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Research Article
Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks
Chang-Hao Zhu, Jie Zhang
Available online   doi: 10.1007/s11633-019-1203-x
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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.
Orientation Measurement for Objects with Planar Surface Based on Monocular Microscopic Vision
Ying Li, Xi-Long Liu, De Xu, Da-Peng Zhang
Available online   doi: 10.1007/s11633-019-1202-y
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Orientation measurement of objects is vital in micro assembly. In this paper, we present a novel method based on monocular microscopic vision for 3-D orientation measurement of objects with planar surfaces. The proposed methods aim to measure the orientation of the object, which does not require calibrating the intrinsic parameters of microscopic camera. In our methods, the orientation of the object is firstly measured with analytical computation based on feature points. The results of the analytical computation are coarse because the information about feature points is not fully used. In order to improve the precision, the orientation measurement is converted into an optimization process base on the relationship between deviations in image space and in Cartesian space under microscopic vision. The results of the analytical computation are used as the initial values of the optimization process. The optimized variables are the three rotational angles of the object and the pixel equivalent coefficient. The objective of the optimization process is to minimize the coordinates differences of the feature points on the object. The precision of the orientation measurement is boosted effectively. Experimental and comparative results validate the effectiveness of the proposed methods.
Integrated Observer-based Fixed-time Control with Backstepping Method for Exoskeleton Robot
Gao-Wei Zhang, Peng Yang, Jie Wang, Jian-Jun Sun, Yan Zhang
Available online   doi: 10.1007/s11633-019-1201-z
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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.
Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm
Sajjad Afrakhteh, Mohammad-Reza Mosavi, Mohammad Khishe, Ahmad Ayatollahi
Available online   doi: 10.1007/s11633-018-1158-3
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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.
HDec-POSMDPs MRS Exploration and Fire Searching Based on IoT Cloud Robotics
Ayman El Shenawy, Khalil Mohamed, Hany Harb
Available online   doi: 10.1007/s11633-019-1187-6
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The multi-robot systems (MRS) exploration and fire searching problem is an important application of mobile robots which require massive computation capability that exceeds the ability of traditional MRS′s. This paper propose a cloud-based hybrid decentralized partially observable semi-Markov decision process (HDec-POSMDPs) model. The proposed model is implemented for MRS exploration and fire searching application based on the Internet of things (IoT) cloud robotics framework. In this implementation the heavy and expensive computational tasks are offloaded to the cloud servers. The proposed model achieves a significant improvement in the computation burden of the whole task relative to a traditional MRS. The proposed model is applied to explore and search for fire objects in an unknown environment; using different sets of robots sizes. The preliminary evaluation of this implementation demonstrates that as the parallelism of computational instances increase the delay of new actuation commands which will be decreased, the mean time of task completion is decreased, the number of turns in the path from the start pose cells to the target cells is minimized and the energy consumption for each robot is reduced.
Research on End-force Output of 8-cable Driven Parallel Manipulator
Sen-Hao Hou, Xiao-Qiang Tang, Ling Cao, Zhi-Wei Cui, Hai-Ning Sun, Ying-Wei Yan
Available online   doi: 10.1007/s11633-019-1195-6
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The return capsule needs to be launched to the moon and return back to earth in the third stage of the Chinese lunar exploration project. Therefore, it is necessary to perform simulations on the ground. This paper presents an 8-cable-driven parallel manipulator to achieve end-force output in a low-gravity environment. End-force output refers to the vector sum of the external force on the end-effector. A model of end-force output is established based on a kinematics model, a dynamic model, and a force analysis of an 8-cable driven parallel manipulator. To obtain end-force output in a low-gravity environment, the cable force has to be controlled to counteract gravity. In addition, a force-position mix control strategy is proposed to proactively control the cable force according to the force optimal distribution given by the closed-form force distribution method. Furthermore, a suitable choice for an end-force output is obtained by modeling the effect of cable force on end-force output. Experimental results show that the actual cable force agrees well with the calculated force distribution, indicating that it is feasible to realize end-force output in a low gravity environment.
Output Feedback Stabilization for MIMO Semi-linear Stochastic Systems with Transient Optimisation
Qi-Chun Zhang, Liang Hu, John Gow
Available online   doi: 10.1007/s11633-019-1193-8
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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.
