• Journal of Information Processing Systems
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• v.15 no.4
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• pp.833-852
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• 2019

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.119-135
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• 2005

Design of location erea(LA) in a cellular network is to partition the network into clusters of cells so as to minimize the cost of location updating and paging. Most research works dealing with the LA design problem assume that the call. arrival rate and mobile flow rate are fixed parameters which can be estimated independently. In this aspect, most Problems addressed so far are deterministic LA design problems(DLADP), known to be NP hard. The mobile flow and call arrival rate are, however, varying with time and should be treated simultaneously because the call arrival rate in a cell during a day is influenced by the change of a population size of the cell. This Paper Presents a new model on IA design problems considering the time-dependent call arrival and mobile flow rate. The new model becomes a stochastic LA design problem(SLADP) because It takes into account the possibility of paging waiting and blocking caused by the changing call arrival rate and finite paging capacity. Un order to obtain the optimal solution of the LA design problem, the SIADP is transformed Into the DLADP by introducing the utilization factor of paging channels and the problem is solved iteratively until the required paging quality is satisfied. Finally, an illustrative example reflecting the metropolitan area, Seoul, is provided and the optimal partitions of a cell structure are presented.

• Structural Engineering and Mechanics
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• v.72 no.6
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• pp.675-687
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• 2019

As the bridges are an integral part of the transportation network, their function as one of the most important vital arteries during an earthquake is fundamental. In a design point of view, the bridges piers, and in particular the wall piers, are considered as effective structural elements in the seismic response of bridge structures due to their cantilever performance. Owing to reduced seismic load during design procedure, the response of these structural components should be ductile. This ductile behavior has a direct and decisive correlation to the development of plastic hinge region at the base of the wall pier. Several international seismic design codes and guidelines have suggested special detailing to assure ductile response in this region. In this paper, the parameters which affect the length of plastic hinge region in the reinforced concrete bridge with wall piers were examined and the sensitivity of these parameters was evaluated on the length of the plastic hinge region. Sensitivity analysis was accomplished by independently variable parameters with one standard deviation away from their means. For this aim, the Monte Carlo simulation, tornado diagram analysis, and first order second moment method were used to determine the uncertainties associated with analysis parameters. The results showed that, among the considered design variables, the aspect ratio of the pier wall (length to width ratio) and axial load level were the most important design parameters in the plastic hinge region, while the yield strength of transverse reinforcements had the least effect on determining the length of this region.

• Advances in aircraft and spacecraft science
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• v.6 no.6
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• pp.529-550
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• 2019

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Advances in Computational Design
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• v.5 no.3
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• pp.323-347
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• 2020

The enhancements of bioenergy production effectiveness require the comprehensively experimental study of several parameters affecting these bioprocesses. The interpretation of the obtained experimental results and the estimation of optimum yield are extremely complicated such as misinterpreting the results of an experiment. The use of mathematical modeling and statistical experimental designs can consistently supply the predictions of the potential yield and the identification of defining parameters and also the understanding of key relationships between factors and responses. This paper summarizes several mathematical models used to achieve an adequate overall and maximal production yield and rate, to screen, to optimize, to identify, to describe and to provide useful information for the effect of several factors on bioenergy production processes. The usefulness, the validity and, the feasibility of each strategy for studying and optimizing the bioenergy-producing processes were discussed and confirmed by the good correlation between predicted and measured values.

• Journal of Sensor Science and Technology
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• v.11 no.4
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• pp.239-246
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• 2002

In this paper a new design of multi-layer perceptron(MLP) for camera calibration is proposed. Most existing techniques determine a transformation from 3D spatial points to their image points and camera parameters are tried to be estimated from the transformation. The technique proposed here, on the other hand, determines rays of sight uniquely from image points and parameters are estimated from the relationship using an MLP. By this approach projection and back-projection can be done more straightforwardly. Being based on a geometric model, a network design process becomes less ambiguous, which is a clear merit compared to other neural net based techniques. An MLP designed according to the technique proposed showed fast and stable learning in tests under various conditions.

• Transactions of Materials Processing
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• v.7 no.2
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• pp.127-138
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• 1998

Process design of multi-step wire drawing process, conducted by means of finite element analysis and ANN(Artificial Neural Network) has been considered. The investigated problem involves the ade-quate selection of the drawing die angle and the correspondent reduction rate in the condition of desired initial and final diameter. Combinations of the process parameters which are used in finite ele-ment simulation are selected by using the orthogonal array. Also the orthogonal array. Also the orthogonal array and the results of finite element simulation which are related to the process energy are used as train data of ANN. In this study it is shown that the application of new technique using ANN and Othogonal array table to the process design of metal forming process is useful method.

• Transactions of Materials Processing
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• v.11 no.4
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• pp.341-348
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• 2002

For the light guide plate of the TFT-LCD, there have been increasing demands for higher brightness, thin and light-weight design, and lower power consumption. To meet these demands, a micro-prism-type frontlight that integrates a prismatic sheet and a light-guiding plate has been developed. In this paper, the influences of processing conditions on the brightness were studied lot the injection molding of the light guide plate. Based on the experiment with an actual mold, the design of experiments and the neural network theory were used lot choosing the optimal processing parameters to increase the brightness and the uniformity. The verification experiment also showed that the brightness and the uniformity were increased dramatically with the chosen processing conditions.

• Computers and Concrete
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• v.10 no.6
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• pp.587-607
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• 2012

The purpose of this research is to propose a design technique of concrete mix proportions satisfying service life through genetic algorithm (GA) and neural network (NN). For this, thirty mix proportions and the related diffusion coefficients in high performance concrete are analyzed and fitness function for diffusion coefficient is obtained considering mix components like w/b (water to binder ratio), cement content, mineral admixture (slag, flay ash and silica fume) content, sand and coarse aggregate content. Through averaging the results of 10 times GA simulations, relative errors to the previous data decrease lower than 5.0% and the simulated mix proportions are verified with the experimental results. Assuming the durability design parameters, intended diffusion coefficient for intended service life is derived and mix proportions satisfying the service life are obtained. Among the mix proportions, the most optimized case which satisfies required concrete strength and the lowest cost is selected through GA algorithm. The proposed technique would be improved with the enhancement of comprehensive data set including wider the range of diffusion coefficients.