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1
Li, Xi, and Dan Feng Feng. "Bayesian Network Learn Method for Machine Tool Thermal Stability Modeling." Advanced Materials Research 284-286 (July 2011): 932–35. http://dx.doi.org/10.4028/www.scientific.net/amr.284-286.932.
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Maintain the thermal stability of the machine tool is a common problem to achieve intelligent and precise processing control, and its difficulty lies in modeling and real-time compensation. In this paper, considering the correlation of various factors, the correlation of those factors according to experiment data was analysis and optimized, and a dynamic model of thermal error compensation of CNC machine tool based on Bayesian Network theory was found. Moreover, because of the self-learning feature of Bayesian network, the model can be continuously optimized by updating dynamic coefficient, and reflect the changes of processing condition. Finally, the feasibility and validation of this model were proved through the experiment.
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Ekwem, Edith N., and Kashif Nisar. "An Experimental Study." International Journal of Advanced Pervasive and Ubiquitous Computing 6, no. 3 (July 2014): 35–53. http://dx.doi.org/10.4018/ijapuc.2014070103.
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A network whose interconnections between nodes are implemented without using wires is referred to as wireless network and is usually related to a telecommunication network. As related to wired local area network, wireless channels are error-prone. Performance study and optimization of Wireless Local Area Network (WLAN) becomes more essential as its gaining popularity. For performance modelling and evaluation of wireless networks, computer simulation has become one of most widespread tools. While numerous network simulators exist for building a variety of network models, selecting a good network simulator tool is vital in modelling and performance study of wireless networks. Optimized Network Engineering Tools (OPNET) Modeller available to academic institutions at no cost is becoming one of the most widespread network simulators. In this study, the authors used OPNET Modeller 14.5 simulator tool to develop and validate a model for campus based WLAN. The results are expected to display that OPNET Modeller offers credible simulation outcomes close to a genuine system. The effect of network parameters such as the processing time on the performance metrics such as delay and throughput of the various scenarios in the entire network was investigated. The analysis of the results from the simulations carried out can assist the management of computer centre that manages the network in identifying the bottleneck node on the network and for future network capacity building. However, this wireless network involves too many numbers of users which OPNET is not capable to simulate; the authors limited the network to only users in the computer building.
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Paluzo-Hidalgo, Eduardo, Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, and Jónathan Heras. "Optimizing the Simplicial-Map Neural Network Architecture." Journal of Imaging 7, no. 9 (September 1, 2021): 173. http://dx.doi.org/10.3390/jimaging7090173.
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Simplicial-map neural networks are a recent neural network architecture induced by simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural networks are universal approximators and that they can be refined to be robust to adversarial attacks. In this paper, the refinement toward robustness is optimized by reducing the number of simplices (i.e., nodes) needed. We have shown experimentally that such a refined neural network is equivalent to the original network as a classification tool but requires much less storage.
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Li, Dian Xin, Hong Lin Zhao, Shi Min Zhang, Dai Geng, Xian Long Liu, and Shan Jun Zheng. "Structure Optimization of Slip by the Combination of Artificial Neural Network and Genetic Algorithm." Advanced Materials Research 199-200 (February 2011): 1223–29. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.1223.
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The bridge plug is a staple tool used in downhole operation and the performance of the slips has a directly influence on the oil well productivity and production safety. We raised an optimize method based on BP network and genetic algorithm to make sure the slips satisfy the high temperature and high pressure demands. Establishing the slips system and making finite element analysis by ANSYS, abtaining sixteen group datas to constitute the BP network training samples, establishing the BP simulation model reflecting curvature radius of the slip fluke, dip angle of the fluke, angle of the fluke and distance between flukes using nonlinearity mapping ability of the neural network, applying optimize design for the simulation model using global optimization ability of the genetic algorithm and abtaining the optimum structure parameters of the slip. The optimized results indicate the whole performance of the slips system has increased notably.
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Alshbatat, Abdel Ilah, and Liang Dong. "Performance Analysis of Mobile Ad Hoc Unmanned Aerial Vehicle Communication Networks with Directional Antennas." International Journal of Aerospace Engineering 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/874586.
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Unmanned aerial vehicles (UAVs) have the potential of creating an ad hoc communication network in the air. Most UAVs used in communication networks are equipped with wireless transceivers using omnidirectional antennas. In this paper, we consider a collection of UAVs that communicate through wireless links as a mobile ad-hoc network using directional antennas. The network design goal is to maximize the throughput and minimize the end-to-end delay. In this respect, we propose a new medium access control protocol for a network of UAVs with directional antennas. We analyze the communication channel between the UAVs and the effect of aircraft attitude on the network performance. Using the optimized network engineering tool (OPNET), we compare our protocol with the IEEE 802.11 protocol for omnidirectional antennas. The simulation results show performance improvement in end-to-end delay as well as throughput.
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Yan, Wang Xiao, Wang Pin, and Lang He. "Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network." IEEE Access 9 (2021): 60682–88. http://dx.doi.org/10.1109/access.2021.3074505.
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Jiang, Tian Ying. "Study on Assessment of Enterprise Intellectual Capital Based on the Genetic Neural Network." Advanced Materials Research 204-210 (February 2011): 237–40. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.237.
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Compared to the neural network BP algorithm, the optimized model of genetic neural network based on the genetic algorithm has a more close assessed result to the expected one and smaller relative mistakes. Practical applications show that the new assess way of enterprise intellectual capital is rational and accessible, and it provides as an important tool to enterprise for intellectual capital decision.
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Li, Bo, Xitian Tian, and Min Zhang. "Thermal error modeling of machine tool spindle based on the improved algorithm optimized BP neural network." International Journal of Advanced Manufacturing Technology 105, no. 1-4 (September 11, 2019): 1497–505. http://dx.doi.org/10.1007/s00170-019-04375-w.
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Hu, Ru Fu, and Xiao Ping Chen. "Research on Structural Dynamic Optimal Design of NC Internal Grinder." Advanced Materials Research 129-131 (August 2010): 814–18. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.814.
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Structural dynamics optimal design of key components is basis to reach optimal design of whole machine tool. The method of sensitivity analysis is applied to optimize the arrangement shapes and parameters of the strengthened bars of components. The BP neural networks model of the spindle system is established and corrected based on comparing with the experimental result, and the structure parameters of the spindle are optimized. These technologies will benefit to realize optimal design of whole NC internal grinder and guide dynamic optimal design of other machine tools.
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Yeh, Yu-En. "Prediction of Optimized Color Design for Sports Shoes Using an Artificial Neural Network and Genetic Algorithm." Applied Sciences 10, no. 5 (February 25, 2020): 1560. http://dx.doi.org/10.3390/app10051560.
