Volume 17, Number 1, 2020
Special Issue on Improving Productivity Through Automation and Computing (pp.1-95)
Driven by the ever increasing demand in function integration, more and more next generation high value-added products, such as head-up displays, solar concentrators and intra-ocular-lens, etc., are designed to possess freeform (i.e., non-rotational symmetric) surfaces. The toolpath, composed of high density of short linear and circular segments, is generally used in computer numerical control (CNC) systems to machine those products. However, the discontinuity between toolpath segments leads to high-frequency fluctuation of feedrate and acceleration, which will decrease the machining efficiency and product surface finish. Driven by the ever-increasing need for high-speed high-precision machining of those products, many novel toolpath interpolation and smoothing approaches have been proposed in both academia and industry, aiming to alleviate the issues caused by the conventional toolpath representation and interpolation methods. This paper provides a comprehensive review of the state-of-the-art toolpath interpolation and smoothing approaches with systematic classifications. The advantages and disadvantages of these approaches are discussed. Possible future research directions are also offered.
Dynamic hand gesture recognition is a desired alternative means for human-computer interactions. This paper presents a hand gesture recognition system that is designed for the control of flights of unmanned aerial vehicles (UAV). A data representation model that represents a dynamic gesture sequence by converting the 4-D spatiotemporal data to 2-D matrix and a 1-D array is introduced. To train the system to recognize designed gestures, skeleton data collected from a Leap Motion Controller are converted to two different data models. As many as 9 124 samples of the training dataset, 1 938 samples of the testing dataset are created to train and test the proposed three deep learning neural networks, which are a 2-layer fully connected neural network, a 5-layer fully connected neural network and an 8-layer convolutional neural network. The static testing results show that the 2-layer fully connected neural network achieves an average accuracy of 96.7% on scaled datasets and 12.3% on non-scaled datasets. The 5-layer fully connected neural network achieves an average accuracy of 98.0% on scaled datasets and 89.1% on non-scaled datasets. The 8-layer convolutional neural network achieves an average accuracy of 89.6% on scaled datasets and 96.9% on non-scaled datasets. Testing on a drone-kit simulator and a real drone shows that this system is feasible for drone flight controls.
Fast high-precision patient-specific vascular tissue and geometric structure reconstruction is an essential task for vascular tissue engineering and computer-aided minimally invasive vascular disease diagnosis and surgery. In this paper, we present an effective vascular geometry reconstruction technique by representing a highly complicated geometric structure of a vascular system as an implicit function. By implicit geometric modelling, we are able to reduce the complexity and level of difficulty of this geometric reconstruction task and turn it into a parallel process of reconstructing a set of simple short tubular-like vascular sections, thanks to the easy-blending nature of implicit geometries on combining implicitly modelled geometric forms. The basic idea behind our technique is to consider this extremely difficult task as a process of team exploration of an unknown environment like a cave. Based on this idea, we developed a parallel vascular modelling technique, called Skeleton Marching, for fast vascular geometric reconstruction. With the proposed technique, we first extract the vascular skeleton system from a given volumetric medical image. A set of sub-regions of a volumetric image containing a vascular segment is then identified by marching along the extracted skeleton tree. A localised segmentation method is then applied to each of these sub-image blocks to extract a point cloud from the surface of the short simple blood vessel segment contained in the image block. These small point clouds are then fitted with a set of implicit surfaces in a parallel manner. A high-precision geometric vascular tree is then reconstructed by blending together these simple tubular-shaped implicit surfaces using the shape-preserving blending operations. Experimental results show the time required for reconstructing a vascular system can be greatly reduced by the proposed parallel technique.
This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN). The important quality variable melt index of polypropylene is hard to measure in industrial processes. Lack of online measurement instruments becomes a problem in polymer quality control. One effective solution is to use soft sensors to estimate the quality variables from process data. In recent years, deep learning has achieved many successful applications in image classification and speech recognition. DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture. It can meet the demand of modelling accuracy when applied to actual processes. Compared to the conventional neural networks, the training of DBN contains a supervised training phase and an unsupervised training phase. To mine the valuable information from process data, DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation. Selection of DBN structure is investigated in the paper. The modelling results achieved by DBN and feedforward neural networks are compared in this paper. It is shown that the DBN models give very accurate estimations of the polymer melt index.
The use of the multiscale generalized radial basis function (MSRBF) neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work. The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems. In this work instead, MSRBF networks are part of an integrated computer-aided diagnosis (CAD) framework for breast cancer detection, which holds three stages: an input-output model is obtained from the image, followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer. In the first stage, the image data is rendered into a multiple-input-single-output (MISO) system. In order to improve the characterisation, the nonlinear autoregressive with exogenous inputs (NARX) model is introduced to rearrange the available input-output data in a nonlinear way. The forward regression orthogonal least squares (FROLS) algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network. In the second stage, once the network model is available, the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform (DCT). In the third stage, we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection. To test the method performance, three different and well-known public image repositories were used: the mini-MIAS and the MMSD for mammography, and the BreaKHis for histopathology images. A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor. Classification results show that the new CAD method reached an accuracy of 93.5% in mini-Mammo graphic image analysis society (mini-MIAS), 93.99% in digital database for screening mammography (DDSM) and 86.7% in the BreaKHis. We found that the MSRBF networks are able to build tailored and precise image models and, combined with the DCT, to extract high-quality features from both black and white and coloured images.
