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A Novel Energy-Efficient MIMO-OFDM Decoder Architecture with Error Detection

IETE JOURNAL OF RESEARCH 2023年第4期69卷 1974-1984页

作者： Preethi, D. Valarmathi, R. S. Bannari Amman Inst Technol Dept Elect & Commun Engn Erode Tamil Nadu India

Medical data transmission is a major challenge in wireless communication to preserve their integrity and coherence. As the transmission takes place at high data rate, the complexity and throughput of the system, pertaining to the fetal ECG signal, is crucial. In addition to the accumulation of noises in the channel, the observation of fetal ECG signal, using diagnostic devices, also provides additive noise, thereby reducing the quality of the transmitted signal. The major motive of this paper is to demarcate the fetal ECG signal from noise at early stages of transmission using Chambolle-Pock based hybridized classifiers. This technique provides sensitivity of 91.1% followed by performance validation of Error Detection and correction adders for different decoder architectures in transceiver. Thereby area and power reduction of 86.3% and 91.1% is achieved. The simulations are carried out with MIT-BIH Arrhythmia using simulation tools, such as MATLAB 2013b, ModelSim 10.0b, and Cadence Virtuoso under 90 nm on over 200 data sets.

关键词： Additive noises Decoder architectures Error detection and correction adders evolution algorithms

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Practical performance models of algorithms in evolutionary program induction and other domains

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ARTIFICIAL INTELLIGENCE 2010年第15期174卷 1254-1276页

作者： Graff, Mario Poli, Riccardo Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems - symbolic regression and Boolean function induction - and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation. (c) 2010 Elsevier B.V. All rights reserved.

关键词： evolution algorithms Program induction Performance prediction Algorithm taxonomies Algorithm selection problem

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A model-based flexural rotor vibration control in cage induction electrical machines by a built-in force actuator

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ELECTRICAL ENGINEERING 2008年第6期90卷 407-421页

作者： Laiho, Antti Tammi, Kari Zenger, Kai Arkkio, Antero VTT Tech Res Ctr Finland Espoo 02044 Finland Aalto Univ Control Engn Lab Helsinki 02015 Finland Aalto Univ Lab Electromech Helsinki 02015 Finland

In this paper active control of flexural rotor vibration in electrical machines is examined. We consider attenuation of discrete low-frequency range forced vibration components by means of an adaptive harmonic control strategy. A built-in force actuator for actively generating force on the machine rotor is investigated. Previously, such an actuator has mainly been used in bearingless machine design for rotor levitation. The action of the actuator is based on electromechanical interaction between the rotor and the stator of the machine. A low-order linear parametric state-space model is derived for the actuator-rotor system. Parameter estimation is carried out using simulation data obtained from a detailed two-dimensional time-stepping finite element field-circuit model of the machine. Hence, model-based control design is performed using the identified model. The controller is verified by embedding it into the finite element analysis. As a result we present a virtual plant of the machine with vibration control. The virtual plant is introduced as a means of vibration control design prior to implementing the control algorithms in a real machine. Simulation results using real machine data and finite element time-stepping method are presented.

关键词： vibration control rotordynamics electrical machines bearingless drives self-bearing machines electromechanics adaptive harmonic control force actuation unbalanced magnetic pull system identification evolution algorithms

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Decomposing the user-preference in multiobjective optimization

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SOFT COMPUTING 2016年第10期20卷 4005-4021页

作者： Yu, Guo Zheng, Jinhua Shen, Ruimin Li, Miqing Xiangtan Univ Dept Informat Engn Xiangtan 411100 Hunan Peoples R China Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England

Preference information (such as the reference point) of the decision maker (DM) is often used in multiobjective optimization;however, the location of the specified reference point has a detrimental effect on the performance of multiobjective evolutionary algorithms (MOEAs). Inspired by multiobjective evolutionary algorithm-based decomposition (MOEA/D), this paper proposes an MOEA to decompose the preference information of the reference point specified by the DM into a number of scalar optimization subproblems and deals with them simultaneously (called MOEA/D-PRE). This paper presents an approach of iterative weight to map the desired region of the DM, which makes the algorithm easily obtain the desired region. Experimental results have demonstrated that the proposed algorithm outperforms two popular preference-based approaches, g-dominance and r-dominance, on continuous multiobjective optimization problems (MOPs), especially on many-objective optimization problems. Moreover, this study develops distinct models to satisfy different needs of the DM, thus providing a new way to deal with preference-based multiobjective optimization. Additionally, in terms of the shortcoming of MOEA/D-PRE, an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems.

