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1
Layana Castro, Pablo E., Joan Carles Puchalt, Antonio García Garví, and Antonio-José Sánchez-Salmerón. "Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm." Sensors 21, no. 16 (2021): 5622. http://dx.doi.org/10.3390/s21165622.
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Automatic tracking of Caenorhabditis elegans (C. egans) in standard Petri dishes is challenging due to high-resolution image requirements when fully monitoring a Petri dish, but mainly due to potential losses of individual worm identity caused by aggregation of worms, overlaps and body contact. To date, trackers only automate tests for individual worm behaviors, canceling data when body contact occurs. However, essays automating contact behaviors still require solutions to this problem. In this work, we propose a solution to this difficulty using computer vision techniques. On the one hand, a skeletonization method is applied to extract skeletons in overlap and contact situations. On the other hand, new optimization methods are proposed to solve the identity problem during these situations. Experiments were performed with 70 tracks and 3779 poses (skeletons) of C. elegans. Several cost functions with different criteria have been evaluated, and the best results gave an accuracy of 99.42% in overlapping with other worms and noise on the plate using the modified skeleton algorithm and 98.73% precision using the classical skeleton algorithm.
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Van den Nest, Maarten. "Simulating quantum computers with probabilistic methods." Quantum Information and Computation 11, no. 9&10 (2011): 784–812. http://dx.doi.org/10.26421/qic11.9-10-5.
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We investigate the boundary between classical and quantum computational power. This work consists of two parts. First we develop new classical simulation algorithms that are centered on sampling methods. Using these techniques we generate new classes of classically simulatable quantum circuits where standard techniques relying on the exact computation of measurement probabilities fail to provide efficient simulations. For example, we show how various concatenations of matchgate, Toffoli, Clifford, bounded-depth, Fourier transform and other circuits are classically simulatable. We also prove that sparse quantum circuits as well as circuits composed of CNOT and $\exp[{i\theta X}]$ gates can be simulated classically. In a second part, we apply our results to the simulation of quantum algorithms. It is shown that a recent quantum algorithm, concerned with the estimation of Potts model partition functions, can be simulated efficiently classically. Finally, we show that the exponential speed-ups of Simon's and Shor's algorithms crucially depend on the very last stage in these algorithms, dealing with the classical postprocessing of the measurement outcomes. Specifically, we prove that both algorithms would be classically simulatable if the function classically computed in this step had a sufficiently peaked Fourier spectrum.
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Hastings, Matthew B. "Classical and Quantum Algorithms for Tensor Principal Component Analysis." Quantum 4 (February 27, 2020): 237. http://dx.doi.org/10.22331/q-2020-02-27-237.
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We present classical and quantum algorithms based on spectral methods for a problem in tensor principal component analysis. The quantum algorithm achieves a quartic speedup while using exponentially smaller space than the fastest classical spectral algorithm, and a super-polynomial speedup over classical algorithms that use only polynomial space. The classical algorithms that we present are related to, but slightly different from those presented recently in Ref. \cite{wein2019kikuchi}. In particular, we have an improved threshold for recovery and the algorithms we present work for both even and odd order tensors. These results suggest that large-scale inference problems are a promising future application for quantum computers.
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Cherckesova, Larissa, Olga Safaryan, Pavel Razumov, Irina Pilipenko, Yuriy Ivanov, and Ivan Smirnov. "Speed improvement of the quantum factorization algorithm of P. Shor by upgrade its classical part." E3S Web of Conferences 224 (2020): 01016. http://dx.doi.org/10.1051/e3sconf/202022401016.
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This report discusses Shor’s quantum factorization algorithm and ρ–Pollard’s factorization algorithm. Shor’s quantum factorization algorithm consists of classical and quantum parts. In the classical part, it is proposed to use Euclidean algorithm, to find the greatest common divisor (GCD), but now exist large number of modern algorithms for finding GCD. Results of calculations of 8 algorithms were considered, among which algorithm with lowest execution rate of task was identified, which allowed the quantum algorithm as whole to work faster, which in turn provides greater potential for practical application of Shor’s quantum algorithm. Standard quantum Shor’s algorithm was upgraded by replacing the binary algorithm with iterative shift algorithm, canceling random number generation operation, using additive chain algorithm for raising to power. Both Shor’s algorithms (standard and upgraded) are distinguished by their high performance, which proves much faster and insignificant increase in time in implementation of data processing. In addition, it was possible to modernize Shor’s quantum algorithm in such way that its efficiency turned out to be higher than standard algorithm because classical part received an improvement, which allows an increase in speed by 12%.
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Сехпосян Арташес. "ИССЛЕДОВАНИЕ НЕКОТОРЫХ КЛАССИЧЕСКИХ АЛГОРИТМОВ ЗАДАЧИ МАРШРУТИЗАЦИИ ТРАНСПОРТА". World Science 1, № 3(43) (2019): 10–14. http://dx.doi.org/10.31435/rsglobal_ws/31032019/6398.
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 The article is devoted to the study of some classical algorithms for transport routing problems. The article describes the existing classical transport routing algorithms, such as the Clark-Wright algorithm and its extended algorithm. The main minus of Clarke-Wright’s algorithm has been revealed, which consists in the efficiency of its operation decreases as it approaches the end of the calculations, while at the beginning of the work, the solutions are relatively successful. To improve the performance of the Clark-Wright algorithm, three approaches have been proposed in its extended algorithm. Also were explored the algorithms of Mole-Jameson and Christofides, Mingozzi and Tossa, which can be used for tasks with an unspecified in advance number of vehicles, are investigated. The algorithm of the Notes, which is used for the initial processing of the problems of ZMT, is proposed. Classical improvement algorithms for MMT are investigated, in which either a single route at a time or several routes are processed. The above algorithms give more interesting results than their predecessors, and are considered optimal in terms of the use of certain resources.
 
 
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Fehér, Áron, and Dénes Nimród Kutasi. "Modelling and Control of Bounded Hybrid Systems in Power Electronics." Acta Universitatis Sapientiae Electrical and Mechanical Engineering 9, no. 1 (2017): 33–42. http://dx.doi.org/10.1515/auseme-2017-0008.
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Abstract In this work, an explicit Model Predictive Control algorithm is devised and compared to classical control algorithms applied to a series resonant DC/DC converter circuit. In the first part, a model of the converter as a hybrid system is created and studied. In the second part, the predictive algorithm is applied and tested on the model. Finally, the designed control algorithm is compared to classical PI and sliding mode controllers.