Image Encryption Algorithm Based on Compressive Sensing and Fractional DCT via Polynomial Interpolation
Ya-Ru Liang, Zhi-Yong Xiao
Available online   doi: 10.1007/s11633-018-1159-2
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Based on compressive sensing and fractional discrete cosine transform (DCT) via polynomial interpolation (PI-FrDCT), an image encryption algorithm is proposed, in which the compression and encryption of an image are accomplished simultaneously. It can keep information secret more effectively with low data transmission. Three-dimensional piecewise and nonlinear chaotic maps are employed to obtain a generating sequence and the exclusive OR (XOR) matrix, which greatly enlarge the key space of the encryption system. Unlike many other fractional transforms, the output of PI-FrDCT is real, which facilitates the storage, transmission and display of the encrypted image. Due to the introduction of a plain-image-dependent disturbance factor, the initial values and system parameters of the encryption scheme are determined by cipher keys and plain-image. Thus, the proposed encryption scheme is very sensitive to the plain-image, which makes the encryption system more secure. Experimental results demonstrate the validity and the reliability of the proposed encryption algorithm.
Spectral-spatial Classification of Hyperspectral Images Using Signal Subspace Identification and Edge-preserving Filter
Negin Alborzi, Fereshteh Poorahangaryan, Homayoun Beheshti
Available online   doi: 10.1007/s11633-019-1188-5
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Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects. In this paper, a new method of classifying hyperspectral images using spectral spatial information has been presented. Here, using the hyperspectral signal subspace identification (HYSIME) method which estimates the signal and noise correlation matrix and selects a subset of eigenvalues for the best representation of the signal subspace in order to minimize the mean square error, subsets from the main sample space have been extracted. After subspace extraction with the help of the HYSIME method, the edge-preserving filtering (EPF), and classification of the hyperspectral subspace using a support vector machine (SVM), results were then merged into the decision-making level using majority rule to create the spectral-spatial classifier. The simulation results showed that the spectral-spatial classifier presented leads to significant improvement in the accuracy and validity of the classification of Indiana, Pavia and Salinas hyperspectral images, such that it can classify these images with 98.79%, 98.88% and 97.31% accuracy, respectively.
A Practical Approach to Representation of Real-time Building Control Applications in Simulation
Azzedine Yahiaoui
Available online   doi: 10.1007/s11633-018-1131-1
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Computer based automation and control systems are becoming increasingly important in smart sustainable buildings, often referred to as automated buildings (ABs), in order to automatically control, optimize and supervise a wide range of building performance applications over a network while minimizing energy consumption and associated green house gas emission. This technology generally refers to building automation and control systems (BACS) architecture. Instead of costly and time-consuming experiments, this paper focuses on development and design of a distributed dynamic simulation environment with the capability to represent BACS architecture in simulation by run-time coupling two or more different software tools over a network. This involves using distributed dynamic simulations as means to analyze the performance and enhance networked real-time control systems in ABs and improve the functions of real BACS technology. The application and capability of this new dynamic simulation environment are demonstrated by an experimental design, in this paper.
Image Encryption Application of Chaotic Sequences Incorporating Quantum Keys
Bin Ge, Hai-Bo Luo
Available online   doi: 10.1007/s11633-019-1173-z
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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.
Hybrid Dynamic Neural Network and PID Control of Pneumatic Artificial Muscle Using the PSO Algorithm
Mahdi Chavoshian, Mostafa Taghizadeh, Mahmood Mazare
Available online   doi: 10.1007/s11633-019-1196-5
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Pneumatic artificial muscles (PAM) have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications. Since accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior, elasticity and ambiguous characteristics, a high performance and efficient control approach should be adopted. Besides of the mentioned challenges, limited course length is another predicament with the PAM control. In this regard, this paper proposes a new hybrid dynamic neural network (DNN) and proportional integral derivative (PID) controller for the position of the PAM. In order to enhance the proficiency of the controller, the problem under study is designed in the form of an optimization trend. Considering the potential of particle swarm optimization, it has been applied to optimally tune the PID-DNN parameters. To verify the performance of the proposed controller, it has been implemented on a real-time system and compared to a conventional sliding mode controller. Simulation and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM.