关键词： Decomposition evolution algorithms Preference information Decision maker Iterative weight

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BESS: Balanced evolutionary semi-stacking for disease detection using partially labeled imbalanced data

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INFORMATION SCIENCES 2022年 594卷 233-248页

作者： Ning, Zhihan Ye, Ziqing Jiang, Zhixing Zhang, David Chinese Univ Hongkong Shenzhen Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

Machine learning offers automatic and objective approaches for disease detection based on biomedical data. However, 1) the percentage of patients in the real world is smaller than that of healthy people;2) the annotations by medical experts are costly. Therefore, medical datasets are often imbalanced and partially labeled. To address this problem, we propose balanced evolutionary semi-stacking (BESS) for disease detection using partially labeled imbalanced (PLI) data. BESS aims to detect illnesses by considering the input of the color, texture, and geometry features extracted from tongue images. Specifically, the proposed method first mitigates the class imbalance problem and leverages the unlabeled data through the so-called balanced evolutionary co-training approach. Then BESS exploits both the data and classifier diversity obtained by balanced evolutionary co-training to improve the performance of the stacking ensemble. We quantitatively evaluate the proposed algorithm based on the PLI tongue image database. BESS achieves the best performance in detecting diabetes mellitus, chronic kidney disease, breast cancer, and chronic gastritis, compared to other state-of-the-art methods. The results of the experiments substantiate the superiority and effectiveness of the proposed algorithm. Codes and datasets have been made publicly available at url: https://***/CUHKSZ-NING/Balanced-evolutionary-Semi-Stacking. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Disease detection Semi-supervised learning Imbalanced data Tongue features evolution algorithms

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Optimal brick layout of masonry walls based on intelligent evolutionary algorithm and building information modeling

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AUTOMATION IN CONSTRUCTION 2021年 129卷 103824-103824页

作者： Xu, Chengran Liu, Jiepeng Li, Sheng Wu, Zhou Chen, Y. Frank Chongqing Univ Sch Civil Engn 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Automat 174 Shazheng St Chongqing 400044 Peoples R China Penn State Univ Dept Civil Engn Middletown PA 17057 USA

The Masonry wall (MW) is an important element of buildings. Currently, the detailed brick layout of a nonmodular MW mostly relies on the experience from a bricklayer. The manual building information modeling (BIM) technique can facilitate the modeling of bricks, but is error-prone and time-consuming. This paper presents a BIM-based framework to generate the layouts and brick take-offs automatically. A computation module is developed to find the optimal brick layout using the three-stage optimization. For a MW, the first-stage determines the vertical position of each course and the second and third stages obtain the horizontal positions and sizes of bricks for odd and even courses respectively. The intelligent algorithm neighborhood field optimization is applied to the computation module. The proposed framework is applied in the automated detailing design of MWs with different bonds in two rooms to demonstrate its practicability and efficiency.

关键词： Masonry walls (MWs) Detailing design Brick layout evolution algorithms Building information modeling (BIM)

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Optimization of Holding Force for a Climbing Robot Based on a Differential evolutionary Algorithm

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年第1期17卷 54-59页

作者： Masike, Rujeko Kaur, Karamjit Arora, Rajesh Shridhara, Somalapura Nagappa Amity Univ Haryana Amity Sch Engn & Technol Dept ECE Panchgaon India HITS Univ Hindustan Inst Technol & Sci Deemed Univ Chennai India Amity Univ Haryana Amity Sch Engn & Technol Panchgaon India

Background: The advancements in robotic technology have completely revolutionized day-to-day life. In industrial applications, the implementation of robotics is quite advantageous as it may help in performing dangerous tasks like climbing high walls, working in a high-temperature environment, high radiation exposure conditions ***;This paper presents the design and development of a wall-climbing robot for dam wall inspection using an adaptive aerodynamic adhesion technique. The optimization of a robot design is done using a differential evolutionary ***: In the proposed model, the principle of Bernoulli adhesion is used for designing the suction pad. The optimization of various variables is done using a differential evolutionary algorithm to improve the efficiency and effectiveness of the wall climbing robot ***: The results of the proposed system show that the approach can find an optimal holding force and can be effectively used for applications like dam wall climbing for inspection.