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van Apeldoorn, Joran, András Gilyén, Sander Gribling, and Ronald de Wolf. "Convex optimization using quantum oracles." Quantum 4 (January 13, 2020): 220. http://dx.doi.org/10.22331/q-2020-01-13-220.
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We study to what extent quantum algorithms can speed up solving convex optimization problems. Following the classical literature we assume access to a convex set via various oracles, and we examine the efficiency of reductions between the different oracles. In particular, we show how a separation oracle can be implemented using O~(1) quantum queries to a membership oracle, which is an exponential quantum speed-up over the Ω(n) membership queries that are needed classically. We show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. Combining this with a simplification of recent classical work of Lee, Sidford, and Vempala gives our efficient separation oracle. This in turn implies, via a known algorithm, that O~(n) quantum queries to a membership oracle suffice to implement an optimization oracle (the best known classical upper bound on the number of membership queries is quadratic). We also prove several lower bounds: Ω(n) quantum separation (or membership) queries are needed for optimization if the algorithm knows an interior point of the convex set, and Ω(n) quantum separation queries are needed if it does not.
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Van den Nest, Maarten. "Efficient classical simulations of quantum Fourier transforms and Normalizer circuits over Abelian groups." Quantum Information and Computation 13, no. 11&12 (2013): 1007–37. http://dx.doi.org/10.26421/qic13.11-12-7.
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The quantum Fourier transform (QFT) is an important ingredient in various quantum algorithms which achieve superpolynomial speed-ups over classical computers. In this paper we study under which conditions the QFT can be simulated efficiently classically. We introduce a class of quantum circuits, called \emph{normalizer circuits}: a normalizer circuit over a finite Abelian group is any quantum circuit comprising the QFT over the group, gates which compute automorphisms and gates which realize quadratic functions on the group. In our main result we prove that all normalizer circuits have polynomial-time classical simulations. The proof uses algorithms for linear diophantine equation solving and the monomial matrix formalism introduced in our earlier work. Our result generalizes the Gottesman-Knill theorem: in particular, Clifford circuits for $d$-level qudits arise as normalizer circuits over the group ${\mathbf Z}_d^m$. We also highlight connections between normalizer circuits and Shor's factoring algorithm, and the Abelian hidden subgroup problem in general. Finally we prove that quantum factoring cannot be realized as a normalizer circuit owing to its modular exponentiation subroutine.
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Nissim, R., and R. Brafman. "Distributed Heuristic Forward Search for Multi-agent Planning." Journal of Artificial Intelligence Research 51 (October 7, 2014): 293–332. http://dx.doi.org/10.1613/jair.4295.
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This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of problem -- one is based on reduction to distributed constraint satisfaction, and the other on partial-order planning techniques. In classical single-agent planning, constraint-based and partial-order planning techniques are currently dominated by heuristic forward search. The question arises whether it is possible to formulate a distributed heuristic forward search algorithm for privacy-preserving classical multi-agent planning. Our work provides a positive answer to this question in the form of a general approach to distributed state-space search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient -- outperforming previous algorithms by orders of magnitude -- while offering similar flexibility to that of forward-search algorithms for single-agent planning. Furthermore, one particular variant of our general approach yields a distributed version of the A* algorithm that is the first cost-optimal distributed algorithm for privacy-preserving planning.
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Fadhil Oudah, Sadeer, Prof Dr Hegazy Zaher, Assoc Prof Dr Naglaa Ragaa Saeid Hassan, and Dr Eman Oun. "Literature Review on Differential Evolution Algorithm." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1577–600. http://dx.doi.org/10.51201/jusst/21/06471.
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Differential evolution algorithm is one of the most efficient metaheuristic approaches. In this paper, a review and analysis is presented in order to help for future research in differential evolution algorithm. It covers an analysis of about 142 papers of the previous work in the modifications of the algorithm including the main parameters of the classical steps of the algorithm and hybridization with other algorithms. The analysis also shows the applications that optimized using the differential evolution algorithm.
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Mahmood, Musaria Karim, Osman Ucan, Zahraa Zaidan, and Sulaiman M. Karim. "Hybrid algorithm for two-terminal reliability evaluation in communication networks." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1185. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1185-1192.
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<span>Network reliability is valuable in establishing a survivable communication network. Reliability evaluation algorithms are used in the design stage and during the network deployment. This work presents a new multistage hybrid technique for two-terminal reliability evaluation problem. It is based on a combination of graph reduction techniques and tie-set method. A new approach has been introduced for deducing tie-sets in a network containing both unidirectional and bi-directional edges. The proposed algorithm can be applied for both simple and complex networks without restrictions. The results confirm that new algorithm evaluates network's reliability with decreasing computing time compared to classical algorithms. The results for a case study of a 20-node network have demonstrated that the required time for reliability evaluation is decreased from (t&gt;1 hour) in the case of using a classical algorithm, to (t&lt;1 second) for the new algorithm.</span>
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Liu, Ye-Chao, Jiangwei Shang, and Xiangdong Zhang. "Coherence Depletion in Quantum Algorithms." Entropy 21, no. 3 (2019): 260. http://dx.doi.org/10.3390/e21030260.
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Besides the superior efficiency compared to their classical counterparts, quantum algorithms known so far are basically task-dependent, and scarcely any common features are shared between them. In this work, however, we show that the depletion of quantum coherence turns out to be a common phenomenon in these algorithms. For all the quantum algorithms that we investigated, including Grover’s algorithm, Deutsch–Jozsa algorithm, and Shor’s algorithm, quantum coherence of the system states reduces to the minimum along with the successful execution of the respective processes. Notably, a similar conclusion cannot be drawn using other quantitative measures such as quantum entanglement. Thus, we expect that coherence depletion as a common feature can be useful for devising new quantum algorithms in the future.
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Montanaro, Ashley. "Quantum speedup of Monte Carlo methods." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2181 (2015): 20150301. http://dx.doi.org/10.1098/rspa.2015.0301.
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Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions. In this work, we describe a quantum algorithm which can accelerate Monte Carlo methods in a very general setting. The algorithm estimates the expected output value of an arbitrary randomized or quantum subroutine with bounded variance, achieving a near-quadratic speedup over the best possible classical algorithm. Combining the algorithm with the use of quantum walks gives a quantum speedup of the fastest known classical algorithms with rigorous performance bounds for computing partition functions, which use multiple-stage Markov chain Monte Carlo techniques. The quantum algorithm can also be used to estimate the total variation distance between probability distributions efficiently.
