• Journal of the Korean Mathematical Society
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• v.58 no.1
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• pp.237-264
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• 2021

In this work the stationary bootstrap of Politis and Romano [27] is applied to the empirical distribution function of stationary and associated random variables. A weak convergence theorem for the stationary bootstrap empirical processes of associated sequences is established with its limiting to a Gaussian process almost surely, conditionally on the stationary observations. The weak convergence result is proved by means of a random central limit theorem on geometrically distributed random block size of the stationary bootstrap procedure. As its statistical applications, stationary bootstrap quantiles and stationary bootstrap mean residual life process are discussed. Our results extend the existing ones of Peligrad [25] who dealt with the weak convergence of non-random blockwise empirical processes of associated sequences as well as of Shao and Yu [35] who obtained the weak convergence of the mean residual life process in reliability theory as an application of the association.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.122-123
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• 2021

This study examines the performance of pretreatment process system as the initial construction stage of the pretreatment process system to use CGS, a by-product generated in IGCC, as a concrete fine aggregate of construction materials. The process undergoes a grinding process capable of grinding to a predetermined particle size during primary grinding and a sorting plant through sieve grading of 2.5 mm or less for particle size correction. Afterwards, it is hoped that the use of coal gasification slag of Korean IGCC as a fine aggregate for concrete will be distributed and expanded by producing quality-improved CGS fine aggregate using water as a medium for removing impurities and particulates.

• Korean Journal of Computational Design and Engineering
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• v.4 no.4
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• pp.371-380
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• 1999

This study proposes a process information model which can integrate various activities during the product development process, the system which can manage the process. Development process information will allow designers and managers to access previous tasks readily and utilize the information in making decisions at hand. While developing products in heterogeneous and distributed environments, concurrent and collaborative designs can be achieved by STEP. The process model in this study divides the task of product development into two fundamental parts, Specific Process(SP) and Normal Process(NP). SP includes the specific information and refer to those defined by prior STEP. NP is routine process excluded from being defined as SP. Due data information can be added to manage development pace and to show delayed tasks. As two or more different processes can be linked to show the entire development flow, Static STEP information can be dynamically interlaced. Remote location operations can be incorporated on the Internet using STEP, and information can be shared. The system has been built upon a process model schema so that task stage, design history, and different development style can be managed and accessed for each component. The system has been proven efficient in information management and exchange by several scenario testings.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.33-40
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• 2010

Because sampling interval for data collection tends to be short compared with the overall processing time, in chemical process, instrumental process related tanks or furnace collected data have a significant autocorrelation. Insufficient control technique and frequent control actions cause unstable condition of the process. Traditional control charts which were developed based on iid (independently and identically distributed) among data cannot be applied on the existence of autocorrelation. Also unstable process is difficult to identity or diagnose. Because large-scale process has a lot of measurable variables and multi-step-structures among data, it is difficult to find relation between measurable variables and nonconformity. In this paper, we suggested an appicable model to diagnose the process and to find relation between measurable variables (CTQ) and nonconformity in the process having autocorrelation, unstable condition frequently, a lot of measurable variables, and multi-step-structure. And we applied this model to real process, to verify that the process engineers could easily and effectively diagnose the process and control the nonconformity.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.103-109
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• 2018

This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.1-10
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• 2012

Many statistical methods have been adapted for terminology recognition to improve its accuracy. However, since previous studies have been carried out in a single core or a single machine, they have difficulties in real-time analysing explosively increasing documents. In this study, the task where bottlenecks occur in the process of terminology recognition is classified into linguistic processing in the process of 'candidate terminology extraction' and collection of statistical information in the process of 'terminology weight assignment'. A terminology recognition system is implemented and experimented to address each task by means of the distributed parallel processing-based MapReduce. The experiments were performed in two ways; the first experiment result revealed that distributed parallel processing by means of 12 nodes improves processing speed by 11.27 times as compared to the case of using a single machine and the second experiment was carried out on 1) default environment, 2) multiple reducers, 3) combiner, and 4) the combination of 2)and 3), and the use of 3) showed the best performance. Our terminology recognition system contributes to speed up knowledge extraction of large scale science and technology documents.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1129-1139
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• 2015

Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

• Bulletin of the Korean Mathematical Society
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• v.34 no.2
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• pp.295-303
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• 1997

Let ${X_k : k = 1, 2, \cdots}$ be a sequence of independent, identically distributed integer-valued random variables with common distribution function F.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.105-107
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• 1991

A chemical process model represented by partial differential equations was studied as one of nonlinear distributed parameter control problems. Using an optimal control theory in the form of maximum principles based on nonlinear integral equations, an algorithm to solve the problem was developed and coded into a computer program.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.715-720
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• 1989

The individual control of machines or processors is subordinate to the management of the entire manufacturing production function. This distinction is necessary in order to provide the appropriate separation of and detailed focus on process activities while still providing acceptable interfaces for the upload of data and the download of instructions, recipes, or commands.