A fuzzy neural network controller with adaptive learning rates for nonlinear slider-crank mechanism

A fuzzy neural network (FNN) controller with adaptive learning rates is proposed to control a nonlinear mechanism system in this study. First, the network structure and the on-line learning algorithm of the FNN is described. To guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the adaptive learning rates of the FNN. Next, a slider-crank mechanism, which is driven by a permanent magnet (PM) synchronous motor, is studied as an example to demonstrate the effectiveness of the proposed control technique; the FNN controller is implemented to control the slider position of the motor-slider-crank nonlinear mechanism. The robust control performance and learning ability of the proposed FNN controller with adaptive learning rates is demonstrated by simulation and experimental results.     

A perturbation-incremental scheme for studying Hopf bifurcation in delayed differential systems

A new method, called perturbation-incremental scheme (PIS), is presented to investigate the periodic solution derived from Hopf bifurcation due to time delay in a system of first-order delayed differential equations. The method is summarized as three steps, namely linear analysis at critical value, perturba- tion and increment for continuation. The PIS can bypass and avoid the tedious calculation of the center manifold reduction (CMR) and normal form. Meanwhile, the PIS not only inherits the advantages of t...     

Soft-Decoding SOM for VQ Over Wireless Channels

A self-organizing map (SOM) approach for vector quantization (VQ) over wireless channels is presented. We introduce a soft decoding SOM-based robust VQ (RVQ) approach with performance comparable to that of the conventional channel optimized VQ (COVQ) approach. In particular, our SOM approach avoids the time-consuming index assignment process in traditional RVQs and does not require a reliable feedback channel for COVQ-like training. Simulation results show that our approach can offer potential performance gain over the conventional COVQ approach. For data sources with Gaussian distribution, the gain of our approach is demonstrated to be in the range of 1–4 dB. For image data, our approach gives a performance comparable to a sufficiently trained COVQ, and is superior with a similar number of training epoches. To further improve the performance, a SOM–based COVQ approach is also discussed.     

Rapid and Superior Bacteria Killing of Carbon Quantum Dots/ZnO Decorated Injectable Folic Acid‐Conjugated PDA Hydrogel through Dual‐Light Triggered ROS and Membrane Permeability

One of the most difficult challenges in the biomedical field is bacterial infection, which causes tremendous harm to human health. In this work, an injectable hydrogel is synthesized through rapid assembly of dopamine (DA) and folic acid (FA) cross‐linked by transition metal ions (TMIs, i.e., Zn²⁺), which was named as DFT‐hydrogel. Both the two carboxyl groups in the FA molecule and catechol in polydopamine (PDA) easily chelates Zn²⁺ to form metal–ligand coordination, thereby allowing this injectable hydrogel to match the shapes of wounds. In addition, PDA in the hydrogel coated around carbon quantum dot‐decorated ZnO (C/ZnO) nanoparticles (NPs) to rapidly generate reactive oxygen species (ROS) and heat under illumination with 660 and 808 nm light, endows this hybrid hydrogel with great antibacterial efficacy against Staphylococcus aureus (S. aureus, typical Gram‐positive bacteria) and Escherichia coli (E. coli, typical Gram‐negative bacteria). The antibacterial efficacy of the prepared DFT‐C/ZnO‐hydrogel against S. aureus and E. coli under dual‐light irradiation is 99.9%. Importantly, the hydrogels release zinc ions over 12 days, resulting in a sustained antimicrobial effect and promoted fibroblast growth. Thus, this hybrid hydrogel exhibits great potential for the reconstruction of bacteria‐infected tissues, especially exposed wounds.     

