• Geomechanics and Engineering
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• v.38 no.1
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• pp.69-77
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• 2024

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.209-214
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• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3515-3524
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• 2023

A reinforcement learning framework is proposed for the control problem of outlet steam pressure of the once-through steam generator(OTSG) in this paper. The double-layer controller using Proximal Policy Optimization(PPO) algorithm is applied in the control structure of the OTSG. The PPO algorithm can train the neural networks continuously according to the process of interaction with the environment and then the trained controller can realize better control for the OTSG. Meanwhile, reinforcement learning has the characteristic of difficult application in real-world objects, this paper proposes an innovative pretraining method to solve this problem. The difficulty in the application of reinforcement learning lies in training. The optimal strategy of each step is summed up through trial and error, and the training cost is very high. In this paper, the LSTM model is adopted as the training environment for pretraining, which saves training time and improves efficiency. The experimental results show that this method can realize the self-adjustment of control parameters under various working conditions, and the control effect has the advantages of small overshoot, fast stabilization speed, and strong adaptive ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.160-163
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• 2006

If an instruction system understood the interest and activeness of a learner in real time, it could provide some interesting factors when a learner is tired of learning. It could work as an adaptive tutoring system to help a learner to understand something difficult to understand. Currently the area of the facial expression recognition mainly deals with the facial expression of adults focusing on anger, hatred, fear, sadness, surprising and gladness. These daily facial expressions couldn't be one of expressions of a learner in e-Learning. They should first study the facial expressions of a learner in e-Learning to recognize the feeling of a learner. Collecting as many expression pictures as possible, they should study the meaning of each expression. This study, as a prior research, analyzes the feelings of learners and facial expressions of learners in e-Learning in relation to the feelings to establish the facial expressions database.

• Advances in nano research
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• v.17 no.2
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• pp.109-124
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• 2024

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.4
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• pp.663-670
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• 2017

The purpose of this study is to define On-line Video Syllabus Template which is based on adaptive mode with interaction.A syllabus has the significance as a teaching and learning plan. However, it has not only been considered as a formal document, has also been limited into a simple query since it has been made into a fragmentary structure, lacking of link between other services and reuse. Additionally, this paper is aimed to design three-dimensional syllabus which makes it possible to provide students with practical information related to teaching and learning and share it with teachers and students. The following is the technique proposed in this paper. It is made to be served for the Syllabus centered on teaching and learning, which is including the definition of hierarchy structure, the media contents application according to the learner's preference and real-time variation function. On-line Video Syllabus provided through LMS has availability and credibility of teaching and learning, in that it enable increased utilization by strengthening convenience.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.632-637
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• 1992

Recently many dynamics control algorithms using robot dynamic equation have been proposed. One of them, Kawato's feedback error learning scheme requires neither an accurate model nor parameter estimation and makes the robot motion closer to the desired trajectory by repeating operation. In this paper, the feedback error learning algorithm is implemented to control a robot system, 5 DOF revolute type movemaster. For this purpose, an actuator dynamic model is constructed considering equivalent robot dynamics model with respect to actuator as well as friction model. The command input acquired from the actuator dynamic model is the sum of products of unknown parameters and known functions. To compute the control algorithm, a parallel processing computer, transputer, is used and real-time computing is achieved. The experiment is done for the three major link of movemaster and its result is presented.