The monolithic integration system of bulk-Si sensors and actuators based on the Au–Au bonding technology

The monolithic integration of bulk-Si MEMS sensors and actuators using the Au–Au bonding technology is presented in this work. With this technology, the substrate isolation and electrical interconnection between the CMOS circuits and bulk-Si CMOS MEMS sensors are achieved perfectly. In order to demonstrate this technology, a pressure sensor and an electrothermal actuator have been monolithically designed and fabricated. In addition, by sputtering aluminium on the bulk silicon electrothermal actuator, the electrothermal actuator is modified and the sensitivity of the actuator is obviously improved. The measurement results show that the sensitivity of the pressure sensor is 0.29fF/hPa. And the sensitivities of the modified and non-modified electrothermal actuators are 20.23 μm/V and 3.34 μm/V, respectively. Compared with the non-modified electrothermal actuators, the sensitivity of the modified electrothermal actuators is improved by 6 times.     

Microinstrument contact force sensing based on cable tension using BLSTM–MLP network

Minimally invasive surgical robotic systems established the foundation for precise and refined surgery, and the intelligentization of robotic systems is an important direction for future development. Among the methods of intelligentization, microinstrument external force sensing is an open and challenging research area. Force sensing information is used not only to ensure that surgeons apply the appropriate amount of force but also to prevent unintentional tissue damage. Because a microinstrument is a compact and small-sized construction, indirect force sensing method instead of the integration of sensors into the microinstrument is used, yielding better biocompatibility, sterilizability and monetary cost savings. This paper focuses on microinstrument-tissue contact force sensing, and the microinstrument used is a three degrees of freedom cable-driven manipulator. A contact force estimation strategy based on the differences in cable tension is established with consideration of the kinematics, dynamics and friction of the manipulator. A principle prototype of a surgical microinstrument force measurement system is developed, and then zero-drift, hysteresis and force loading experiments are studied. Based on the experimental data of the force loading experiments, the relationship between cable tension and contact forces is established by using a bidirectional long short-term memory plus multilayer perceptron network. The results show that the L2 cost of the network in the training set converges to 0.006 and that the RMSE of the network in the testing set converges to 0.053, and the network can meet the measurement requirements without overfitting. Therefore, the indirect force estimation method is a viable method of measuring forces of cable-driven microinstrument and can be used to integrate force sensing information into surgical robotic systems to improve the operability of surgical robots.

     

Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules

In this paper, we develop a diagnosis model based on particle swarm optimization (PSO), support vector machines (SVMs) and association rules (ARs) to diagnose erythemato-squamous diseases. The proposed model consists of two stages: first, AR is used to select the optimal feature subset from the original feature set; then a PSO based approach for parameter determination of SVM is developed to find the best parameters of kernel function (based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy, and PSO is a promising tool for global searching). Experimental results show that the proposed AR_PSO–SVM model achieves 98.91% classification accuracy using 24 features of the erythemato-squamous diseases dataset taken from UCI (University of California at Irvine) machine learning database. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.     

Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information

In this study, a novel digital modulation classification model has been proposed for automatically recognizing six different modulation types including amplitude shift keying (ASK), frequency shift keying (FSK), phase-shift keying (PSK), quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase-shift keying (QPSK). The determination of modulation type is significant in military communication, satellite communication systems, and submarine communication. To classify the modulation types, we have proposed a two-stage hybrid method combining short-time Fourier transform (STFT) and convolutional neural network (CNN). In the first stage, as the data source, the time–frequency information from these modulation signals have been extracted with STFT. This information has been obtained as 2D images to feed the input of the CNN deep learning method. In the second stage, the obtained 2D time–frequency information has been given to the input of the CNN algorithm to classify the modulation types. In this work, noises at various SNR values from 0 dB to 25 dB were created and added to the modulated signals. Even in the presence of noise, the proposed hybrid deep learning model achieved excellent results in the noised-modulation signals.     

