Display Method:
Review
Computational Intelligence in Remote Sensing Image Registration: A survey
Yue Wu, Jun-Wei Liu, Chen-Zhuo Zhu, Zhuang-Fei Bai, Qi-Guang Miao, Wen-Ping Ma, Mao-Guo Gong
doi: 10.1007/s11633-020-1248-x
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Abstract:
In recent years, computational intelligence has been widely used in many fields and achieved remarkable performance. Evolutionary computing and deep learning are important branches of computational intelligence. Many methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image registration. This paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep learning. In the part of remote sensing image registration based on evolutionary calculation, the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is discussed. The application of deep learning in remote sensing image registration is also discussed. At the same time, the development status and future of remote sensing image registration are summarized and their prospects are examined.
Research Article
A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition
Wei Jia, Jian Gao, Wei Xia, Yang Zhao, Hai Min, Jing-Ting Lu
doi: 10.1007/s11633-020-1257-9
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Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
Camera-based Basketball Scoring Detection Using Convolutional Neural Network
Xu-Bo Fu, Shao-Long Yue, De-Yun Pan
doi: 10.1007/s11633-020-1259-7
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Recently, deep learning methods have been applied in many real scenarios with the development of convolutional neural networks (CNNs). In this paper, we introduce a camera-based basketball scoring detection (BSD) method with CNN based object detection and frame difference-based motion detection. In the proposed BSD method, the videos of the basketball court are taken as inputs. Afterwards, the real-time object detection, i.e., you only look once (YOLO) model, is implemented to locate the position of the basketball hoop. Then, the motion detection based on frame difference is utilized to detect whether there is any object motion in the area of the hoop to determine the basketball scoring condition. The proposed BSD method runs in real-time with satisfactory basketball scoring detection accuracy. Our experiments on the collected real scenario basketball court videos show the accuracy of the proposed BSD method. Furthermore, several intelligent basketball analysis systems based on the proposed method have been installed at multiple basketball courts in Beijing, and they provide good performance.
Type Synthesis and Dynamics Performance Evaluation of a Class of 5-DOF Redundantly Actuated Parallel Mechanisms
Bing-Shan Jiang, Hai-Rong Fang, Hai-Qiang Zhang
doi: 10.1007/s11633-020-1255-y
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This paper presents a five degree of freedom (5-DOF) redundantly actuated parallel mechanism (PM) for the parallel machining head of a machine tool. A 5-DOF single kinematic chain is evolved into a secondary kinematic chain based on Lie group theory and a configuration evolution method. The evolutional chain and four 6-DOF kinematic chain SPS (S represents spherical joint and P represents prismatic joint) or UPS (U represents universal joint) can be combined into four classes of 5-DOF redundantly actuated parallel mechanisms. That SPS-(2UPR)R (R represents revolute joint) redundantly actuated parallel mechanism is selected and is applied to the parallel machining head of the machine tool. All formulas of the 4SPS-(2UPR)R mechanism are deduced. The dynamic model of the mechanism is shown to be correct by Matlab and automatic dynamic analysis of mechanical systems (ADAMS) under no-load conditions. The dynamic performance evaluation indexes including energy transmission efficiency and acceleration performance evaluation are analyzed. The results show that the 4SPS-(2UPR)R mechanism can be applied to a parallel machining head and have good dynamic performance.
Computational Decision Support System for ADHD Identification
Senuri De Silva, Sanuwani Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna, Anne M. P. Michalek
doi: 10.1007/s11633-020-1252-1
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Attention deficit/hyperactivity disorder (ADHD) is a common disorder among children. ADHD often prevails into adulthood, unless proper treatments are facilitated to engage self-regulatory systems. Thus, there is a need for effective and reliable mechanisms for the early identification of ADHD. This paper presents a decision support system for the ADHD identification process. The proposed system uses both functional magnetic resonance imaging (fMRI) data and eye movement data. The classification processes contain enhanced pipelines, and consist of pre-processing, feature extraction, and feature selection mechanisms. fMRI data are processed by extracting seed-based correlation features in default mode network (DMN) and eye movement data using aggregated features of fixations and saccades. For the classification using eye movement data, an ensemble model is obtained with 81% overall accuracy. For the fMRI classification, a convolutional neural network (CNN) is used with 82% accuracy for the ADHD identification. Both ensemble models are proved for overfitting avoidance.
Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning
Punyanuch Borwarnginn, Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Kittikhun Thongkanchorn
doi: 10.1007/s11633-020-1261-0
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Dog breed identification is essential for many reasons, particularly for understanding individual breeds′ conditions, health concerns, interaction behavior, and natural instinct. This paper presents a solution for identifying dog breeds using their images of their faces. The proposed method applies a deep learning based approach in order to recognize their breeds. The method begins with a transfer learning by retraining existing pre-trained convolutional neural networks (CNNs) on the public dog breed dataset. Then, the image augmentation with various settings is also applied on the training dataset, in order to improve the classification performance. The proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental comparisons. The proposed model achieves a promising accuracy of 89.92% on the published dataset with 133 dog breeds.
Image Inpainting Based on Structural Tensor Edge Intensity Model
Jing Wang, Yan-Hong Zhou, Hai-Feng Sima, Zhan-Qiang Huo, Ai-Zhong Mi
doi: 10.1007/s11633-020-1256-x
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In the exemplar-based image inpainting approach, there are usually two major problems: the unreasonable calculation of priority and only considering the color features in the patch lookup strategy. In this paper, we propose an image inpainting approach based on the structural tensor edge intensity model. First, we use the progressive scanning inpainting method to avoid the image filling order being affected by the priority function. Then, we use the edge intensity model to build the patches similarity function for correctly identifying the local image structure. Finally, the balance operator is used to restrict the excessive propagation of structural information to ensure the correct structural reconstruction. The experimental results show that the our approach is comparable and even superior to some state-of-the-art inpainting algorithms.
Event-triggered Control of Positive Switched Systems with Actuator Saturation and Time-delay
Jun-Feng Zhang, Lai-You Liu, Shi-Zhou Fu, Shuo Li
doi: 10.1007/s11633-020-1245-0
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This paper investigates the event-triggered control of positive switched systems with randomly occurring actuator saturation and time-delay, where the actuator saturation and time-delay obey different Bernoulli distributions. First, an event-triggering condition is constructed based on a 1-norm inequality. Under the presented event-triggering scheme, an interval estimation method is utilized to deal with the error term of the systems. Using a co-positive Lyapunov functional, the event-triggered controller and the cone attraction domain gain matrices are designed via matrix decomposition techniques. The positivity and stability of the resulting closed-loop systems are reached by guaranteeing the positivity of the lower bound of the systems and the stability of the upper bound of the systems, respectively. The proposed approach is developed for interval and polytopic uncertain systems, respectively. Finally, two examples are provided to illustrate the effectiveness of the theoretical findings.
Rail Detection Based on LSD and the Least Square Curve Fitting
Yun-Shui Zheng, Yan-Wei Jin, Yu Dong
doi: 10.1007/s11633-020-1241-4
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It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not. A convenient and fast method based on line segment detector (LSD) and the least square curve fitting to identify the rail in the image is proposed in this paper. The image in front of the train can be obtained through the camera on-board. After preprocessing, it will be divided equally along the longitudinal axis. Utilizing the characteristics of the LSD algorithm, the edges are approximated into multiple line segments. After screening the terminals of the line segments, it can generate the mathematical model of the rail in the image based on the least square. Experiments show that the algorithm in this paper can fit the rail curve accurately and has good applicability and robustness.
Design of an Executable ANFIS-based Control System to Improve the Attitude and Altitude Performances of a Quadcopter Drone
Mohammad Al-Fetyani, Mohammad Hayajneh, Adham Alsharkawi
doi: 10.1007/s11633-020-1251-2
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Nowadays, quadcopters are presented in many life applications which require the performance of automatic takeoff, trajectory tracking, and automatic landing. Thus, researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques. Due to high nonlinearities, strong couplings and under-actuation, the control design process of a quadcopter is a rather challenging task. Therefore, the main objective of this work is demonstrated through two main aspects. The first is the design of an adaptive neuro-fuzzy inference system (ANFIS) controller to develop the attitude and altitude of a quadcopter. The second is to create a systematic framework for implementing flight controllers in embedded systems. A suitable model of the quadcopter is also developed by taking into account aerodynamics effects. To show the effectiveness of the ANFIS approach, the performance of a well-trained ANFIS controller is compared to a classical proportional-derivative (PD) controller and a properly tuned fuzzy logic controller. The controllers are compared and tested under several different flight conditions including the capability to reject external disturbances. In the first stage, performance evaluation takes place in a nonlinear simulation environment. Then, the ANFIS-based controllers alongside attitude and position estimators, and precision landing algorithms are implemented for executions in a real-time autopilot. In precision landing systems, an IR-camera is used to detect an IR-beacon on the ground for precise positioning. Several flight tests of a quadcopter are conducted for results validation. Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.