Tracking Registration Algorithm for Augmented Reality Based on Template Tracking
Peng-Xia Cao, Wen-Xin Li, Wei-Ping Ma
Available online   doi: 10.1007/s11633-019-1198-3
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Tracking registration is a key issue in augmented reality applications, particularly where there are no artificial identifier placed manually. In this paper, an efficient markerless tracking registration algorithm which combines the detector and the tracker is presented for the augmented reality system. We capture the target images in real scenes as template images, use the random ferns classifier for target detection and solve the problem of reinitialization after tracking registration failures due to changes in ambient lighting or occlusion of targets. Once the target has been successfully detected, the pyramid Lucas-Kanade (LK) optical flow tracker is used to track the detected target in real time to solve the problem of slow speed. The least median of squares (LMedS) method is used to adaptively calculate the homography matrix, and then the three-dimensional pose is estimated and the virtual object is rendered and registered. Experimental results demonstrate that the algorithm is more accurate, faster and more robust.
Modeling of a Smart Nano Force Sensor Using Finite Elements and Neural Networks
Farid Menacer, Abdelmalek Kadr, Zohir Dibi
Available online   doi: 10.1007/s11633-018-1155-6
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The aim of this work is to model and analyze the behavior of a new smart nano force sensor. To do so, the carbon nanotube has been used as a suspended gate of a metal-oxide-semiconductor field-effect transistor (MOSFET). The variation of the applied force on the carbon nanotube (CNT) generates a variation of the capacity of the transistor oxide-gate and therefore the variation of the threshold voltage, which allows the MOSFET to become a capacitive nano force sensor. The sensitivity of the nano force sensor can reach 0.124 31 V/nN. This sensitivity is greater than results in the literature. We have found through this study that the response of the sensor depends strongly on the geometric and physical parameters of the CNT. From the results obtained in this study, it can be seen that the increase in the applied force increases the value of the MOSFET threshold voltage VTh. In this paper, we first used artificial neural networks to faithfully reproduce the response of the nano force sensor model. This neural model is called direct model. Then, secondly, we designed an inverse model called an intelligent sensor which allows linearization of the response of our developed force sensor.
An Operator-based Nonlinear Vibration Control System Using a Flexible Arm with Shape Memory Alloy
Hiroki Matsumori, Ming-Cong Deng, Yuichi Noge
Available online   doi: 10.1007/s11633-018-1149-4
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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.
Deep Learning Based Hand Gesture Recognition and UAV Flight Controls
Bin Hu, Jiacun Wang
Available online   doi: 10.1007/s11633-019-1194-7
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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.
Expression Analysis Based on Face Regions in Read-world Conditions
Zheng Lian, Ya Li, Jian-Hua Tao, Jian Huang, Ming-Yue Niu
Available online   doi: 10.1007/s11633-019-1176-9
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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.
Low-Latency Data Gathering with Reliability Guaranteeing in Heterogeneous Wireless Sensor Networks
Tian-Yun Shi, Jian Li, Xin-Chun Jia, Wei Bai, Zhong-Ying Wang, Dong Zhou
Available online   doi: 10.1007/s11633-017-1074-y
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Composite Control of Nonlinear Singularly Perturbed Systems via Approximate Feedback Linearization
Aleksey Kabanov, Vasiliy Alchakov
Available online   doi: 10.1007/s11633-017-1076-9
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Optimal Design of Fuzzy-AGC Based on PSO&RCGA to Improve Dynamic Stability of Interconnected Multi Area Power Systems
Ali Darvish Falehi
Available online   doi: 10.1007/s11633-017-1064-0
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Study of performance and reliability of urethral valve driven by ultrasonic-vaporized steam
Zhen Hu, Xiao Li, Ting Guan
Available online   doi: 10.1007/s11633-016-1026-y
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A Novel Self-adaptive Circuit Design Technique based on Evolvable Hardware
Jun-Bin Zhang, Jin-Yan Cai, Ya-Feng Meng, Tian-Zhen Meng
Available online   doi: 10.1007/s11633-016-1000-8
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Simultaneous Identification of Process Structure, Parameter and Time-Delay Based on Non-Negative Garrote
Jian-Guo Wang, Qian-Ping Xiao, Tiao Shen, Shi-Wei Ma, Wen-Tao Rao, Yong-Jie Zhang
Available online   doi: 10.1007/s11633-015-0948-0
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Energy Efficient Scheduler of Aperiodic jobs for Real-Time Embedded Systems
Hussein El Ghor, E. M. Aggoune
Available online   doi: 10.1007/s11633-016-0993-3
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Energy Efficient Scheduler of Aperiodic jobs for Real-Time Embedded Systems
Design of Ethernet based data acquisition system for yaw rate and longitudinal velocity measurement in automobiles
K. Arun Venkatesh, N. Mathivanan
Available online   doi: 10.1007/s11633-016-0968-4
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Review
Toolpath Interpolation and Smoothing for Computer Numerical Control Machining of Freeform Surfaces: A Review
Wen-Bin Zhong, Xi-Chun Luo, Wen-Long Chang, Yu-Kui Cai, Fei Ding, Hai-Tao Liu, Ya-Zhou Sun
Available online   doi: 10.1007/s11633-019-1190-y
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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.
Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions
Quan Qi, Qing-De Li, Yongqiang Cheng, Qing-Qi Hong
Available online   doi: 10.1007/s11633-019-1189-4
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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.

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Review
A Survey on 3D Visual Tracking of Multicopters
Qiang Fu, Xiang-Yang Chen, Wei He
2019,  vol. 16,  no. 6,   pp. 707-719 ,  doi: 10.1007/s11633-019-1199-2
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Three-dimensional (3D) visual tracking of a multicopter (where the camera is fixed while the multicopter is moving) means continuously recovering the six-degree-of-freedom pose of the multicopter relative to the camera. It can be used in many applications, such as precision terminal guidance and control algorithm validation for multicopters. However, it is difficult for many researchers to build a 3D visual tracking system for multicopters (VTSMs) by using cheap and off-the-shelf cameras. This paper firstly gives an overview of the three key technologies of a 3D VTSMs: multi-camera placement, multi-camera calibration and pose estimation for multicopters. Then, some representative 3D visual tracking systems for multicopters are introduced. Finally, the future development of the 3D VTSMs is analyzed and summarized.
Research Article
Transfer Hierarchical Attention Network for Generative Dialog System
Xiang Zhang, Qiang Yang
2019,  vol. 16,  no. 6,   pp. 720-736 ,  doi: 10.1007/s11633-019-1200-0
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In generative dialog systems, learning representations for the dialog context is a crucial step in generating high quality responses. The dialog systems are required to capture useful and compact information from mutually dependent sentences such that the generation process can effectively attend to the central semantics. Unfortunately, existing methods may not effectively identify importance distributions for each lower position when computing an upper level feature, which may lead to the loss of information critical to the constitution of the final context representations. To address this issue, we propose a transfer learning based method named transfer hierarchical attention network (THAN). The THAN model can leverage useful prior knowledge from two related auxiliary tasks, i.e., keyword extraction and sentence entailment, to facilitate the dialog representation learning for the main dialog generation task. During the transfer process, the syntactic structure and semantic relationship from the auxiliary tasks are distilled to enhance both the word-level and sentence-level attention mechanisms for the dialog system. Empirically, extensive experiments on the Twitter Dialog Corpus and the PERSONA-CHAT dataset demonstrate the effectiveness of the proposed THAN model compared with the state-of-the-art methods.
An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals
Siuly Siuly, Varun Bajaj, Abdulkadir Sengur, Yanchun Zhang
2019,  vol. 16,  no. 6,   pp. 737-747 ,  doi: 10.1007/s11633-019-1178-7
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This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram (EEG) data in an automatic way. This study introduces an optimum allocation based sampling (OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes (e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods (decision table, support vector machine (SVM), k-nearest neighbor (k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD (i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to 9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals.
Multi-objective Dimensional Optimization of a 3-DOF Translational PKM Considering Transmission Properties
Song Lu, Yang-Min Li, Bing-Xiao Ding
2019,  vol. 16,  no. 6,   pp. 748-760 ,  doi: 10.1007/s11633-019-1184-9
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Multi-objective dimensional optimization of parallel kinematic manipulators (PKMs) remains a challenging and worthwhile research endeavor. This paper presents a straightforward and systematic methodology for implementing the structure optimization analysis of a 3-prismatic-universal-universal (PUU) PKM when simultaneously considering motion transmission, velocity transmission and acceleration transmission. Firstly, inspired by a planar four-bar linkage mechanism, the motion transmission index of the spatial parallel manipulator is based on transmission angle which is defined as the pressure angle amongst limbs. Then, the velocity transmission index and acceleration transmission index are derived through the corresponding kinematics model. The multi-objective dimensional optimization under specific constraints is carried out by the improved non-dominated sorting genetic algorithm (NSGA II), resulting in a set of Pareto optimal solutions. The final chosen solution shows that the manipulator with the optimized structure parameters can provide excellent motion, velocity and acceleration transmission properties.
