• Journal of applied mathematics & informatics
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• v.3 no.1
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• pp.11-24
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• 1996

The focus of this research is the class of sequential al-gorithms called predictive sorting algorithms for sorting a given set of n elements using pairwise comparisons. The order in which these pairwise comparisons are made is defined by a fixed sequence of all un-ordered pairs of distinct integers{1,2 ···,n} called a sort sequence. A predictive sorting algorithm associated with a sort sequence spec-ifies pairwise comparisons of elements in the input set in the order defined by the sort sequence except that the comparisons whose out-comes can be inferred from the preceding pairs of comparisons are not performed. in this paper predictive sorting algorithms are obtained based on known sorting algorithms and are shown to be required on the average O(n log n) comparisons.

• Korean Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.234-241
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• 1996

Recently, Genetic Algorithms(GAs), which consist of genetic operators named selection crossover and mutation, are widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GAs are very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GAs. So, they can be easily applicable to wide territory of design optimization problems. Also, virtue to multi-point search procedure, they have higher probability of convergence to global optimum compared with traditional techniques which take one-point search method. The introduction of basic theory on GAs, and the application examples in combination optimization of ten-member truss structure are presented in this paper.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.2
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• pp.1-6
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• 1986

Capacitated facility location problems have received a great deal of attention in the past decade, resulting in a proliferation of algorithms for solving them. As is the case with mixed 1-1 integer programming problems, the computational success of such algorithms depends greatly on how to obtain lower bounds in good quality within a resonable time. The objective of this paper is to provide a comparative analysis of those algorithms in terms of lower bounds they produce. Analyses of the strategies for generating lower bounds as well as the quality of generated lower bounds are provided.

• Nonlinear Functional Analysis and Applications
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• v.29 no.2
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• pp.307-332
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• 2024

Two inertial extragradient-type algorithms are introduced for solving convex pseudomonotone variational inequalities with fixed point problems, where the associated mapping for the fixed point is a 𝜌-demicontractive mapping. The algorithm employs variable step sizes that are updated at each iteration, based on certain previous iterates. One notable advantage of these algorithms is their ability to operate without prior knowledge of Lipschitz-type constants and without necessitating any line search procedures. The iterative sequence constructed demonstrates strong convergence to the common solution of the variational inequality and fixed point problem under standard assumptions. In-depth numerical applications are conducted to illustrate theoretical findings and to compare the proposed algorithms with existing approaches.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.41-47
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• 2012

Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students' survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students' views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students' survey data.

• Nonlinear Functional Analysis and Applications
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• v.27 no.1
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• pp.203-221
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• 2022

In this paper, we introduce new hybrid inertial contraction projection algorithms for solving variational inequality problems over the intersection of the fixed point sets of demicontractive mappings in a real Hilbert space. The proposed algorithms are based on the hybrid steepest-descent method for variational inequality problems and the inertial techniques for finding fixed points of nonexpansive mappings. Strong convergence of the iterative algorithms is proved. Several fundamental experiments are provided to illustrate computational efficiency of the given algorithm and comparison with other known algorithms

• Genomics & Informatics
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• v.21 no.1
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• pp.15.1-15.4
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• 2023

Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-by-step processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.606-609
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• 2004

Maximum power point tracking(MPPT) is usually used for a solar power system. Many maximum power tracking techniques have been considered in the past. The microprocessors with appropriate MPPT algorithms are favored because of their flexibility and compatibility with different solar arrays. In this paper, four MPPT algorithms are analyzed and studied. Perturbation and Observation(P&O), Incremental Conductance(IncCond), which are used from the past. Improved P&O and Two-mode , which are developed P&O and IncCond algorithms. Also, the author introduces grid-connected fransformerless PV PCS to apply MPPT control. MPPT efficiency is measured by changing irradiance from $0.1kW/m^2\;to\;1kW/m^2$ and simulation was performed for each MPPT algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.306-311
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• 1992

We propose software algorithms which provide the characteristics of acceleration/deceleration essential to high dynamic performance at the transient state where industrial robots or CNC machine tools start and stop. The path error, which is one of the most significant factors in performance evaluation of industrial robots and CNC machine tools, is analyzed for linear, exponential, and parabolic acceleration/deceleration algorithms in case of circular interpolation. The analysis shows that the path error depends on the acceleration/deceleration routine and the servo control system. In experiments, the entire control algorithm including the proposed acceleration/deceleration algorithms is executed on the motion control system with a floating point digital signal processor(DSP) TMS320C30 as a CPU. The experimental results demonstrate that the proposed algorithms are very effective in controlling axes of motion of industrial robots or CNC machine tools with the desired characteristics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.