• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Advances in nano research
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• v.12 no.2
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• pp.167-183
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• 2022

In this paper, the free and forced vibration analysis of rotating cantilever nanoscale cylindrical beams and tubes is investigated under the external dynamic load to examine the nonlocal effect. A couple of nonlocal strain gradient theories with different beams and tubes theories, involving the Euler-Bernoulli, Timoshenko, Reddy beam theory along with the higher-order tube theory, are assumed to the mathematic model of governing equations employing the Hamilton principle in order to derive the nonlocal governing equations related to the local and accurate nonlocal boundary conditions. The two-dimensional functional graded material (2D-FGM), made by the axially functionally graded (AFG) in conjunction with the porosity distribution in the radial direction, is considered material modeling. Finally, the derived Partial Differential Equations (PDE) are solved via a couple of the generalized differential quadrature element methods (GDQEM) with the Newmark-beta techniques for the time-dependent results. It is indicated that the boundary conditions equations play a crucial task in responding to nonlocal effects for the cantilever structures.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Journal of the korean academy of Pediatric Dentistry
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• v.33 no.2
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• pp.233-243
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• 2006

The aim of this study was to investigate the relationship between the C-factor and shrinkage strain values of composite resin and examine the strain values in different incremental filling techniques. Experiment consisted two aims. First, we compared with strain value in two different C-factors(3.7 and 1.0). Second, we examined the strain values in three different filling techniques. The results of the present study can be summarized as follows : 1. High C-factor groups showed higher contraction stress values than low C-factor groups at 900 sec after polymerization. 2. Hybrid resin showed higher contraction stress values than flowable resin in high C-factor cavities. But contraction stress was not revealed significant difference between hybrid resin and flowable resin in low C-factor cavities (P>0.05). 3. Bulk felling with hybrid resin(Group 1) showed high contraction stress and lining with flowable resin followed hybrid resin (Group 5) showed lower contraction stress. 4. Contraction stress were increased during 900 sec after polymerization in high C-factor groups but decreased gradually after 900 sec. 5. Low C-factor groups showed tight marginal seal between resin and cavity wall but high C-factor groups showed gaps formed between resin and cavity wall in part. On the basis above results, layering techniques in high C-factor cavity showed advantages in reducing contraction stress and gap formation between cavity wall and resin restoration.

• Investigative Magnetic Resonance Imaging
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• v.6 no.1
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• pp.64-72
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• 2002

Purpose : To introduce and demonstrate the advantages of the new hybrid two-dimensional (2D) proton spectroscopic imaging (SI) over the single voxel spectroscopy (SVS) and conventional 2D SI in the clinical application of spectroscopy for pediatric cerebral disease. Materials and Methods : Eighty-one hybrid 2D proton spectroscopic imaging was performed in 79 children (36 normal infants and children, 10 with hypoxic-ischemic injury, 20 with toxic-metabolic encephalopathy, seven with brain tumor, three with meningoencephalitis, one with neurofibromatosis, one with Sturge-Weber syndrome and one with lissencephaly) ranging in age from the third day of life to 15 years. In adult volunteers (n=5), all three techniques including hybrid 2D proton SI, SVS using PRESS sequence, and conventional 2D proton SI were performed. Both hybrid 2D proton SI and SVS using PRESS sequence were performed in clinical cases (n=). All measurements were performed with a 1.5-T scanner using standard head quadrature coil. The 16$\times$16 phase encoding steps were set on variable field of view (FOV) depending on the size of the brain. The hybrid volume of interest inside FOV was set as $75{\times}75{\times}15{\;}\textrm{mm}^3$ or smaller to get rid of unwanted fat signal. Point-resolved spectroscopy (TR/TE=1,500 msec/135 or 270msec) was employed with standard chemical shift selective saturation (CHESSI pulses for water suppression. The acquisition time and spectral quality of hybrid 2D proton SI were compared with those of SVS and conventional 2D proton SI. Results : The hybrid 2D proton SI was successfully conducted upon all patients.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.357-371
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• 2014

There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.2
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• pp.168-180
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• 1992

Commumication technology has been continuously developed to overcome the distance and time for the transmission of information to the human society. Wireless mobile communication, which had been used mostly in the military and police is widely used these days for enterprise and individuals. Therefore the domestic usage of the advanced mobile phone service are progressively gaining wide popularity. The modulation techniques used usually in mobile communications were the analog techniques such as AM and FM, but they are getting replaced by the digital techniques, However, the major disadvantage of the digital communications is the increase of the transmission bandwidth. Therefore, it is very important to use efficiently the limited frequency bandwidth. The domestic research and development on the subject seems quite limited and in order to establish the technology of the digital mobile communications. This thesis presents the design of the frequency hopping synthesizer providing 124 channels with a channel spcing of 200KHz. VCD used in the synthesizer employs a semi-rigid cable for higher purity of signal spectrum, and a hybrid pgase detector is realized with a sample hold phase detector in conjuction with a tri-state phase detedctor.