Hyperspectral anomaly detection using spectral–spatial features based on the human visual system

ABSTRACT

Anomaly detection (AD) is one of the most attracting topics within the recent 10 years in hyperspectral imagery (HSI). The goal of the AD is to label the pixels with significant spectral or spatial differences to their neighbours, as targets. In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the retina and the visual cortex functionality, the multiscale multiresolution analysis is applied to some principal components of hyperspectral data, to extract features from different spatial levels of the image. Then the global and local relations between features are considered based on inspiring the visual attention mechanism and inferotemporal (IT) part of the visual cortex. The effects of the attention mechanism are implemented using the logarithmic function which well highlights, small variations in pixels’ grey levels in global features. Also, the maximum operation is used over the local features for imitating the function of IT. Finally, the information theory concept is used for generating the final detection map by weighting the global and local detection maps to obtain the final anomaly map. The result of the proposed method is compared with some state-of-the-art methods such as SSRAD, FLD, PCA, RX, KPCA, and AED for two well-known real hyperspectral data which are San Diego airport and Pavia city, and a synthetic hyperspectral data. The results demonstrate that the proposed method effectively improves the AD capabilities, such as enhancement of the detection rate, reducing the false alarm rate and the computation complexity.     

Object-oriented change detection based on the Kolmogorov–Smirnov test using high-resolution multispectral imagery

In this article, we propose a novel method of object-oriented change detection for high-resolution remote-sensing imagery. The method consists of three main parts: image segmentation, object adjusting and change detection. We use the Fractal Net Evolution Approach to segment the multi-temporal images. Then we adjust the object maps. By merging the objects in relatively large areas, the object -adjusting algorithm aims to obtain a set of objects with different sizes, which coincide better with the real ground objects than the single-scale results. In the third part, the Kolmogorov–Smirnov two-sample test detects each pair of objects in the multi-temporal object maps with multi-scale. The calculated value of the D-statistic is compared to the threshold of a user-defined significance level. Through these three processes, we can make full use of the spatial and spectral features in high- resolution images to detect changes. According to our experiments in two study areas employing QuickBird imagery, the overall errors of our method decreased by more than 1000 pixels compared with the conventional object-oriented change vector analysis. The proposed method can also avoid the errors resulting from classification in the method of post-classification comparison.     

Orchard identification using landform and landscape factors based on a spatial–temporal classification framework

Ecological restoration measures have been undertaken in loess hilly and gully regions since the 1970s to prevent soil loss and to improve the ecological environment in those regions. Orchard construction was the main ecological measure undertaken in the Luo-Yu-Gou watershed, and in this article we propose a coupled maximum a posteriori decision rule and Markov random field (MAP-MRF) framework for orchard identification based on landform and landscape factors. Support vector machine (SVM) classification was first performed to obtain initial classification results for the years 2003 and 2008. A series of factors including landform factor, landscape factor, and the spatial–temporal neighbourhood factor are used to obtain land-cover change information including the change in orchard class. Finally, field experiments were carried out in the case study region of the Luo-Yu-Gou watershed, and based on the experimental results, it was found that the quantity error and the allocation error of the classification results for 2008 were 0.0441 and 0.1037, respectively.     

A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems

pantograph–catenary system is one of the critical components used in electrical trains. It ensures the transmission of the electrical energy to the train taken from the substation that is required for electrical trains. The condition monitoring and early diagnosis for pantograph–catenary systems are very important in terms of rail transport disruption. In this study, a new method is proposed for arc detection in the pantograph–catenary system based signal processing and S-transform. Arc detection and condition monitoring were achieved by using current signals received from a real pantograph–catenary system. Firstly, model based current data for pantograph–catenary system is obtained from Mayr arc model. The method with S-transform is developed by using this current data. Noises on the current signal are eliminated by applying a low pass filter to the current signal. The peak values of the noiseless signals are determined by taking absolute values of these signals in a certain frequency range. After the data of the peak points has been normalized, a new signal will be obtained by combining these points via a linear interpolation method. The frequency-time analysis was realized by applying S-transform on the signal obtained from peak values. Feature extraction that obtained by S-matrix was used in the fuzzy system. The current signal is detected the contdition as healthy or faulty by using the outputs of the fuzzy system. Furthermore the real-time processing of the proposed method is examined by applying to the current signal received from a locomotive.     

