scholarly journals QUANTUM-INSPIRED ARTIFICIL NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS METHODS APPLIED TO MODELING OF THE POLISH ELECTRIC POWER EXCHANGE USING THE DAY-AHEAD MARKET DATA

2018 ◽  
Vol 7 (3) ◽  
pp. 201-212
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Electric Power ◽  
Quantum Computer ◽  
Neural Model ◽  
Power Exchange

The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).

scholarly journals EVOLUTIONARY ALGORITHM INSPIRED BY THE METHODS OF QUANTUM COMPUTER SCIENCES FOR THE IMPROVEMENT OF A NEURAL MODEL OF THE ELECTRIC POWER EXCHANGE

2017 ◽  
Vol 6 (4) ◽  
pp. 343-355 ◽  
Author(s):  
Jerzy Tchórzewski
Keyword(s):  
Evolutionary Algorithm ◽  
Electric Power ◽  
Quantum Computer ◽  
Electrical Energy ◽  
Neural Model ◽  
Initial Population ◽  
Mutation Operators ◽  
Power Exchange ◽  
Using Data ◽  
Crossover And Mutation

The work contains results of research on the possibility to improve the neural model of the Electric Power Exchange (polish: Towarowa Giełda Energii Elektrycznej – TGEE) in MATLAB and Simulink environment using evolutionary algorithm inspired by quantum computer science. The developed artificial neural network was trained using data for the Day Ahead Market, assuming the joint volume of supplied and sold electrical energy [MWh] as the input quantities in each hour of the 24-hour day, and average prices [PLN/MWh] as output quantities. The obtained model of the exchange system was improved using the evolutionary algorithm, and further improvement in the accuracy of the model by supplementing the evolutionary algorithm using quantum solutions, related to the initial population, crossover and mutation operators, selection, etc. were proposed.

scholarly journals What is Quantum Computing and How it Works

2020 ◽  
pp. 1-5
Author(s):  
Bahman Zohuri ◽  
◽  
Farhang Mossavar Rahmani ◽  
Keyword(s):  
Artificial Intelligence ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Time Frame ◽  
Basic Unit ◽  
Next Generation ◽  
Intel Corporation ◽  
Encrypt Data ◽  
Near Term ◽  
Incremental Step

Companies such as Intel as a pioneer in chip design for computing are pushing the edge of computing from its present Classical Computing generation to the next generation of Quantum Computing. Along the side of Intel corporation, companies such as IBM, Microsoft, and Google are also playing in this domain. The race is on to build the world’s first meaningful quantum computer—one that can deliver the technology’s long-promised ability to help scientists do things like develop miraculous new materials, encrypt data with near-perfect security and accurately predict how Earth’s climate will change. Such a machine is likely more than a decade away, but IBM, Microsoft, Google, Intel, and other tech heavyweights breathlessly tout each tiny, incremental step along the way. Most of these milestones involve packing more quantum bits, or qubits—the basic unit of information in a quantum computer—onto a processor chip ever. But the path to quantum computing involves far more than wrangling subatomic particles. Such computing capabilities are opening a new area into dealing with the massive sheer volume of structured and unstructured data in the form of Big Data, is an excellent augmentation to Artificial Intelligence (AI) and would allow it to thrive to its next generation of Super Artificial Intelligence (SAI) in the near-term time frame.

scholarly journals Heliostat Field Layout Optimization with Evolutionary Algorithms

10.29007/7p6t ◽  
2018 ◽  
Author(s):  
Pascal Richter ◽  
David Laukamp ◽  
Levin Gerdes ◽  
Martin Frank ◽  
Erika Ábrahám
Keyword(s):  
Power Plant ◽  
Convex Optimization ◽  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Energy Supply ◽  
Optimization Problem ◽  
Convex Optimization Problem ◽  
New Genotype

The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for optimization. This work addresses the problem of finding an optimal heliostat field arrangement for a solar tower power plant.We propose a solution to this global, non-convex optimization problem by using an evolutionary algorithm. We show that the convergence rate of a conventional evolutionary algorithm is too slow, such that modifications of the recombination and mutation need to be tailored to the problem. This is achieved with a new genotype representation of the individuals.Experimental results show the applicability of our approach.

scholarly journals To contemplate quantitative and qualitative water features by neural networks method

2011 ◽  
Vol 48 (No. 7) ◽  
pp. 322-326 ◽  
Author(s):  
M. Neruda ◽  
R. Neruda
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
Computer Science ◽  
Hybrid System ◽  
Back Propagation ◽  
Neural Model ◽  
Black Box ◽  
Layer Number ◽  
Accurate Forecast ◽  
Hidden Layer

An application deals with calibration of neural model and Fourier series model for Ploučnice catchment. This approach has an advantage, that the network choice is independent of other example’s parameters. Each networks, and their variants (different units and hidden layer number) can be connected in as a black box and tested independently. A Stuttgart neural simulator SNNS and a multiagent hybrid system Bang2 developed in Institute of Computer Science, AS CR have been used for testing. A perceptron network has been constructed, which was trained by back propagation method improved with a momentum term. The network is capable of an accurate forecast of the next day runoff based on the runoff and rainfall values from previous day.

scholarly journals Application of Artificial Intelligence for Optimization of Organic Solar Cells Production Process

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Grazia Lo Sciuto
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Solar Cells ◽  
Computer Science ◽  
Organic Solar Cells ◽  
Full Potential ◽  
Production Chain ◽  
Lecture Notes ◽  
Occurrence Matrix ◽  
Conversion Efficiencies

