27,024 results
Search Results
2. Angle of the Perforation Line to Optimize Partitioning Efficiency on Toilet Papers.
- Author
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Vieira, Joana Costa, Vieira, André Costa, Ribeiro, Marcelo L., Fiadeiro, Paulo T., and Costa, Ana Paula
- Subjects
- *
TOILET paper , *GENETIC algorithms , *ANGLES - Abstract
Currently, tissue product producers try to meet consumers' requirements to retain their loyalty. In perforated products, such as toilet paper, these requirements involve the paper being portioned along the perforation line and not outside of it. Thus, it becomes necessary to enhance the behavior of the perforation line in perforated tissue papers. The current study aimed to verify if the perforation line for 0° (the solution found in commercial perforated products) is the best solution to maximize the perforation efficiency. A finite element (FE) simulation was used to validate the experimental data, where the deviations from the experiments were 5.2% for the case with a 4 mm perforation length and 8.8% for a perforation of 2 mm, and optimize the perforation efficiency using the genetic algorithm while considering two different cases. In the first case, the blank distance and the perforation line angle were varied, with the best configuration being achieved with a blank distance of 0.1 mm and an inclination angle of 0.56°. For the second case, the blank distance was fixed to 1.0 mm and the only variable to be optimized was the inclination angle of the perforation line. It was found that the best angle inclination was 0.67°. In both cases, it was verified that a slight inclination in the perforation line will favor partitioning and therefore the perforation efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. COLLABORATIVE PRODUCTION SCHEDULING WITH MULTI-ENTERPRISE IDLE CAPACITY SHARING.
- Author
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Liao, Y. G., Zhang, H., Ren, N., and Wang, T. Y.
- Subjects
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DIGITAL twins , *ELECTRONIC paper , *MANUFACTURING processes , *DIGITAL computer simulation , *GENETIC algorithms - Abstract
This paper proposes a digital twin enhanced approach for optimizing collaborative production scheduling in multi-enterprise manufacturing systems. A multi-objective model is developed incorporating practical constraints such as limited time windows, different production capacities, and transportation considerations. To solve the model, an Improved Non-dominated Sorting Genetic Algorithm (INSGA-II) is designed with specialized operators and strategies. The digital twin simulation is enriched with Multi-objective Decision Making based on Interaction Structures (MDIS) to obtain higher-quality solutions. Experiments demonstrate that the MDIS digital twin approach reduces manufacturing lead times and costs while improving utilization and quality compared to standard methods. This research provides an effective optimization framework to leverage cloud manufacturing resources across organizations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Modelling and Simulation of Intelligent English Paper Generating Based on SSA-GA.
- Author
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Han, Limin, Gao, Hong, and Zhai, Rongjie
- Subjects
GENETIC algorithms ,SEARCH algorithms ,MATHEMATICAL models ,SIMULATION methods & models - Abstract
To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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5. Editorial: Special Issue on Selected Papers from the 33rd Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2021).
- Author
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Tsihrintzis, George A., Virvou, Maria, and Hatzilygeroudis, Ioannis
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ARTIFICIAL intelligence ,DEEP learning ,CONFERENCES & conventions ,GENETIC algorithms ,PROGRAMMING languages ,GENE regulatory networks - Published
- 2023
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6. Automatic Question Paper Pattern Generation using GA Approach.
- Author
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PAUL, DIMPLE VALAYIL
- Subjects
CONSTRAINED optimization ,GENETIC algorithms ,GENERATIONS ,QUESTIONING - Abstract
This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation system. By using Genetic Algorithm (GA) and educational taxonomies, this paper analyses the initial population generation, does chromosome encoding, applies genetic manipulations and experimentally proves that the generated question paper templates are best suited for the dynamic examination paper generation system. This new approach outperforms traditional algorithms that randomly generate examination papers in terms of their topic coverage, learning domains and marks distribution. [ABSTRACT FROM AUTHOR]
- Published
- 2020
7. Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm
- Author
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Khanduja, Rajiv and Tewari, P.C.
- Published
- 2013
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8. Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm
- Author
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Khanduja, Rajiv, Tewari, P.C., and Chauhan, R.S.
- Published
- 2011
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9. Integrating process optimization with energy-efficiency scheduling to save energy for paper mills.
- Author
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Zeng, Zhiqiang, Hong, Mengna, Li, Jigeng, Man, Yi, Liu, Huanbin, Li, Zeeman, and Zhang, Huanhuan
- Subjects
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ENERGY consumption , *PAPER mills , *DRYING , *ENERGY conservation , *GENETIC algorithms - Abstract
With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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10. A Review Paper on Different Application of Genetic Algorithm for Mobile Ad-hoc Network (MANET).
