A review of stochastic resonance in rotating machine fault detection

Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

Quantum-inspired evolutionary algorithms: a survey and empirical study

Review on improvement for air source heat pump units during frosting and defrosting

The energy consumption in buildings accounts for a significant proportion of total energy and carbon emissions over the world. To reduce the building energy consumption and achieve sustainable development of buildings, nearly zero energy buildings (NZEBs) have attracted widespread attention as a future building energy target. This paper reviews energy-efficient measures (EEMs) and renewable energy technologies (RETs) in NZEBs and discusses the application and suitability of the key technologies, i.e. minimizing the energy demand in NZEBs by EEMs and adopting RETs to meet the remaining energy needs. EEMs contain efficient thermal insulation system, high-performance window system, good airtightness and fresh air heat recovery system. RETs are composed of solar photovoltaic/thermal (PV/T) system, air source heat pump system (ASHP), ground source heat pump system (GSHP), and wind power. In addition, this paper emphasizes further research works, which involve life cycle energy analysis (LCEA), meteorological parameters change, intelligent building operation management (IBOM) system, energy storage system and social policy issues. This paper will provide some references for the further development of NZEBs in different climatic regions in China.

Application and suitability analysis of the key technologies in nearly zero energy buildings in China

Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.

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Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

Experimental study on frosting suppression for a finned-tube evaporator using ultrasonic vibration

Experimental investigation on the defrosting performance of a finned-tube evaporator using intermittent ultrasonic vibration

In this paper, a novel approach to adjusting the weightings of fuzzy neural networks using a Real-coded Chaotic Quantum-inspired genetic Algorithm (RCQGA) is proposed. Fuzzy neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, RCQGA algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional quantum genetic algorithms (QGA) is not satisfactory. In this paper, a real-coded chaotic quantum-inspired genetic algorithm (RCQGA) is proposed based on the chaotic and coherent characters of Q-bits. In this algorithm, real chromosomes are inversely mapped to Q-bits in the solution space. Q-bits probability-guided real cross and chaos mutation are applied to the evolution and searching of real chromosomes. Chromosomes consisting of the weightings of the fuzzy neural network are coded as an adjustable vector with real number components that are searched by the RCQGA. Simulation results have shown that faster convergence of the evolution process in searching for an optimal fuzzy neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy neural network via the RCQGA are demonstrated to illustrate the effectiveness of the proposed method.

Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks

Recently, cross-domain fault diagnosis based on transfer learning methods has been extensively explored and well-addressed when class-balance data with supervision information are available. However, data under machine faulty states are mostly difficult to collect; there is a huge divide between current transfer learning methods based on implicit class-balance data and real industrial applications. In this article, we propose a class-imbalance adversarial transfer learning (CIATL) network with input being imbalanced data to learn domain-invariant and knowledge. Within this framework, class-imbalance learning is embedded into the adversarial training process to learn class-separate diagnostic knowledge with imbalanced data, double-level adversarial transfer learning including marginal and conditional distribution adaptations is conducted to learn domain-invariant knowledge. Extensive experiments on a planetary gearbox rig with imbalanced data verify the effectiveness and generalization of the proposed method and show its superior performance over contrastive transfer learning methods. Moreover, the proposed method relaxes the underlying assumption that the success of current transfer learning regimes is rooted in class-balance data and extends the application of the transfer learning method for real-industrial cross-domain diagnosis tasks.

Tangfei Tao

Papers

Experimental study on frosting suppression for a finned-tube evaporator using ultrasonic vibration

Experimental investigation on the defrosting performance of a finned-tube evaporator using intermittent ultrasonic vibration

Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks

Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data

Experimental study on defrosting mechanism of intermittent ultrasonic resonance for a finned-tube evaporator