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Fang, Zhuang, Xuming Yi, and Liming Tang. "An Adaptive Boosting Algorithm for Image Denoising." Mathematical Problems in Engineering 2019 (February 18, 2019): 1–14. http://dx.doi.org/10.1155/2019/8365932.
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Image denoising is an important problem in many fields of image processing. Boosting algorithm attracts extensive attention in recent years, which provides a general framework by strengthening the original noisy image. In such framework, many classical existing denoising algorithms can improve the denoising performance. However, the boosting step is fixed or nonadaptive; i.e., the noise level in iteration steps is set to be a constant. In this work, we propose a noise level estimation algorithm by combining the overestimation and underestimation results. Based on this, we further propose an adaptive boosting algorithm that excludes intricate parameter configuration. Moreover, we prove the convergence of the proposed algorithm. Experimental results that are obtained in this paper demonstrate the effectiveness of the proposed adaptive boosting algorithm. In addition, compared with the classical boosting algorithm, the proposed algorithm can get better performance in terms of visual quality and peak signal-to-noise ratio (PSNR).
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He, Yue Shun, and Jun Fang Xiao. "Improved Methods on Association Rules Mining Algorithms." Key Engineering Materials 460-461 (January 2011): 148–52. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.148.
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Among the many mining algorithms of association rules, Apriori Algorithm is a classical algorithm that has caused the most discussion; it can effectively carry out the mining association rules. However, based on Apriori Algorithm, most of the traditional algorithms exist "item sets generation bottleneck" problem, and are very time-consuming. An enhanced algorithm associating Apriori with transaction reduction and item reduction technique is put forward by the paper, in the algorithm candidate item sets generation and the support calculation are created after each transaction is compressed and connected, and the key word identifying is adopted in the candidate set, thus the process of pruning and string pattern matching is removed from Apriori algorithm. Original algorithm and improved algorithm implementation steps are presented by examples, the results show that the new algorithm reduces the storage space, improve the efficiency of the algorithm and improve the performance of data mining technology.
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Gushanskiy, Sergey, Viktor Potapov, and Omar Correa Madrigal. "Implementation of Classic Image Transformation Algorithm to Quantum State, Boundary Extraction and Transformation of Half-Tone Image to Binary." Cybernetics and Information Technologies 20, no. 2 (2020): 70–78. http://dx.doi.org/10.2478/cait-2020-0017.
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AbstractThe aim of the research is computer simulation of a quantum algorithm to solve the problem of transforming a classical image using quantum computing tools and methods, studying recognition algorithms and creating a recognition model using quantum methods. The method of quantum modeling makes it possible to convert a classical image into a quantum state, select boundaries and convert a grayscale image to a binary one, and shows the possibilities of the quantum information theory in interpreting classical problems. The main results of the article are the developed quantum algorithm that allows recognizing objects, as well as the quantum method aimed at representing/processing a color pixel image. The scientific novelty of the article is expressed in the construction of a quantum system, an exponential increase in the speed of solving computational NP-complete problems, which on classical machines can be solved in unacceptable time. The motivation for writing the work was a high growth interest in quantum computing and the benefits that they guarantee. The development of the theoretical foundations of creating software systems and the design of algorithms for new information technologies and specialized computing systems is a dynamic field, as evidenced by the number of existing works in this direction. The developed algorithms for various problems of complexity classes can give a significant gain in efficiency in comparison with existing classical ones and provide a solution to a number of complex mathematical (including cryptographic) problems.
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V.V. Kumar, K., and P. V.V. Kishore. "Indian classical dance action identification using adaptive graph matching from unconstrained videos." International Journal of Engineering & Technology 7, no. 1.1 (2017): 500. http://dx.doi.org/10.14419/ijet.v7i1.1.10156.
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Extracting and recognizing complex human movements from unconstraint online video sequence is a challenging task. In this work the problem becomes complicated by the use of unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern features for segmentation. We also explore multiple feature fusion models with early fusion and late fusion techniques for improving the classification process. The extracted features were represented as a graph and a novel adaptive graph matching algorithm is proposed. We test the algorithms on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.
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Slate, N., E. Matwiejew, S. Marsh, and J. B. Wang. "Quantum walk-based portfolio optimisation." Quantum 5 (July 28, 2021): 513. http://dx.doi.org/10.22331/q-2021-07-28-513.
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This paper proposes a highly efficient quantum algorithm for portfolio optimisation targeted at near-term noisy intermediate-scale quantum computers. Recent work by Hodson et al. (2019) explored potential application of hybrid quantum-classical algorithms to the problem of financial portfolio rebalancing. In particular, they deal with the portfolio optimisation problem using the Quantum Approximate Optimisation Algorithm and the Quantum Alternating Operator Ansatz. In this paper, we demonstrate substantially better performance using a newly developed Quantum Walk Optimisation Algorithm in finding high-quality solutions to the portfolio optimisation problem.
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Wu, Jingqiao, Xiaoyue Feng, Renchu Guan, and Yanchun Liang. "Cancer Research Trend Analysis Based on Fusion Feature Representation." Entropy 23, no. 3 (2021): 338. http://dx.doi.org/10.3390/e23030338.
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Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research.
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Bravyi, Sergey, Dan Browne, Padraic Calpin, Earl Campbell, David Gosset, and Mark Howard. "Simulation of quantum circuits by low-rank stabilizer decompositions." Quantum 3 (September 2, 2019): 181. http://dx.doi.org/10.22331/q-2019-09-02-181.
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Recent work has explored using the stabilizer formalism to classically simulate quantum circuits containing a few non-Clifford gates. The computational cost of such methods is directly related to the notion of stabilizerrank, which for a pure state ψ is defined to be the smallest integer χ such that ψ is a superposition of χ stabilizer states. Here we develop a comprehensive mathematical theory of the stabilizer rank and the related approximate stabilizer rank. We also present a suite of classical simulation algorithms with broader applicability and significantly improved performance over the previous state-of-the-art. A new feature is the capability to simulate circuits composed of Clifford gates and arbitrary diagonal gates, extending the reach of a previous algorithm specialized to the Clifford+T gate set. We implemented the new simulation methods and used them to simulate quantum algorithms with 40-50 qubits and over 60 non-Clifford gates, without resorting to high-performance computers. We report a simulation of the Quantum Approximate Optimization Algorithm in which we process superpositions of χ∼106 stabilizer states and sample from the full n-bit output distribution, improving on previous simulations which used ∼103 stabilizer states and sampled only from single-qubit marginals. We also simulated instances of the Hidden Shift algorithm with circuits including up to 64 T gates or 16 CCZ gates; these simulations showcase the performance gains available by optimizing the decomposition of a circuit's non-Clifford components.