- Author
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Jinfa Shi, Habib, Misbah, and Hai Yan
- Subjects
GENETIC algorithms ,ROUTING algorithms ,SEARCH algorithms ,NATURAL selection ,RADIO waves ,AD hoc computer networks - Abstract
A Genetic algorithm is a search algorithm depends on the methodology of natural selection and natural genetics. A Mobile Ad hoc network (MANET) is a type of wireless nodes (Devices) which are free to move anywhere in the network without any constraints. The nodes which are in range can communicate each other through radio waves and those who are not in range use any routing algorithm for communication. In this review paper, we focus on the problems of MANET that has been solved by applying GA for it and highlights the characteristic and challenges of MANET in the literature. More specifically, we present the summary of review papers and basic solutions that use and in the last, we present some future direction. Consequently, we concluded that modification in Fitness function (Evaluation function) according to the problem is the base of Genetic algorithm and variation in algorithm parameters can give solutions in a reasonable time. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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11. System closure in pulp and paper mills: network analysis by genetic algorithm
- Author
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Shafiei, S., Domenech, S., Koteles, R., and Paris, J.
- Subjects
- *
PAPER industry , *PRODUCTION engineering , *GENETIC algorithms , *LINEAR programming - Abstract
An approach based on the characterisation of individual operations by water demands and sources has been utilised to study an operating integrated pulp and paper mill. The search for novel whitewater network configurations as a function of specified process constraints and objective functions was done by means of a genetic algorithm coupled with linear programming. Potential solutions are derived from an initial superstructure representation which embodies all possible source and demand connections by an evolutive process akin to natural selection and adaptation. Various objective functions were investigated. Innovative strategies have been identified and are being considered for implementation. [Copyright &y& Elsevier]
- Published
- 2004
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12. Optimal Feature Selection through Search-Based Optimizer in Cross Project.
- Author
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Faiz, Rizwan bin, Shaheen, Saman, Sharaf, Mohamed, and Rauf, Hafiz Tayyab
- Subjects
FEATURE selection ,RANDOM forest algorithms ,GENETIC algorithms ,INDEPENDENT variables ,FILTER paper - Abstract
Cross project defect prediction (CPDP) is a key method for estimating defect-prone modules of software products. CPDP is a tempting approach since it provides information about predicted defects for those projects in which data are insufficient. Recent studies specifically include instructions on how to pick training data from large datasets using feature selection (FS) process which contributes the most in the end results. The classifier helps classify the picked-up dataset in specified classes in order to predict the defective and non-defective classes. The aim of our research is to select the optimal set of features from multi-class data through a search-based optimizer for CPDP. We used the explanatory research type and quantitative approach for our experimentation. We have F1 measure as our dependent variable while as independent variables we have KNN filter, ANN filter, random forest ensemble (RFE) model, genetic algorithm (GA), and classifiers as manipulative independent variables. Our experiment follows 1 factor 1 treatment (1F1T) for RQ1 whereas for RQ2, RQ3, and RQ4, there are 1 factor 2 treatments (1F2T) design. We first carried out the explanatory data analysis (EDA) to know the nature of our dataset. Then we pre-processed our data by removing and solving the issues identified. During data preprocessing, we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compared to NN filter with base paper (all, ckloc, IG). Then we applied search-based optimizer i.e., random forest ensemble (RFE) to get the best features set for a software prediction model and we get 30% to 50% significant results compared with genetic instance selection (GIS). Then we used a classifier to predict defects for CPDP. We compare the results of the classifier with base paper classifier using F1-measure and we get almost 35% more than base paper. We validate the experiment using Wilcoxon and Cohen's d test. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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13. Genetic algorithm modeling of European Union firms' competitive advantage
- Author
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Dias, Alexandre Teixeira, Martins, Henrique Cordeiro, Santos, Valdeci Ferreira, Verga Matos, Pedro, and Morais, Greiciele Macedo
- Published
- 2024
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14. Multiple interaction strategies for mobile robots based on improved dynamic window approach in unknown dynamic environments
- Author
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He, Li, Zhang, Shuai, Zhang, Heng, and Yuan, Liang
- Published
- 2024
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15. Energy optimization in a pulp and paper mill cogeneration facility
- Author
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Marshman, D.J., Chmelyk, T., Sidhu, M.S., Gopaluni, R.B., and Dumont, G.A.