Broadband high‐gain slot grid array antenna for millimeter wave applications

A broadband high‐gain slot grid array antenna (SGAA) is proposed in this paper. Based on the electromagnetic complementarity principle, the metal elements in the traditional microstrip grid array antenna (GAA) are replaced by a wide slot element. Compared with the GAA, the proposed SGAA achieves broadband and high‐gain performance. In order to demonstrate this concept, a prototype with 9‐element SGAA is designed using wide slot radiation elements and fabricated on Rogers 5880 printed circuit board (PCB) substrates, which is fed by a 50 Ω coaxial probe. The measured and simulated results show a good agreement. The proposed SGAA achieves a measured peak gain of 14.8 dBi at 26.0 GHz, a 10‐dB impedance bandwidth from 22.2 to 28.5 GHz with a fractional bandwidth of 24.9%. These results indicate that the SGAA is with high performance and it is suitable for the fifth‐generation (5G) millimeter wave (mmW) wireless communication system.     

Performance comparisons of intelligent load forecasting structures and its application to energy-saving load regulation

This study mainly focuses on the development of intelligent forecasting structures via a similar time method with historical load change rates for the hourly, daily and monthly load forecasting simultaneously based on the basic frameworks of fuzzy neural network (FNN) and particle swarm optimization (PSO). In the regulative aspect of network parameters, conventional back-propagation (BP) and PSO tuning algorithms are used, and varied learning rates are designed in the sense of discrete-time Lyapunov stability theory. The performance comparisons of different intelligent forecasting structures including neural network (NN) structure with BP tuning algorithm (NN-BP), FNN structure with BP tuning algorithm (FNN-BP), FNN structure with BP tuning algorithm and varied learning rates (FNN-BP-V), FNN structure with PSO tuning algorithm (FNN-PSO) and newly-designed adaptive PSO (APSO) structure are verified by numerical simulations. In order to verify the effectiveness of the superior APSO forecasting structure in practical energy-saving load regulation, the load forecasting during every 15 min is also given, and its result is used to manipulate the scheduled unloading control of a real case in Taiwan campus.     

Sliding-mode-controlled slider-crank mechanism with fuzzy neuralnetwork

The dynamic response of a sliding-mode-controlled slider-crank mechanism, which is driven by a permanent-magnet (PM) synchronous servo motor, is studied in this paper. First, a position controller is developed based on the principles of sliding-mode control. Moreover, to relax the requirement of the bound of uncertainties in the design of a sliding-mode controller, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which a FNN is adopted to adjust the control gain in a switching control law on line to satisfy the sliding mode condition. In addition, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNN. Numerical and experimental results show that the dynamic behaviors of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances. Furthermore, compared with the sliding-mode controller, smaller control effort results and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller     

SAHPPA: a novel power pinch analysis approach for the design of off-grid hybrid energy systems

This work proposes a novel approach called stand-alone hybrid system power pinch analysis (SAHPPA), which is particularly applicable for the design of off-grid distributed energy generation systems. The enhanced graphical tool employs new ways of utilising the recently introduced demand composite curve and supply composite curve while honouring and adapting fundamental energy systems engineering concepts. The SAHPPA method is capable of optimising the capacity of both the power generators and energy storage for biomass (i.e. non-intermittent) and solar photovoltaic (i.e. intermittent) energy technologies, which is a contribution to the emerging area of power pinch analysis. In addition, the procedure considers all possible efficiency losses in the overall system encompassing the charging–discharging and current inversion processes.     

Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement

The paper targets to devise a genuine Knowledge Management (KM) performance measurement model in a stochastic setting based on Data Envelopment Analysis (DEA), Monte Carlo simulation and Genetic Algorithm (GA). The proposed model evaluates KM using a set of proxy measures correlated with the major KM processes. Data Collection Budget Allocation (DCBA) that maximizes the model accuracy is determined using GA. Additional data are generated and analyzed using a Monte-Carlo-enhanced DEA model to obtain the overall KM efficiency and KM processes’ efficiency scores. An application of the model has been carried out to evaluate KM performance in higher educational institutions. It is found that with GA, the accuracy of the model has been greatly improved. Lastly, comparing with a conventional deterministic DEA model, the results from the proposed model would be more useful for managers to determine future strategies to improve their KM.