• Computers and Concrete
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• v.7 no.2
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• pp.87-102
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• 2010

This paper discusses new capabilities developed for the Virtual Penetration Laboratory (VPL) software package to address the challenges of determining Penetration Resistance (PR) equations for concrete materials. Specifically, the paper introduces a three-invariant concrete constitutive model recently developed by the authors. The Advanced Fundamental Concrete (AFC) model was developed to provide a fast-running predictive model to simulate the behavior of concrete and other high-strength geologic materials. The Continuous Evolutionary Algorithms (CEA) automatic fitting algorithms used to fit the new model are discussed, and then examples are presented to demonstrate the effectiveness of the new AFC model. Finally, the AFC model in conjunction with the VPL software package is used to develop a PR equation for a concrete material.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.279-287
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• 2009

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality providing cross-sectional images of a conductivity distribution inside an electrically conducting object. MREIT has rapidly progressed in its theory, algorithm and experimental technique and now reached the stage of in vivo animal and human experiments. Conductivity image reconstructions in MREIT require various steps of carefully implemented numerical computations. To facilitate MREIT research, there is a pressing need for an MREIT software package with an efficient user interface. In this paper, we present an example of such a software, called CoReHA which stands for conductivity reconstructor using harmonic algorithms. It offers various computational tools including preprocessing of MREIT data, identification of boundary geometry, electrode modeling, meshing and implementation of the finite element method. Conductivity image reconstruction methods based on the harmonic $B_z$ algorithm are used to produce cross-sectional conductivity images. After summarizing basics of MREIT theory and experimental method, we describe technical details of each data processing task for conductivity image reconstructions. We pay attention to pitfalls and cautions in their numerical implementations. The presented software will be useful to researchers in the field of MREIT for simulation as well as experimental studies.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.51-60
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• 2019

While the quality of life is enhanced as many types of diseases are remedied, there is a high demand for analysis and research on gene-related diseases. There exists various forms and requirements in analyzing the relevance between genes and diseases, and the runtime efficiency can be decreased due to the level of algorithm optimization. This paper proposes a platform for analyzing gene disease relevance, provides API for remedying the variability issue, and suggests two algorithms which optimize the runtime efficiency. And, we conduct experiments for measuring the relevancy using the analysis API, and compare the two algorithms. The first algorithm is to improve the runtime efficiency comparing to the conventional methods, and the second algorithm is to improve the runtime efficiency with lower accuracy. This platform can be well utilized for analyzing various forms of gene disease analytics.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.14-22
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• 1994

This paper proposes two Bayes estimators and their evaluation algorithms of the software reliability at the end testing stage in the Smith's Bayesian software reliability growth model under the data prior distribution BE(a, b), which is more general than uniform distribution, as a class of prior information. We consider both a squared-error loss function and the Harris loss function in the Bayesian estimation procedures. We also compare the MSE performances of the Bayes estimators and their algorithms of software reliability using computer simulations. And we conclude that the Bayes estimator of software reliability under the Harris loss function is more efficient than other estimators in terms of the MSE performances as a is larger and b is smaller, and that the Bayes estimators using the beta prior distribution as a conjugate prior is better than the Bayes estimators under the uniform prior distribution as a noninformative prior when a＞b.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• Nuclear Engineering and Technology
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• v.44 no.6
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• pp.663-672
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• 2012

As software based digital I&C (Instrumentation and Control) systems are used more prevalently in nuclear plants, enhancement of software dependability has become an important issue in the area of nuclear I&C systems. Critical attributes of software dependability are safety and reliability. These attributes are tightly related to software failures caused by faults. Software testing and V&V (Verification and Validation) activities are hence important for enhancing software dependability. If the risky modules of safety-critical software can be predicted, it will be possible to focus on testing and V&V activities more efficiently and effectively. It should also make it possible to better allocate resources for regulation activities. We propose a prediction technique to estimate risky software modules by adopting machine learning models based on software complexity metrics. An empirical study with various machine learning algorithms was executed for comparing the prediction performance. Experimental results show SVMs (Support Vector Machines) perform as well or better than the other methods.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.10-17
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• 2009

Surface detection and parameter estimation in point cloud is a relevant subject in CAD/CAM, reverse engineering, computer vision, coordinate metrology and digital factory. In this paper we present a software for a fully automatic surface detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting, which work interactively. Our newly developed algorithms for orthogonal distance fitting(ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. We demonstrate the performance of the software on a variety of point clouds generated by laser radar, computer tomography, and stripe-projection method.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1334-1336
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• 2003

On the basis of comprehensive analysis and summarization of the image fusion algorithms provided by PCI Geomatica software, deficiencies in image fusion processing functions of this software are put forwarded in this paper. This limitation could be improved by further developing PCI Geomatica on the user’ side. Five effective algorithms could be added into PCI Geomatica. In this paper, the detailed description of how to customize and further develop PCI Geomatica by using Microsoft Visual C++ 6.0, PCI SDK Kit and GDB technique is also given. Through this way, the remote sensing imagery fusion functions of PCI Geomatica software can be extended.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.535-538
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• 2005

Today, most fixed-wing aircrafts are equipped with the antiskid brake system. It can modulate braking moments in the wheels optimally, when an aircraft is landing. So it can reduce landing distance and increase safeties. The antiskid brake system for an aircraft are mainly composed of braking moment modulators (hydraulic control valves) and brake control unit. In this paper, a Mark IV type - fully digital - brake controller is studied. For the development of its control algorithms, a 5-DOF (Degree of Freedom) aircraft landing model is composed in the form of matlab/simulink model at first. Then, braking moment control algorithms using wheel decelerations and slips are made. The developed algorithms are tested in software simulations using state-flow toolboxes in matlab/simulink model. Also, a real-time simulation systems are made, which use hydraulic brake systems of a real aircraft, pressure control valves and its controller as hardware components of HIL(Hardware In-the-Loop) simulation. Algorithms tested in software simulations are coded into the controller and the real-time landing simulations are made in very severe road conditions. The real-time simulation results are presented.