- Subjects
- *
PAPER mills , *PULP mills , *ENERGY management , *ELECTRIC power production , *PROFIT margins , *HEURISTIC , *DYNAMIC programming , *ENERGY development , *GENETIC algorithms - Abstract
Abstract: Alarmingly low pulp prices in early 2009 left pulp and paper mills across North America desperate for any way to improve thin profit margins. One solution that continues to gain popularity among the industry is improved energy management systems for cogeneration systems, which use steam for two purposes – to provide heat for the pulping process and to generate electricity for sale to regional providers. This paper presents an energy optimization algorithm for use in a pulp and paper mill cogeneration system. The algorithm is applicable to a number of popular mill configurations, power sale contracts, and fuel purchasing scenarios. The method is also extended to address weather-dependent cooling limitations encountered by a mill cogeneration facility, in which case an iterative solution is proposed in order to maintain convexity of the optimization problem. Results are presented in the form of three case studies. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
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16. Assessing university students' perception of academic quality using machine learning
- Author
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Guillén Perales, Alberto, Liébana-Cabanillas, Francisco, Sánchez-Fernández, Juan, and Herrera, Luis Javier
- Published
- 2024
- Full Text
- View/download PDF
17. Quality evaluation of water disclosure of Chinese papermaking enterprises based on accelerated genetic algorithm.
- Author
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He, Chaoran, Shen, Juqin, Xu, Jiawei, Sun, Fuhua, and Wang, Bing
- Subjects
WATER quality ,ENVIRONMENTAL reporting ,GENETIC algorithms ,WATER management ,DISCLOSURE ,PAPERMAKING - Abstract
As the carrier of enterprise water resources management disclosure, water information disclosure is a means of expression of enterprises' environmental responsibility. First, a corporate water information disclosure quality evaluation index system and evaluation method are established, and with the help of the projection tracing method of accelerated genetic algorithm, 27 paper companies in China are selected as a sample and the disclosure quality level is analyzed empirically. Then, the analysis is carried out in terms of three changes in vertical trends, horizontal trends and changes in laws, regulations and policies, and the results show that Chinese paper and paper product enterprises have low quality of water information disclosure, weak disclosure content and low voluntary disclosure. Finally, feasible suggestions are made based on the evaluation of disclosure issues. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
18. Research on Photovoltaic Energy Storage System and Supply-Side Power Dispatch Model in Paper Mill.
- Author
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Ying Gao, Jigeng Li, Mengna Hong, and Yi Man
- Subjects
PHOTOVOLTAIC power systems ,ENERGY storage ,PAPER mills ,GENETIC algorithms ,OPERATING costs - Abstract
With the increased awareness of environmental protection and the establishment of the spot market of electric power, renewable energy plays a more and more important role in energy structure adjustment. But the intermittence and instability of renewable energy often result in energy waste. Micro-grid energy storage can effectively improve the utilization rate, economy and reliability of renewable energy. To ensure the economical operation of the micro-grid system and improve the load curve, a two-stage optimal dispatch model was established based on non-dominated sorting genetic algorithm (NSGA-II) and deep Q-value reinforcement learning algorithm (DQN). In the first stage, to get the appropriate battery capacity, the minimum comprehensive operation cost and the minimum capacity fading of battery in the cycle life were selected as the optimization objectives. The optimal solution was calculated by using the improved non-dominated sorting genetic algorithm (NSGA-II). In the second stage, the DQN Algorithm was used to propose a scheduling strategy. Compared with the GA method, the DQN method was able to reduce the operating cost from 0.5829% to 1.2294%. The results showed that the method was feasible and effective. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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- View/download PDF
19. Research on Algorithms based on Web Self-adaptive Study and Intelligent Test Paper Construction and their Applications
- Author
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Ying Liu, Lihua Huang, Xuming Han, Limin Wang, Juan Sang, and Gu Zhenshan
- Subjects
algorithms of intelligent test paper construction ,Large Number Law ,Item pool ,Bernoulli's principle ,business.industry ,Computer science ,Self adaptive ,Artificial intelligence ,Physics and Astronomy(all) ,business ,Self-adaptive study ,genetic algorithms ,Test (assessment) - Abstract
A novel system based on Bernoulli Theorem of Large Number Law and the genetic algorithms was designed and realized in this paper, which had many advantages such as self-adaptive study for difficulty coefficient of item pool and intelligent test paper construction etc. At present, the system has applied in the examination of paperless computer tests of Jinlin university of finance and economics and some satisfactory results have been also obtained.
- Published
- 2012
20. Research on Algorithms based on Web Self-adaptive Study and Intelligent Test Paper Construction and their Applications.
- Author
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Liu, Ying, Wang, Limin, Huang, Lihua, Han, Xuming, Gu, Zhenshan, and Sang, Juan
- Subjects
WEB services ,ADAPTIVE computing systems ,INTELLIGENT agents ,BERNOULLI numbers ,LAW of large numbers ,GENETIC algorithms ,TEST design - Abstract
Abstract: A novel system based on Bernoulli Theorem of Large Number Law and the genetic algorithms was designed and realized in this paper, which had many advantages such as self-adaptive study for difficulty coefficient of item pool and intelligent test paper construction etc. At present, the system has applied in the examination of paperless computer tests of Jinlin university of finance and economics and some satisfactory results have been also obtained. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
