• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Journal of Mechanical Science and Technology
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• v.14 no.6
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• pp.622-632
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• 2000

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on. The cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling processes for various cutting conditions, their mathematical model is important and the model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging forces of cutting tests. In this paper the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in the learning stage as the omitted number of experimental data increase the average errors increase as well.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.44-52
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• 1994

In this paper, we assume that the dynamics of DC motor and nonlinear load are unknown. We propose an inverse dynamic model of DC motor and nonlinear load using the artificial neural network and construck speed control system based on the proposed dynamic model. We also propose another dynamic model with speed prediction scheme using the artificial neural network that removes the undesirable time delay effect caused by the computation time during the real-time control. We suggest a dynamic model which has arbitrary number of speed arguments and is especially effective when the motor and load has large moment of inertia. Next, we suggest a controller that combine the neurocontrol and PID control with constant gain. We show that the proposed neurocontrol systems have capabilities of noise rejection and generalization to have good velocity tracking through computer simulations and experiments.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.220-228
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• 2022

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.10
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• pp.1-7
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• 2003

In this paper, we propose load balanced routing and wavelength assignment (RWA) algorithm for static model. The proposed algorithm arranges the routing paths over the link uniformly and assigns routing paths according to the length of routing paths orderly. Thus, the proposed algorithm can efficiently utilize the network resources. Through the computer simulation on layered-graph model, we prove that the proposed algorithm improves network throughput and reduces blocking probability comparing to first-fit algorithm [1］. Moreover, the proposed algorithm considerably reduces computational time.

• Structural Engineering and Mechanics
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• v.21 no.5
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• pp.495-506
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• 2005

A neural network based model is developed for the structural analysis of masonry infilled steel frames, which can account for the non-linearities in the material properties and structural behaviour. Using the data available from the analytical methods, an ANN model with input parameters consisting of dimension of frame, size of infill, properties of steel and infill was developed. It was found to be acceptable in predicting the failure modes of infilled frames and corresponding failure load subject to limitations in the training data and the predicted results are tested using the available experimental results. The study shows the importance of validating the ANN models in simulating structural behaviour especially when the data are limited. The ANN model was also compared with the available experimental results and was found to perform well.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.300-301
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• 2015

Accurate and robust load forecasting model plays very important role in power system operation. In case of short-term electric load forecasting, its results offer standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve accuracy of load forecasting. This paper proposes a newly forecasting model for weather sensitive season including temperature and Cooling Degree Hour(C.D.H) data as an input. This Forecasting model consists of previous electric load and preprocessed temperature, constant, parameter. It optimizes load forecasting model to fit actual load by PSO and results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows better performance than comparison groups.

• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.1
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• pp.28-36
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• 2010

Hardware implementation methods for Artificial Neural Network (ANN) have been researched for a long time to achieve high performance. We have proposed a Network on Chip (NoC) for ANN, and this architecture can reduce communication load and increase performance when an implemented ANN is small. In this paper, a multiple NoC models are proposed for ANN, which can implement both a small size ANN and a large size one. The simulation result shows that the proposed multiple NoC models can reduce communication load, increase system performance of connection-per-second (CPS), and reduce system running time compared with the existing hardware ANN. Furthermore, this architecture is reconfigurable and reparable. It can be used to implement different applications of ANN.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.893-910
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• 1996

Recently, there ary many researches on local area multichannel network as WDM technology developes. An ideal media access protocol in a multichannel network is one that shows short access delay under low load and high throughput under heavy load. This paper proposed a new media access protocol for WDM passive star coupler network. The proposed one is a random access rpotocol based on reservation. Access delay is short under low load by using random access method, and high throughput is achieved under heavy load by usin greservation access. Analytic model for the performance analysis of the proposed protocol is developed and performance of the proposed protocol is compared with the previous ones. The effect on the performance of the number of the nodes and channels, and the number of transceivers in each node are analyzed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1001-1006
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• 2017

In this study the NARX was proposed as a novel approach to forecast electric load more accurately. The NARX model is a recurrent dynamic network. ISO-NewEngland dataset was employed to evaluate and validate the proposed approach. Obtained results were compared with NAR network and some other popular statistical methods. This study showed that the proposed approach can be applied to forecast electric load and NARX has high potential to be utilized in modeling dynamic systems effectively.