• Tunnel and Underground Space
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• v.25 no.3
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• pp.221-230
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• 2015

This study examined and analyzed several mine management software programs developed in domestic and oversea countries. In the oversea countries, many companies have developed and commercialized mine management softwares such as Dispatch, $Cat^{(R)}$ $MineStar^{TM}$ and FARA. These softwares provide many functionalities including real-time machine tracking, machine assignment optimization, productivity management, equipment health monitoring and remote control. For the domestic cases, this study reviewed two software programs (i.e., GEMISIMS, Truck-Shovel fleet optimization) developed by several researchers because there is no mine management software currently commercialized in Korea. In addition, this paper reports the two cases at the Jwaneng mine in Botswana and at the Robinson mine in United States where mine management software programs are used to support mine operations.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.211-218
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• 2010

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. Moreover, there are difficulties in practical application, such as complexity and nonlinear relationships among quality characteristics and design (control) factors, and interactions occurred among control factors. Neural networks have a learning capability and model free characteristics. There characteristics support neural networks as a competitive tool in processing multivariable input-output implementation. In this paper we propose a substantially simpler optimization procedure for parameter design using neural network without resorting to SN. An example is illustrated to compare the difference between the Taguchi method and neural network method.

• Journal of the Ergonomics Society of Korea
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• v.27 no.4
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• pp.45-58
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• 2008

Ergonomic techniques have been required to analyze the effectiveness of functional clothing design improvement in a systematic and analytic manner. The goals of the present study are to: (1) comprehensively and analytically examine the effectiveness of clothing improvement by using the relationship analysis between clothing design components (D) and ergonomic evaluation measures (E) and (2) prove the usefulness of cost-effectiveness analysis for clothing design optimization. The cost effectiveness analysis is comprised of the preliminary evaluation based on expertise and the in-depth evaluation where the D-E relationship analysis is applied. As a result of the cost effectiveness analysis applied to flame-proof clothing, an optimal design was identified by analyzing costs and qualitative/quantitative effects. In the preliminary evaluation, the expected effectiveness of each design alternative on wear efficiency and wear comfort was estimated. In the in-depth evaluation, however, the effectiveness of each design alternative was analyzed by quantitative evaluation in a wearing test using a questionnaire prepared based on the D-E relationship analysis. It was concluded that the D-E relationship analysis and the cost-effectiveness analysis are useful for comprehensive evaluation and optimization of functional clothing design.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.120-127
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• 2008

HMIPv6 draws lots of attentions in recent years for providing an efficient handover and reducing the signaling overhead. HMIPv6 employs MAP(Mobility Anchor Point) in order to minimize a signaling overhead and a local mobility management. MAP completes an efficient mobility management in HMIPv6 network environment with frequent handover. However, HMIPv6 causes load concentration at a paricular MAP and may have unnecessary latency between HN(Mobile Node) and CN(Correspondent Node) within the same network. A MAP may also disturb the route optimization in HMIPv6 network because all packets must be transmitted through a MAP. In this paper, we propose a scheme to optimize the route in HMIPv6 networks according to MAP load. We configure a threshold in order to support the better service into MAP domain. The packets do not pass through MAP and are directly transmitted to AR(Access Router) if the number of current MNs attached to the MAP exceed the desired threshold. We simulate the performance of the proposed scheme and compare with HMIPv6. Resultly, the proposed scheme reduces signaling costs and mitigates concentration of a paticular MAP as well.

• Computational Structural Engineering
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• v.10 no.2
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• pp.233-242
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• 1997

The main purpose of this paper is to investigate the dynamic optimal design of continuous beams. The computer-aided optimization technique is used to obtain the near-optimal parameters of continuous beam. The computer program is developed to obtain the natural frequency parameters and the forced vibration responses to a transit point load for the continuous beam with variable support spacing, mass and stiffness. The model test data is in good agreement with the computer calculation, which serves to validate the mathematical analysis. The optimization function to describe the design efficiency is defined as a linear combination of four dimensionless span characteristics; the maximum dynamic stress; the stress difference between span segments; the rms deflection under the transit point load; and the total span mass. Studies of three span beams show that the beam with near-optimal parameters can improve design efficiency when compared to a uniform beam with even spacing of the same total span length.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.138-145
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• 2018

In the field of optimization, many studies have been performed on various types of Vehicle Routing Problem (VRP) for a long time. A variety of models have been derived to extend the basic VRP model, to consider multiple truck terminal, multiple pickup and delivery, and time windows characteristics. A lot of research has been performed to find better solutions in a reasonable time for these models with heuristic approaches. In this paper, by considering realtime traffic characteristics in Map Navigation environment, we proposed a method to manage realistic optimal path allocation for the logistics trucks and cargoes, which are dispersed, in order to realize the realistic cargo mixing allowance and time constraint enforcement which were required as the most important points for an online logistics brokerage service company. Then we developed a prototype system that can support above functionality together with delivery status monitoring on Map Navigation environment. First, through Map Navigation system, we derived information such as navigation-based travel time required for logistics allocation scheduling based on multiple terminal multiple pickup and delivery models with time constraints. Especially, the travel time can be actually obtained by using the Map Navigation system by reflecting the road situation and traffic. Second, we made a mathematical model for optimal path allocation using the derived information, and solved it using an optimization solver. Third, we constructed the prototype system to provide the proposed method together with realtime logistics monitoring by arranging the allocation results in the Map Navigation environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.195-215
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• 2021

Multipath Transport Control Protocol (MPTCP) is a transport layer protocol that enables multiple TCP connections across various paths. Due to path heterogeneity, it incurs more energy in a multipath wireless network. Recent work presents a set of approaches described in the literature to support systems for energy consumption in terms of their performance, objectives and address issues based on their design goals. The existing solutions mainly focused on the primary system model but did not discourse the overall system performance. Therefore, this paper capitalized a novel stochastically multipath scheduling scheme for data and path capacity variations. The scheduling problem formulated over MPTCP as a stochastic optimization, whose objective is to maximize the average throughput, avoid network congestion, and makes the system more stable with greater energy efficiency. To design an online algorithm that solves the formulated problem over the time slots by considering its mindrift-plus penalty form. The proposed solution was examined under extensive simulations to evaluate the anticipated stochastic optimized MPTCP (so-MPTCP) outcome and compared it with the base MPTCP and the energy-efficient MPTCP (eMPTCP) protocols. Simulation results justify the proposed algorithm's credibility by achieving remarkable improvements, higher throughput, reduced energy costs, and lower-end to end delay.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.109-117
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• 2022

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

• Smart Media Journal
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• v.11 no.2
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• pp.70-76
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• 2022

IoT application development using a cloud server causes problems such as data transmission and reception delay, network traffic, and cost for real-time processing support in network connected hardware. To solve this problem, edge cloud-based platforms can use neuromorphic hardware to enable fast data transfer. In this paper, we propose a model optimization method for supporting spiking neural networks on FPGA hardware. We focused on auto-adjusting network model parameters optimized for neuromorphic hardware. The proposed method performs optimization to show higher performance based on user requirements for accuracy. As a result of performance analysis, it satisfies all requirements of accuracy and showed higher performance in terms of expected execution time, unlike the naive method supported by the existing open source framework.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.