21. Test Paper Generating Method Based on Genetic Algorithm.
- Author
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Yan, Li, Shuhong, Li, and Xiurong, Li
- Subjects
CONTINUING education ,GENETIC algorithms ,PARAMETER estimation ,COMPARATIVE studies ,EDUCATIONAL technology ,COMBINATORIAL optimization - Abstract
Abstract: The continuation education is very important for people who have left school to work to increase their competence and skills. To avoid the disadvantages of the common test paper generating methods, genetic algorithm is used to generate the test paper automatically. The concrete design process of test paper generating based on genetic algorithm is discussed in this paper, and some corresponding parameters setting have been compared and defined. The application results demonstrated that the genetic algorithm was an effective tool in the test paper generating. [Copyright &y& Elsevier]
- Published
- 2012
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22. Reasearch on Shared Intelligent Test Paper Generating Algorithm Based on Multi Branches Tree.
- Author
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Zhang, Yajuan, Ye, Maogong, Li, Hongbao, and Fu, Hui
- Subjects
GENETIC algorithms ,ARTIFICIAL intelligence ,MATHEMATICAL optimization ,TREE graphs ,DIFFERENTIAL evolution ,INFORMATION technology - Abstract
Abstract: The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm. [Copyright &y& Elsevier]
- Published
- 2011
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23. Comparative Corrigendum note on papers “Fuzzy adaptive GA for multi-objective assembly line balancing” continued “Modified GA for different types of assembly line balancing with fuzzy processing times”: differences and...
- Author
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Tarimoradi, Mosahar, Alavidoost, M.H., and Zarandi, M.H. Fazel
- Subjects
FUZZY logic ,GENETIC algorithms ,ASSEMBLY line balancing ,COMPARATIVE studies ,PROBLEM solving - Abstract
In the very few recently published paper by Alavidoost et al. [1] , they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2] , as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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24. CROSS-BORDER E-COMMERCE LOGISTICS OPTIMIZATION ALGORITHM FOR COLLABORATION BETWEEN THE INTERNET OF THINGS AND LOGISTICS.
- Author
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SHILING PAN and JUAN CHENG
- Subjects
OPTIMIZATION algorithms ,CROSS-border e-commerce ,INTERNET of things ,GENETIC algorithms ,LOGISTICS ,HOME computer networks - Abstract
This paper proposes the shortest path optimization algorithm for domestic and overseas e-commerce logistics based on a bilateral search method. This paper uses the logistics distribution route optimization algorithm based on the shortest path to set the collaborative parameters. Then, it builds an adaptive optimization model for the grid planning of domestic and overseas e-commerce logistics path. The route is optimized. Then, the PSO and genetic algorithm are integrated to establish the logistics path planning model of domestic and overseas e-commerce. The superiority of the proposed route optimization algorithm in domestic and overseas e-commerce logistics distribution is verified through simulation experiments. This algorithm has high spatial positioning efficiency and high transportation efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. A technical note on the paper “hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems”.
- Author
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Derhami, Shahab and Smith, Alice E.
- Subjects
GENETIC algorithms ,FUZZY systems ,RULE-based programming ,DIMENSIONAL analysis ,PROBLEM solving ,INTEGER programming - Abstract
This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1] . They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
26. Research on Digital Steganography and Image Synthesis Model Based on Improved Wavelet Neural Network.
- Author
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Li, Xujie, Yao, Rujing, and Lee, Jonghan
- Subjects
DIGITAL images ,CRYPTOGRAPHY ,FEEDFORWARD neural networks ,ARTIFICIAL neural networks ,ELECTRONIC paper ,LINEAR network coding ,GENETIC algorithms - Abstract
Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
27. Determination of moisture diffusion coefficient in transformer paper using thermogravimetric analysis
- Author
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García, Diego F., García, Belén, Burgos, Juan Carlos, and García-Hernando, Néstor
- Subjects
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DIFFUSION , *THERMOGRAVIMETRY , *PAPER in electrical insulation , *GENETIC algorithms , *MOISTURE measurement , *TEMPERATURE - Abstract
Abstract: This paper presents the methodology used and the results obtained in the determination of moisture diffusion coefficient of non-impregnated transformer insulating paper. In order to establish the diffusion coefficient, drying curves of paper samples were obtained by means of thermogravimetric experiments. The diffusion coefficient parameters were found by applying an optimization process based on genetic algorithms. The error function between measured and simulated curves was determined, and the parameters achieving the best correspondence between measured and estimated values were obtained. As a result, a new equation for the diffusion coefficient of non-impregnated insulating paper is proposed, depending on average moisture concentration, temperature and insulation thickness. The proposed coefficient was validated through experimental cases finding a good agreement between the experimental drying curves and those obtained by simulation using the diffusion coefficient. The proposed diffusion coefficient can be used for the determination of the time required to dry power transformers in factory. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
