted problems are known to be unavoidable. In this paper, we present design and implementation details of GUI software which can be valuable tool to maintain and develop neural networks. It has capability of displaying every state of synaptic weights with network nodal relation in each learning step.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.147-147
• /
• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.29-30
• /
• 2019

본 논문에서는 다중 컨트롤러가 존재하는 분산 SDN 환경에서 과도한 제어 메시지로 인한 과부하된 컨트롤러의 부하를 줄이기 위하여 이주할 스위치를 K-means 군집화와 Harmony Search(HS)를 기반으로 선정 하는 기법을 제안하였다. 기존에 HS를 이용하여 이주할 스위치를 선택하는 기법이 제시되었으나, 시간 소모에 비하여 정확도가 부족한 단점이 있다. 또한 Harmony Memory(HM) 구축을 위해 메모리 소모 또한 크다. 이를 해결하기 위하여 본 논문에서는 유클리드 거리를 기반으로 하는 K-means 군집화를 이용하여 이주할 스위치를 골라내어 HM의 크기를 줄이고 이주 효율을 향상 시킨다.

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.22-28
• /
• 2019

This paper presents the monitoring method of machining error and quality check to improve the productivity of boring manufacturing process. Machining error usually appears as the offset of spatial location of actual cutting path compared to ideal cutting path. In order to monitor an error of workpiece, multiple factors affecting quality of boring, such as distortion of workpiece, clamping error, radial rotation error of the spindle and motion error of machine tools, were took into account. To verify the productive quality, we propose the quality check system. The system based on IT convergence analyzes the process error rate and saves the analyzed data in memory. Also, these play important roles in detecting an inferior production goods and can decrease the production cost and loss of bearing.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.19 no.12
• /
• pp.21-27
• /
• 2020

Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.26 no.3
• /
• pp.183-191
• /
• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.