• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.421-430
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• 2000

This paper presents a new approach which uses $A^*$ search and genetic algorithms for solving large scale multi-objective shortest path problem. The focus of this paper is motivated by the problem of finding Pareto optimal paths for an advanced traveler information system(ATIS) in the context of intelligent transportation system(ITS) application. The individual description, the decoding rule, the selection strategy and the operations of crossover and mutation are proposed for this problem. The keynote points of the algorithm are how to represent individuals and how to calculate the fitness of each individual. The high performance of the proposed algorithm is demonstrated by computer simulations.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.149-157
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• 2004

This paper presents a new method for hi-objective optimization. Ordinary weighted sum method is easy to implement, but it has two significant drawbacks: (1) the solution distribution by the weighted sum method is not uniform, and (2) the method cannot determine any solutions that reside in non-convex regions of a Pareto front. The proposed adaptive weighted sum method does not solve a multiobjective optimization in a predetermined way, but it focuses on the regions that need more refinement by imposing additional inequality constraints. It is demonstrated that the adaptive weighted sum method produces uniformly distributed solutions and finds solutions on non-convex regions. Two numerical examples and a simple structural problem are presented to verify the performance of the proposed method.

• Korean Journal of Optics and Photonics
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• v.3 no.3
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• pp.155-160
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• 1992

1)iscussed in this paper are the effects of phase modulation on the line spread functions (LSF) and MTFs of ;I binocular objective system. The binocular objective lens is made in Korea. It has rotationally symmetric aberrations. The LSFs and MTFs are measured experimentally. The phase modulation is carried out by applying phase retardation n on the aperture. The area where the phase is not retarded presents a double annular type. The OTF curves of phase modulated aperture are compared with that of unmodulated aperture. The comparison shows that there is the aberration compensation effect in aberration loaded system. Therefore the performance of many optical system can be improved without any loss of light energy by properly modulating the phase on the aperture.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.109-114
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• 2004

A combined optimal design problem of structural and control systems is discussed by taking a 3-D flexible structure as an object. We consider a minimum weight design problem for structural system and disturbance suppression problem for the control system. The conditions for the existence of controller are expressed in terms of linear matrix inequalities (LMI). By minimizing the linear sum of the normalized structural objective function and control objective function, it is possible to make optimal design by which the balance of the structural weight and the control performance is taken. We showed in this paper the validity of combined optimal design of structural and control systems.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.56-62
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• 2024

To enhance energy efficiency and reduce emissions in prefabricated construction, optimizing the production scheduling of precast concrete is considered an effective approach. Due to the unique characteristics of precast concrete during production, traditional scheduling models are no longer applicable. This present study introduces practical considerations, such as a limited number of molds, buffers, uncertainty of order arrivals and vehicles. Furthermore, to meet the requirements of contemporary industrial development, a mulit-objective optimization scheduling model is formulated by integrating total processing time, on-time delivery rate and work station idle time. A solution based on reinforcement learning algorithm is devised. Results indicate that this method can effectively undergo training and achieve outstanding performance in addressing such issues. The model has the potential to significantly reduce decision-making time in precast production, thereby contributing to the sustainable development of prefabricated construction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.921-927
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• 2013

The performance estimation of a landing gear with uncertain parameters is presented. In actual use, many parameters can have certain degrees of variations that affect the energy absorbing performance. For example, the shock strut gas pressure, oil volume, tire pressure, and temperature can deviate from their nominal values. The objective function in this study is the ground reaction during touchdown, which is a function of the abovementioned parameters and time. To consider the uncertain properties, convex modeling and interval analysis are used to calculatethe objective function. The numerical results show that the ground reaction characteristics are quite different from those of the deterministic method. The peak load, which affects the efficiency and structural integrity, is increases considerably when the uncertainties are considered. Therefore, it is important to consider the uncertainties, and the proposed methodology can serve as an efficient method to estimate the effect of such uncertainties.

• Journal of Biosystems Engineering
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• v.36 no.6
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• pp.491-499
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• 2011

The objective of this study was to develop the export strategic core technologies for IT fusion next generation agricultural machines by the analysis of comprehensive and cooperative systems of industries, universities, and institutes. In order to achieve the objective of this study, an expert panel was formed and operated. The first survey was conducted by the Delphi method. For this the export strategic core technologies were surveyed and analyzed using the questionnaire. Based on the results of the first survey, the second survey was conducted. The questionnaire used for the second survey was designed by results of the first survey. The results of the second survey was analyzed by AHP method. The third survey was conducted based on the second one, and the final results were analyzed and the export strategic core technologies were developed through the expert meeting. The study results showed six export strategic core technologies as the followings : 1) environment-friendly engine technology for high performance 2) high performance/high efficiency power transmission system technology 3) development of measurement system technology for safety of agricultural products 4) field application of sensor networks 5) large size combine development technology for high performance 6) quality evaluation technology for agricultural products.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.559-566
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• 2007

Subjective sentences express opinions, emotions, evaluations and other subjective ideas relevant to products or events. These expressions sometimes can be seen in only part of a sentence, thus extracting features from a full-sentence can degrade the performance of subjective-sentence-classification. This paper presents a method for improving the performance of a subjectivity classifier by combining two classifiers generated from the different representations of an input sentence. One representation is a sentimental phrase that represents an automatically identified subjective expression or objective expression and the other representation is a full-sentence. Each representation is used to extract modified n-grams that are composed of a word and its contextual words' polarity information. The best performance, 79.7% accuracy, 2.5% improvement, was obtained when the phrase-level classifier and the sentence-level classifier were merged.