28. The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm.
- Author
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Wei, Zhiguo, Wu, Jun, Li, Zhe, Cheng, Shangfang, Yan, Xiaojiang, and Wang, Shunsen
- Subjects
OPTIMIZATION algorithms ,MATHEMATICAL optimization ,PARTICLE swarm optimization ,NAVAL architecture ,GENETIC algorithms ,ELBOW - Abstract
The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships' single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper's algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution's efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Artificial intelligence applied to investment in variable income through the MACD (moving average convergence/divergence) indicator
- Author
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Agudelo Aguirre, Alberto Antonio, Duque Méndez, Néstor Darío, and Rojas Medina, Ricardo Alfredo
- Published
- 2021
- Full Text
- View/download PDF
30. Stealth Unmanned Aerial Vehicle Penetration Efficiency Optimization Based on Radar Detection Probability Model.
- Author
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Yuan, Chengen, Ma, Dongli, Jia, Yuhong, and Zhang, Liang
- Subjects
OPTIMIZATION algorithms ,LIFE cycles (Biology) ,GENETIC algorithms ,RADAR ,PROBABILITY theory - Abstract
Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. Downlink Scheduling via Genetic Algorithms for Multiuser Single-Carrier and Multicarrier MIMO Systems With Dirty Paper Coding.
- Author
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Elliott, Robert C. and Krzymieñ, Witold A.
- Subjects
- *
MIMO systems , *BIT rate , *WIRELESS communications , *COMPUTER input-output equipment , *FREQUENCY-deviation meters , *BROADBAND communication systems , *QUALITY of service , *GENETIC algorithms , *COMPUTER scheduling , *COMPUTATIONAL complexity , *ORTHOGONAL frequency division multiplexing - Abstract
Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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32. Multi-Robot Cooperative Multi-Area Coverage Based on Circular Coding Genetic Algorithm.
- Author
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Xin, Bin, Wang, Heng, and Li, Ming
- Subjects
GENETIC algorithms ,GENETIC code ,ROBOT design & construction ,LINEAR network coding ,PARTICIPATORY design ,ROBOTS - Abstract
This paper studies the cooperative multi-area coverage problem with obstacles, which requires a group of robots to cover an area while avoiding collisions. This problem is very common in scenarios such as garbage removal, mine clearance, and regional information collection. The currently proposed algorithms usually have the problem of high redundancy and weak scene scalability. This paper designs a cooperative area coverage algorithm for multiple robots. First, a set of rules is proposed to divide the area to be covered into several small areas. Then, a genetic algorithm based on circular coding is designed to allocate these divided areas to several robots. Finally, the coverage path of the robot is designed using the zigzag method, so that the robot can cover the area allocated to it. Through computational experiments, it has been verified that this algorithm has efficiency advantages over state-of-the-art algorithms in certain scenarios and has scalability for different scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Evolutional Based Optimization Analysis for Three-element Control System.
- Author
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Chew, I. M., Juwono, Filbert H., and Wong, W. K.
- Subjects
OPTIMIZATION algorithms ,GRAPHICAL user interfaces ,PARTICLE swarm optimization ,PID controllers ,GENETIC algorithms - Abstract
This paper presents a multi-objective optimization analysis to improve the controller tuning of three-element control loop for the best fit to both its servo and regulatory control objectives during the process operations. The existing Proportional-Integral-Derivative (PID) controller tuning for the three-element control loop is challenging because the best setting of each controller is obtained during the concurrent analysis, but all controller settings affect the control performance of other control loops and the output responses. Furthermore, this paper highlights the determination of Upper Limit (UL) and Lower Limit (LL) bounds by using the necessity criterion of Routh-Hurwitz stability analysis. The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used as the optimization algorithms to improve the control performances. Both optimization analysis are operated by using a developed Graphical User Interface (GUI) via MATLAB software. At the same time, the optimized PID controller settings are applied to the steam boiler drum function of the LOOP-PRO simulator. Both GA and PSO outperform the manual tuning for the three-element loop. Among them, GA performs better than PSO even though both methods are capable of suggesting highly satisfactory performances. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Vehicle direct yaw moment control system based on the improved linear quadratic regulator
- Author
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Xie, Xianyi, Jin, Lisheng, Baicang, Guo, and Shi, Jian
- Published
- 2021
- Full Text
- View/download PDF
35. The Cyclic Performance and Macro-Simplified Analytical Model of Internal Joints in RC-Assembled Frame Structures Connected by Unbonded Prestressed Strands and Mortise-Tenon Based on Numerical Studies.