Selection of Observation Position and Orientation in Visual Servoing with Eye-in-vehicle Configuration for Manipulator
Hong-Xuan Ma, Wei Zou, Zheng Zhu, Chi Zhang, Zhao-Bing Kang
2019,  vol. 16,  no. 6,   pp. 761-774 ,  doi: 10.1007/s11633-019-1181-z
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In this paper, we propose a method to select the observation position in visual servoing with an eye-in-vehicle configuration for the manipulator. In traditional visual servoing, the images taken by the camera may have various problems, including being out of view, large perspective aberrance, improper projection area of object in images and so on. In this paper, we propose a method to determine the observation position to solve these problems. A mobile robot system with pan-tilt camera is designed, which calculates the observation position based on an observation and then moves there. Both simulation and experimental results are provided to validate the effectiveness of the proposed method.
Type Synthesis and Kinematics Performance Analysis of a Class of 3T2R Parallel Mechanisms with Large Output Rotational Angles
Bing-Shan Jiang, Hai-Rong Fang, Hai-Qiang Zhang
2019,  vol. 16,  no. 6,   pp. 775-785 ,  doi: 10.1007/s11633-019-1192-9
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Abstract:
Based on Lie group theory and the integration of configuration, a class of 3T2R (T denotes translation and R denotes rotation) parallel mechanisms with large output rotational angles is synthesized through a five degree of freedom single limb evolving into two five degree of freedom limbs and constraint coupling of each kinematics chain. A kind of 3T2R parallel mechanisms with large rotational angles was selected from type synthesis of 3T2R parallel mechanisms, inverse kinematics and velocity Jacobian matrix of the parallel mechanism are established. The performance indices including workspace, rotational capacity, singularity and dexterity of the parallel mechanism are analyzed. The results show that the parallel mechanism has not only large output rotational angles but also better dexterity.
An Integrated MCI Detection Framework Based on Spectral-temporal Analysis
Jiao Yin, Jinli Cao, Siuly Siuly, Hua Wang
2019,  vol. 16,  no. 6,   pp. 786-799 ,  doi: 10.1007/s11633-019-1197-4
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Abstract:
Aiming to differentiate between mild cognitive impairment (MCI) patients and elderly control subjects, this study proposes an integrated framework based on spectral-temporal analysis for the automatic analysis of resting-state electroencephalogram (EEG) recordings. This framework firstly eliminates noise by employing stationary wavelet transformation (SWT). Then, a set of features is extracted through spectral-temporal analysis. Next, a new wrapper algorithm, named three-dimensional (3-D) evaluation algorithm, is proposed to derive an optimal feature subset. Finally, the support vector machine (SVM) algorithm is adopted to identify MCI patients on the optimal feature subset. Decision tree and K-nearest neighbors (KNN) algorithms are also used to test the effectiveness of the selected feature subset. Twenty-two subjects are involved in experiments, of which eleven persons were in an MCI condition and the rest were elderly control subjects. Extensive experiments show that our method is able to classify MCI patients and elderly control subjects automatically and effectively, with the accuracy of 96.94% achieved by the SVM classifier. Decision tree and KNN algorithms also achieved superior results based on the optimal feature subset extracted by the proposed framework. This study is conducive to timely diagnosis and intervention for MCI patients, and therefore to delaying cognitive decline and dementia onset.
A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics
Snehasis Banerjee, Tanushyam Chattopadhyay, Utpal Garain
2019,  vol. 16,  no. 6,   pp. 800-811 ,  doi: 10.1007/s11633-019-1185-8
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Abstract:
This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things (IoT) analytics problems targeted on 1-dimensional (1-D) sensor data. As feature recommendation is a major bottleneck for general IoT-based applications, this paper shows how this step can be successfully automated based on a Wide Learning architecture without sacrificing the decision-making accuracy, and thereby reducing the development time and the cost of hiring expensive resources for specific problems. Interpretation of meaningful features is another contribution of this research. Several data sets from different real-world applications are considered to realize the proof-of-concept. Results show that the interpretable feature recommendation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in development time.