Hybrid Approach to Document Anomaly Detection: An Application to Facilitate RPA in Title Insurance
Abhijit Guha, Debabrata Samanta
doi: 10.1007/s11633-020-1247-y
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Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is no exception. Robotic process automation (RPA) is taking over manual tasks in TI business processes, but it has its limitations without the support of artificial intelligence (AI) and machine learning (ML). With increasing data dimensionality and in composite population scenarios, the complexity of detecting anomalies increases and AD in automated document management systems (ADMS) is the least explored domain. Deep learning, being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI. We present a hybrid model for AD, using autoencoders (AE) and a one-class support vector machine (OSVM). In the present study, OSVM receives input features representing real-time documents from the TI business, orchestrated and with dimensions reduced by AE. The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range, regarding accuracy and performance.
Saliency Detection via Manifold Ranking Based on Robust Foreground
Wei-Ping Ma, Wen-Xin Li, Jin-Chuan Sun, Peng-Xia Cao
doi: 10.1007/s11633-020-1246-z
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The graph-based manifold ranking saliency detection only relies on the boundary background to extract foreground seeds, resulting in a poor saliency detection result, so a method that obtains robust foreground for manifold ranking is proposed in this paper. First, boundary connectivity is used to select the boundary background for manifold ranking to get a preliminary saliency map, and a foreground region is acquired by a binary segmentation of the map. Second, the feature points of the original image and the filtered image are obtained by using color boosting Harris corners to generate two different convex hulls. Calculating the intersection of these two convex hulls, a final convex hull is found. Finally, the foreground region and the final convex hull are combined to extract robust foreground seeds for manifold ranking and getting final saliency map. Experimental results on two public image datasets show that the proposed method gains improved performance compared with some other classic methods in three evaluation indicators: precision-recall curve, F-measure and mean absolute error.
A Position Synchronization Controller for Co-ordinated Links (COOL) Dual Robot Arm Based on Integral Sliding Mode: Design and Experimental Validation
Sumi Phukan, Chitralekha Mahanta
doi: 10.1007/s11633-020-1242-3
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In this study, a simple position synchronization control algorithm based on an integral sliding mode is developed for dual-arm robotic manipulator systems. A first-order sliding surface is designed using cross-coupling error in order to ensure position synchronization of dual-arm manipulators. The design objective of the proposed controller is to ensure stability as well as to synchronize the movement of both arms while maintaining the trajectory as desired. The integral sliding mode eliminates the reaching phase and guarantees robustness throughout the whole operating period. Additionally, a low pass filter is used to smoothen the discontinuous element and minimize unwanted chattering. Lyapunov stability theory is utilized to prove the asymptotic stability of the controlled system. Simulation studies are performed to validate the proposed controller′s effectiveness. Also, to investigate the possibility of realizing the proposed dynamic control method in practical applications, experiments are conducted on a 14DoF coordinated links (COOL) dual-arm robotic manipulator system. Experimental evidence indicates adequate efficiency in trajectory tracking and guarantees robustness in the presence of parametric uncertainty and external disturbance.
