Extreme learning machine (ELM) has become a popular topic in machine learning in recent years. ELM is a new kind of single-hidden layer feedforward neural network with an extremely low computational ...cost. ELM, however, has two evident drawbacks: 1) the output weights solved by Moore-Penrose generalized inverse is a least squares minimization issue, which easily suffers from overfitting and 2) the accuracy of ELM is drastically sensitive to the number of hidden neurons so that a large model is usually generated. This brief presents a sparse Bayesian approach for learning the output weights of ELM in classification. The new model, called Sparse Bayesian ELM (SBELM), can resolve these two drawbacks by estimating the marginal likelihood of network outputs and automatically pruning most of the redundant hidden neurons during learning phase, which results in an accurate and compact model. The proposed SBELM is evaluated on wide types of benchmark classification problems, which verifies that the accuracy of SBELM model is relatively insensitive to the number of hidden neurons; and hence a much more compact model is always produced as compared with other state-of-the-art neural network classifiers.
Hysteresis nonlinearity degrades the positioning accuracy of a piezostage and requires a suppression for precision micro-/nanopositioning applications. This paper proposes two new approaches to ...modeling and compensating the rate-dependent hysteresis of a piezostage driven by piezoelectric stack actuators. By formulating the hysteresis modeling as an online nonlinear regression problem, online least squares support vector machine (SVM) (LS-SVM) and online relevance vector machine (RVM) models are proposed to capture the hysteretic behavior. Both hysteresis models are capable of updating continually with subsequent samples. After a comparative study on modeling performances, an inverse model-based feedforward combined with proportional-integral-derivative feedback control is presented to alleviate the hysteresis effect. Experimental results show that the LS-SVM model-based control scheme is over 86% more accurate than the RVM model-based one in the motion tracking task, whereas the latter is 14 times faster than the former in terms of updating time. Moreover, both LS-SVM and RVM model-based control schemes can suppress the rate-dependent hysteresis to a negligible level, which validates the feasibility and effectiveness of the proposed approaches.
•The framework is trained only with single-faults patterns to diagnose simultaneous-faults.•Probabilistic classifier method offers the probabilities of all possible faults.•An optimizer is designed ...to confirm the optimal weight and decision threshold.•A new ensemble method for combining the multi-classifier outputs is proposed.•The framework is superior to the diagnostic system using single classifiers.
Intelligent fault diagnosis of rotating machinery is vital for industries to improve fault prediction performance and reduce the maintenance cost. The new fault diagnostic framework is proposed which consists of three stages: (1) signal processing and feature extraction, (2) fault diagnosis by combining the classification results through a probabilistic ensemble method, and (3) parameter optimization and performance evaluation. In the first stage, ensemble empirical mode decomposition (EEMD) decomposes the acquired signal into a suite of intrinsic mode functions (IMF) which encounters redundant components and large data problems. To eliminate the redundant IMF and select fault feature from residual IMF, correlation coefficient (CC) and singular value decomposition (SVD) method are applied respectively. In the second stage, to improve the performance of fault diagnosis based on single classifier and increase the number of detectable fault, a new probabilistic committee machine (PCM) method is proposed, in which multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM) are individually trained using air ration, ignition pattern and engine sound signal. In addition, each classifier is assigned with an optimal weight in accordance with their reliability and accuracy so that a reliable and widely-covered fault diagnostic result can be obtained from the weighted combination of the members. To verify the effectiveness of the proposed fault diagnostic framework, it is applied to automotive engine fault detection. The evaluation results show the proposed framework is superior to the existing single classifier in terms of both single- and simultaneous-faults in automotive engine.
Mechanical forces play important roles in the regulation of various biological processes at the molecular and cellular level, such as gene expression, adhesion, migration, and cell fate, which are ...essential to the maintenance of tissue homeostasis. In this review, we discuss emerging bioengineered tools enabled by microscale technologies for studying the roles of mechanical forces in cell biology. In addition to traditional mechanobiology experimental techniques, we review recent advances of microelectromechanical systems (MEMS)-based approaches for cell mechanobiology and discuss how microengineered platforms can be used to generate in vivo-like micromechanical environment in in vitro settings for investigating cellular processes in normal and pathophysiological contexts. These capabilities also have significant implications for mechanical control of cell and tissue development and cell-based regenerative therapies.
