• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.253-260
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• 2022

Existing web-based cost databases have proved invaluable for construction cost estimating. These databases have been utilized to compute approximate cost estimates using assembly rates, unit rates, and etc. These web-based databases can be used independently with traditional cost estimation methods (manual methods) or used to support BIM-based cost estimating platforms. However, these databases are rigid, costly, and require a lot of manual inputs to reflect recent trends in prices or prices relative to a construction project's location. To address this gap, this study integrated deep learning techniques with web-based price analysis to develop a database that incorporates a project's location cost estimating standards and current cost trends in generating a cost estimate. The proposed method was tested in a case study project in Lagos, Nigeria. A cost estimate was successfully generated. Comparison of the experimental results with results using current industry standards showed that the proposed method achieved a 98.16% accuracy. The results showed that the proposed method was successful in generating approximate cost estimates irrespective of project's location.

• Journal of Engineering Education Research
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• v.26 no.3
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• pp.42-48
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• 2023

We compare engineering students' learning performance in introductory Statistics classes of the two class types, traditional in-classroom classes with a few reports and real-time online classes with quizzes. Rates of missing classes and turning in homeworks are also included to explain learning attitude. Scores of quizzes, midterm test and final test are used to assess performance. Upto the midterm, the class type is not significant, but rates of missing classes and turning in homeworks are significant. Since the midterm, in-classroom class type reveals better final performance than real-time online class type, rate of turning in homeworks is significant, but rate of missing classes is not significant.

• Journal of IKEEE
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• v.26 no.4
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• pp.686-692
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• 2022

In this paper, the matching error rates of modified area-based, energy-based algorithms, and learning-based structures were compared for stereo image matching. Census transform (CT) based on region and life propagation (BP) algorithm based on energy were selected, respectively.Existing algorithms have been improved and implemented in an embedded processor environment so that they can be used for stereo image matching in mobile systems. Even in the case of the learning base to be compared, a neural network structure that utilizes small-scale parameters was adopted. To compare the error rates of the three matching methods, Middlebury's Tsukuba was selected as a test image and subdivided into non-occlusion, discontinuous, and disparity error rates for accurate comparison. As a result of the experiment, the error rate of modified CT matching improved by about 11% when compared with the existing algorithm. BP matching was about 87% better than conventional CT in the error rate. Compared to the learning base using neural networks, BP matching was about 31% superior.

• Archives of Plastic Surgery
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• v.41 no.6
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• pp.773.1-773.1
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• 2014

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.1-14
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• 2020

This study analyzed the illustrations presented in the elementary science paper textbooks and digital textbooks under the 2015 revised curriculum in terms of gender role stereotypes. The frequency of pupils and adults on illustrations was counted by gender, and the types of activities and behavior of characters were analyzed. For pupils, paper textbooks showed a higher frequency of girls than boys, and digital textbooks showed similar gender rates. In the aspect of the activity, paper textbooks showed similar gender rates in non-learning activities, but more girls in learning activities. In digital textbooks, the learning activity was balanced by gender, but the non-learning activities presented boys more frequently. For both paper and digital textbooks, most pupils were described to be active regardless of gender. For adults, paper textbooks were balanced in the frequency by gender, but digital textbooks had a higher proportion of men. Paper textbooks showed similar gender rates in out-of-home activities, but more men in housework activities. In digital textbooks, housework activities were balanced by gender, but out-of-home activities presented men more frequently. For both paper and digital textbooks, men appeared in a broader range of occupations than women. Female scientists appeared more than male scientists in paper textbooks, and opposite tendency appeared in digital textbooks. As a result of analyzing the characteristics of adults, both paper and digital textbooks showed gender stereotypes in specific behavioral characteristics.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.709-715
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• 2023

In this study, the performance evaluation of image quality for noise reduction was implemented using the U-net deep learning architecture in computed tomography (CT) images. In order to generate input data, the Gaussian noise was applied to ground truth (GT) data, and datasets were consisted of 8:1:1 ratio of train, validation, and test sets among 1300 CT images. The Adagrad, Adam, and AdamW were used as optimizer function, and 10, 50 and 100 times for number of epochs were applied. In addition, learning rates of 0.01, 0.001, and 0.0001 were applied using the U-net deep learning model to compare the output image quality. To analyze the quantitative values, the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. Based on the results, deep learning model was useful for noise reduction. We suggested that optimized hyper parameters for noise reduction in CT images were AdamW optimizer function, 100 times number of epochs and 0.0001 learning rates.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.808-813
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• 1999

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.5 no.6
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• pp.69-78
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• 1982

Traditional CVP (Cost-Volume-Profit) analysis employs linear cost and revenue functions within some specified time period and range of operations. Therefore CVP analysis is assumption of constant labor productivity. The use of linear cost functions implicity assumes, among other things, that firm's labor force is either a homogenous group or a collection homogenous subgroups in a constant mix, and that total production changes in a linear fashion through appropriate increase or decrease of seemingly interchangeable labor unit. But productivity rates in many firms are known to change with additional manufacturing experience in employee skill. Learning curve is intended to subsume the effects of all these resources of productivity. This learning phenomenon is quantifiable in the form of a learning curve, or manufacturing progress function. The purpose d this study is to show how alternative assumptions regarding a firm's labor force may be utilize by integrating conventional CVP analysis with learning curve theory, Explicit consideration of the effect of learning should substantially enrich CVP analysis and improve its use as a tool for planning and control of industry.