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Product design is a complicated activity that is highly reliant on individual impressions, feelings and emotions. Back-propagated neural networks have already been applied in Kansei engineering to solve difficult design problems. However, artificial neural networks (ANNs) have a slow rate of convergence, and find it difficult to devise a suitable network structure and find the global optimal solution. This study developed an ANN-based predictive model enhanced with a genetic algorithm (GA) optimization technique to search for close-to-optimal sports shoe color schemes for a given product image. The design factors of the sports shoe were set as the network inputs, and the Kansei objective value was the output of the GA-based ANN model. The results show that a model built with three hidden layers (28 × 38 × 19) could predict the object value reliably. The R2 of the preference objective was equal to 0.834, suggesting that the developed model is a feasible and efficient tool for predicting the objective value of product images. This study also found that the prediction accuracy for shoes with two colors was higher than that for shoes with only one color. In addition, the prediction accuracy for shoes with a relatively familiar shape was also higher. However, the prediction of color preferences is relatively difficult, because the respondents had different individual color preferences. Exploring the sensitivity and importance of the visual factors (form, color, texture) for various image words is a worthy topic for future research in this field.
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Checcucci, Matteo, Federica Sazzini, Michele Marconcini, Andrea Arnone, Mario Coneri, Luigi De Franco, and Matteo Toselli. "Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application." International Journal of Rotating Machinery 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/817547.
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This work provides a detailed description of the fluid dynamic design of a low specific-speed industrial pump centrifugal impeller. The main goal is to guarantee a certain value of the specific-speed number at the design flow rate, while satisfying geometrical constraints and industrial feasibility. The design procedure relies on a modern optimization technique such as an Artificial-Neural-Network-based approach (ANN). The impeller geometry is parameterized in order to allow geometrical variations over a large design space. The computational framework suitable for pump optimization is based on a fully viscous three-dimensional numerical solver, used for the impeller analysis. The performance prediction of the pump has been obtained by coupling the CFD analysis with a 1D correlation tool, which accounts for the losses due to the other components not included in the CFD domain. Due to both manufacturing and geometrical constraints, two different optimized impellers with 3 and 5 blades have been developed, with the performance required in terms of efficiency and suction capability. The predicted performance of both configurations were compared with the measured head and efficiency characteristics.
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Swarup, M. Prem, and A. Prabhu Kumar. "Effective Implementation of Value Engineering Using Artificial Neural Network Aid of Optimization Techniques." International Journal of Air-Conditioning and Refrigeration 27, no. 03 (September 2019): 1950022. http://dx.doi.org/10.1142/s2010132519500226.
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Value Engineering (VE) is a method for characterizing the developed requirements of a product, and it is concerned with the selection of less excessive conditions. VE can understand and improve the optimal outcome such as quantity, security, unwavering quality and convertibility of each managerial unit. It is an incredible solving tool that can diminish costs while preserving or improving performance and quality requirements. In this research work, VE is presented to calculate the heating cost and cooling cost of the air conditioner with the assistance of an Artificial Neural Network (ANN) with an optimization model. This ANN model effectively chooses the maximum number of sources obtainable and the source respective method with low functional cost and energy consumption. For improving the prediction accuracy of VE in the ANN model, we have incorporated some training algorithms and optimized the network hidden layer and hidden neuron by Opposition Genetic Algorithm (OGA). From the results, trained ANN with OGA predicts the output with 96.02% accuracy and also minimum errors compared with the existing GA process.
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Schwennig, B., and S. Jerems. "Smart Tools: intelligente Werkzeuge/Smart Tools - For a quick and safe production sequence." wt Werkstattstechnik online 108, no. 01-02 (2018): 9–13. http://dx.doi.org/10.37544/1436-4980-2018-01-02-9.
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Werkzeugprozesskosten haben einen signifikanten Anteil an den Fertigungskosten in der zerspanenden Fertigung. Diese Kosten zu senken und die Produktion zu optimieren, waren die Forschungsschwerpunkte des vom BMBF geförderten Projektes „Smart Tool – Intelligente Werkzeuge für die vernetzte Fertigung von morgen“. Smart Tool vernetzt mithilfe eines cyber-physischen Systems (CPS) den gesamten Werkzeugkreislauf. So ist eine lückenlose und durchgängige Nachverfolgung der Werkzeuge gegeben. In den Werkzeughalter integrierte Sensoren lassen darüber hinaus Rückschlüsse auf den Zustand des Werkzeugs zu. Tool process costs have a significant share of the production costs in machining production. To reduce these costs and to optimize production are the main research areas of the BMBF-funded project „Smart Tool – Intelligent Tools for the Networked Manufacturing of Tomorrow“. Smart Tool uses a cyber-physical system (CPS) to network the entire tool cycle. Thus, a complete and continuous follow-up of the tools is given. Sensors integrated in the tool holders also allow conclusions on the condition of the tool.
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Li, Deng Wan, Hong Li Gao, Yun Shou, Peng Du, and Ming Heng Xu. "Tool Condition Monitoring Based on Radial Basis Probabilistic Neural Networks and Improved Genetic Algorithm." Advanced Materials Research 139-141 (October 2010): 2522–26. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2522.
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In order to accurately estimate tool life for milling operation, a novel tool condition monitoring system was proposed to improve classifying precision in different cutting condition. Lots of features were extracted from cutting forces signal, vibration signal and acoustic emission signal by different signal processing method, only a few features selected by principal component analysis (PCA) according to contribution rate, and constructed as input vector. The relation between tool condition and features was built by radial basis probability neural network which control parameter of kernel function and hidden central vector were optimized by improved genetic algorithm. The experimental results show that the method proposed in the paper achieves higher recognition rate, good generalization ability and better available practicality.
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Huang, Zhiwen, Jianmin Zhu, Jingtao Lei, Xiaoru Li, and Fengqing Tian. "Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling." Mathematical Problems in Engineering 2021 (June 30, 2021): 1–14. http://dx.doi.org/10.1155/2021/9976939.
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Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.
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Dong, Weihang, Xiaolei Guo, Yong Hu, Jinxin Wang, and Guangjun Tian. "Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals." BioResources 16, no. 2 (February 5, 2021): 2369–84. http://dx.doi.org/10.15376/biores.16.2.2369-2384.
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Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.
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T., Senthilnathan, Sujay Aadithya B., and Balachandar K. "Prediction of mechanical properties and optimization of process parameters in friction-stir-welded dissimilar aluminium alloys." World Journal of Engineering 17, no. 4 (May 28, 2020): 519–26. http://dx.doi.org/10.1108/wje-01-2020-0019.