System reliability for non-repairable multi-state series–parallel system using fuzzy Bayesian inference based on prior interval probabilities

In this article, the fuzzy concepts are applied in analysis of the system reliability problem. The fuzzy number is used to construct the fuzzy reliability of the non-repairable multi-state series–parallel system (NMSS). The fuzzy failure rate function is represented by an exponential fuzzy number. By using this innovative approach, the fuzzy system reliability of NMSS is created. In order to analyse this fuzzy system reliability, the fuzzy Bayesian point estimate of fuzzy system reliability is made by the conventional Bayesian formula. And, the posterior fuzzy system reliability of NMSS is developed by Bayesian inference with fuzzy probabilities. Finally, the performance of the method is measured by the mean square error of fuzzy Bayesian point estimate for the fuzzy system reliability of NMSS.     

Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation

Most of the reported prognostic techniques use a small number of condition indicators and/or use a thresholding strategies in order to predict the remaining useful life (RUL). In this paper, we propose a reliability-based prognostic methodology that uses condition monitoring (CM) data which can deal with any number of condition indicators, without selecting the most significant ones, as many methods propose. Moreover, it does not depend on any thresholding strategies provided by the maintenance experts to separate normal and abnormal values of condition indicators. The proposed prognostic methodology uses both the age and CM data as inputs to estimate the RUL. The key idea behind this methodology is that, it uses Kaplan–Meier as a time-driven estimation technique, and logical analysis of data as an event-driven diagnostic technique to reflect the effect of the operating conditions on the age of the monitored equipment. The performance of the estimated RUL is measured in terms of the difference between the predicted and the actual RUL of the monitored equipment. A comparison between the proposed methodology and one of the common RUL prediction technique; Cox proportional hazard model, is given in this paper. A common dataset in the field of prognostics is employed to evaluate the proposed methodology.     

Robust synchronisation tracking control of networked Euler–Lagrange systems using reference trajectory estimation based on virtual double-integrators

This paper considers the problem of distributed synchronisation tracking control of multiple Euler–Lagrange systems on a directed graph which contains a spanning tree with the leader node being the root. To design the high performance distributed controllers, a virtual double-integrator is introduced in each agent and is controlled by a virtual distributed linear high-gain synchronisation tracking controller, so that the position and velocity of each agent track those of the reference trajectory with arbitrarily short transient time and small ultimate tracking error. Then taking the double-integrator's position and velocity as the estimates of those of the reference trajectory, in each generalised coordinate of each Euler–Lagrange agent, a local controller with a disturbance observer and a sliding mode control term is designed, to suppress the mutual interactions among the agents and the modelling uncertainties. The boundedness of the overall signals and the synchronisation tracking control performance are analysed, and the conditions for guaranteed control performance are clarified. Simulation examples are provided to demonstrate the performance of the distributed controllers.     

Multigrid Methods for the Stokes Equations using Distributive Gauss–Seidel Relaxations based on the Least Squares Commutator

A distributive Gauss–Seidel relaxation based on the least squares commutator is devised for the saddle-point systems arising from the discretized Stokes equations. Based on that, an efficient multigrid method is developed for finite element discretizations of the Stokes equations on both structured grids and unstructured grids. On rectangular grids, an auxiliary space multigrid method using one multigrid cycle for the Marker and Cell scheme as auxiliary space correction and least squares commutator distributive Gauss–Seidel relaxation as a smoother is shown to be very efficient and outperforms the popular block preconditioned Krylov subspace methods.     

Adaptive control based on IF–THEN rules for grasping force regulation with unknown contact mechanism

An industrial gripping application with unknown contact mechanism is considered as a class of unknown nonlinear discrete-time systems. The control scheme is developed by an adaptive network called multi-input fuzzy rules emulated network (MiFREN) within discrete-time domain. The network structure is directly constructed regarding to IF–THEN rules related to gripper and contact mechanism properties. All adjustable parameters require only the on-line learning phase to improve the closed loop performance. The time varying learning rate is devised for gradient reach with the proof of stability analysis. Furthermore, the estimated sensitivity of system dynamic is directly considered within the parameter adaptation. The experimental system with an industrial parallel grip model WSG-50 validates the performance of the proposed controller.     

