• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.1
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• pp.69-77
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• 1991

A collision-free trajectory planning algorithm using an iterative learning concept is proposed for dual robot arms in a 3-D common workspace to accurately follow their specified paths with constant velocities. Specifically, a collision-free trajectory minimizing the trajectory error is obtained first by employing the linear programming technique. Then the total operating time is iteratively adjusted based on the maximum trajectory error of the previous iteration so that the collision-free trajectory has no deviation from the specified path and also that the operating time is near-minimal. To show the validity of the proposed algorithm, a numerical example is presented based on two planar robots.

• Korean Chemical Engineering Research
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• v.44 no.5
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• pp.425-434
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• 2006

The operability and productivity of continuous processes, especially in petrochemical industries have made remarkable improvement during the past twenty years through advanced process control (APC) typified by model-based predictive control. On the other hand, APC have not been actively practiced in industrial batch processes typified by batch polymerization reactors. Perhaps the main cause for this has been the lack of reliable batch process APC techniques that can overcome the unique problems in industrial batch processes. Recently, some noteworthy progress is being made in this area. New high-performance batch process control techniques that can accommodate and also overcome the unique problems of industrial batch processes have been proposed on the basis of iterative learning control (ILC). In this review paper, recent advancement in the batch process APC techniques are presented, with a particular focus on the variations of the so called Q-ILC method, with the hope that they are widely practiced in different industrial batch processes and enhance their operations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.78-89
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• 2012

The generated torque of a switched reluctance(SR) motor is highly nonlinear, which makes it difficult to determine the reference current commands for minimum torque ripples. In this paper, we present a computationally simple and efficient method to minimize torque ripples of SR motors based on iterative learning control. The reference current command of each phase minimizing torque ripples is identified in 2-dimensional look-up table form. Our learning control algorithm does not require the torque model, so our method is not affected by model errors and hence is very accurate. In order to justify our work, we present some computer simulation results.

• Journal of applied mathematics & informatics
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• v.42 no.5
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• pp.1063-1076
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• 2024

This research introduces an innovative approach to revolutionize inventory management strategies amid unpredictable demand and uncertainties. Introducing a Fuzzy Economic Order Quantity (EOQ) model, enriched with the centroid defuzzification method and supervised machine learning, the study offers a comprehensive solution for optimized decision-making. The model transcends traditional inventory paradigms by seamlessly integrating fuzzy logic and advanced machine learning, emphasizing adaptability in fast-paced business landscapes. The research unfolds against the backdrop of agile inventory management advocacy, with key contributions including the centroid defuzzification method for crisp interpretation and the integration of linear regression for cost prediction. The study employs a real-life bakery scenario to demonstrate the efficacy of both crisp and fuzzy models, underscoring the latter's superiority in handling uncertainties. Comparative analysis reveals nuanced impacts of uncertainty on inventory decisions, while linear regression establishes statistical relationships for cost predictions. The findings underscore the pivotal role of fuzzy logic in optimizing inventory management, paving the way for future enhancements, advanced machine learning integration, and real-world validation. This research not only contributes to adaptive inventory management evolution but also sets the stage for further exploration and refinement in dynamic business landscapes.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.201-209
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• 2009

According to previous researches about online evaluation in many e-Learning contents, it took too much time and effort to generate test questions for formative or achievement tests using a database as an item pool. Furthermore, it is hard to measure accomplishment of learners for each unit through overall tests provided by existing e-learning contents. In this paper, to efficiently cope with problems described above, the item pool based on Item Form was transformed into Interaction Date Model in Run-Time Environment of SCORM2004. And the contents for the math concepts and principles that students would learn from regular classroom were developed in accordance with SCORM. In addition, Confidence Factor Function was used to take an objective view in measuring the accomplishment of learners through the items automatically generated by LMS(Learning Management System).

• 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.

• Journal of East Asia Management
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• v.2 no.1
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• pp.93-103
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• 2021

Struggle refers to the process of overcoming various difficulties for a goal. The spirit of struggle is a positive attitude and reaction reflected in the process of struggle. Cultivating the spirit of struggle of college students is the call of the new era. In essence, the cultivation of the spirit of struggle is a process of learning, which is in line with Bandura's Observation Learning Theory(Bandura, 1977):Attention, Maintenance, Reproduction and Motivation. The cultivation of College Students' spirit of struggle in the new era is also a learning process of enriched experience. It is necessary to cultivate the spirit of struggle into the soul of college students and make it become a habit of students. Moreover, it is crucial to carry out adaptive transformation of Bandura's observation learning theory. By studying the mechanism of the spirit of struggle of college students, taking innovation and entrepreneurship education as a means, and aiming at cultivating the connotation of President Xi's thought on socialism with Chinese characteristics for a new era, this paper constructs the AIST model for cultivating the spirit of struggle of college students in the new era. This model includes online learning acceptance platform(Acceptance), classroom experience stimulation platform(Inspiration), iterative training solidified platform (Solidification), and competition practice transfer platform(Transfer). The purpose of this model is to provide a practical way for universities to fulfill the fundamental task of moral education and cultivate qualified socialist builders and successors. The number of students using the online learning acceptance platform ranked the first among that of the similar courses in China; The classroom experience stimulation platform and the iterative training solidified platform support each other, with an effective rate of 97%; The competition practice transfer platform has realized the continuous growth of the number of awards won in competitions for three years. The direction of future efforts is to establish the external mechanism of the spirit of struggle, to ensure the effectiveness of classroom experience and iterative training, to cultivate teachers with coaching skills, and to accurately measure the transformation point of external and endogenous motivation.

• Transactions of the Society of Information Storage Systems
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• v.3 no.4
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• pp.167-172
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• 2007

The recently-developed off-line learning control approaches for the rejection of periodic disturbances utilize the specific property that the learning system tends to oscillate in steady state. Unfortunately, the prior works have not clarified how closely the learning system should approach the steady state to achieve the rejection of periodic disturbances to satisfactory level. In this paper, we address this issue extensively for the class of linear systems. We also attempt to remove the effect of other aperiodic disturbances on the rejection of the periodic disturbances effectively. In fact, the proposed learning control algorithm can provide very fast convergence performance in the presence of aperiodic disturbance. The effectiveness and practicality of our work is demonstrated through mathematical performance analysis as well as various simulation results.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.6
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• pp.591-597
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• 1990

As a means of controlling a plant whose dynamics is not fully known, an iterative learning control method is proposed in this paper. The proposed method employs the structure of the

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.302-307
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• 1989

A pressure tracking control of hydroforming process is considered in this paper. To account for nonlinearities and uncertainties of the process, an iterative learning control scheme is proposed using Cerebellar Model Arithmatic Computer (CMAC). The experimental result shows that the proposed learning control is superior to any fixed gain controller in the sense that it enables the system to do the same work more effectively as the number of operation increases.