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Purpose This study aims to predict the mechanical properties such as equivalent tensile strength and micro-hardness of friction-stir-welded dissimilar aluminium alloy plates AA 6063-O and AA 2014-T6, using artificial neural network (ANN). Design/methodology/approach The ANN model used for the experiment was developed through back propagation algorithm. The input parameter of the model consisted of tool rotational speed and weld-traverse speed whereas the output of the model consisted of mechanical properties (tensile strength and hardness) of the joint formed by friction-stir welding (FSW) process. The ANN was trained for 60% of the experimental data. In addition, the impact of the process parameters (tool rotational speed and weld-traverse speed) on the mechanical properties of the joint was determined by Taguchi Grey relational analysis. Findings Subsequently, testing and validation of the ANN were done using experimental data, which were not used for training the network. From the experiment, it was inferred that the outcomes of the ANN are in good agreement with the experimental data. The result of the analyses showed that the tool rotational speed has more impact than the weld-traverse speed. Originality/value The developed neural network can be used to predict the mechanical properties of the weld. Results indicate that the network prediction is similar to the experiment results. Overall regression value computed for training, validation and testing is greater than 0.9900 for both tensile strength and microhardness. In addition, the percentage error between experimental and predicted values was found to be minimal for the mechanical properties of the weldments. Therefore, it can be concluded that ANN is a potential tool for predicting the mechanical properties of the weld formed by FSW process. Similarly, the results of Taguchi Grey relational analysis can be used to optimize the process parameters of the weld process and it can be applied extensively to ascertain the most prominent factor. The results of which indicates that rotational speed of 1,270 rpm and traverse speed of 30 mm/min are to be the optimized process parameters. The result also shows that tool rotational speed has more impact on the mechanical properties of the weld than that of traverse speed.
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Moayedi, Hossein, and Amir Mosavi. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework." Energies 14, no. 4 (February 23, 2021): 1196. http://dx.doi.org/10.3390/en14041196.
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Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.
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Khoshdel, Vahab, and Alireza Akbarzadeh. "An optimized artificial neural network for human-force estimation: consequences for rehabilitation robotics." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 416–23. http://dx.doi.org/10.1108/ir-10-2017-0190.
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Purpose This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to estimate human force from Electromyogram (sEMG) signals for rehabilitation robotics. Physiotherapists believe, to make a precise therapeutic exercise, we need to design and perform therapeutic exercise base on patient muscle activity. Therefore, sEMG signals are the best tool for using in therapeutic robots because they are related to the muscle activity. Using sEMG signals as input for therapeutic robots need precise human force estimation from sEMG. Furthermore, the ANN estimator performance is highly dependent on the accuracy of the target date and setting parameters. Design/methodology/approach In the previous studies, the force data, which are collected from the force sensors or dynameters, has widely been used as target data in the training phase of learning ANN. However, force sensors or dynameters could measure only contact force. Therefore, the authors consider the contact force, limb’s dynamic and time in target data to increase the accuracy of target data. Findings There are plenty of algorithms that are used to obtain optimal ANN settings. However, to the best of our knowledge, they do not use regression analysis to model the effect of each parameter, as well as present the contribution percentage and significance level of the ANN parameters for force estimation. Originality/value In this paper, a new model to estimate the force from sEMG signals is presented. In this method, the sum of the limb’s dynamics and the contact force is used as target data in the training phase. To determine the limb’s dynamics, the patient’s body and the rehabilitation robot are modeled in OpenSim. Furthermore, in this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi is used to find the optimal parameters settings. Next, analysis of variance technique is used to obtain significance level, as well as contribution percentage of each parameter, to optimize ANN’s modeling in human force estimation. The results indicate that the presented model can precisely estimate human force from sEMG signals.
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Fereshteh-Saniee, F., A. Sepahi-Boroujeni, and S. Sepahi-Boroujeni. "Optimized tool design for expansion equal channel angular extrusion (Exp-ECAE) process using FE-based neural network and genetic algorithm." International Journal of Advanced Manufacturing Technology 86, no. 9-12 (February 15, 2016): 3471–82. http://dx.doi.org/10.1007/s00170-016-8487-6.
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C, Jaymar, and Marifel Grace Capili-Kummer. "Giddy Ion Reloaded: Desktop Manager, Optimizer with Multi Utility Tool." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 154–58. http://dx.doi.org/10.35940/ijrte.e5238.019521.
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The performance of our computer is vital in fulfilling the task of the user. The paper presents a solution for maintaining the performance of the computer specifically computers with Windows operating systems. In this article, the fundamental difference and problem of the Windows operating system are defined which roots in the architectural design of using single configuration storage. The security hole of windows authentication, the exploitation of Microsoft EFS, and the acquisition of password hashes from Microsoft SAM are also discussed. Various existing utility software is evaluated to investigate if they meet the user define criteria. This paper also proposes a user-level implementation of the AES 256 encryption algorithm for securing user files and a Network Blocking algorithm based on ARP Spoofing techniques that provide a user-level network monitoring capability. The proposed application is called “Giddy-ION Reloaded” which consists of four main modules; machine information acquisition and monitoring, machine optimization, machine cleaning, and tools module that is divided into submodule; encryption and decryption, network monitoring, desktop management, network optimization/ control, and task automation. The testing was conducted with the participants coming from a computer college, continuing education trainer/faculty, and various IT experts. The response from these groups was statistically treated and analyzed, where the Giddy ION rank top and shows promising results. The study is limited to windows machines with 64-bit support architecture. The developed application is ready for implementation and deployment as evidenced by its high overall performance rating as evaluated by the participants against the ISO 25010 standards.
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Mandal, Sudip, Abhinandan Khan, Goutam Saha, and Rajat Kumar Pal. "Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm." Journal of Bioinformatics and Computational Biology 14, no. 03 (June 2016): 1650010. http://dx.doi.org/10.1142/s0219720016500104.
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The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.
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Örmeci, Banu. "Rheology as a tool for measurement of sludge shear." Water Science and Technology 58, no. 7 (October 1, 2008): 1379–84. http://dx.doi.org/10.2166/wst.2008.724.