- Author
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Wang, Junwei, Zhang, Wenxue, and Zhang, Cheng
- Subjects
STRUCTURAL frames ,STRESS-strain curves ,REINFORCED concrete testing ,FAILURE mode & effects analysis ,GENETIC algorithms ,CURVES ,NUMERICAL analysis - Abstract
This paper introduces a novel type of connection that integrates unbonded prestressed strands (UPS) and mortise-tenon in an assembly frame structure (UPS-MTF). First, the damage process and failure modes of the joints under reciprocating horizontal loads were systematically analyzed using refined numerical models. The recommended values of the design parameters of the joints were derived from the parametric analysis results. Refined numerical modeling results reveal the diagonal compression strut mechanism within the core region of the joint. The diagonal compression struts model assists in establishing the theoretical calculation formula for the skeleton curve of shear stress–strain in the core region. Second, a genetic algorithm (GA) parameter was identified for the restoring force model of the core region to determine the parameters of the hysteresis rules. Finally, a macro-simplified analytical model of the joint was created based on the restoring force model of the core region, and parameter analysis was conducted to verify the applicability of this macro-simplified analytical model. The research results prove that the damaged form of the joint proposed in this paper originates from the shear and relative slip damage between the components in the core region. The axial compression ratio significantly affects the hysteretic performance of the joints, and the upper and lower limit values were identified for the axial compression ratio of the joints. The area and initial effective stress of the UPS exert a minimal effect on the hysteretic performance of the joint. Based on the method proposed in this paper for determining the restoring force model in the core region of the joints, the hysteresis curves obtained from the macro-simplified analytical model closely match the refined numerical analysis model results. This correspondence verifies the applicability of the macro-simplified analytical model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. A implementation of an automatic examination paper generation system
- Author
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Cen, Guang, Dong, Yuxiao, Gao, Wanlin, Yu, Lina, See, Simon, Wang, Qing, Yang, Ying, and Jiang, Hongbiao
- Subjects
- *
ENGINEERING design , *JAVASCRIPT programming language , *RELIABILITY in engineering , *USER-centered system design , *GENETIC algorithms , *JACKSON structured programming , *ARCHITECTURAL design - Abstract
Abstract: This article uses lightweight J2EE tools based on B/S architecture to design an auto-generated paper management system. This system includes several modules, namely: user management, subject management, classification management, question entry, question management, paper generation, and paper management. The design process performs the analysis and composes the examination paper using an efficient algorithm with a high possibility of success. With this algorithm, the user needs to identify the subject, the question type and the difficulty level. From this input, the examination paper will be generated automatically. On the basis of a study of the JSP and Microsoft Word interface, we find that the system combines with Word very well. Therefore the editing of questions and answers is performed using Word and the final paper may be stored as “.doc” files. The system has characteristics such as easy operation, a good interface, good usability, high security, high stability, and reliability. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
37. A Survey Paper in Transportation Logistics based on Artificial Intelligence.
- Author
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Emam, Osama, Younis Haggag, Riham Mohamed, and Mohamed, Nanees Nabil
- Subjects
ARTIFICIAL intelligence ,SWARM intelligence ,ARTIFICIAL neural networks ,FUZZY logic ,GENETIC algorithms ,FUZZY sets - Abstract
In the recent era, Transportation considers the most powerful component of the business logistics system. Likewise, there is an interdependent relationship between the transportation and logistics systems. This paper aims to make a comparative study of logistics transportation problems based on intelligence algorithms. The researchers surveyed the previous studies conducted in the Artificial Intelligent field to solve complex problems. In this research study, the authors focused on techniques that are mostly applied in transportation and logistics systems, especially, Artificial Neural Network, Genetic Algorithm, and Fuzzy Logic models. Also, a proposed model and algorithm was done to obtain customers' and organizations' satisfaction. Artificial Neural Network uses as a decision tool that combines the system stat sets and the operation state-dependent sets. As well, the genetic algorithm combines the best parameters as a method to finds the best evaluation solutions. And fuzzy logic uses a fuzzy set to help decision-makers in making the best decisions in multiple fields. Finally, authors recommended to work in two new areas which are FGA, NFGA Algorithms to solve complex and multimodal problems that faces transportation logistics sector. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
38. Application of Improved SROM Based on RBF Neural Network Model in EMC Worst Case Estimation.
- Author
-
Bing Hu, Yingxin Wang, Shenghang Huo, and Jinjun Bai
- Subjects
ARTIFICIAL neural networks ,RADIAL basis functions ,REDUCED-order models ,DESIGN protection ,GENETIC algorithms - Abstract
The Stochastic Reduced-Order Models (SROMs) are a non-embedded uncertainty analysis method that has the advantages of high computational efficiency, easy implementation, and no dimensional disasters. Recently, it has been widely used in the field of EMC simulation. In the process of optimizing electromagnetic protection design, the worst-case estimation value is an extremely important uncertainty quantification simulation result. However, the SROMs have a large error in providing this result, which limits its application in the field of EMC simulation prediction. An improved SROM based on the Radial Basis Function (RBF) neural network algorithm is proposed in this paper, which improves the fitness function in the genetic algorithm center clustering process and constructs an RBF neural network model to obtain accurate worst-case estimation results. The accuracy improvement effect of the algorithm proposed in this paper in worst-case estimation is quantitatively verified by using a parallel cable crosstalk prediction example from published literature. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling Problem.