Robust Disturbance Rejection Based Control with Extended-state Resonant Observer for Sway Reduction in Uncertain Tower-cranes
Horacio Coral-Enriquez, Santiago Pulido-Guerrero, John Cortés-Romero
2019,  vol. 16,  no. 6,   pp. 812-827 ,  doi: 10.1007/s11633-019-1179-6
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Abstract:
In this paper, the problem of load transportation and robust mitigation of payload oscillations in uncertain tower-cranes is addressed. This problem is tackled through a control scheme based on the philosophy of active-disturbance-rejection. Here, a general disturbance model built with two dominant components: polynomial and harmonic, is stated. Then, a disturbance observer is formulated through state-vector augmentation of the tower-crane model. Thus, better performance of estimations for system states and disturbances is achieved. The control law is then formulated to actively reject the disturbances but also to accommodate the closed-loop system dynamics even under system uncertainty. The proposed control schema is validated via experimentation using a small-scale tower-crane, and compared with other relevant active disturbance rejection control (ADRC)-based techniques. The experimental results show that the proposed control scheme is robust under parametric uncertainty of the system, and provides improved attenuation of payload oscillations even under system uncertainty.
Determination of Vertices and Edges in a Parametric Polytope to Analyze Root Indices of Robust Control Quality
Sergey Gayvoronskiy, Tatiana Ezangina, Ivan Khozhaev, Viktor Kazmin
2019,  vol. 16,  no. 6,   pp. 828-837 ,  doi: 10.1007/s11633-019-1182-y
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Abstract:
The research deals with the methodology intended to root robust quality indices in the interval control system, the parameters of which are affinely included in the coefficients of a characteristic polynomial. To determine the root quality indices we propose to depict on the root plane not all edges of the interval parametric polytope (as the edge theorem says), but its particular vertex-edge route. In order to define this route we need to know the angle sequence at which the edge branches depart from any integrated pole on the allocation area. It is revealed that the edge branches can integrate into the route both fully or partially due to intersection with other branches. The conditions which determine the intersection of one-face edge images have been proven. It is shown that the root quality indices can be determined by its ends or by any other internal point depending on a type of edge branch. The conditions which allow determining the edge branch type have been identified. On the basis of these studies we developed the algorithm intended to construct a boundary vertex-edge route on the polytope with the interval parameters of the system. As an illustration of how the algorithm can be implemented, we determined and introduced the root indices reflecting the robust quality of the system used to stabilize the position of an underwater charging station for autonomous unmanned vehicles.
Continuous Probabilistic SLAM Solved via Iterated Conditional Modes
J. Gimenez, A. Amicarelli, J. M. Toibero, F. di Sciascio, R. Carelli
2019,  vol. 16,  no. 6,   pp. 838-850 ,  doi: 10.1007/s11633-019-1186-7
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Abstract:
This article proposes a simultaneous localization and mapping (SLAM) version with continuous probabilistic mapping (CP-SLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field (MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes (ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.
Correlation of Direct Piezoelectric Effect on EAPap under Ambient Factors
Li-Jie Zhao, Chang-Ping Tang, Peng Gong
2010,  vol. 7,  no. 3,   pp. 324-329, doi: 10.1007/s11633-010-0510-z
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Feature Selection and Feature Learning for High-dimensional Batch Reinforcement Learning: A Survey
De-Rong Liu, Hong-Liang, Li Ding Wang
2015,  vol. 12,  no. 3,   pp. 229-242, doi: 10.1007/s11633-015-0893-y
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The Effect of Interatomic Potentials on the Molecular Dynamics Simulation of Nanometric Machining
Akinjide Oluwajobi, Xun Chen
2011,  vol. 8,  no. 3,   pp. 326-332, doi: 10.1007/s11633-011-0588-y
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Adaptive Terminal Sliding Mode Control for Rigid Robotic Manipulators