关键词： Bernoulli pad wall-climbing robot evolution algorithms adaptive holding force dynamic variations variable optimization climbing robot

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Chaotic attractors of discrete dynamical systems used in the core of evolutionary algorithms: state of art and perspectives

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JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS 2023年第9-12期29卷 1202-1227页

作者： Zelinka, Ivan Senkerik, Roman VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic Tomas Bata Univ Zlin Fac Appl Informat AI Lab Zlin Czech Republic Tomas Bata Univ Zlin Fac Appl Informat AI Lab TG Masaryka 5555 Zlin 76001 Czech Republic

This paper delves into the intersection between discrete dynamical systems and bio-inspired metaheuristic algorithms. Chaos, an inherent phenomenon manifested by both continuous and discrete dynamic nonlinear systems has become an essential component of engineering design. This study examines the relationship between chaos and bio-inspired metaheuristic algorithms from two distinct angles: the presence of chaos within the realm of algorithms, and the application of bio-inspired algorithms for the identification, control, or synthesis of complex systems. Furthermore, this paper offers an in-depth exploration of the interplay between metaheuristics and complex nonlinear dynamics, including potential avenues for future research. The presented simulation studies, along with the design of objective functions for optimization and the implementation of metaheuristics, serve as a valuable foundation for subsequent experiments involving discrete nonlinear systems and complex systems with time delays. Such systems may benefit from multiparametric or multicriteria optimization approaches, paving the way for novel advancements in the field.

关键词： Deterministic chaos metaheuristics evolution algorithms swarm intelligence discrete chaotic dynamical systems chaotic maps

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Testing and Verification of the Deep Neural Networks Against Sparse Pixel Defects 1

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41st International Conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Szczepankiewicz, Michal Radlak, Krystian Szczepankiewicz, Karolina Popowicz, Adam Zawistowski, Pawel NVIDIA Warsaw Poland Warsaw Univ Technol Warsaw Poland Vay Technol Berlin Germany Silesian Tech Univ Gliwice Poland

ISBN: (数字)9783031148620

ISBN: (纸本)9783031148620;9783031148613

Deep neural networks can produce outstanding results when applied to image recognition tasks but are susceptible to image defects and modifications. Substantial degradation of the image can be detected by automatic or interactive prevention techniques. However, sparse pixel defects may have a significant impact on the dependability of safety-critical systems, especially autonomous driving vehicles. Such perturbations can limit the perception capabilities of the system while remaining undetected by human observer. The effective generation of such cases facilitates the simulation of real-life challenges caused by sparse pixel defects, like occluded or stained objects. This work introduces a novel sparse adversarial attack generation method based on differential evolution strategy. Additionally, we introduce a novel framework for sparse adversarial attack generation, which can be integrated into the safety-critical systems development process. An empirical evaluation demonstrates that the proposed method outperforms and complements state-of-the-art techniques allowing for complete evaluation of an image recognition system.

关键词： Dependability Adversarial attacks Deep learning evolution algorithms Differential evolution

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Hyperspectral imagery vegetation index and temporal analysis for corn yield estimation

Hyperspectral imagery vegetation index and temporal analysis...

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Conference on Monitoring Food Safety, Agriculture and Plant Health

作者： Yao, HB Tian, L Univ Illinois Dept Agr & Biol Engn Urbana IL 61801 USA

ISBN: (纸本)0819451606

Aerial hyperspectral imagery has been used to find the temporal relationship between image and corn yield. A total of five hyperspectral images were taken during the growing season. For each image, the optimal vegetation index was selected among many candidate vegetation indices. At the same time, the optimal band subset was selected to calculate the vegetation index. The optimal band subset has the minimum number of bands and represents the most significant image bands (or wavelength) for yield prediction. The optimization process used the EAVI (evolutionary Algorithm based Vegetation Index generation) algorithm. Results showed that the EAVI algorithm generated the best vegetation index among many comparison indices for yield estimation. For image taken at different date, the algorithm selected a different optimal vegetation index and image bands. The most common sensitive wavelength identified was in the red edge at 700 nm and in the NIR region at 826 nm. This study showed that images taken from the beginning of full canopy coverage to the corn ear formation period provided the best and stable result for corn yield estimation. It is suggested that this period of time during the growing season would have great potential for remote sensing based corn yield prediction.

关键词： remote sensing aerial hyperspectral image yield monitoring temporal resolution vegetation index evolution algorithms

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