• Advances in Computational Design
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• v.1 no.2
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• pp.175-194
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• 2016

Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins for navigation and hunting in various environments. This ability of dolphins is mimicked in this paper to develop a new optimization method. Dolphin Echolocation Optimization (DEO) is an optimization method based on dolphin's approach for hunting food and exploration of environment. DEO has already been developed for discrete optimization search space and here it is extended to continuous search space. DEO has simple rules and is adjustable for predetermined computational cost. DEO provides the optimum results and leads to alternative optimality curves suitable for the problem. This algorithm has a few parameters and it is applicable to a wide range of problems like other metaheuristic algorithms. In the present work, the efficiency of this approach is demonstrated using standard benchmark problems.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.123-137
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• 1995

Most of solution methods in scheduling attempt to generate good solutions by either developing algorithms or heuristic rules. However, scheduling problems in the real world require considering more factors such as multiple objectives, different combinations of heuristic rules due to problem characteristics. In this respect, the traditional mathematical a, pp.oach showed limited performance so that new a, pp.oaches need to be developed. Expert system is one of them. When an expert system is developed for scheduling one of the most difficult processes faced could be knowledge acquisition on scheduling heuristics. In this paper we propose a method for the acquisition of knowledge on the selection of scheduling heuristics using Dempster-Shafer Theory(DST). We also show the examples in the multi-objectives environment.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.366-370
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• 2005

The construction environment is dynamic in nature and is characterized by various degrees of uncertainties. The uncertainties such as lack of coordination, non availability of resources, condition of temporary structures and varying weather conditions have a significant impact on estimating the duration of activities. These are subjective, vague and imprecisely defined and are expressed in subjective measures rather than mathematical terms. Conventionally, various quantitative techniques such as CPM and PERT have emerged in construction industry. These techniques cannot solve the above problems and rely on human experts which may not always be possible. In such situations Artificial Intelligence tools such as fuzzy sets and neural networks handle such variables and provide global strategies. The present paper evaluates the effect of qualitative factors to identify the activity duration using new intelligent approach. The results are compared with conventional methods for effective project management. A case study is considered to demonstrate the applicability of fuzzy logic for project scheduling.

• Journal of Gifted/Talented Education
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• v.3_4 no.1
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• pp.37-77
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• 1994

In addition to exceptional abilities and domain-specific aptitudes, frequently creativity potentials are used to explain high achievements in science and technology. In the Guilford tradition, research focuses increasingly on convergent versus divergent thinking, that is, a suspected dichotomy between intelligence and creativity. Despite important insights from this about relationship of ability and creativity, a number of important questions remain unanswered. These relate not only to conceptualization and measurement problems regarding the hypothetical constructs "scientific ability" and "creativity", but also their diagnosis and nurturance in childhood and adolescence. It would appear that, in view of current research paradigms, the role of ability and creativity needs to be redefinded in order to more reliably predict and explain excellent achievements in science and technology. Advances are mostly expected from synthetic approaches. Thus, I will be presenting new theoretical models and empirical research results. Finally, consequences for the prediction and promotion of mathematical-scientific and technical talents will be discussed including the consideration of sex-related problems.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.2-4
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• 2023

Sequence data plays an important role in the field of intelligence, especially for industrial control, traffic control and other aspects. Finding abnormal parts in sequence data has long been an application field of AI technology. In this paper, we propose an anomaly detection method for sequence data using a diffusion model. The diffusion model has two major advantages: interpretability derived from rigorous mathematical derivation and unrestricted selection of backbone models. This method uses the diffusion model to predict and reconstruct the sequence data, and then detects the abnormal part by comparing with the real data. This paper successfully verifies the feasibility of the diffusion model in the field of anomaly detection. We use the combination of MLP and diffusion model to generate data and compare the generated data with real data to detect anomalous points.

• Korean Journal of Mathematics
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• v.31 no.4
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• pp.419-431
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• 2023

In this paper, we propose an iterative method for a symmetric interior penalty Galerkin method for heterogeneous elliptic problems. The iterative method consists mainly of two parts based on a non-overlapping domain decomposition approach. One is an intermediate preconditioner constructed by understanding the properties of the discontinuous finite element functions and the other is a preconditioning related to the dual-primal finite element tearing and interconnecting (FETI-DP) methodology. Numerical results for the proposed method are presented, which demonstrate the performance of the iterative method in terms of various parameters associated with the elliptic model problem, the finite element discretization, and non-overlapping subdomain decomposition.

• Journal of applied mathematics & informatics
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• v.42 no.4
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• pp.725-737
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• 2024

This article introduces the concepts of Energy and Laplacian Energy (LE) of Domination in Hesitancy fuzzy graph (DHFG). Also, the adjacency matrix of a DHFG is defined and proposed the definition of the energy of domination in hesitancy fuzzy graph, and Laplacian energy of domination in hesitancy fuzzy graph is given.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1407-1420
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• 2001

There is an increasing concern about computer education with the age of knowledge-based society. The learning programming language is taking an important role of computer education. However, the special emphasis in learning programming language has been attached to memorizing the programming language by rote and learning computer programs. Therefore, those were not much useful tools to develope a logical intelligence of the meanings of programming language and the methods of realization. It is positively necessary to improve the programming education efficiently because of the objects of knowledge of computing and raising an efficiency of problem solving. Under the circumstances, this research is aimed at representing an useful education model through developing a mathmatical program into each part of the C programming language, which would be a new supplier of an basic insight into the programming language and techniques. Accordingly it is thought that the research material will be an useful model to increase interests and concerns as well as to raise an efficiency of problem solving or a logical intelligence going through the process of studying programming language.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.59-66
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• 2015

A stochastic modeling approach as a mathematical method for workflow intelligence is widely used for analyzing and simulating workflow models in the literature. In particular, as a resource-centric modeling approach, this paper proposes a stochastic model to represent work-handover relationships between performers in a workflow-supported social network. Calculating probabilities for the work-handover relationships are determined by two types of probabilities. One is the work-transition probability between activities, and the other is the task assignment probability between activities and performers. In this paper, we describe formal definitions of stochastic workflow models and stochastic work-handover relationship models, as well. Then, we propose an algorithm for extracting a stochastic work-handover relationship model from a stochastic workflow model. As a consequence, the proposed model ought to be useful in performing resource-centric workflow simulations and model-log comparison analyses.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.167-173
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• 2021

The purpose of this study is to discuss the types of algorithms and data categories in AI education for elementary school students. The study surveyed 11 pre-elementary teachers after providing education and practice on various data, artificial intelligence algorithm, and AI education platform for 15 weeks. The categories of data and algorithms considering the elementary school level, and educational tools were presented, and their suitability was analyzed. Through the questionnaire, it was concluded that it is most suitable for the teacher to select and preprocess data in advance according to the purpose of the class, and the classification and prediction algorithms are suitable for elementary AI education. In addition, it was confirmed that Entry is most suitable as an AI educational tool, and materials that explain mathematical knowledge are needed to educate the concept of learning of AI. This study is meaningful in that it specifically presents the categories of algorithms and data with in AI education for elementary school students, and analyzes the need for related mathematics education and appropriate AI educational tools.