Mezghani Ben Romdhane Neila, Damak Tarak
2011,  vol. 8,  no. 2,   pp. 215-220, doi: 10.1007/s11633-011-0576-2
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A New Method for Modelling and Simulation of the Dynamic Behaviour of the Wheel-rail contact
Arthur Anyakwo, Crinela Pislaru, Andrew Ball
2012,  vol. 9,  no. 3,   pp. 237-247, doi: 10.1007/s11633-012-0640-6
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Adaptive Fuzzy Sliding Mode Power System Stabilizer Using Nussbaum Gain
Emira Nechadi, Mohamed Naguib Harmas, Najib Essounbouli, Abdelaziz Hamzaoui
2013,  vol. 10,  no. 4,   pp. 281-287, doi: 10.1007/s11633-013-0722-0
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Exponential Nonlinear Observer Based on the Differential State-dependent Riccati Equation
Hossein Beikzadeh, Hamid D. Taghirad
2012,  vol. 9,  no. 4,   pp. 358-368, doi: 10.1007/s11633-012-0656-y
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Unknown Inputs Observer for a Class of Nonlinear Uncertain Systems: An LMI Approach
Kamel Mohamed, Mohammed Chadli, Mohamed Chaabane
2012,  vol. 9,  no. 3,   pp. 331-336, doi: 10.1007/s11633-012-0652-2
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Coordination Control of Greenhouse Environmental Factors
Feng Chen, Yong-Ning Tang, Ming-Yu Shen
2011,  vol. 8,  no. 2,   pp. 147-153, doi: 10.1007/s11633-011-0567-3
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Second-order Sliding Mode Approaches for the Control of a Class of Underactuated Systems
Sonia Mahjoub, Faiçal Mnif, Nabil Derbel
2015,  vol. 12,  no. 2,   pp. 134-141, doi: 10.1007/s11633-015-0880-3
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Adaptive Tracking Control of an Autonomous Underwater Vehicle
Basant Kumar Sahu, Bidyadhar Subudhi
2014,  vol. 11,  no. 3,   pp. 299-307, doi: 10.1007/s11633-014-0792-7
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Fault Tolerant Control for Networked Control Systems with Packet Loss and Time Delay
Ming-Yue Zhao, He-Ping Liu, Zhi-Jun Li, De-Hui Sun
2011,  vol. 8,  no. 2,   pp. 244-253, doi: 10.1007/s11633-011-0579-z
Abstract PDF SpringerLink
A Method for Trust Management in Cloud Computing: Data Coloring by Cloud Watermarking
Yu-Chao Liu, Yu-Tao Ma, Hai-Su Zhang, De-Yi Li, Gui-Sheng Chen
2011,  vol. 8,  no. 3,   pp. 280-285, doi: 10.1007/s11633-011-0583-3
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Position Control of Electro-hydraulic Actuator System Using Fuzzy Logic Controller Optimized by Particle Swarm Optimization
Daniel M. Wonohadidjojo, Ganesh Kothapalli, Mohammed Y. Hassan
2013,  vol. 10,  no. 3,   pp. 181-193, doi: 10.1007/s11633-013-0711-3
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State Feedback Sliding Mode Control without Chattering by Constructing Hurwitz Matrix for AUV Movement
Huan-Yin Zhou, Kai-Zhou Liu, Xi-Sheng Feng
2011,  vol. 8,  no. 2,   pp. 262-268, doi: 10.1007/s11633-011-0581-5
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Self-contained Capsubot Propulsion Mechanism
Nazmul Huda, Hong-Nian Yu, Samuel Oliver
2011,  vol. 8,  no. 3,   pp. 348-356, doi: 10.1007/s11633-011-0591-3
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A New Sliding Function for Discrete Predictive Sliding Mode Control of Time Delay Systems
Abdennebi Nizar, Ben Mansour Houda, Nouri Ahmed Said
2013,  vol. 10,  no. 4,   pp. 288-295, doi: 10.1007/s11633-013-0723-z
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Adaptive Backstepping Sliding Mode Trajectory Tracking Control for a Quad-rotor
Xun Gong, Zhi-Cheng Hou, Chang-Jun Zhao, Yue Bai, Yan-Tao Tian
2012,  vol. 9,  no. 5,   pp. 555-560, doi: 10.1007/s11633-012-0679-4
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Sliding Mode Control with Disturbance Observer for Class of Nonlinear Systems
Lei-Po Liu, Zhu-Mu Fu, Xiao-Na Song
2012,  vol. 9,  no. 5,   pp. 487-491, doi: 10.1007/s11633-012-0671-z
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Delay Dependent Robust Stability of Singular Systems with Additive Time-varying Delays
N. Chaibi, E. H. Tissir, A. Hmamed
2013,  vol. 10,  no. 1,   pp. 85-90 , doi: 10.1007/s11633-013-0700-6
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Current Issue

2019 Vol.16 No.6

Table of Contents

ISSN 1476-8186

E-ISSN 1751-8520

CN 11-5350/TP

Editors-in-chief
Tieniu TAN, Chinese Academy of Sciences Guoping LIU, University of South Wales Huosheng HU, University of Essex
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