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Liu, Wei, Yongkun Huang, Zhiwei Ye, et al. "Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm." Applied Sciences 10, no. 9 (2020): 3225. http://dx.doi.org/10.3390/app10093225.
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Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.
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Zhu, Yongkuan, Gurjot Singh Gaba, Fahad M. Almansour, Roobaea Alroobaea, and Mehedi Masud. "Application of data mining technology in detecting network intrusion and security maintenance." Journal of Intelligent Systems 30, no. 1 (2021): 664–76. http://dx.doi.org/10.1515/jisys-2020-0146.
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Abstract In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm.
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Alharbey, Rania A., and Kiran Sultan. "Evolutionary Algorithm Based Solution of Rössler Chaotic System Using Bernstein Polynomials." Journal of Computational and Theoretical Nanoscience 17, no. 7 (2020): 2932–39. http://dx.doi.org/10.1166/jctn.2020.9272.
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Chaotic systems have gained enormous research attention since the pioneering work of Lorenz. Rössler system stands among the extensively studied classical chaotic models. This paper proposes a technique based on Bernstein Polynomial Basis Function to convert the three-dimensional Rössler system of Ordinary Differential Equations (ODEs) into an error minimization problem. First, the properties of Bernstein Polynomials are applied to derive the fitness function of Rössler chaotic system. Second, in order to obtain the values of unknown Bernstein coefficients to optimize the fitness function, the problem is solved using two versatile algorithms from the family of Evolutionary Algorithms (EAs), Genetic Algorithm (GA) hybridized with Interior Point Algorithm (IPA) and Differential Algorithm (DE). For validity of the proposed technique, simulation results are provided which verify the global stability of error dynamics and provide accurate estimation of the desired parameters.
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Banchi, Leonardo, and Gavin E. Crooks. "Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule." Quantum 5 (January 25, 2021): 386. http://dx.doi.org/10.22331/q-2021-01-25-386.
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Hybrid quantum-classical optimization algorithms represent one of the most promising application for near-term quantum computers. In these algorithms the goal is to optimize an observable quantity with respect to some classical parameters, using feedback from measurements performed on the quantum device. Here we study the problem of estimating the gradient of the function to be optimized directly from quantum measurements, generalizing and simplifying some approaches present in the literature, such as the so-called parameter-shift rule. We derive a mathematically exact formula that provides a stochastic algorithm for estimating the gradient of any multi-qubit parametric quantum evolution, without the introduction of ancillary qubits or the use of Hamiltonian simulation techniques. The gradient measurement is possible when the underlying device can realize all Pauli rotations in the expansion of the Hamiltonian whose coefficients depend on the parameter. Our algorithm continues to work, although with some approximations, even when all the available quantum gates are noisy, for instance due to the coupling between the quantum device and an unknown environment.
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Ravikumar, C. P., Vikas Jain, and Anurag Dod. "Distributed Fault Simulation Algorithms on Parallel Virtual Machine." VLSI Design 12, no. 1 (2001): 81–99. http://dx.doi.org/10.1155/2001/58303.
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In this paper, we describe distributed algorithms for combinational fault simulation assuming the classical stuck-at fault model. Our algorithms have been implemented on a network of Sun workstations under the Parallel Virtual Machine (PVM) environment. Two techniques are used for subdividing work among processors – test set partition and fault set partition. The sequential algorithm for fault simulation, used on individual nodes of the network, is based on a novel path compression technique proposed in this paper. We describe experimental results on a number of ISCAS′85 benchmark circuits.
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Skačkauskas, Paulius, and Edgar Sokolovskij. "Analysis of the Hybrid Global Path Planning Algorithm for Different Environments." Transport and Telecommunication Journal 20, no. 1 (2019): 1–11. http://dx.doi.org/10.2478/ttj-2019-0001.
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Abstract To achieve the overall goal of realising an efficient and advantageous participation of autonomous ground vehicles in the transport system as fast as possible, a lot of work is being done in different and specific research fields. One of the most important research fields, which has a large impact on safe autonomous ground vehicle realisation, is the development of path planning algorithms. Therefore, this work describes in detail the development and application of a hybrid path planning algorithm. The described algorithm is based on classical and heuristic path planning approaches and can be applied in unstructured and structured environments. The efficiency of the algorithm was investigated by applying the algorithm and executing theoretical and experimental tests. The theoretical and experimental tests were executed while optimising different complexity paths. Results analysis demonstrated that the described algorithm can generate a smooth, dynamically feasible and collision-free path.
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Zou, Xiang, Kai Li, and Bing Pan. "The Effect of Low-pass Pre-filtering on Subvoxel Registration Algorithms in Digital Volume Correlation: A revisited study." Measurement Science Review 20, no. 5 (2020): 202–9. http://dx.doi.org/10.2478/msr-2020-0025.
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AbstractIn digital volume correlation (DVC), random image noise in volumetric images leads to increased systematic error and random error in the displacements measured by subvoxel registration algorithms. Previous studies in DIC have shown that adopting low-pass pre-filtering to the images prior to the correlation analysis can effectively mitigate the systematic error associated with the classical forward additive Newton-Raphson (FA-NR) algorithm. However, the effect of low-pass pre-filtering on the state-of-the-art inverse compositional Gauss-Newton (ICGN) algorithm has not been investigated so far. In this work, we focus on the effect of low-pass pre-filtering on two mainstream subvoxel registration algorithms (i.e., 3D FA-NR algorithm and 3D IC-GN algorithm) used in DVC. Basic principles and theoretical error analyses of the two algorithms are described first. Then, based on numerical experiments with precisely controlled subvoxel displacements and noise levels, the influences of image noise on the displacements measured by two subvoxel algorithms are examined. Further, the effects of low-pass pre-filtering on these two subvoxel algorithms are examined for simulated image sets with different noise levels and deformation modes. The results show that the low-pass pre-filtering can effectively suppress the systematic errors for the 3D FA-NR algorithm, which is consistent with the previously drawn conclusion in DIC. On the contrary, different form the 3D FA-NR algorithm, the 3D IC-GN algorithm itself can reduce the influence of image noise, and the effect of low-pass pre-filtering on it is not so obvious as on 3D FA-NR algorithm.
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Cheng, Yong Hong, Li Hua Ouyang, and Xin Yan Liu. "Research on the Mining Technology of Multimedia Communication Data Flow Based on Computer Network." Applied Mechanics and Materials 380-384 (August 2013): 3570–74. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3570.
