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A CNN SAT-solver robust to noise

A CNN SAT-solver robust to noise

引用

IEEE international workshop on Cellular Nanoscale Networks and their applications, (CNNA)

作者： Botond Molnár Róbert Sumi Mária Ercsey-Ravasz Faculty of Physics Babe§-Bolyai University Cluj-Napoca Romania

In a recent study we presented a cellular neural network model for solving the Boolean satisfiability (SAT) problem. When solving hard problems the CNN presents transiently chaotic dynamics, raising the question of the viability of the system in presence of noise, which is unavoidable on analog devices. Here we test the robustness of the system in presence of white and colored (1/f 2 ) noises. We also test the effects of potential errors in connection weights. The obtained results show that the probability of finding a solution is robust to noise and the developed CNN model tolerates surprisingly large noise intensities. Noise can even improve the performance by increasing the optimal parameter region of the model.

关键词： Robustness Physics White noise Colored noise optimization Heuristic algorithms

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Hybrid Artificial Intelligent Systems 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Emilio Corchado Václav Snášel Ajith Abraham Michał Woźniak Manuel Graña Sung-Bae Cho

ISBN: (数字)9783642289316

ISBN: (纸本)9783642289309

The two LNAI volumes 7208 and 7209 constitute the proceedings of the 7th international Conference on Hybrid Artificial Intelligent Systems, HAIS 2012, held in Salamanca, Spain, in March 2012. The 118 papers published in these proceedings were carefully reviewed and selected from 293 submissions. They are organized in topical sessions on agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, systems, man, and cybernetics by HAIS workshop, methods of classifier fusion, HAIS for computer security (HAISFCS), data mining: data preparation and analysis, hybrid artificial intelligence systems in management of production systems, hybrid artificial intelligent systems for ordinal regression, hybrid metaheuristics for combinatorial optimization and modelling complex systems, hybrid computational intelligence and lattice computing for image and signal processing and nonstationary models of pattern recognition and classifier combinations.

关键词： Artificial Intelligence Algorithm Analysis and Problem Complexity Information Systems applications (incl. Internet) Computation by Abstract Devices Database Management Information Storage and Retrieval

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Hybrid Artificial Intelligent Systems 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Emilio Corchado Václav Snášel Ajith Abraham Michał Woźniak Manuel Graña Sung-Bae Cho

ISBN: (数字)9783642289422

ISBN: (纸本)9783642289415

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8th international Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

8th International Conference on Scale Space and Variational ...

引用

8th international Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

ISBN: (纸本)9783030755485

The proceedings contain 45 papers. The special focus in this conference is on Scale Space and Variational Methods in Computer Vision. The topics include: Multiscale Registration;challenges for Optical Flow Estimates in Elastography;an Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation;low-Rank Registration of Images Captured Under Unknown, Varying Lighting;towards Efficient Time Stepping for Numerical Shape Correspondence;first Order Locally Orderless Registration;first-Order Geometric multilevel optimization for Discrete Tomography;bregman Proximal Gradient algorithms for Deep Matrix Factorization;Hessian Initialization Strategies for -BFGS Solving Non-linear Inverse Problems;inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting;quantisation Scale-Spaces;A Scaled and Adaptive FISTA Algorithm for Signal-Dependent Sparse Image Super-Resolution Problems;Convergence Properties of a Randomized Primal-Dual Algorithm with applications to Parallel MRI;wasserstein Generative Models for Patch-Based Texture Synthesis;sketched Learning for Image Denoising;Translating Numerical Concepts for PDEs into Neural Architectures;CLIP: Cheap Lipschitz Training of Neural Networks;variational Models for Signal Processing with Graph Neural Networks;synthetic Images as a Regularity Prior for Image Restoration Neural Networks;geometric Deformation on Objects: Unsupervised Image Manipulation via Conjugation;learning Local Regularization for Variational Image Restoration;Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations;on the Correspondence Between Replicator Dynamics and Assignment Flows;learning Linear Assignment Flows for Image Labeling via Exponential Integration;on the Geometric Mechanics of Assignment Flows for Metric Data Labeling;a Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration.

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An Intrusion Detection Model Based on Improved Whale optimization Algorithm and XGBoost

An Intrusion Detection Model Based on Improved Whale Optimiz...

引用

IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Xinlu Zong Ruicheng Li Zhiwei Ye Hubei University of Technology Wuhan China

ISBN: (纸本)9781665426060

Hyperparameters optimization is an effective way to improve the detection performance of intrusion detection models based on machine learning. However, hyperparameters optimization is a typical NP-hard problem. Therefore, it is difficult to find the optimal parameters within an acceptable time by using traditional parameters optimization methods. To solve this problem, this paper proposed an intrusion detection model named IWOA-XGB which combines improved Whale optimization Algorithm (WOA) and eXtreme Gradient Boosting (XGBoost). Firstly, the spiral update maneuver of the original WOA is modified by setting the parameter $l$ to a linearly decreasing random value. Then, the improved WOA is used to optimize several valued parameters of tree booster in the XGBoost. Finally, the optimized XGBoost is applied for intrusion detection so that the performance of the intrusion detection model can be improved. To evaluate the efficacy of IWOA-XGB, the NSL-KDD intrusion detection data set is used for simulation tests. The simulation results show that compared to the original XGBoost and other evolutionary algorithms, IWOA-XGB could effectively improve the macro F1-score of the detection model, demonstrating good intrusion detection performance.

关键词： Spirals algorithms NP-hard problem Computational modeling Simulation Intrusion detection optimization methods

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A pattern-based evolving mechanism for genetic algorithm to solve combinatorial optimization problems

A pattern-based evolving mechanism for genetic algorithm to ...