This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and ...optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective.
•A new modeling framework with optimization of biodiesel ratio for diesel engines.•A new application of kernel-based extreme learning machine to biodiesel engine modelling.•A first application of cuckoo search to biodiesel engine optimization problem.•A flexible objective function for multi-objective optimization of biodiesel ratio.•A comparison of various techniques for biodiesel engine modeling and optimization problem.
Summary
This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front ...steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation.
Amperometric biosensors are widely applied for rapid biomarker detection in physiological and environmental samples. The dynamics and linearity of the current signal, however, are only partially ...understood. This study investigates the diffusion-reaction kinetics of amperometric biosensing using a self-assembled monolayer (SAM) based biosensor for bacterial 16S rRNA. A numerical model is developed to optimize the chamber dimensions and elucidate the concentration dependences of the biosensor. The results revealed that depletion of substrates associated with the chamber dimension can limit the current signal in a target concentration dependent manner. This study provides practical guidelines in the design and interpretation of microfluidic amperometric biosensors for biochemical applications.
Real-time fault diagnostic system is very important to maintain the operation of the gas turbine generator system (GTGS) in power plants, where any abnormal situation will interrupt the electricity ...supply. The GTGS is complicated and has many types of component faults. To prevent from interruption of electricity supply, a reliable and quick response framework for real-time fault diagnosis of the GTGS is necessary. As the architecture and the learning algorithm of extreme learning machine (ELM) are simple and effective respectively, ELM can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). This paper therefore proposes a new application of ELM for building a real-time fault diagnostic system in which data pre-processing techniques are integrated. In terms of data pre-processing, wavelet packet transform and time-domain statistical features are proposed for extraction of vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features in order to shorten the fault identification time and improve accuracy. To evaluate the system performance, a comparison between ELM and the prevailing SVM on the fault detection was conducted. Experimental results show that the proposed diagnostic framework can detect component faults much faster than SVM, while ELM is competitive with SVM in accuracy. This paper is also the first in the literature that explores the superiority of the fault identification time of ELM.
•A novel adaptive AFR control method is proposed for dual-injection engines under biofuel blends.•Lyapunov analysis is used to verify the stability of the proposed controller.•An SI engine is ...retrofitted for operating under dual-injection strategy.•The proposed controller is verified experimentally under three testing conditions.•Comparison with traditional PID-based AFR controller is conducted.
Dual-injection engines, which allow real-time control and injection of two different fuels, are capable of varying the ratio of biofuel blends at different engine operating conditions for optimal engine performance. However, while many experiments have been carried out on these engines to demonstrate their advantages, very few studies have focused on the corresponding air–fuel ratio (AFR) control strategy. In order to achieve stable engine operation, it is essential to maintain transient AFR during the change of fuel blend ratio. Therefore, this study proposes an adaptive controller for AFR control of dual-injection engines. The proposed controller is designed based on a recently developed machine learning method called extreme learning machine, and its stability is verified with Lyapunov analysis. Simulations have been performed on an industry-level engine simulation software to verify the controller. Since dual-injection engines are not available in the market, a spark-ignition engine has been retrofitted for dual-injection operation so that the proposed controller can be implemented and evaluated experimentally. Both simulation and experiment results show that the proposed controller can effectively regulate the AFR to desired level. The results also show that the proposed controller outperforms the engine built-in AFR controller, indicating its significance for dual-injection engines.
Simultaneous-fault diagnosis is a common problem in many applications and well-studied for time-independent patterns. However, most practical applications are of the type of time-dependent patterns. ...In our study of simultaneous-fault diagnosis for time-dependent patterns, two key issues are identified: 1) the features of the multiple single faults are mixed or combined into one pattern which makes accurate diagnosis difficult, 2) the acquisition of a large sample data set of simultaneous faults is costly because of high number of combinations of single faults, resulting in many possible classes of simultaneous-fault training patterns. Under the assumption that the time-frequency features of a simultaneous fault are similar to that of its constituent single faults, these issues can be effectively resolved using our proposed framework combining feature extraction, pairwise probabilistic multi-label classification, and decision threshold optimization. This framework has been applied and verified in automotive engine-ignition system diagnosis based on time-dependent ignition patterns as a test case. Experimental results show that the proposed framework can successfully resolve the issues.