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Shear intensity, shear time, and polymer dose are the main parameters that determine the dewaterability of wastewater sludge. Polymer dose required to condition the sludge increases with the increase of shear intensity (G) and shear time (t). Therefore, in order to minimize the polymer demand during conditioning and dewatering, shear should be optimized. Optimization of shear can be achieved if the total shear that the sludge network is exposed to during conditioning and dewatering can be measured and quantified. This is quite a challenge since total shear includes unintended shear introduced during piping and pumping, and currently there is no direct or indirect technique that can measure this unintended shear. Unintended shear increases the polymer demand and shifts the optimum polymer dose to a higher dose, which in turn decreases the cake solids concentration and the efficiency of the dewatering process. Thus, quantification of the unintended shear and adjustment of the polymer dose accordingly are essential for the optimization of dewatering processes. The main objective of this study was to develop a method for sludge shear measurement based on the rheological characteristics of sludge and illustrate its possible applications at treatment plants. The results of this study indicate that the rheological characteristics of sludge can be used to estimate an unknown amount of shear that sludge network is exposed to, and to match the jar-test mixing conditions to that of the full-scale mixers employed at treatment plants.
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Liu, Lanjun, Denghui Liu, Han Wu, and Xinyu Wang. "The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization." Advances in Civil Engineering 2020 (December 18, 2020): 1–15. http://dx.doi.org/10.1155/2020/6692130.
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The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method.
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Cheremukhin, Petr S., and Aleksandr A. Shumeyko. "Educational Robotics as a Factor in the Development of Network Interaction in the System of Engineering Training." Integration of Education, no. 3 (September 28, 2018): 535–50. http://dx.doi.org/10.15507/1991-9468.092.022.201803.535-550.
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Introduction. Educational robotics is a new learning technology and an effective tool for training engineering staff. Networking of educational organizations and enterprises expands their potential in the system of level engineering training. The main idea of the article is to create and test a local model of an effective networked educational system in the context of federal and regional concepts and programs that would meet the development trends of modern society and at the same time would allow the preparation of schoolchildren for real participation in practical activities. Materials and Methods. We conducted a theoretical analysis of foreign and domestic literature. The method of scientific modeling, namely, the creation of a graphic hierarchical model was applied to develop an integrated system of engineering education for schoolchildren. When organizing the practical use of the model, pedagogical design, comparative analysis of verification works, sociological tools and criterial formative evaluation are used. Results. Authors made an attempt of systematization of subjects and forms of lifelong engineering education at the stages from preschool to higher, based on research conducted over six years. It is defined that the subject of inter-agency coordination network between participants of educational organizations. A tool to ensure continuity in the transition to a new level of education, and the implementation of interdisciplinary component pre-engineering education are interdisciplinary programs, in particular, robotics. Implementation of programs on robotics is carried out through curricular and extracurricular activities, additional education program, vacation employment and other forms of work, provided resources as the base of the organization and network partners. The author’s summer program of the camp “Technosphere” was developed and approved with the day-time stay of children during the vacation period. The model of the Integrated System of Level Engineering Engineering for Schoolchildren was developed and introduced into the city’s education system. Discussion and Conclusions.The system of level engineering training, which combines the levels of education, additional educational programs and the potential of network interaction, allows to optimize all directions and forms of organization of the educational process. As a result of the study, a model of an integrated system of level engineering training at the stage of pre-school and primary general education was developed.
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Yuan, Song Mei, Zhong Fei Zhan, and Yao Li. "Optimum Design of Machine Tool Structures Based on BP Neural Network and Genetic Algorithm." Advanced Materials Research 655-657 (January 2013): 1291–95. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.1291.
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In order to estimate and optimize the static and dynamic characteristics of machine tool, the full parameterized FEM model of it is established and studied in the paper. After the FEM analysis of bed, this paper takes a machine bed as example, presents a method of combination of BP Neural-Network(NN) and Genetic-Algorithm(GA) to optimize dynamic characteristics and realizes the structural optimization of the bed. It proved that this method takes less time, and more precision compared to traditional method.
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Nishida, Isamu, and Keiichi Shirase. "Machine Tool Assignment Realized by Automated NC Program Generation and Machining Time Prediction." International Journal of Automation Technology 13, no. 5 (September 5, 2019): 700–707. http://dx.doi.org/10.20965/ijat.2019.p0700.
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The present study proposed a method to automatically generate a numerical control (NC) program by referring to machining case data for each machine tool with only 3D-CAD models of a product and workpiece as the input data, and to select machine tools for machining the target removal region among several machine tools with different characteristics. The special features of the proposed method are described as follows. The removal volume can be automatically obtained from the total removal volume (TRV), which is extracted from the workpiece and product using a Boolean operation by dividing it on the XY plane. The removal region changed according to the determined machining sequence. The conditions for machining the removal region is automatically determined according to the machining case data, which is stored by linking the geometric properties of the removal region with the machining conditions determined by experienced operators. Furthermore, an NC program is automatically generated based on the machining conditions. The machine tools for machining the target region are selected according to the predicted machining time of each machine tool connected by a network. A case study was conducted to validate the effectiveness of the proposed system. The results confirm that machining can be conducted using only 3D-CAD models as input data. It was suggested that the makespan would be shortened by changing the machining sequence from the optimized machining sequence when machining a plurality of products.
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Fang, Kuan, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, and Silvio Savarese. "Learning task-oriented grasping for tool manipulation from simulated self-supervision." International Journal of Robotics Research 39, no. 2-3 (August 29, 2019): 202–16. http://dx.doi.org/10.1177/0278364919872545.
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Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and, thus, properly grasping and manipulating the tool to achieve the task. Most work in robotics has focused on task-agnostic grasping, which optimizes for only grasp robustness without considering the subsequent manipulation tasks. In this article, we propose the Task-Oriented Grasping Network (TOG-Net) to jointly optimize both task-oriented grasping of a tool and the manipulation policy for that tool. The training process of the model is based on large-scale simulated self-supervision with procedurally generated tool objects. We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering. Our model achieves overall 71.1% task success rate for sweeping and 80.0% task success rate for hammering.
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Badejo, O. T., O. T. Jegede, H. O. Kayode, O. O. Durodola, and S. O. Akintoye. "Modelling and prediction of water current using artificial neural networks: A case study of the commodore channel." Nigerian Journal of Technology 39, no. 3 (September 16, 2020): 942–52. http://dx.doi.org/10.4314/njt.v39i3.37.
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Water current modelling and prediction techniques along coastal inlets have attracted growing concern in recent years. This is largely so because water current component continues to be a major contributor to movement of sediments, tracers and pollutants, and to a whole range of offshore applications in engineering, environmental observations, exploration and oceanography. However, most research works are lacking adequate methods for developing precise prediction models along the commodore channel in Lagos State. This research work presents water current prediction using Artificial Neural Networks (ANNs). The Back Propagation (BP) technique with feed forward architecture and optimized training algorithm known as Levenbergq-Marquardt was used to develop a Neural Network Water Current Prediction model-(NNWLM) in a MATLAB programming environment. It was passed through model sensitivity analysis and afterwards tested with data from the Commodore channel (Lagos Lagoon). The result revealed prediction accuracy ranging from 0.012 to 0.045 in terms of Mean Square Error (MSE) and 0.80 to 0.83 in terms of correlation coefficient (R-value). With this high performance, the Neural network developed in this work can be used as a veritable tool for water current prediction along the Commodore channel and in extension a wide variety of coastal engineering and development, covering sediment management program: dredging, sand bypassing, beach-contingency plans, and protection of beaches vulnerable to storm erosion and monitoring and prediction of long-term water current variations in coastal inlets.