Display Method:
Review
A Survey on Fault Diagnosis and Fault Tolerant Methodologies for Permanent Magnet Synchronous Machines
Erphan A. Bhuiyan, Md. Maeenul Azad Akhand, Sajal K. Das, Md. F. Ali, Z. Tasneem, Md. R. Islam, D. K. Saha, Faisal R. Badal, Md. H. Ahamed, S. I. Moyeen
2020,  vol. 17,  no. 6, pp. 763-787,  doi: 10.1007/s11633-020-1250-3
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This paper presents a comprehensive survey of fault diagnosis and fault tolerant approaches for permanent magnet synchronous machines (PMSM). PMSMs are prominent in the pervading usage of electric motors, for their high efficiency, great robustness, reliability and low torque inertia. In spite of their extensive appliance, they can be quite non-resilient and inadequate in operation when faults appear in motor drive apparatus such as inverters, stator windings, sensors, etc. These may lead to insulation failure, torque fluctuations, overcurrent or even system collapse. On that account, fault diagnosis and fault tolerant methods are equipped to enhance the stability and robustness in PMSMs. Progressive methodologies of PMSM fault diagnosis and tolerance are classified, discussed, reviewed and compared in this paper, beginning with mathematical modeling of PMSM and then scrutinizing various fault conditions in PMSMs. Finally, the scope of research on the topic is highlighted. The contribution of this review is to emphasize optimistic schemes and to assist researchers with the latest trends in this field for future directions.
Research Article
Study on Statistical Outlier Detection and Labelling
Paweł D. Domański
2020,  vol. 17,  no. 6, pp. 788-811,  doi: 10.1007/s11633-020-1243-2
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Outliers accompany control engineers in their real life activity. Industrial reality is much richer than elementary linear, quadratic, Gaussian assumptions. Outliers appear due to various and varying, often unknown, reasons. They meet research interest in statistical and regression analysis and in data mining. There are a lot of interesting algorithms and approaches to outlier detection, labelling, filtering and finally interpretation. Unfortunately, their impact on control systems has not been found sufficient attention in research. Their influence is frequently unnoticed, ignored or not mentioned. This work focuses on the subject of outlier detection and labelling in the context of control system performance analysis. Selected statistical data-driven approaches are analyzed, as they can be easily implemented with limited a priori knowledge. The study consists of a simulation study followed by the analysis of real control data. Different generation mechanisms are simulated, like overlapping Gaussian processes, symmetric and asymmetric, artificially shifted points and fat-tailed distributions. Simulation observations are confronted with industrial control loops datasets. The work concludes with a practical procedure, which should help practitioners in dealing with outliers in control engineering temporal data.
A Computational Model for Measuring Trust in Mobile Social Networks Using Fuzzy Logic
Farzam Matinfar
2020,  vol. 17,  no. 6, pp. 812-821,  doi: 10.1007/s11633-020-1232-5
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Large-scale mobile social networks (MSNs) facilitate communications through mobile devices. The users of these networks can use mobile devices to access, share and distribute information. With the increasing number of users on social networks, the large volume of shared information and its propagation has created challenges for users. One of these challenges is whether users can trust one another. Trust can play an important role in users′ decision making in social networks, so that, most people share their information based on their trust on others, or make decisions by relying on information provided by other users. However, considering the subjective and perceptive nature of the concept of trust, the mapping of trust in a computational model is one of the important issues in computing systems of social networks. Moreover, in social networks, various communities may exist regarding the relationships between users. These connections and communities can affect trust among users and its complexity. In this paper, using user characteristics on social networks, a fuzzy clustering method is proposed and the trust between users in a cluster is computed using a computational model. Moreover, through the processes of combination, transition and aggregation of trust, the trust value is calculated between users who are not directly connected. Results show the high performance of the proposed trust inference method.
Control of a 3-RRR Planar Parallel Robot Using Fractional Order PID Controller
Auday Al-Mayyahi, Ammar A. Aldair, Chris Chatwin
2020,  vol. 17,  no. 6, pp. 822-836,  doi: 10.1007/s11633-020-1249-9
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3-RRR planar parallel robots are utilized for solving precise material-handling problems in industrial automation applications. Thus, robust and stable control is required to deliver high accuracy in comparison to the state of the art. The operation of the mechanism is achieved based on three revolute (3-RRR) joints which are geometrically designed using an open-loop spatial robotic platform. The inverse kinematic model of the system is derived and analyzed by using the geometric structure with three revolute joints. The main variables in our design are the platform base positions, the geometry of the joint angles, and links of the 3-RRR planar parallel robot. These variables are calculated based on Cayley-Menger determinants and bilateration to determine the final position of the platform when moving and placing objects. Additionally, a proposed fractional order proportional integral derivative (FOPID) is optimized using the bat optimization algorithm to control the path tracking of the center of the 3-RRR planar parallel robot. The design is compared with the state of the art and simulated using the Matlab environment to validate the effectiveness of the proposed controller. Furthermore, real-time implementation has been tested to prove that the design performance is practical.