The study of organic solar cells (OSCs) has been rapidly developed in recent years. Organic solar cell technology is sought after mainly due to the ease of manufacture and their exclusive properties such as mechanical flexibility, light-weight, and transparency. These properties of OSCs are well-suited for unconventional applications with power conversion efficiencies more high than 10%. The flexibility of the used substrates and the thinness of the devices make OSCs ideal for roll-to-roll production. However the organic solar cells still have very low conversion efficiencies due to degradation and stability of the technology. In order to extract their full potential, OSCs have to be optimized. On the other hand the production chain of the organic solar cells (OSC) can take advantage of the use of artificial intelligence (AI). In fact the integration into the production workflow makes solar cells more competitive and efficient. This paper presents some applications of the AI for optimization of OSCs production processes Full Text: PDF ReferencesLo Sciuto, G., Capizzi, G., Coco, S., Shikler, R., "Geometric shape optimization of organic solar cells for efficiency enhancement by neural networks." (2017) Lecture Notes in Mechanical Engineering, pp. 789-796. CrossRef Barnea, S.N., Lo Sciuto, G., Hai, N., Shikler, R., Capizzi, G., Wozniak, M., Polap, D., "Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network." (2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, pp. 378-389. CrossRef Ye, L.; Hu, H.; Ghasemi, M.; Wang, T.; Collins, B.A.; Kim, J.H.; Jiang, K.; Carpenter, J.H.; Li, H.; Li, Z.; et al. "Quantitative relations between interaction parameter, miscibility and function in organic solar cells." Nat. Mater. 2018, 17, 253-260. CrossRef Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610-621 (1973) CrossRef Capizzi, G., Sciuto, G.L., Napoli, C., Tramontana, E., Wozniak, M.: Automatic classification of fruit defects based on co-occurrence matrix and neural networks. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 861-867, September 2015. CrossRef

scholarly journals Evolutionary algorithms with and without adaptive mutation in AI based cryptography

2018 ◽  
Vol 21 ◽  
pp. 00008
Author(s):  
Mateusz Tybura
Keyword(s):  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Transfer Learning ◽  
Adaptive Mutation ◽  
Cryptographic Keys ◽  
Similar Task

The key role of cryptography is to make cipher so hard to reproduce without knowing all the details that no one besides the recipient could decipher the message. Those algorithms which are used nowadays gets its security mostly from highly reliable algorithms and/or complicated cryptographic keys. Unfortunately, those human-made methods aren‘t invulnerable so sooner or later they compromise. So, it could be really useful to make a cipher which could change. But currently only neural networks are capable of thing known as transfer learning. In this article similar method was proposed in order to make it possible to re-learn already established evolutionary algorithm to do new, similar task.

scholarly journals Analog environment vs. digital communications

2020 ◽  
Vol 1 ◽  
pp. 11-34
Author(s):  
Rusi Marinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
New Technologies ◽  
Digital Communications ◽  
Radical Change ◽  
Digital Tools ◽  
Human Environment ◽  
Interaction Models ◽  
Traditional Approaches

In this article, I discuss problems associated with new technologies, digital communications and the future of analog interaction models. I also analyze the development possibilities of artificial intelligence and neural networks based on analog computhttping systems. The transformation today, involves a radical change in existing models and the rediscovery of the benefits of some traditional approaches, which in another context can be much more effective than existing information and digital tools. In this case, it is the analog approaches of quantum computing in combination with new technologies that lead to better results, development of society and the creation of a more human environment.

scholarly journals Implementation of Shor’s Quantum Factoring Algorithm using ProjectQ Framework

2019 ◽  
Vol 8 (6S3) ◽  
pp. 52-56
Keyword(s):  
Quantum Computing ◽  
Computer Science ◽  
Polynomial Time ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
Prime Factorization ◽  
Shor's Algorithm ◽  
Computer Simulators ◽  
Testing And Evaluation ◽  
Factoring Algorithm

Prime number factorization is a problem in computer science where the solution to that problem takes super-polynomial time classically. Shor’s quantum factoring algorithm is able to solve the problem in polynomial time by harnessing the power of quantum computing. The implementation of the quantum algorithm itself is not detailed by Shor in his paper. In this paper, an approach and experiment to implement Shor’s quantum factoring algorithm are proposed. The implementation is done using Python and a quantum computer simulator from ProjectQ. The testing and evaluation are completed in two computers with different hardware specifications. User time of the implementation is measured in comparison with other quantum computer simulators: ProjectQ and Quantum Computing Playground. This comparison was done to show the performance of Shor’s algorithm when simulated using different hardware. There is a 33% improvement in the execution time (user time) between the two computers with the accuracy of prime factorization in this implementation is inversely proportional to the number of qubits used. Further improvements upon the program that has been developed for this paper is its accuracy in terms of finding the factors of a number and the number of qubits used, as previously mentioned.

scholarly journals Studies of Manufacturing Process Control: A Technological Transfer of Soft Computing

2008 ◽  
pp. 31-37
Author(s):  
J. P. Panda ◽  
R. N. Satpathy
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Technology Transfer ◽  
Evolutionary Algorithms ◽  
Soft Computing ◽  
Manufacturing Processes ◽  
Technological Transfer ◽  
Manufacturing Process Control

The field of soft computing embraces several techniques that have been inspired by nature but are mathematical. These techniques are artificial neural networks, fuzzy logic and evolutionary algorithms. Often these techniques are considered part of artificial intelligence, however the name artificial intelligence is more properly given to techniques which try to capture and emulate biological intelligence, such as expert systems and thinking computers. This paper focuses on the technology transfer issues and solutions when using soft computing for off line control of manufacturing processes. This paper will discuss each of these three techniques – neural networks, fuzzy logic and evolutionary algorithms - in turn and how they might be used in manufacturing. The kind of problems these techniques are best suited for will be defined, and competing techniques will be compared and contrasted.

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