Keywords: Artificial Neural Network, Commodore Channel, Coastal Inlet, Water Current, Back Propagation.
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Li, Yong. "Research on Dynamic Weight WFQ Algorithm of Wireless Networks." Advanced Materials Research 129-131 (August 2010): 90–94. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.90.
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Excellent scheduling algorithms can optimize allocation and improve utilization of wireless resources. To improve QoS of wireless networks, this paper presents a Dynamic Weight-Weight Fair Queuing (DW-WFQ) algorithm with differentiated service that is suited to characteristics of wireless networks based on WFQ algorithm. The proposed algorithm adaptively adjusts the weights of queues according to the dynamical measurement of link state. It can fully make use of limited wireless network resources and improve the overall throughput of network.The simulation experiment results using OPNET software tool demonstrate that the proposed algorithm can improve the network throughput and reduce the average end-to-end delay, compared with WFQ algorithm.
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Wang, Jianchen, Tao Jiang, Junquan Shen, Junhao Dai, Zequan Pan, and Xiaolei Deng. "Thermal Error Compensation of Spindle System of Computer Numerically Controlled Machine Tools Through Experiments and Modeling." Instrumentation Mesure Métrologie 19, no. 4 (September 30, 2020): 301–9. http://dx.doi.org/10.18280/i2m.190408.
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This paper attempts to solve the insufficient machining precision of computer numerically controlled (CNC) machine tools, which is induced by the thermal error of the spindle. Firstly, the relationship between machining error and thermal sensitive points was analyzed through experiments. On this basis, the backpropagation neural network (BPNN) was improved by particle swarm optimization (PSO). Next, the improved network (PSO-BPNN) was used to build a thermal error compensation (TCE) model for the spindle of machine tools. Taking VM-500T precision machine tool as the object, the temperature data were grouped through the optimization based on thermal imaging, grey relational analysis (GRA), and fuzzy clustering, to determine the temperature sensitive items that causes the thermal error. To speed up network convergence, the PSO algorithm was introduced to optimize the number of hidden layers and the number of hidden layer nodes of the BPNN, lifting the network from the local optimum trap. To enhance the generalization ability, the weights and thresholds of the BPNN were also improved by the PSO. After that, two TCE models were established for the spindle of the machine tool, respectively based on the original BPNN and PSO-BPNN. Contrastive experiments show that the PSO-BPNN TCE model achieved the better generalization ability, and improved the prediction accuracy of the machining error of the CNC machine tool.
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Krishnaraj, Vijayan. "Optimisation of End Milling Parameters on Aluminium/SiC Composites Using Response Surface and Artificial Neural Network Methodologies." Materials Science Forum 766 (July 2013): 59–75. http://dx.doi.org/10.4028/www.scientific.net/msf.766.59.
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In recent years, the utilization of metal matrix composites (MMC) have increased in engineering fields of automobile, aviation, aerospace, construction and microelectronics. In conjunction with innovations in these advanced materials, machining them to obtain good dimensional accuracy and surface integrity has become a challenge. In this present work, 15 % (by weight) SiC particle reinforced aluminium is synthesized by stir casting technique. The synthesized MMC is end milled using Ø16 mm carbide end mill in a CNC milling machine and the machining parameters explicitly cutting speed, feed rate and depth of cut are optimised for minimum surface roughness and total force acting on the tool using response surface methodology (RSM) and artificial neural network (ANN). The un-coated cutting tool insert was compared with the nanocomposite coated insert on tool wear at optimised cutting conditions.
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Abrahart, Robert J., Linda See, and Pauline E. Kneale. "Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model." Journal of Hydroinformatics 1, no. 2 (October 1, 1999): 103–14. http://dx.doi.org/10.2166/hydro.1999.0009.
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Four design tool procedures are examined to create improved neural network architectures for forecasting runoff from a small catchment. Different algorithms are used to remove nodes and connections so as to produce an optimised forecasting model, thereby reducing computational expense without loss in performance. The results also highlight issues in selecting analytical methods to compare outputs from different forecasting procedures.
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Ma, Wei, Rongqi Wang, Xiaoqin Zhou, and Xuefan Xie. "The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 235, no. 1-2 (July 1, 2020): 265–77. http://dx.doi.org/10.1177/0954405420932442.
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The cutting forces will generally suffer massive complex factors, such as material deformation, tool eccentricity and system vibration, which will inevitably induce many great difficulties in accurately modeling the cutting force predictions that are very significant to investigate cutting processes. Therefore, the genetic algorithm optimized back-propagation and particle swarm optimization neural networks will be adopted to effectively construct cutting force prediction models. In these two back-propagation prediction models, the main milling parameters will be defined into their input vectors, and the transient milling forces along three different directions will be selected as their output vectors, then the implicit relationships between input and output vectors can be directly generated through practically training and learning these two built back-propagation models with a set of experimental milling force data. Meanwhile, the finite element analysis method will be also used to predict milling forces through programming two easy-to-operate plug-ins that can efficiently construct finite element analysis models, conveniently define processing parameters, and automatically perform mesh generation. Subsequently, the milling forces predicted by the established genetic algorithm optimized back-propagation and particle swarm optimization back-propagation models will be analytically compared with finite element analysis simulations and experiments; also the stress distribution and chip formations of finite element analysis and experiments will be comparatively investigated. Finally, the obtained results clearly indicate that these two back-propagation models built by artificial neural networks can well agree with finite element analysis simulations and experiments, but the particle swarm optimization back-propagation model is superior to the genetic algorithm optimized back-propagation model, which clearly demonstrate the particle swarm optimization back-propagation model has higher efficiencies and accuracies in predicting the average and transient cutting forces for different milling processes on aluminum alloy 7050.
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Kumar, Adarsh, Saurabh Jain, and Divakar Yadav. "A novel simulation-annealing enabled ranking and scaling statistical simulation constrained optimization algorithm for Internet-of-things (IoTs)." Smart and Sustainable Built Environment 9, no. 4 (March 6, 2020): 675–93. http://dx.doi.org/10.1108/sasbe-06-2019-0073.
