• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.188-193
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• 2004

3D spatial-modeling can be used in various safety-enhancement applications and for as-built data acquisition in project-control systems. The objective of the research reported herein was to provide spatial-modeling methods that represent construction sites in an efficient manner and to validate the proposed methods by testing them in an actual construction environment. Algorithms to construct construction-site scenes and to carry out coordinate transformations in order to merge data from different acquisition locations are presented. Field experiments were conducted to establish performance parameters and validation for the proposed methods and models. Initial experimental work has demonstrated the feasibility of this approach.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• Journal of Korea Multimedia Society
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• v.17 no.10
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• pp.1189-1197
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• 2014

Recently, general purpose graphic processing units(GPUs) are being widely used in mobile embedded systems such as smart phone and tablet PCs. Because of architectural limitations of mobile GPGPUs, only a single program is allowed to occupy a GPU at a time in a non-preemptive way. As a result, it is difficult to meet performance requirements of applications such as frame rate or response time if applications running on a GPU are not scheduled properly. To tackle this difficulty, we propose to specify applications using synchronous data flow model of computation such that applications are formed with edges and nodes. Then nodes of applications are scheduled onto a GPU unlike conventional scheduling an application as a whole. This approach allows applications to share a GPU at a finer granularity, node (or task)-level, providing several benefits such as eliminating need for manually partitioning applications and better GPU utilization. Furthermore, any scheduling policy can be applied in response to the characteristics of applications.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.668-685
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• 2019

Provision of higher throughput without sacrificing consistency guarantees in replication systems is a critical problem. In this paper, we propose a novel approach called Bidirectional Chain Replication (BCR) to improve throughput in traditional Chain Replication (CR) through better utilization of computing and communication resources of the chain. Unlike CR where the whole replicated data store is treated as a single unit, in BCR the replicated shared data at each server in the chain is split into two disjoint Logical Partitions ($LP_1$, $LP_2$). This forms two chains running concurrently on the same hardware in two opposite directions; the first chain ($CR_1$) exclusively manipulates data objects in $LP_1$, while the second chain ($CR_2$) exclusively manipulates data objects in $LP_2$, therefore, conflict is avoided and concurrency is guaranteed. The simultaneous employment of these two chains results in better utilization of hardware in the sense that the two chains can evenly share the workload, hence, throughput can be improved without sacrificing consistency. Experimental results showed an improvement of approximately 85% in throughput of BCR over CR.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.147-147
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• 2021

A partition of water distribution network (WDN) into district metered areas (DMAs) brings the efficiency and efficacy for water network operation and management (O&M), especially in monitoring pressure and leakage. Traditionally, the DMA configurations (i.e., number, shape, and size of DMAs) are permanent and cannot be changed occasionally. This leads to changes in water quality and reduced network redundancy lowering network resilience against abnormal conditions such as water demand variability and mechanical failures. This study proposes a framework to automatically divide a WDN into dynamic DMA configurations, in which the DMA layouts can self-adapt in response to abnormal scenarios. To that aim, a complex graph theory is adopted to sectorize a WDN into multiscale DMA layouts. Then, different failure-based scenarios are investigated on the existing DMA layouts. Here, an optimization-based model is proposed to convert existing DMA layouts into dynamic layouts by considering existing valves and possibly placing new valves. The objective is to minimize the alteration of flow paths (i.e., flow direction and velocity in the pipes) while preserving the hydraulic performance of the network. The proposed method is tested on a real complex WDN for demonstration and validation of the approach.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.268-280
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• 2023

Several companies have migrated their existing monolithic web applications to microservice architectures. Consequently, research on the identification of microservices from monolithic web applications has been conducted. Meanwhile, the use-case model plays a crucial role in outlining the system's functionalities at a high level of abstraction, and studies have been conducted to identify microservices by utilizing this model. However, previous studies on microservice identification utilizing use-cases did not consider the components executed in the presentation layer. Unlike existing approaches, this paper proposes a technique that considers all three layers of web applications (presentation, business logic, and data access layers). Initially, the components used in the three layers of a web application are extracted by executing all the scenarios that constitute its use-cases. Thereafter, the usage rate of each component is determined for each use-case and the component is allocated to the use-case with the highest rate. Then, each use-case is realized as a microservice. To verify the proposed approach, microservice identification is performed using open-source web applications.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.2-9
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• 2024

In Japan, when constructing frames using Precast Concrete (PCa) methods, unique building components are used. These include integrating column tops with beam ends or using cast-in-place concrete in the panel zone. Planning these components requires considering various factors such as the loading capacity of trailers, crane lifting capacity, joining methods, and equipment penetrations. Building Information Modeling (BIM) technology has become increasingly common in construction planning. However, extracting the necessary information for construction planning directly from the design BIM model is challenging. This difficulty arises because the design BIM model organizes columns and beams in different division units than those used in construction. To address this issue, our study models the concept of the "panel zone" and proposes a method for representing a PCa BIM model composed of panel zones, columns, and beams as PCa products. The study decomposes and combines columns and beams, with parametric changes applied to the panel zone range. Additionally, our study analyzes factors related to the design and planning of column and beam PCa products through interviews and questionnaire surveys conducted with general contractors. An evaluation mechanism for the proposed column and beam division was also established. Based on the findings, a BIM-based method was developed for planning the PCa construction method of the frame using a genetic algorithm. This approach provides a technological solution that supports the planning of frame division, considering the construction rationale at the early design stage.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.177-188
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• 2018

The scale of graph data has been increased rapidly because of the growth of mobile Internet applications and the proliferation of social network services. This brings upon the imminent necessity of efficient distributed and parallel graph processing approach since the size of these large-scale graphs are easily over a capacity of a single machine. Currently, there are two popular parallel graph processing approaches, vertex-centric graph processing and block centric processing. While a vertex-centric graph processing approach can easily be applied to the parallel processing system, a block-centric graph processing approach is proposed to compensate the drawbacks of the vertex-centric approach. In these systems, the initial quality of graph partition affects to the overall performance significantly. However, it is a very difficult problem to divide the graph into optimal states at the initial phase. Thus, several dynamic load balancing techniques have been studied that suggest the progressive partitioning during the graph processing time. In this paper, we present a load balancing algorithms for the block-centric graph processing approach where most of dynamic load balancing techniques are focused on vertex-centric systems. Our proposed algorithm focus on an improvement of the graph partition quality by dynamically reassigning blocks in runtime, and suggests block split strategy for escaping local optimum solution.