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computer network multimedia communication has spread all over daily work and life fields, people also have put forward higher requirements for computer multimedia communication network. Based on current network multimedia communication, there are a large amount of data transmission, high-speed and dynamic characteristics, the mining technology of its communication data flow is carried out related to research. In this paper, the frequent item sets of data stream mining technology is carried out related to research, and the classical HCOUNT algorithm is carried out relevant analysis, according to the relevant analysis, the classical HCOUNT algorithm is improved, in order to alleviate possible error problem in data mining. Finally, the data stream mining algorithms are carried out relevant analysis of actual experimental data. Study on the technical aspects of the data flow mining in computer network multimedia communication, it is an important role in the development of the computer network multimedia communication. Study on the improvement of the current algorithm, it provides reference in the similar field of algorithm research
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Ben-Asher, Yosi, and Esti Stein. "Basic Algorithms for the Asynchronous Reconfigurable Mesh." VLSI Design 15, no. 1 (2002): 441–54. http://dx.doi.org/10.1080/106551402100002057.
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Many constant time algorithms for various problems have been developed for the reconfigurable mesh (RM) in the past decade. All these algorithms are designed to work with synchronous execution, with no regard for the fact that large size RMs will probably be asynchronous. A similar observation about the PRAM model motivated many researchers to develop algorithms and complexity measures for the asynchronous PRAM (APRAM). In this work, we show how to define the asynchronous reconfigurable mesh (ARM) and how to measure the complexity of asynchronous algorithms executed on it. We show that connecting all processors in a row of an n×n ARM (the analog of barrier synchronization in the APRAM model) can be solved with complexity Θ(nlogn). Intuitively, this is average work time for solving such a problem. Next, we describe general a technique for simulating T -step synchronous RM algorithms on the ARM with complexity of Θ(T⋅n2logn). Finally, we consider the simulation of the classical synchronous algorithm for counting the number of non-zero bits in an n bits vector using (k<n)×n RM. By carefully optimizing the synchronization to the specific synchronous algorithm being simulated, one can (at least in the case of counting) improve upon the general simulation.
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Chen, Qun, and Zong-Xiao Yang. "Identification of Duhem-type piezoelectric hysteresis using a modified bee colony algorithm." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 39, no. 2 (2019): 249–64. http://dx.doi.org/10.1108/compel-05-2019-0194.
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Purpose The determination of parameters of Duhem model that can describe piezoelectric hysteresis is usually a great challenge. The purpose of this paper is to find a way to identify the parameters of Duhem model by using a modified bee colony algorithm. Design/methodology/approach The promising bee colony algorithm has great potential to identify hysteresis nonlinearity, but has not yet been used to identify the Duhem-type hysteresis in the literatures. To explore this possibility, the classical bee colony algorithm is modified to enhance its performance regarding both searching capability and convergence speed. In the modification, the current optimal solution is used to guide the search direction, which can balance the local and global searching ability. Moreover, a new searching formula for scout bees is proposed to enhance the convergence ability of the algorithm. Findings Through a series of experiments, the modified algorithm can attain the optimal parameters with a 0.61 µm peak valley error and a 0.12 µm root-mean-square error. Compared to the particle swarm optimization and classical bee colony algorithms, the modified bee colony algorithm can reach higher parameter identification accuracy. Based on 50 trials, the robustness of the posed algorithm was also proved. Originality/value The well-performed modified bee colony algorithm is a good candidate in parameter identification of Duhem-type hysteresis nonlinear systems. As there is no work studying the parameter identification of Duhem model using a bee colony algorithm in the literatures, this work closed this gap and explored the ability of bee colony algorithm to identify piezoelectric hysteresis with superb accuracy and robustness.
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Sharma, Oshin, and Hemraj Saini. "Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment." International Journal of Business Data Communications and Networking 13, no. 1 (2017): 45–57. http://dx.doi.org/10.4018/ijbdcn.2017010104.
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In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.
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Hammad, Baraa Tareq, Ali Maki Sagheer, Ismail Taha Ahmed, and Norziana Jamil. "A comparative review on symmetric and asymmetric DNA-based cryptography." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2484–91. http://dx.doi.org/10.11591/eei.v9i6.2470.
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Current researchers have focused on DNA-based cryptography, in fact, DNA or deoxyribonucleic acid, has been applied in cryptography for performing computation as well as storing and transmitting information. In the present work, we made use of DNA in cryptographic, i.e. its storing capabilities (superior information density) and parallelism, in order to improve other classical cryptographic algorithms. Data encryption is made possible via DNA sequences. In this paper, two cases utilizing different DNA properties were studied by combining the DNA codes with those conventional cryptography algorithms. The first case concerned on symmetric cryptography that involved DNA coding with OTP (one time pad) algorithms. Asymmetric cryptography was considered in the second case by incorporating the DNA codes in RSA algorithm. The efficiencies of DNA coding in OTP, RSA, and other algorithms were given. As observed, the computational time of RSA algorithm combined with DNA coding was longer. In order to alleviate this problem, data redundancy was reduced by activating the GZIP compressed algorithm. The present experimental results showed that DNA symmetric cryptography worked quite well in both time and size analyses. Nevertheless, it was less efficient than the compressed DNA asymmetric cryptography.
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Yücel, Melda, Aylin Ece Kayabekir, Gebrail Bekdaş, Sinan Melih Nigdeli, Sanghun Kim, and Zong Woo Geem. "Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls." Sustainability 13, no. 4 (2021): 1639. http://dx.doi.org/10.3390/su13041639.
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In the optimum design of reinforced concrete (RC) structural members, the robustness of the employed method is important as well as solving the optimization problem. In some cases where the algorithm parameters are defined as non-effective values, local-optimum solutions may prevail over the existing global optimum results. Any metaheuristic algorithm can be effective to solve the optimization problem but must give the same results for several runs. Due to the randomization nature of these algorithms, the performance may vary with respect to time. The essential and novel work done in this study is the comparative investigation of 10 different metaheuristic algorithms and two modifications of harmony search (HS) algorithm on the optimum cost design of RC retaining walls constrained with geotechnical and structural state limits. The employed algorithms include classical ones (genetic algorithm (GA), differential evaluation (DE), and particle swarm optimization (PSO)), proved ones on structural engineering applications (harmony search, artificial bee colony, firefly algorithm), and recent algorithms (teaching–learning-based optimization (TLBO), flower pollination algorithm (FPA), grey wolf optimization, Jaya algorithm (JA)). The modifications of HS include adaptive HS (AHS) concerning the automatic change of algorithm parameters and hybridization of AHS with JA that is developed for the investigated problem. According to the numerical investigations, recent algorithms such as TLBO, FPA, and JA are generally the best at finding the optimum values with less deviation than the others. The adaptive-hybrid HS proposed in this study is also competitive with these algorithms, while it can reach the best solution by using a lower population number which can lead to timesaving in the optimization process. By the minimization of material used in construction via best optimization, sustainable structures that support multiple types of constraints are provided.