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PurposeSimulation-based optimization is a decision-making tool for identifying an optimal design of a system. Here, optimal design means a smart system with sensing, computing and control capabilities with improved efficiency. As compared to testing the physical prototype, computer-based simulation provides much cheaper, faster and lesser time-and resource-consuming solutions. In this work, a comparative analysis of heuristic simulation optimization methods (genetic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed.Design/methodology/approachIn this work, a comparative analysis of heuristic simulation optimization methods (genertic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed. Further, a novel simulation annealing-based heuristic approach is proposed for critical infrastructure.FindingsA small scale network of 50–100 nodes shows that genetic simulation optimization with multi-criteria and multi-dimensional features performs better as compared to other simulation optimization approaches. Further, a minimum of 3.4 percent and maximum of 16.2 percent improvement is observed in faster route identification for small scale Internet-of-things (IoT) networks with simulation optimization constraints integrated model as compared to the traditional method.Originality/valueIn this work, simulation optimization techniques are applied for identifying optimized Quality of service (QoS) parameters for critical infrastructure which in turn helps in improving the network performance. In order to identify optimized parameters, Tabu search and ant-inspired heuristic optimization techniques are applied over QoS parameters. These optimized values are compared with every monitoring sensor point in the network. This comparative analysis helps in identifying underperforming and outperforming monitoring points. Further, QoS of these points can be improved by identifying their local optimum values which in turn increases the performance of overall network. In continuation, a simulation model of bus transport is taken for analysis. Bus transport system is a critical infrastructure for Dehradun. In this work, feasibility of electric recharging units alongside roads under different traffic conditions is checked using simulation. The simulation study is performed over five bus routes in a small scale IoT network.
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Shi, Yaochen, Hongyan Liu, Xuechen Zhang, Qinghua Li, and Xiaocheng Guo. "Wear Identification of Vibration Drilling Bit Based on Improved LMD and BP Neural Network." Mathematical Problems in Engineering 2020 (August 14, 2020): 1–9. http://dx.doi.org/10.1155/2020/2386721.
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In view of the low accuracy of single signal monitoring for the wear state of vibration drilling bit, a multisignal acquisition system for the wear state of ultrasonic axial vibration drilling bit is built to collect the drilling force, vibration, and acoustic emission signals under three different wear states. The drilling force, vibration and acoustic emission signals of the bit in the drilling process are processed by using wavelet decomposition technology, and the signals are extracted from the wear state of the bit, The wavelet energy coefficient with high state correlation is used as the feature parameter to identify the bit wear state. The feature parameter is trained by the combination of noise assisted LMD method and BP neural network. The experiment of single signal and multisignal fusion monitoring bit wear state is carried out, and the neural network structure is optimized according to the error. The results show that the accuracy of monitoring bit wear with a single signal of drilling force is 83.3%, the accuracy of monitoring bit wear with a single signal of vibration is 91.6%, the accuracy of monitoring bit wear with a single signal of acoustic emission is 91.6%, and the accuracy of monitoring bit wear with multisignal fusion is 95.8%; when the number of network layer is 4, the vibration is monitored with the fusion of force signal, acoustic emission signal, and vibration signal The accuracy of the state of drilling tool is up to 100%. The structure model of neural network is optimized reasonably to improve the recognition rate of bit wear in vibration drilling.
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Shayanfar, Elham, Paul M. Schonfeld, and J. Jason Wang. "Prioritizing Highway Development Projects Based on Market Access in Appalachia." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (April 25, 2019): 333–42. http://dx.doi.org/10.1177/0361198119835506.
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The objective of this study is to develop a method for prioritizing the implementation of transportation investment projects constrained by limited resources while dealing with interrelations among projects. This study considers transportation (travel time, vehicle operating, and safety costs) and economic impacts (accessibility to buyer–supplier market) of completing interrelated highway projects within an optimization framework. The goal is to determine the optimized sequence and schedule of projects to optimize a performance metric (consisting of transportation and economic impacts) subject to budget flow constraints. This problem can be formulated as a non-linear integer optimization problem that can be solved using heuristic search methods. The study proposes to integrate a user equilibrium traffic assignment model and the SHRP2 C11 market access analysis tool with a genetic algorithm (GA) to solve the optimization problem. The proposed methodology is applied to a large actual network located in central Appalachian region of the U.S., and numerical results are provided to showcase its real-world applicability. This study constitutes a useful framework for state planners and regional decision makers that can effectively guide the project prioritization process. Notably, prioritization with consideration of network effects and interrelations is feasible using the proposed framework. This can lead to well-informed decisions on the basis of both direct transportation and wider economic benefits while accounting for project interrelations.
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Caputo, Davide, Francesco Grimaccia, Marco Mussetta, and Riccardo E. Zich. "Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks." Applied Computational Intelligence and Soft Computing 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/523943.
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In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications.
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Kübeck, Ch, W. van Berk, and A. Bergmann. "Modelling raw water quality: development of a drinking water management tool." Water Science and Technology 59, no. 1 (January 1, 2009): 117–24. http://dx.doi.org/10.2166/wst.2009.766.
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Ensuring future drinking water supply requires a tough management of groundwater resources. However, recent practices of economic resource control often does not involve aspects of the hydrogeochemical and geohydraulical groundwater system. In respect of analysing the available quantity and quality of future raw water, an effective resource management requires a full understanding of the hydrogeochemical and geohydraulical processes within the aquifer. For example, the knowledge of raw water quality development within the time helps to work out strategies of water treatment as well as planning finance resources. On the other hand, the effectiveness of planed measurements reducing the infiltration of harmful substances such as nitrate can be checked and optimized by using hydrogeochemical modelling. Thus, within the framework of the InnoNet program funded by Federal Ministry of Economics and Technology, a network of research institutes and water suppliers work in close cooperation developing a planning and management tool particularly oriented on water management problems. The tool involves an innovative material flux model that calculates the hydrogeochemical processes under consideration of the dynamics in agricultural land use. The program integrated graphical data evaluation is aligned on the needs of water suppliers.
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Yu, Zhao, Yun Bai, Qian Fu, Yao Chen, and Baohua Mao. "An Estimation Model on Electricity Consumption of New Metro Stations." Journal of Advanced Transportation 2020 (January 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/3423659.
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Electricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consumption of a newly constructed metro station. The model considers some major factors influencing the electricity consumption of metro station in terms of both the interior design scheme of a station (e.g., layout of the station and allocation of facilities) and external factors (e.g., passenger volume, air temperature and relative humidity). A genetic algorithm with five-fold cross-validation is used to optimize the hyper-parameters of the SVR model in order to improve its accuracy in estimating the electricity consumption of a metro station (ECMS). With the optimized hyper-parameters, results from case studies on the Beijing Subway showed that the estimating accuracy of the proposed SVR model could reach up to 95% and the correlation coefficient was 0.89. It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations.
