• Journal of Construction Engineering and Project Management
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• v.6 no.3
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• pp.1-7
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• 2016

The purpose of this paper is to develop a model for selecting the best contractor in the Gaza Strip using the Artificial Neural Network (ANN). The contractor's selection methods and criteria were identified using a field survey. Fifty four engineers were asked to fill a questionnaire that covers factors related to the selection criteria of contractors practiced in Gaza Strip. The results shows that the dominant part of respondents (91%) confirmed that the current awarding method "the lowest bid price" is considered one of the major problems of the construction sector, "award the bid to the highest weight after combination of the technical and financial scores" represented 50% of the respondents. The criteria weights were determined based on Relative Importance Index (RII. Ninety-one tenders(13 projects) were used to train and test the ANN model after re-evaluating the contractors depend on the weights of factors to select the best contractor who achieves the highest score. Neurosolution software was used to train the models. The results of the trained models indicated that neural network reasonably succeeded in selection the best contractor with 95.96% accuracy. The performed sensitivity analysis showed that the profitability and capital of company are the most influential parameters in selection contractors. This model gives chance to the owner to be more accurate in selecting the most appropriate contractor.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.265-270
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• 2014

Existing speech recognition algorithm have a problem with not distinguish the order of vocabulary, and the voice detection is not the accurate of noise in accordance with recognized environmental changes, and retrieval system, mismatches to user's request are problems because of the various meanings of keywords. In this article, we proposed to event based semantic ontology inference model, and proposed system have a model to extract the speech recognition feature extract using ERB filter. The proposed model was used to evaluate the performance of the train station, train noise. Noise environment of the SNR-10dB, -5dB in the signal was performed to remove the noise. Distortion measure results confirmed the improved performance of 2.17dB, 1.31dB.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.183-188
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• 2009

The High speed electrical multiple train having a distributed electrical motor system has just been developing to aim the experimental maximum speed at 400km/h since August, 2007. This project comes in stage of basic design and so, it needs to take some review and analysis the characteristics of suspensions on the view of basic design. The vehicle model for dynamic analysis is made from the concept design model that used for the preliminary design review with Vampire program and it is modeled with three linear secondary dampers and two shear springs separated from the bush elements in previous model. The optimization technique is applied to search the proper range of linear characteristics for the suspension elements to satisfy the stability performance at speed 130m/s (about 460km/h). The results shows there are some optimum points according to the variation of primary and secondary suspension characteristics and it would be useful to make a decision to select the proper suspension elements in the precision design that will be done by the manufacturing company.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• 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 Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Journal of Hydrogen and New Energy
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• v.35 no.2
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• pp.121-128
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• 2024

In this study, a dynamic simulation model of a heavy-duty truck, equipped with a fuel cell power-train, has been developed and the dynamic behavior of the fuel cell stack has bee investigated using. Output change simulations were performed according to several drive cycle load change of a fuel cell truck. Mathworks' Simulink and Simscape program were used to develop the model. The model is comprised of fuel cell power train, power converter system and truck vehicle part. The vehicle runs at targeted speed of the truck, which is set as the load of the system. The dynamic behavior of the fuel cell stack according to the weight difference were analyzed, and based on this, the dynamic characteristics of the fuel cell output power and battery state with simple load was analyzed.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.119-126
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• 2024

In building information model (BIM), it is difficult to train an artificial intelligence (AI) model due to the lack of sufficient data about individual projects in an architecture firm. In this paper, we present a methodology to correctly train an AI neural network model based on a large language model (LLM) to predict the steel structure product weight ratios in BIM. The proposed method, with the aid of the LLM, can overcome the inherent problem of limited data availability in BIM and handle a combination of natural language and numerical data. The experimental results showed that the proposed method demonstrated significantly higher accuracy than methods based on a smaller language model. The potential for effectively applying large language models in BIM is confirmed, leading to expectations of preventing building accidents and efficiently managing construction costs.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.7 no.1
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• pp.79-86
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• 2003

A myoelectric signal, under sustained isometric contraction of muscle the modelled as the output of a linear time-varying system whose input is constant number of pulse train. The proposed model considered localized muscle fatigue by metabolic by-products during sustained fatiguing contraction. To characterize muscle fatiguing model of myoelectric signal, We calculated median frequency of generated signal as fatiguing index of muscle during sustained isometric contraction.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1172-1178
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• 2009

The Training using a simulator comes into the spotlight as effective and safety training tool in a lot of fields. Driving simulator of KTX-II is for driver's actual training using virtual reality to be improved their ability of operation against when they are faced with urgent situation and various accidents. This paper describes the construction of the propulsion and brake model for KTX-II driving simulator and the utility of the model as a result in comparison with the design data of the real train.