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Popov, Evgeny, and Yury Shornikov. "Detection of different type events in hybrid dynamical systems." Science Bulletin of the Novosibirsk State Technical University, no. 4 (December 18, 2020): 159–76. http://dx.doi.org/10.17212/1814-1196-2020-4-159-176.
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Hybrid dynamical or simply hybrid systems (HS) are a modern apparatus for modeling discrete-continuous processes in different applications such as power engineering, aeronautics, manufacturing, economics, transport dispatching, etc. The key difference of HSs from classical dynamic systems is the presence of continuous mode switching events. Event times are defined by the zeroes of continuous event functions. If it is impossible to symbolically compute an event time. To do it one uses an event detection algorithm working together with a differential equation integration algorithm. Events in HSs are traditionally divided into state events and time events. Only explicit time events with event functions in the form of linear polynomials in time are usually considered in the literature. This paper addresses the class of implicit time events and lists their possible sources. Moreover, the traditional classification of events into unilateral, bilateral and accuracy critical events is expanded by adding difficult-to-detect events. These events are characterized by event functions crossing zero several times within one integration step. Not all algorithms can guarantee detecting events of this type. Heterogeneous HSs including processes of different physical nature are in general characterized by significantly stiff and high-dimensional modes usually defined in a form of differential-algebraic systems of equations with events of different types. The last feature limits the application of classical event detection algorithms oriented to a single event type. That is why the paper proposes the methodology of complex event detection consisting in using separate event detection algorithm for each event type. The joint work of several algorithms can ensure correct detection of events of different types and also may improve the efficiency. A complex event detection algorithm guaranteeing detection of all events is constructed for a particular HS. The complex algorithm demonstrates an average speed up of 17%.
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STRZAŁKA, DOMINIK, and FRANCISZEK GRABOWSKI. "TOWARDS POSSIBLE NON-EXTENSIVE THERMODYNAMICS OF ALGORITHMIC PROCESSING — STATISTICAL MECHANICS OF INSERTION SORT ALGORITHM." International Journal of Modern Physics C 19, no. 09 (2008): 1443–58. http://dx.doi.org/10.1142/s0129183108013011.
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Tsallis entropy introduced in 1988 is considered to have obtained new possibilities to construct generalized thermodynamical basis for statistical physics expanding classical Boltzmann–Gibbs thermodynamics for nonequilibrium states. During the last two decades this q-generalized theory has been successfully applied to considerable amount of physically interesting complex phenomena. The authors would like to present a new view on the problem of algorithms computational complexity analysis by the example of the possible thermodynamical basis of the sorting process and its dynamical behavior. A classical approach to the analysis of the amount of resources needed for algorithmic computation is based on the assumption that the contact between the algorithm and the input data stream is a simple system, because only the worst-case time complexity is considered to minimize the dependency on specific instances. Meanwhile the article shows that this process can be governed by long-range dependencies with thermodynamical basis expressed by the specific shapes of probability distributions. The classical approach does not allow to describe all properties of processes (especially the dynamical behavior of algorithms) that can appear during the computer algorithmic processing even if one takes into account the average case analysis in computational complexity. The importance of this problem is still neglected especially if one realizes two important things. The first one: nowadays computer systems work also in an interactive mode and for better understanding of its possible behavior one needs a proper thermodynamical basis. The second one: computers from mathematical point of view are Turing machines but in reality they have physical implementations that need energy for processing and the problem of entropy production appears. That is why the thermodynamical analysis of the possible behavior of the simple insertion sort algorithm will be given here.
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Liu, Kai, Yi Zhang, Kai Lu, Xiaoping Wang, Xin Wang, and Guojing Tian. "MapEff: An Effective Graph Isomorphism Agorithm Based on the Discrete-Time Quantum Walk." Entropy 21, no. 6 (2019): 569. http://dx.doi.org/10.3390/e21060569.
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Graph isomorphism is to determine whether two graphs have the same topological structure. It plays a significant role in areas of image matching, biochemistry, and information retrieval. Quantum walk, as a novel quantum computation model, has been employed to isomorphic mapping detection to optimize the time complexity compared with a classical computation model. However, these quantum-inspired algorithms do not perform well—and even cease to work—for graphs with inherent symmetry, such as regular graphs. By analyzing the impacts of graphs symmetry on isomorphism detection, we proposed an effective graph isomorphism algorithm (MapEff) based on the discrete-time quantum walk (DTQW) to improve the accuracy of isomorphic mapping detection, especially for regular graphs. With the help of auxiliary edges, this algorithm can distinguish the symmetric nodes efficiently and, thus, deduct the qualified isomorphic mapping by rounds of selections. The experiments tested on 1585 pairs of graphs demonstrated that our algorithm has a better performance compared with other state-of-the-art algorithms.
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Zeghba, O., S. Chakroune, D. E. Khodja, and S. Belhamdi. "Design Optimization of Induction Motor Using On-Line Improved Genetic Algorithms." Modelling, Measurement and Control A 93, no. 1-4 (2020): 19–25. http://dx.doi.org/10.18280/mmc_a.931-403.
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The main objective of this work is the application of a new architecture of genetic algorithms to the induction machine design in order to improve their performance. The latter is proposed by our research team based on modified crossing and mutation operators who have fixed values for conventional genetic algorithms. In addition, this version is characterized by a double loop and a random crossover. Firstly, to demonstrate the ability to locate the global optimum with this version algorithm a mathematical function was used. Then we approached the second phase which its application in real time to the induction motor optimized design problem. Knowing that, the machine is a highly coupled with multivariable system and constraints. Finally, the results obtained have been analyzed where we have found that satisfactory and can be declared that adaptation algorithm is effective in locating rapidly the region in which the global optimum exists in relation to the classical genetic algorithm.
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Dadashzadeh, Mohammad, and Sara Dadashzadeh. "The Match: A Case Study In Algorithm Analysis Of The National Resident Matching Program." Journal of Business Case Studies (JBCS) 11, no. 4 (2015): 141–50. http://dx.doi.org/10.19030/jbcs.v11i4.9443.
