• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.9-16
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• 2009

Because Grid system consists of heterogenous computing resources, which are distributed on a wide scale, it is impossible to efficiently execute applications with scheduling algorithms of a conventional parallel system that, in contrast, aim at homogeneous and controllable resources. To suggest an algorithm that can fully reflect the characteristics of a grid system, our research is focused on examining the type of information used in current scheduling algorithms and consequently, deriving factors that could develop algorithms further. The results from the analysis of these algorithms not only show that static information of resources such as capacity or the number of processors can facilitate the scheduling algorithms but also verified a decrease in efficiency in case of utilizing real time load information of resources due to the intrinsic characteristics of a grid system relatively long computing time, and the need for the means to evade unfeasible resources or ones with slow processing time. In this paper, we propose a new algorithm, which is revised to reflect static information in the logic of WQR(Workqueue Replication) algorithms and show that it provides better performance than the one used in the existing method through simulation.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Smart Structures and Systems
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• v.34 no.2
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• pp.117-127
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• 2024

A novel approach that the smart control of robotics can be realized by a fuzzy controller and an appropriate Runge-Kutta scheme in this paper. A recently proposed integral inequality is selected based on the free weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs). We demonstrate that this target information obtained through image processing is subjected to smart control with computer-vision robotic to Arduino, and the infrared beacon was utilized for the operation of practical illustrations. A fuzzy controller derived with a fuzzy Runge-Kutta type functions is injected into the system and then the system is stabilized asymptotically. In this study, a fuzzy controller and a fuzzy observer are proposed via the parallel distributed compensation technique to stabilize the system. This paper achieves the goal of real-time following of three vehicles and there are many areas where improvements were made. Finally, each information is transmitted to Arduino via I2C to follow the self-propelled vehicle. The proposed calculation is approved in reproductions and ongoing smart control tests.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.57-62
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• 2009

Purpose: Brain SPECT study is more sensitive to motion than other studies. Especially, when applying 1-day subtraction method for Diamox SPECT, it needs shorter study time in order to prevent reexamination. We were required to have new study condition and analysing method on dual detector system because triple head camera in Seoul National University Hospital is to be disposed. So we have tried to increase image quality and make the dual and triple head to have equivalent study time by using a new analysing program. Materials and Methods: Using IEC phantom, we estimated contrast, SNR and FWHM. In Hoffman 3D brain phantom which is similar with real brain, we were on the supposition that 5% of injected doses were distributed in brain tissue. To compare with existing FBP method, we used fan-beam collimator. And we applied 15 sec, 25 sec/frame for each SEPCT studies using LEHR and LEUHR. We used OSEM2D and Onco-flash3D reconstruction method and compared reconstruction methods between applied Gaussian post-filtering 5mm and not applied as well. Attenuation correction was applied by manual method. And we did Brain SPECT to patient injected 15 mCi of $^{99m}Tc$-HMPAO according to results of Phantom study. Lastly, technologist, MD, PhD estimated the results. Results: The study shows that reconstruction method by Flash3D is better than exiting FBP and OSEM2D when studied using IEC phantom. Flowing by estimation, when using Flash3D, both of 15 sec and 25 sec are needed postfiltering 5 mm. And 8 times are proper for subset 8 iteration in Flash3D. OSEM2D needs post-filtering. And it is proper that subset 4, iteration 8 times for 15sec and subset 8, iteration 12 times for 25sec. The study regarding to injected doses for a patient and study time, combination of input parameter-15 sec/frame, LEHR collimator, analysing program-Flash3D, subset 8, iteration 8times and Gaussian post-filtering 5mm is the most appropriate. On the other hands, it was not appropriate to apply LEUHR collimator to 1-day subtraction method of Diamox study because of lower sensitivity. Conclusions: We could prove that there was also an advantage of short study time effectiveness in Dual camera same as Triple gamma camera and get great result of alternation from existing fan-beam collimator to parallel collimator. In addition, resolution and contrast of new method was better than FBP method. And it could improve sensitivity and accuracy of image because lesser subjectivity was input than Metz filter of FBP. We expect better image quality and shorter study time of Brain SPECT on Dual detector system.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2461-2469
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• 1997

Target detection and recognition problems, in which neural networks are widely used, require translation invariant and real-time processing in addition to the requirements that general pattern recognition problems need. This paper presents a novel architecture that meets the requirements and explains effective methodology to train the network. The proposed neural network is an architectural extension of the shared-weight neural network that is composed of the feature extraction stage followed by the pattern recognition stage. Its feature extraction stage performs correlational operation on the input with a weight kernel, and the entire neural network can be considered a nonlinear correlation filter. Therefore, the output of the proposed neural network is correlational plane with peak values at the location of the target. The architecture of this neural network is suitable for implementing with parallel or distributed computers, and this fact allows the application to the problems which require realtime processing. Net training methodology to overcome the problem caused by unbalance of the number of targets and non-targets is also introduced. To verify the performance, the proposed network is applied to detection and recognition problem of a specific automobile driving around in a parking lot. The results show no false alarms and fast processing enough to track a target that moves as fast as about 190 km per hour.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1277-1286
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• 2018

In this paper, we provide flexible scalability of computing resources in cloud environment and Apache Hadoop based cloud environment for analysis of public medical information big data. In fact, it includes the ability to quickly and flexibly extend storage, memory, and other resources in a situation where log data accumulates or grows over time. In addition, when real-time analysis of accumulated unstructured log data is required, the system adopts Hadoop-based analysis module to overcome the processing limit of existing analysis tools. Therefore, it provides a function to perform parallel distributed processing of a large amount of log data quickly and reliably. Perform frequency analysis and chi-square test for big data analysis. In addition, multivariate logistic regression analysis of significance level 0.05 and multivariate logistic regression analysis of meaningful variables (p<0.05) were performed. Multivariate logistic regression analysis was performed for each model 3.