- Author
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Sun, Xiaohan, Ren, Yuan, and Yu, Linghui
- Subjects
REMOTE sensing ,BIOLOGICAL evolution ,GLOBAL optimization ,GENETIC algorithms ,QUANTUM operators - Abstract
The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme. Firstly, we comprehensively considered the time-dependent characteristics of agile remote sensing satellites, attitude maneuverability, energy balance, and data storage constraints and established a satellite scheduling model that integrates multiple constraints. Then, quantum register operators, adaptive evolution operations, and adaptive mutation transfer operations were introduced to ensure global optimization while reducing time consumption. Finally, this paper demonstrated, through computational experiments, that the MAS-HOQGA exhibits high computational efficiency and excellent global optimization ability in the scheduling process of agile remote sensing satellites for large-scale tasks, while effectively avoiding the problem that the traditional QGA has, namely low solution efficiency and the tendency to easily fall into local optima. This method can be considered for application to the engineering practice of agile remote sensing satellite scheduling for large-scale tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Integrated Optimization of Route and Frequency for Rail Transit Feeder Buses under the Influence of Shared Motorcycles.
- Author
-
Cai, Jing, Li, Zhuoqi, and Long, Sihui
- Subjects
BUS stops ,PUBLIC transit ,BUS travel ,TRAVEL costs ,GENETIC algorithms ,SUBWAY stations - Abstract
In this paper, we develop a multi-objective integrated optimization method for feeder buses of rail transit based on realistic considerations. We propose a bus stop selection method that considers the influence of shared motorcycles, which can score the importance of alternative bus stops and select those with the highest scores as objectives. The objective of the model in this paper is to minimize both the travel costs of passengers and the operating costs of the bus company. This is achieved by optimizing feeder bus routes, the frequency of departures, and interchange discounts to enhance the connectivity between feeder buses and rail transit. In addition, to ensure the feasibility of generated routes in the real road network, a genetic algorithm encoded with priority is used to solve this model. We use the Xingyao Road subway station in Kunming as an example, and the results show that the optimization method is effective. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. A new proposed moisture diffusion coefficient for transformer paper
- Author
-
García, Diego F.
- Subjects
- *
MOISTURE , *DIFFUSION , *COEFFICIENTS (Statistics) , *THERMAL insulation , *ELECTRIC transformers , *THERMOGRAVIMETRY , *GENETIC algorithms , *MATHEMATICAL models - Abstract
Abstract: In a previous work, a moisture diffusion coefficient for non-impregnated transformer insulation paper was proposed, which is useful for analysing the moisture dynamics in electrical power transformer insulation during its construction stage. This diffusion coefficient was determined using a set of experimental drying curves obtained in a thermogravimetric analyzer and an optimization process that employs genetic algorithms. The expression for the moisture diffusion coefficient proposed in that previous work is dependent on the temperature, insulation thickness and average moisture concentration. The use of average moisture concentration is not common in diffusion models. In this paper, a new expression for the moisture diffusion coefficient is proposed in which the dependence on average moisture has been substituted by a dependence on the local concentration. This new expression was obtained that is more suitable for diffusion models. To obtain that expression, the same experimental data used in the previous study have been used, but the genetic algorithm’s optimization process was modified. In this paper, a description of the applied procedure is detailed, and the new proposed expression is presented. Finally, a comparison between both equations, the one dependent on average moisture and the one dependent on the local moisture, is performed using a drying diffusion model implemented in a finite element commercial software package. [Copyright &y& Elsevier]
- Published
- 2013
- Full Text
- View/download PDF
42. Behavior prediction of washing system in a paper industry using GA and fuzzy lambda–tau technique
- Author
-
Sharma, S.P., Kumar, Dinesh, and Kumar, Ajay
- Subjects
- *
GENETIC algorithms , *FUZZY systems , *MANUFACTURED products , *MATHEMATICAL models , *NUMERICAL calculations , *FUZZY numbers - Abstract
Abstract: Availability analysis has been an important issue in the design field of any Industrial system as the system structure has become more complicated. Also, the system availability is affected by many factors such as design, manufacturing, installation, etc., and so it may be extremely difficult to model, analyze and predict the failure behavior of the system. The purpose of this paper is to develop a new approach for computing various performance measures, namely reliability, availability, MTBF (mean time between failures), ENOF (expected number of failures), failure rate and repair time, for any industrial system. In the proposed approach, the failure rates and repair times of all constituent components are obtained using genetic algorithms and then various performance measures are computed using fuzzy lambda–tau methodology. Washing system, the major part of paper industry is the subject of study. The interactions among the working components are modeled using Petri nets. Failure and repair rates are represented using triangular fuzzy numbers as they allow expert opinion, linguistic variables, operating conditions, uncertainty and imprecision in reliability information to be incorporated into system model. Based on calculated reliability parameters, a structured framework has been developed that may help the maintenance engineers to analyze and predict the system behavior. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
43. Multi-objective optimization of safety instrumented systems maintenance strategy: a case study
- Author
-
Touahar, Hafed, Ouazraoui, Nouara, Khanfri, Nor El Houda, Korichi, Mourad, Bachi, Bilal, and Boukrouma, Houcem Eddine
- Published
- 2021
- Full Text
- View/download PDF
44. Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed.