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DebRoy, T., A. De, H. K. D. H. Bhadeshia, V. D. Manvatkar, and A. Arora. "Tool durability maps for friction stir welding of an aluminium alloy." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2147 (July 25, 2012): 3552–70. http://dx.doi.org/10.1098/rspa.2012.0270.
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Friction stir welding is not used for hard alloys because of premature tool failure. A scheme is created that exploits the physical three-dimensional heat and mass flow models, and implements them into a fast calculation algorithm, which, when combined with damage accumulation models, enables the plotting of tool durability maps that define the domains of satisfactory tool life. It is shown that fatigue is an unlikely mechanism for tool failure, particularly for the welding of thin plates. Plate thickness, welding speed, tool rotational speed, shoulder, and pin diameters and pin length all affect the stresses and temperatures experienced by the tool. The large number of these variables makes the experimental determination of their effects on stresses and temperatures intractable and the use of a well-tested, efficient friction stir welding model a realistic undertaking. An artificial neural network that is trained and tested with results from a phenomenological model is used to generate tool durability maps that show the ratio of the shear strength of the tool material to the maximum shear stress on the tool pin for various combinations of welding variables. These maps show how the thicker plates and faster welding speeds adversely affect tool durability and how that can be optimized.
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Tan, Yang, Chang, and Zhao. "Prediction of the First Weighting from the Working Face Roof in a Coal Mine Based on a GA-BP Neural Network." Applied Sciences 9, no. 19 (October 3, 2019): 4159. http://dx.doi.org/10.3390/app9194159.
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The accidents caused by roof pressure seriously restrict the improvement of mines and threaten production safety. At present, most coal mine pressure forecasting methods still rely on expert experience and engineering analogies. Artificial neural network prediction technology has been widely used in coal mines. This new approach can predict the surface pressure on the roof, which is of great significance in coal mine production safety. In this paper, the mining pressure mechanism of coal seam roofs is summarized and studied, and 60 sets of initial pressure data from multiple working surfaces in the Datong mining area are collected for gray correlation analysis. Finally, 12 parameters are selected as the input parameters of the model. Suitable back propagation (BP) and GA(genetic algorithm)-BP initial roof pressure prediction models are established for the Datong mining area and trained with MATLAB programming. By comparing the training results, we found that the optimized GA-BP model has a larger determination coefficient, smaller error, and greater stability. The research shows that the prediction method based on the GA-BP neural network model is relatively reliable and has broad engineering application prospects as an auxiliary decision-making tool for coal mine production safety.
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Zhang, Degan, Changle Gong, Kaiwen Jiang, Xiaodan Zhang, and Ting Zhang. "A kind of new method of intelligent trust engineering metrics (ITEM) for application of mobile ad hoc network." Engineering Computations 37, no. 5 (December 19, 2019): 1617–43. http://dx.doi.org/10.1108/ec-12-2018-0579.
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Purpose This paper aims to put forward a kind of new method of intelligent trust engineering metrics for application of mobile ad hoc network (MANET). Design/methodology/approach The new method calculates the communication trust by using the number of data packets among the nodes of MANET, predicts intelligently the trust and calculates the comprehensive trust based on the historical trust; then calculates the energy trust based on the residual energy of the nodes of MANET, calculates the direct trust based on the communication trust and energy trust. The new method calculates the recommendation trust based on the recommendation reliability; adopts the adaptive weighting to calculate the integrated direct trust by considering the direct trust with recommendation trust. Findings Based on the integrated direct trust and the factor of trust propagation distance, the indirect trust among the nodes of MANET is calculated. The above process can be optimized based on the dynamic machine learning presented in this study. The advantage of the new method is its intelligent ability to discover malicious nodes. Originality/value The advantage of the new method is its intelligent ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and proceeds to protect the network. The authors have done the experiments based on the tool kits such as NS3, QualNet, OMNET++ and MATLAB. The experimental results show that this study’s approach can effectively avoid the attacks of malicious nodes, and more conformable to the actual engineering application of MANET.
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Tang, Yu. "Establish the Queue Length Function Model." Advanced Materials Research 962-965 (June 2014): 2691–94. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2691.
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A queue length function mapping model is built through the neural network, and can be optimized based on the white noise principle. Thus, a nonlinear mapping, which the input and output nodes are considered as independent variable and the dependent variable respectively, is obtained. After training the learning samples, which can be got from counting the data of the accident duration, traffic upstream, the passed coefficient, and the vehicles queue length, we can acquired the weights and thresholds, which are used to represent the mapping between the nodes. Also, through the optimization based on the white noise principle, the impact caused by the error can be reduced and a more accurate model is obtained as a predicting measuring tool of the queue length.
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Bahmani, Mojtaba, Mehdi Nejati, Amin GhasemiNejad, Fateme Nazari Robati, Mehrdad Lashkary, and Naeeme Amani Zarin. "A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption." Mathematical Problems in Engineering 2021 (February 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/6189329.
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In this paper, we develop a function of population, GDP, import, and export by applying a hybrid bat algorithm (BAT) and artificial neural network (ANN). We apply these methods to forecast oil consumption in Iran. For this purpose, an improved artificial neural network (ANN) structure, which is optimized by the BAT is proposed. The variables between 1980 and 2017 were used, partly for installing and testing the method. This method would be helpful in forecasting oil consumption and would provide a level playing field for checking the energy policy authority impacts on the structure of the energy sector in an economy such as Iran with high economic interventionism by the government. The result of the model shows that the findings are in close agreement with the observed data, and the performance of the method was excellent. We demonstrate that its efficiency could be a helpful and reliable tool for monitoring oil consumption.
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Khurshid, Burhan, and Roohie Naaz. "Technology - Dependent Optimization of FIR Filters based on Carry - Save Multiplier and 4:2 Compressor unit." Electronics ETF 20, no. 2 (July 14, 2017): 43. http://dx.doi.org/10.7251/els1620043k.
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This work presents an FPGA implementation of FIR filter based on 4:2 compressor and CSA multiplier unit. The hardware realizations presented in this pa per are based on the technology-dependent optimization of these individual units. The aim is to achieve an efficient mapping of these isolated units on Xilinx FPGAs. Conventional filter implementations consider only technology-independent optimizations and rely on Xilinx CAD tools to map the logic onto FPGA fabric. Very often this results in inefficient mapping. In this paper, we consider the traditional CSA-4:2 compressor based FIR filte rs and restructure these units to achieve improved integration levels. The technology optimized Boolean networks are then coded using instantiation based coding strategies. The Xilinx tool then uses its own optimization strategies to further optimize the networks and generate circuits with high logic densities and reduced depths. Experimental results indicate a significant improvement in performance over traditional realizations. An important property of technology-dependent optimizations is the simultaneous improvement in all the performance parameters. This is in contrast to the technology-independent optimizations where there is always an application driven trade-off between different performance parameters.
