• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1576-1589
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• 2006

Most of surface water ways in Egypt suffer from the infestation of aquatic weeds especially submerged ones which cause lots of problems for the open channels and the water structures such as increasing water losses, obstructing the water flow, and reducing the efficiency of the water structures. Accurate simulation of the water flow behavior in such channels is very essential for water distribution decision makers. Artificial Neural Network (ANN) has been widely utilized in the past ten years in civil engineering applications for the simulation and prediction of the different physical phenomena and has proven its capabilities in the different fields. The present study aims towards introducing the use of ANN technique to model and predict the impact of the existence of submerged aquatic weeds on the hydraulic performance of open channels. Specifically the current paper investigates utilizing the ANN technique in developing a simulation and prediction model for the flow behavior in an open channel experiment that simulates the existence of submerged weeds as branched flexible elements. This experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results of current manuscript showed that ANN technique was very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds' cases that were considered in the ANN development process.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.865-873
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• 2010

In this paper, a novel schedulability criteria is developed to provide handover calls with Quality of Service (QoS) guarantees in terms of both minimum available bandwidth, maximum tolerated packet delay, and other additive QoS constraints as required by the real-time mobile traffic. This requires prediction of the handover time using mobility trends on the mobile station, which is used as input to this work. After the handover time and the QoS are negotiated, the destination base station makes attempts to give priority to handover calls over new calls, and pre-reserves resources that will have more chance of being available during the actual handover.

• Journal of Industrial Technology
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• v.39 no.1
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• pp.27-31
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• 2019

Semiconductor logistics systems are facing difficulties in increasing production as production processes become more complicated due to the upgrading of fine processes. Therefore, the purpose of the research is to design predictive models that can predict traffic during the pre-planning stage, identify the risk zones that occur during the production process, and prevent them in advance. As a solution, we build FABs using automode simulation to collect data. Then, the traffic prediction model of the areas of interest is constructed using deep learning techniques (keras - multistory conceptron structure). The design of the predictive model gave an estimate of the traffic in the area of interest with an accuracy of about 87%. The expected effect can be used as an indicator for making decisions by proactively identifying congestion risk areas during the Fab Design or Factory Expansion Planning stage, as the maximum traffic per section is predicted.

• Steel and Composite Structures
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• v.29 no.5
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• pp.659-667
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• 2018

This paper deals with the experimental study on strength the strength and deformation characteristics of short circular Concrete Filled Steel Tube (CFST) columns. Effect of vertical stiffeners on the behavior of the column is studied under axial compressive loading. Intermittently welded vertical stiffeners are used to strengthen the tubes. Stiffeners are attached to the inner surface of tube by welding through pre drilled holes on the tube. The variable of the study is the spacing of the weld between stiffeners and circular tube. A total of 5 specimens with different weld spacing (60 mm, 75 mm, 100 mm, 150 mm and 350 mm) were prepared and tested. Short CFST columns of height 350 mm, outer tube diameter of 165 mm and thickness of 4.5 mm were used in the study. Concrete of cube compressive strength $41N/mm^2$ and steel tubes with yield strength $310N/mm^2$ are adopted. The test results indicate that the strength and deformation of the circular CFST column is found to be significantly influenced by the weld spacing. The ultimate axial load carrying capacity was found to increase by 11% when the spacing of weld is reduced from 350 mm to 60 mm. The vertical stiffeners are found to effective in enhancing the initial stiffness and ductility of CFST columns. The prediction models were developed for strength and deformation of CFST columns. The prediction is found to be in good agreement with the corresponding test data.

• Progress in Superconductivity and Cryogenics
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• v.24 no.1
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• pp.23-28
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• 2022

A sorption cooler, which utilizes helium-4 as a working fluid, was previously developed and tested in KAIST. The cooler consists of a sorption pump and a thermosyphon. The developed sorption cooler aims to pre-cool a certain amount of the magnetic refrigerant of an adiabatic demagnetization refrigerator (ADR) from 4.5 K to 2.5 K. To simulate the high heat capacitance of the magnetic refrigerant, liquid helium was utilized not only as a refrigerant for the sorption cooling but also as a thermal capacitor. The previous experiment, however, showed that the lowest temperature of 2.7 K which was slightly higher than the target temperature (2.5 K) was achieved due to the radiation heat leak. This excessive heat leak would not occur when the sorption cooler is completely integrated with the ADR. Thus, based on the experimentally obtained pumping speed, the prediction model for the sorption cooler is developed in this study. The presented model in this paper assumes the sorption cooler is integrated with the ADR and the heat leak is negligible. The model predicts the amount of the liquid helium and the required time for the sorption cooling process. Furthermore, it is confirmed that the performance of the sorption cooler is enhanced by reducing the volume of the thermosiphon. The detailed results and discussions are summarized.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Earthquakes and Structures
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• v.25 no.6
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.