- Author
-
Xu, Yan, Hu, Peng, Zhang, Fengyang, and Zhou, Tao
- Subjects
DISTRIBUTED power generation ,SPEED limits ,DISTRIBUTION planning ,GENETIC algorithms - Abstract
With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. A Boundary Scan Test Vectors Optimization Method Based on Improved GA-AO* Approach Considering Fault Probability Model.
- Author
-
Su, Yuanzhang, Guo, Xinfeng, Luo, Hang, Wang, Jingyuan, and Liu, Zhen
- Subjects
HEURISTIC ,GENETIC algorithms ,PROBABILITY theory - Abstract
The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. A DNN Architecture Generation Method for DDoS Detection via Genetic Alogrithm.
- Author
-
Zhao, Jiaqi, Xu, Ming, Chen, Yunzhi, and Xu, Guoliang
- Subjects
ARTIFICIAL neural networks ,DENIAL of service attacks ,GENETIC algorithms - Abstract
Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer's experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization ability to tackle intricate network scenarios. This paper presents a fitness function that guarantees the best model generated possesses a specific level of real-time capability. Additionally, the proposed method employs multiple datasets to joint models generated, thereby enhancing the model's generalization performance. This paper conducts several experiments to validate the viability of the proposed method. Firstly, the best model generated with one dataset is compared with existing DNN models on the CICDDoS2019 dataset. The experimental results indicate that the model generated with one dataset has higher precision and F1-score than the existing DNN models. Secondly, model generation experiments are conducted on the CICIDS2017 and CICIDS2018 datasets, and the best model generated still performs well. Finally, this paper conducts comparative experiments on multiple datasets using the best model generated with six datasets and the best model generated by existing methods. The experimental results demonstrate that the best model generated with six datasets has better generalization ability and real-time capability. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. Optimization of earthworks planning: a systematic mapping study.
- Author
-
Fernandes, Pedro G.P.S., Júnior, Ernesto F. Nobre, and Prata, Bruno de A.
- Subjects
EARTHWORK ,MIXED integer linear programming ,MATHEMATICAL optimization ,SNOWBALL sampling ,LINEAR programming ,GENETIC algorithms ,BUDGET - Abstract
Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
- Full Text
- View/download PDF
48. Exam paper generation based on performance prediction of student group.
- Author
-
Wu, Zhengyang, He, Tao, Mao, Chenjie, and Huang, Changqin
- Subjects
- *
FORECASTING , *DYNAMIC programming , *ABILITY grouping (Education) , *GENETIC algorithms , *EXAMINATIONS - Abstract
Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
49. A Joint Optimization Algorithm for UAV Location and Offloading Decision Based on Wireless Power Supply.
- Author
-
Sun, Shuo and Zhu, Qi
- Subjects
OPTIMIZATION algorithms ,POWER resources ,APPROXIMATION algorithms ,ENERGY harvesting ,ENERGY consumption ,GENETIC algorithms ,DRONE aircraft - Abstract
In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)'s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection.
- Author
-
Bustamante-Mesa, Santiago, Gonzalez-Sanchez, Jorge W., Saldarriaga-Zuluaga, Sergio D., López-Lezama, Jesús M., and Muñoz-Galeano, Nicolás
- Subjects
INJECTION wells ,RESEARCH personnel ,PYTHON programming language ,GENETIC algorithms - Abstract
The data presented in this paper are related to the paper entitled "Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection", available in the Energies journal. Here, data are included to show the results of an Under-Frequency Load Shedding (UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained in six cases which were considered and analyzed are shown. In this paper, each case represents a different frequency response configuration in the event of generation loss, taking into account the presence or absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of virtual inertia by the VSC-HVDC link. Data for each example contain the state of the relay, threshold, position in every delay, load shed, and relay configuration parameters. Data were obtained through Digsilent Power Factory and Python simulations. The purpose of this dataset is so that other researchers can reproduce the results reported in our paper. Dataset: 10.5281/zenodo.11371324 Dataset License: Creative Commons Attribution 4.0 International (CC BY 4.0) [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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