To achieve the fast convergence and tracking precision of a robotic upper-limb exoskeleton, this paper proposes an observer-based integrated fixed-time control scheme with a backstepping method. Firstly, a typical 5 DoF (degrees of freedom) dynamics is constructed by Lagrange equations and processed for control purposes. Secondly, second-order sliding mode controllers (SOSMC) are developed and novel sliding mode surfaces are introduced to ensure the fixed-time convergence of the human-robot system. Both the reaching time and settling time are proved to be bounded with certain values independent of initial system conditions. For the purpose of rejecting the matched and unmatched disturbances, nonlinear fixed-time observers are employed to estimate the exact value of disturbances and compensate the controllers online. Ultimately, the synthesis of controllers and disturbance observers is adopted to achieve the excellent tracking performance and simulations are given to verify the effectiveness of the proposed control strategy.
This paper investigates the stabilisation problem and consider transient optimisation for a class of the multi-input-multi-output (MIMO) semi-linear stochastic systems. A control algorithm is presented via an m-block backstepping controller design where the closed-loop system has been stabilized in a probabilistic sense and the transient performance is optimisable by optimised by searching the design parameters under the given criterion. In particular, the transient randomness and the probabilistic decoupling will be investigated as case studies. Note that the presented control algorithm can be potentially extended as a framework based on the various performance criteria. To evaluate the effectiveness of this proposed control framework, a numerical example is given with simulation results. In summary, the key contributions of this paper are stated as follows: 1) one block backstepping-based output feedback control design is developed to stabilize the dynamic MIMO semi-linear stochastic systems using a linear estimator; 2) the randomness and probabilistic couplings of the system outputs have been minimized based on the optimisation of the design parameters of the controller; 3) a control framework with transient performance enhancement of multi-variable semi-linear stochastic systems has been discussed.
Facial emotion recognition is an essential and important aspect of the field of human-machine interaction. Past research on facial emotion recognition focuses on the laboratory environment. However, it faces many challenges in real-world conditions, i.e., illumination changes, large pose variations and partial or full occlusions. Those challenges lead to different face areas with different degrees of sharpness and completeness. Inspired by this fact, we focus on the authenticity of predictions generated by different <emotion, region> pairs. For example, if only the mouth areas are available and the emotion classifier predicts happiness, then there is a question of how to judge the authenticity of predictions. This problem can be converted into the contribution of different face areas to different emotions. In this paper, we divide the whole face into six areas: nose areas, mouth areas, eyes areas, nose to mouth areas, nose to eyes areas and mouth to eyes areas. To obtain more convincing results, our experiments are conducted on three different databases: facial expression recognition + ( FER+), real-world affective faces database (RAF-DB) and expression in-the-wild (ExpW) dataset. Through analysis of the classification accuracy, the confusion matrix and the class activation map (CAM), we can establish convincing results. To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis. Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.
A brain-computer interface (BCI) system is one of the most effective ways that translates brain signals into output commands. Different imagery activities can be classified based on the changes in μ and β rhythms and their spatial distributions. Multi-layer perceptron neural networks (MLP-NNs) are commonly used for classification. Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently. Conventional methods for training NNs, such as gradient descent and recursive methods, have some disadvantages including low accuracy, slow convergence speed and trapping in local minimums. In this paper, in order to overcome these issues, the MLP-NN trained by a hybrid population-physics-based algorithm, the combination of particle swarm optimization and gravitational search algorithm (PSOGSA), is proposed for our classification problem. To show the advantages of using PSOGSA that trains NNs, this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization (PSO), gravitational search algorithm (GSA) and new versions of PSO. The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics. The results show that the proposed algorithm in most subjects of encephalography (EEG) dataset has very better or acceptable performance compared to others.
This paper proposes an image encryption algorithm LQBPNN (logistic quantum and back propagation neural network) based on chaotic sequences incorporating quantum keys. Firstly, the improved one-dimensional logistic chaotic sequence is used as the basic key sequence. After the quantum key is introduced, the quantum key is incorporated into the chaotic sequence by nonlinear operation. Then the pixel confused process is completed by the neural network. Finally, two sets of different mixed secret key sequences are used to perform two rounds of diffusion encryption on the confusing image. The experimental results show that the randomness and uniformity of the key sequence are effectively enhanced. The algorithm has a secret key space greater than 2182. The adjacent pixel correlation of the encrypted image is close to 0, and the information entropy is close to 8. The ciphertext image can resist several common attacks such as typical attacks, statistical analysis attacks and differential attacks.
In the past, arms used in the fields of industry and robotics have been designed not to vibrate by increasing their mass and stiffness. The weight of arms has tended to be reduced to improve speed of operation, and decrease the cost of their production. Since the weight saving makes the arms lose their stiffness and therefore vibrate more easily, the vibration suppression control is needed for realizing the above purpose. Incidentally, the use of various smart materials in actuators has grown. In particular, a shape memory alloy (SMA) is applied widely and has several advantages: light weight, large displacement by temperature change, and large force to mass ratio. However, the SMA actuators possess hysteresis nonlinearity between their own temperature and displacement obtained by the temperature. The hysteretic behavior of the SMA actuators affects their control performance. In previous research, an operator-based control system including a hysteresis compensator has been proposed. The vibration of a flexible arm is dealt with as the controlled object; one end of the arm is clamped and the other end is free. The effectiveness of the hysteresis compensator has been confirmed by simulations and experiments. Nevertheless, the feedback signal of the previous designed system has increased exponentially. It is difficult to use the system in the long-term because of the phenomenon. Additionally, the SMA actuator generates and radiates heat because electric current passing through the SMA actuator provides heat, and strain on the SMA actuator is generated. With long-time use of the SMA actuator, the environmental temperature around the SMA actuator varies through radiation of the heat. There exists a risk that the ambient temperature change dealt with as disturbance affects the temperature and strain of the SMA actuator. In this research, a design method of the operator-based control system is proposed considering the long-term use of the system. In the method, the hysteresis characteristics of the SMA actuator and the temperature change around the actuator are considered. The effectiveness of the proposed method is verified by simulations and experiments.