引用

IEEE workshop on Soft Computing in Industrial applications (SMCia)

作者： Qing Wang Kai Leung Yung Wai Hung Ip Department of Industrial and Systems Engineering Hong Kong Polytechnic University Kowloon Hong Kong China

ISBN: (纸本)0780378555

The combinatorial optimization problem always is ubiquitous in various applications and has been proved to be well known NP-hard problem that classical mathematical methods cannot solve within the polynomial time. To solve it, many approaches have been developed to find best or near best solutions. As one of such approaches, genetic algorithm is well known as being able to find satisfied solution within acceptable time, it is controlled by evolving mechanism to achieve optimization searching in the solutions space. In this paper, we propose a new evolving mechanism for GA to improve the solution quality and searching efficiency as well. This evolving mechanism can extract a generalized pattern from elite individuals in the whole population. The pattern is used to determine the selection probability to experience the genetic operations such as crossover, mutation, replication, etc. moreover, the evolving mechanism includes a replacement mechanism to substitute the worse individual for the potential excellent individual to expand searching space. The computation results show that the proposed evolving mechanism can work effectively and find satisfactory solutions better than traditional evolving mechanisms, even though the solution space increases with the problem size.

关键词： Genetic algorithms Evolution (biology) optimization methods Polynomials Genetic mutations Resource management State-space methods Systems engineering and theory NP-hard problem Mathematics

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Rapid antenna prototyping method by evolutionary computation and inkjet printing

Rapid antenna prototyping method by evolutionary computation...

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IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： Xinyang Shi Yoshiaki Narusue Yoshihiro Kawahara Tohru Asami Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Japan Society for the Promotion of Science Kojimachi Business Center Building Tokyo Japan JST PRESTO Research Division Gobancho Building Tokyo Japan

Antenna designs using evolutionary computation methods have been widely used because of the rapid development of wireless technologies. However, a large number of antennas must be analyzed when evolutionary computation methods are adopted. Running a simulation is the main method of obtaining information about an antenna, such as bandwidth and input impedance, but it is very time-consuming because complicated calculations are needed. In this paper, we proposed a method that quickly realizes optimization of structures of inkjet-printed antennas. We propose the use of inkjet technology to implement candidates generated by evolutionary computation methods and use the measurement results instead of complex calculations. This method also solves the problem in which the fitness of candidates is sometimes difficult to directly realize from simulations. This paper presents the optimization of an inkjet-printed antenna for energy harvesting from TV broadcast waves using the proposed technique and a genetic algorithm. An antenna in which the output voltage is 1.4 times that of a rectangular dipole was designed.

关键词： Dipole antennas Antenna measurements optimization Voltage measurement TV broadcasting Genetic algorithms

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Neural Network Learning Based on Chaotic Imperialist Competitive Algorithm

Neural Network Learning Based on Chaotic Imperialist Competi...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Marjan Abdechiri Karim Faez Helena Bahrami Department of Electrical Computer & IT Qazvin Azad University Qazvin Iran Department of EE Amirkabir University of Technology슠 Tehran Iran

ISBN: (纸本)9781424458721;9781424458745

Imperialist Competitive Algorithm (ICA) is a novel optimization algorithm that inspired by socio-political process of imperialistic competition. ICA shown its excellent capability in diverse optimization tasks. In this paper, a new method for training an Artificial Neural Network using Chaotic Imperialist Competitive Algorithm is proposed. In Chaotic Imperialist Competitive Algorithm (CICA) the chaos theory has been used to adjust the movement angle of colonies towards the imperialists. Using Chaotic Imperialist Competitive Algorithm (CICA), the weights of the Neural Network in its training phase are updated. In this paper, a multi layer Perceptron Neural Network used for prediction of the maximum worth of the stocks change in Tehran's Bourse Market. We trained this Neural Network with CICA, ICA, PSO and GA algorithms and compared the experimental results obtained from these four methods. The consideration of results showed that the training and test error of the network trained by CICA algorithm has been reduced in comparison to the other three methods. However, the run time of the proposed algorithm in training the neural network is less than PSO and GA algorithm it is a little more than ICA algorithm.

关键词： Neural networks Chaos Independent component analysis Artificial neural networks Evolutionary computation Absorption optimization methods Testing Computer networks Pattern recognition

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Adaptive trained filters with SSIM optimization and pixel value truncation for video upscaling

Adaptive trained filters with SSIM optimization and pixel va...

引用

IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Zenoviy Veres Adrian Nakonechny Lviv Polytechnic National University Lviv Ukraine

The adaptive trained filter for video upscaling based on optimization of structural similarity index is presented. Experimental results show that proposed algorithm has improved results for upscaled videos with low bi... 详细信息

关键词： Information filters Training Filtering algorithms Interpolation High definition video Image quality

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Performance evaluation of symmetric cryptography in embedded systems

Performance evaluation of symmetric cryptography in embedded...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Ondrej Hyncica Pavel Kucera Petr Honzik Petr Fiedler Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

We consider the problem of implementing security algorithms into embedded systems deployed in automation applications. Such systems are typically built on embedded microcontrollers with limited resources and as hardware changes may not be possible or convenient, the software based cryptography is a suitable solution. In this paper we present results of performance benchmarks of different software-implemented symmetric cryptography algorithms on 8 and 16-bit microcontroller platforms. The contribution of the work is in comparing performance of different algorithms, embedded microcontroller platforms, effects of optimizations and different implementations.

关键词： Microcontrollers Encryption optimization Libraries Throughput Software algorithms

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