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There are rare opportunities when solving an easily-understood problem can bring together application of skills taught in diverse courses in a Computer Science (CS) or Management Information Systems (MIS) program. This paper presents such an opportunity in the typical database management systems course taught at the junior or senior level. Specifically, we describe the case study of solving the classical Hospitals/Residents problem in Microsoft Access. The solution, based on classical Gale-Shapely algorithm for the Stable Marriage problem, offers pedagogical opportunities in data modeling, algorithm and data structure considerations for program development, Visual Basic for Applications (VBA) and embedded SQL (Structured Query Language) programming, and empirical analysis of running time complexity of algorithms that work remarkably well in teaching students the value of each tool in the toolset they take away from required courses as a part of their undergraduate education in CS or MIS.
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Tejani, Ghanshyam G., Vimal J. Savsani, Vivek K. Patel, and Seyedali Mirjalili. "An improved heat transfer search algorithm for unconstrained optimization problems." Journal of Computational Design and Engineering 6, no. 1 (2018): 13–32. http://dx.doi.org/10.1016/j.jcde.2018.04.003.
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Abstract In this work, an improved heat transfer search (IHTS) algorithm is proposed by incorporating the effect of the simultaneous heat transfer modes and population regeneration in the basic HTS algorithm. The basic HTS algorithm considers only one of the modes of heat transfer (conduction, convection, and radiation) for each generation. In the proposed algorithms, however, the system molecules are considered as the search agents that interact with each other as well as with the surrounding to a state of the thermal equilibrium. Another improvement is the integration of a population regenerator to reduce the probability of local optima stagnation. The population regenerator is applied to the solutions without improvements for a pre-defined number of iterations. The feasibility and effectiveness of the proposed algorithms are investigated by 23 classical benchmark functions and 30 functions extracted from the CEC2014 test suite. Also, two truss design problems are solved to demonstrate the applicability of the proposed algorithms. The results show that the IHTS algorithm is more effective as compared to the HTS algorithm. Moreover, the IHTS algorithm provides very competitive results compared to the existing meta-heuristics in the literature. Highlights An improved Heat Transfer Search (HTS) algorithm is proposed. A novel population regenerator is integrated to the improved HTS. A set of 53 test functions is employed to test the performance of the proposed algorithm. The results are compared with several techniques in the literature.
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Chien, Su Fong, Heng Siong Lim, Michail Alexandros Kourtis, Qiang Ni, Alessio Zappone, and Charilaos C. Zarakovitis. "Quantum-Driven Energy-Efficiency Optimization for Next-Generation Communications Systems." Energies 14, no. 14 (2021): 4090. http://dx.doi.org/10.3390/en14144090.
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The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.
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Negulescu, Alina Eugenia, Sorin Constantin Negulescu, and Ioan Dzitac. "Balancing Between Exploration and Exploitation in ACO." International Journal of Computers Communications & Control 12, no. 2 (2017): 265. http://dx.doi.org/10.15837/ijccc.2017.2.2879.
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In order to balance the preference of the artificial entities towards exploration or exploitation (in their transition rule), a novel technique is proposed for replacing the random function used by the classical Ant Colony Optimization (ACO) algorithms for solving the Traveling Salesman Problem (TSP). The proposed Beta Distribution function (B), or random:betavariate(a; b) has the proven capability (depicted through test-runs) of influencing the algorithm’s solution quality and convergence speed. Consequently, this paper will introduce in the related work section the classical ACO algorithm, with a focus on the transition rule used for choosing the next node in the problem’s associated graph, followed by the related work on this topic, and it will continue with the introduction of the B function which will be presented both from a theoretical and practical perspective in relation with the scope: balancing between exploration and exploitation in order to improve the performance of the ACO algorithm for the TSP. The paper concludes that the B-EAS has the ability to find better solution than EAS for a set of benchmarks from the TSPLib library.
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Liu, Zhiguo, Changqing Ren, and Wenzhu Cai. "Overview of clustering analysis algorithms in unknown protocol recognition." MATEC Web of Conferences 309 (2020): 03008. http://dx.doi.org/10.1051/matecconf/202030903008.
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In the process of identifying unknown protocol of bit stream, the clustering of data sets of bit stream in the protocol is the basis of further identifying unknown protocol. Therefore, on the one hand, this paper analyses the classical clustering algorithms used in unknown protocol recognition from three perspectives: the whole process of clustering analysis, similarity measurement and clustering result evaluation. On the other hand, the development trend of clustering algorithm in unknown protocol recognition is summarized, and other problems in unknown protocol recognition can be solved by clustering algorithm according to the characteristics of bit stream data set, which can provide reference for future research work. Finally, the challenges faced by the Research Institute and the prospects for future work are given.
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Лун, Сюй, Xu Long, Йан Йихуа, Yan Yihua, Чэн Цзюнь, and Cheng Jun. "Guided filtering for solar image/video processing." Solnechno-Zemnaya Fizika 3, no. 2 (2017): 10–17. http://dx.doi.org/10.12737/22596.
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A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image en-hancement algorithms, thus facilitating easier determi-nation of interesting solar burst activities from recorded images/movies.
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Лун, Сюй, Xu Long, Йан Йихуа, Yan Yihua, Чэн Цзюнь, and Cheng Jun. "Guided filtering for solar image/video processing." Solar-Terrestrial Physics 3, no. 2 (2017): 9–15. http://dx.doi.org/10.12737/stp-3220172.
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A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image en-hancement algorithms, thus facilitating easier determi-nation of interesting solar burst activities from recorded images/movies.
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Ye, Gan Zhen, and Dae-Ki Kang. "Extended Evolutionary Algorithms with Stagnation-Based Extinction Protocol." Applied Sciences 11, no. 8 (2021): 3461. http://dx.doi.org/10.3390/app11083461.
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Extinction has been frequently studied by evolutionary biologists and is shown to play a significant role in evolution. The genetic algorithm (GA), one of popular evolutionary algorithms, has been based on key concepts in natural evolution such as selection, crossover, and mutation. Although GA has been widely studied and implemented in many fields, little work has been done to enhance the performance of GA through extinction. In this research, we propose stagnation-driven extinction protocol for genetic algorithm (SDEP-GA), a novel algorithm inspired by the extinction phenomenon in nature, to enhance the performance of classical GA. Experimental results on various benchmark test functions and their comparative analysis indicate the effectiveness of SDEP-GA in terms of avoiding stagnation in the evolution process.