The study of dual COX-2/5-LOX inhibitors by using electronic-topological approach based on data on the ligand–receptor interactions

Structural and electronic factors influencing selective inhibition of cyclooxygenase-2 and 5-lipoxygenase (COX-2/5-LOX) were studied by using Electronic-Topological Method combined with Neural Networks (ETM–NN), molecular docking, and Density Functional Theory (DFT) in a large set of molecules. The results of the ETM–NN calculations allowed for the selection of pharmacophoric molecular fragments, which could be taken as a basis for a system capable of predicting the COX-2/5-LOX inhibitory activity. For the more effective extraction of the pharmacophoric molecular fragments, docking of molecules into the active sites of the two enzymes was carried out to get data on the ligand–receptor interaction. To make an assessment of these interactions, stabilization energies were calculated by using Natural Bond Orbital (NBO) analysis. Docking and data on the electronic structures of active sites of enzymes helped to reveal effectively the peculiarities of the ligand–receptor binding. The system for the selective COX-2/5-LOX inhibitory activity prediction that has been developed as the result of the ETM–NN study recognized correctly 93% of compounds as highly active ones. Thus, this system can be successfully used for carrying out computer screening and synthesis of potent inhibitors of COX-2/5-LOX with diverse molecular skeletons.     

Numerical analysis on electroosmotic flows in a microchannel with rectangle-waved surface roughness using the Poisson–Nernst–Planck model

The present study has numerically investigated two-dimensional electroosmotic flows in a microchannel with dielectric walls of rectangle-waved surface roughness to understand the roughness effect. For the study, numerical simulations are performed by employing the Nernst–Planck equation for the ionic species and the Poisson equation for the electric potential, together with the traditional Navier–Stokes equation. Results show that the steady electroosmotic flow and ionic-species transport in a microscale channel are well predicted by the Poisson–Nernst–Planck model and depend significantly on the shape of surface roughness such as the amplitude and periodic length of wall wave. It is found that the fluid flows along the surface of waved wall without involving any flow separation because of the very strong normal component of EDL (electric double layer) electric field. The flow rate decreases exponentially with the amplitude of wall wave, whereas it increases linearly with the periodic length. It is mainly due to the fact that the external electric-potential distribution plays a crucial role in driving the electroosmotic flow through a microscale channel with surface roughness. Finally, the present results using the Poisson–Nernst–Planck model are compared with those using the traditional Poisson–Boltzmann model which may be valid in these scales.     

Stability robustness bounds for linear state–space models with structured uncertainty based on ellipsoidal set-theoretic approach

This paper is concerned with the problem of robust stability of linear dynamic systems with structured uncertainty by means of ellipsoidal set-theoretic approach. In this paper, the uncertainty in the physical parameters is expressed in terms of an ellipsoidal set in appropriate vector space. Two ellipsoidal set-theoretic approaches are presented for giving sufficient conditions for robust stability property of the systems with structured uncertainty. The bound produced by the ellipsoidal extension function theorem is shown to be less conservative than the one predicted by the Lagrange multiplier method. In order to introduce the ellipsoidal extension function theorem, in Appendix A of this paper, we try to present the theory of ellipsoidal algebra, following the thought of interval analysis. First of all, we give the concept of ellipsoidal numbers and define their arithmetic operations. Based on them, we finally introduce ellipsoidal vectors and ellipsoidal functions. In terms of the inclusion monotonic property of ellipsoidal functions, we present and prove the ellipsoidal extension function theorem.     

Simultaneous estimations of temperature-dependent thermal conductivity and heat capacity using a time efficient novel strategy based on MEGA–NN

The aim of the presented novel strategy is to find the best values of input parameters, while the objective functions are not explicitly known in terms of input parameters and their values only can be calculated by a time-consuming simulation. In this paper, a hybrid modified elitist genetic algorithm–neural network (MEGA–NN) strategy is proposed for such optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of modified elitist genetic algorithm (MEGA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and MEGA is adopted in searching optimal designs based on the predicted fitness values. The proposed strategy (MEGA–NN) is used to estimate the temperature-dependent thermal conductivity and heat capacity using inverse heat transfer method. In order to demonstrate the accuracy and time efficiency of the proposed strategy, the results are compared to those of pre-selected parameters and MEGA. Finally, the results show that proposed MEGA–NN could save a great deal of time depending on the case.