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Li, Xin, Ming Wei, Jia Hu, Yun Yuan, and Huifu Jiang. "An Agent-Based Model for Dispatching Real-Time Demand-Responsive Feeder Bus." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6925764.
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This research proposed a feeder bus dispatching tool that reduces rides’ effort to reach a feeder bus. The dispatching tool takes in real-time user specific request information and optimizes total cost accordingly (passenger access time cost and transit operation cost) by choosing the best pick-up locations and feeder buses’ routes. The pick-up locations are then transmitted back to passengers along with GPS guidance. The tool fits well with the Advanced Traveler Information Services (ATIS) which is one of the six high-priority dynamic mobility application bundles currently being promoted by the United State Department of Transportation. The problem is formulated into a Mixed Integer Programming (MIP) model. For small networks, out-of-the-shelf commercial solvers could be used for finding the optimal solution. For large networks, this research developed a GA-based metaheuristic solver which generates reasonably good solutions in a much shorter time. The proposed tool is evaluated on a real-world network in the vicinity of Jiandingpo metro station in Chongqing, China. The results demonstrated that the proposed ATIS tool reduces both buses operation cost and passenger walking distance. It is also able to significantly bring down computation time from more than 1 hour to about 1 min without sacrificing too much on solution optimality.
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Mukhtar, Muhammad Fahad, Muhammad Shiraz, Qaisar Shaheen, Kamran Ahsan, Rizwan Akhtar, and Wang Changda. "RBM: Region-Based Mobile Routing Protocol for Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021 (February 4, 2021): 1–11. http://dx.doi.org/10.1155/2021/6628226.
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Wireless sensor networks (WSNs) are employed for different applications for the reason of small-sized and low-cost sensor nodes. However, several challenges that include a low powered battery of the sensor nodes restrict their functionality. Therefore, saving energy in the routing process to extend network life is a serious concern while deploying applications on WSN. To this end, the key technology is clustering, which helps maximize scalability and network lifecycle. Base station (BS) collects data, aggregates it, and extracts the required information. To obtain the maximum outcome, the lifetime of the network is maximized by the use of different techniques and protocols. Data transmissions consume most of the network energy, and the transmissions over normal ranges require less energy as compared to transmissions over long ranges. Moreover, the nodes closer to the BS deplete their energy faster as compared to distant nodes because of traffic overload. The proposed protocol is aimed at reducing energy consumption and increasing the network lifetime. For this purpose, the network is divided into two regions: region 1 closer to the BS communicating directly, whereas region 2 farther away from the BS having routing nodes to communicate with the BS. Routing nodes do not take part in sensing function but will only move in region 2 collecting data and forwarding it to BS. MATLAB is used as the simulation tool for evaluation, and the results are compared with the existing optimized region-based efficient routing (AORED) and low-energy adaptive clustering hierarchical protocol (LEACH) techniques. The comparison showed that energy conservation and lifetime increased by 15%, and throughput is increased by more than 5% approximately.
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Tlouyamma, Joseph, and Mthulisi Velempini. "Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks." Wireless Personal Communications 119, no. 4 (April 26, 2021): 3161–78. http://dx.doi.org/10.1007/s11277-021-08392-5.
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AbstractA major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm. This study focuses on designing a channel selection algorithm to efficiently utilize the spectrum. Channels are ordered and grouped to allow faster discovery of channel access opportunities. The ordering is based on descending order of channel’s idling probabilities. Grouping of channels ensured that channels are sensed simultaneously. These two techniques greatly reduce delays and maximized throughput of SU. Hence, Extended Generalized Predictive Channel Selection Algorithm, a proposed scheme has significantly performed better than its counterpart (Generalized Predictive Channel Selection Algorithm). Matlab simulation tool was used to simulate and plot the results of the proposed channel selection algorithm.
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Kausik, Ravinath, Augustin Prado, Vasileios-Marios Gkortsas, Lalitha Venkataramanan, Harish Datir, and Yngve Bolstad Johansen. "Dual Neural Network Architecture for Determining Permeability and Associated Uncertainty." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 62, no. 1 (February 1, 2021): 122–34. http://dx.doi.org/10.30632/pjv62n1-2021a8.
Full textAbstract:
The computation of permeability is vital for reservoir characterization because it is a key parameter in the reservoir models used for estimating and optimizing hydrocarbon production. Permeability is routinely predicted as a correlation from near-wellbore formation properties measured through wireline logs. Several such correlations, namely Schlumberger-Doll Research (SDR) permeability and Timur-Coates permeability models using nuclear magnetic resonance (NMR) measurements, K-lambda using mineralogy, and other variants, have often been used, with moderate success. In addition to permeability, the determination of the uncertainties, both epistemic (model) and aleatoric (data), are important for interpreting variations in the predictions of the reservoir models. In this paper, we demonstrate a novel dual deep neural network framework encompassing a Bayesian neural network (BNN) and an artificial neural network (ANN) for determining accurate permeability values along with associated uncertainties. Deep-learning techniques have been shown to be effective for regression problems but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. This is especially vital for petrophysical answer products because these algorithms need the ability to flag data from new geological formations that the model was not trained on as “out of distribution” and assign them higher uncertainty. Additionally, the model outputs need sensitivity to heteroscedastic aleatoric noise in the feature space arising due to tool and geological origins. Reducing these uncertainties is key to designing intelligent logging tools and applications, such as automated log interpretation. In this paper, we train a BNN with NMR and mineralogy data to determine permeability with associated epistemic uncertainty, obtained by determining the posterior weight distributions of the network by using variational inference. This provides us the ability to differentiate in- and out-of-distribution predictions, thereby identifying the suitability of the trained models for application in new geological formations. The errors in the prediction of the BNN are fed into a second ANN trained to correlate the predicted uncertainty to the error of the first BNN. Both networks are trained simultaneously and therefore optimized together to estimate permeability and associated uncertainty. The machine-learning permeability model is trained on a “ground-truth” core database and demonstrates considerable improvement over traditional SDR and Timur-Coates permeability models on wells from the Ivar Aasen Field. We also demonstrate the value of information (VOI) of different logging measurements by replacing the logs with their median values from nearby wells and studying the increase in the mean square errors.