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Ngoko, Yanik, Christophe Cérin, and Denis Trystram. "Solving Sat in a Distributed Cloud: A Portfolio Approach." International Journal of Applied Mathematics and Computer Science 29, no. 2 (2019): 261–74. http://dx.doi.org/10.2478/amcs-2019-0019.
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Abstract We introduce a new parallel and distributed algorithm for the solution of the satisfiability problem. It is based on an algorithm portfolio and is intended to be used for servicing requests in a distributed cloud. The core of our contribution is the modeling of the optimal resource sharing schedule in parallel executions and the proposition of heuristics for its approximation. For this purpose, we reformulate a computational problem introduced in a prior work. The main assumption is that it is possible to learn optimal resource sharing from traces collected on past executions on a representative set of instances. We show that the learning can be formalized as a set coverage problem. Then we propose to solve it by approximation and dynamic programming algorithms based on classical greedy algorithms for the maximum coverage problem. Finally, we conduct an experimental evaluation for comparing the performance of the various algorithms proposed. The results show that some algorithms become more competitive if we intend to determine the trade-off between their quality and the runtime required for their computation.
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Hulianytskyi, Leonid, and Sergii Chornozhuk. "Genetic Algorithm with New Stochastic Greedy Crossover Operator for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 19–29. http://dx.doi.org/10.34229/2707-451x.20.2.3.
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Introduction. The spatial protein structure folding is an important and actual problem in biology. Considering the mathematical model of the task, we can conclude that it comes down to the combinatorial optimization problem. Therefore, genetic and mimetic algorithms can be used to find a solution. The article proposes a genetic algorithm with a new greedy stochastic crossover operator, which differs from classical approaches with paying attention to qualities of possible ancestors. The purpose of the article is to describe a genetic algorithm with a new greedy stochastic crossover operator, reveal its advantages and disadvantages, compare the proposed algorithm with the best-known implementations of genetic and memetic algorithms for the spatial protein structure prediction, and make conclusions with future steps suggestion afterward. Result. The work of the proposed algorithm is compared with others on the basis of 10 known chains with a length of 48 first proposed in [13]. For each of the chain, a global minimum of free energy was already precalculated. The algorithm found 9 out of 10 spatial structures on which a global minimum of free energy is achieved and also demonstrated a better average value of solutions than the comparing algorithms. Conclusion. The quality of the genetic algorithm with the greedy stochastic crossover operator has been experimentally confirmed. Consequently, its further research is promising. For example, research on the selection of optimal algorithm parameters, improving the speed and quality of solutions found through alternative coding or parallelization. Also, it is worth testing the proposed algorithm on datasets with proteins of other lengths for further checks of the algorithm’s validity. Keywords: spatial protein structure, combinatorial optimization, genetic algorithms, crossover operator, stochasticity.
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Fava, Leandro, João Furtado, Gilson Helfer, et al. "A Multi-Start Algorithm for Solving the Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints." Symmetry 13, no. 9 (2021): 1697. http://dx.doi.org/10.3390/sym13091697.
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This work presents a multistart algorithm for solving the capacitated vehicle routing problem with 2D loading constraints (2L-CVRP) allowing for the rotation of goods. Research dedicated to graph theory and symmetry considered the vehicle routing problem as a classical application. This problem has complex aspects that stimulate the use of advanced algorithms and symmetry in graphs. The use of graph modeling of the 2L-CVRP problem by undirected graph allowed the high performance of the algorithm. The developed algorithm is based on metaheuristics, such as the Constructive Genetic Algorithm (CGA) to construct promising initial solutions; a Tabu Search (TS) to improve the initial solutions on the routing problem, and a Large Neighborhood Search (LNS) for the loading subproblem. Although each one of these algorithms allowed to solve parts of the 2L-CVRP, the combination of these three algorithms to solve this problem was unprecedented in the scientific literature. In our approach, a parallel mechanism for checking the loading feasibility of routes was implemented using multithreading programming to improve the performance. Additionally, memory structures such as hash-tables were implemented to save time by storing and querying previously evaluated results for the loading feasibility of routes. For benchmarks, tests were done on well-known instances available in the literature. The results proved that the framework matched or outperformed most of the previous approaches. As the main contribution, this work brings higher quality solutions for large-size instances of the pure CVRP. This paper involves themes related to the symmetry journal, mainly complex algorithms, graphs, search strategies, complexity, graph modeling, and genetic algorithms. In addition, the paper especially focuses on topic-related aspects of special interest to the community involved in symmetry studies, such as graph algorithms and graph theory.
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Suau, Adrien, Gabriel Staffelbach, and Henri Calandra. "Practical Quantum Computing." ACM Transactions on Quantum Computing 2, no. 1 (2021): 1–35. http://dx.doi.org/10.1145/3430030.
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In the last few years, several quantum algorithms that try to address the problem of partial differential equation solving have been devised: on the one hand, “direct” quantum algorithms that aim at encoding the solution of the PDE by executing one large quantum circuit; on the other hand, variational algorithms that approximate the solution of the PDE by executing several small quantum circuits and making profit of classical optimisers. In this work, we propose an experimental study of the costs (in terms of gate number and execution time on a idealised hardware created from realistic gate data) associated with one of the “direct” quantum algorithm: the wave equation solver devised in [32]. We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm. We also explain in great detail the implementation steps and discuss some possibilities of improvements. Finally, our implementation proves experimentally that some PDE can be solved on a quantum computer, even if the direct quantum algorithm chosen will require error-corrected quantum chips, which are not believed to be available in the short-term.
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Memon, Tayab, Paul Beckett, and Amin Z. Sadik. "Sigma-Delta Modulation Based Digital Filter Design Techniques in FPGA." ISRN Electronics 2012 (November 14, 2012): 1–10. http://dx.doi.org/10.5402/2012/538597.
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In this paper efficient digital filter design techniques categorized as sigma-delta modulation based short word length (SWL) and multibit (or contemporary) techniques are reviewed in terms of hardware complexity, area, performance and power tradeoffs, synthesis issues, and algorithm versatility. More recent, general purpose DSP applications including classical LMS algorithms reported using sigma-delta modulation encoding are reviewed thoroughly. A small number of basic arithmetic circuits designed using sigma-delta modulation encoding and synthesized by using FPGAs are also described. Finally, recent FPGA based area-performance-power analysis of single-bit ternary FIR filtering is discussed and compared to its corresponding multi-bit system. This work shows that in most cases single-bit ternary FIR-like filters are able to outperform their equivalent multi-bit filters in terms of area, power, and performance.