Integration of Facial Thermography in EEG-based Classification of ASD
Dilantha Haputhanthri, Gunavaran Brihadiswaran, Sahan Gunathilaka, Dulani Meedeniya, Sampath Jayarathna, Mark Jaime, Christopher Harshaw
2020,  vol. 17,  no. 6, pp. 837-854,  doi: 10.1007/s11633-020-1231-6
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Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting social, communicative, and repetitive behavior. The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging, requiring highly trained clinical practitioners. The development of automated approaches to ASD classification, based on integrated psychophysiological measures, may one day help expedite the diagnostic process. This paper provides a novel contribution for classifing ASD using both thermographic and EEG data. The methodology used in this study extracts a variety of feature sets and evaluates the possibility of using several learning models. Mean, standard deviation, and entropy values of the EEG signals and mean temperature values of regions of interest (ROIs) in facial thermographic images were extracted as features. Feature selection is performed to filter less informative features based on correlation. The classification process utilizes Naïve Bayes, random forest, logistic regression, and multi-layer perceptron algorithms. The integration of EEG and thermographic features have achieved an accuracy of 94% with both logistic regression and multi-layer perceptron classifiers. The results have shown that the classification accuracies of most of the learning models have increased after integrating facial thermographic data with EEG.
A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence
Zun-Ran Wang, Chen-Guang Yang, Shi-Lu Dai
2020,  vol. 17,  no. 6, pp. 855-866,  doi: 10.1007/s11633-020-1240-5
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In this paper, a novel compression framework based on 3D point cloud data is proposed for telepresence, which consists of two parts. One is implemented to remove the spatial redundancy, i.e., a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box. The other part is applied to remove the temporal redundancy of the 3D point cloud data. The temporal redundancy between point clouds is removed by using the motion vector, i.e., the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame. The first, the B-SHOT (binary signatures of histograms orientation) descriptor is applied to represent the point feature for matching the corresponding point between two frames. The second, the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame. The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames. Finally, the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the current and the motion vectors are transmitted into the remote end. In order to reduce calculation time of the B-SHOT descriptor, we introduce an octree structure into the B-SHOT descriptor. In particular, in order to improve the robustness of the matching operation, we design the cluster feature to estimate the similarity between two clusters. Experimental results have shown the better performance of the proposed method due to the lower calculation time and the higher compression ratio. The proposed method achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.
PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution
Hong-Tao Ye, Zhen-Qiang Li
2020,  vol. 17,  no. 6, pp. 867-872,  doi: 10.1007/s11633-015-0917-7
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For complex systems with high nonlinearity and strong coupling, the decoupling control technology based on proportion integration differentiation (PID) neural network (PIDNN) is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem, a hybrid particle swarm optimization (PSO) and differential evolution (DE) algorithm (PSO-DE) is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show that the proposed method has better decoupling capabilities and control quality than the previous approaches.
Simultaneous Identification of Process Structure, Parameters 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
2020,  vol. 17,  no. 6, pp. 873-882,  doi: 10.1007/s11633-015-0948-0
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In practice, the model structure, parameters and time-delay of the actual process may vary simultaneously.However, the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application.In view of the fact that variable selection procedure can ensure a compact model with robust input-output relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure, parameters and time-delay, non-negative garrote (NNG) algorithm is introduced and applied to system identification and the corresponding procedures are presented.The application of NNG variable selection algorithm to the identification of single input single output (SISO) system, multiple input multiple output (MIMO) system and Wood-Berry tower industry are investigated.The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square (OLS) algorithms.The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure, parameters and time-delay with high precision.
Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles
K.A. Venkatesh, N. Mathivanan
2020,  vol. 17,  no. 6, pp. 883-890,  doi: 10.1007/s11633-016-0968-4
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Design of an Ethernet network compatible data acquisition system for the measurement of yaw rate and longitudinal velocity in automobiles is presented.The data acquisition system includes a base node and a remote node.The remote node consists of a micro electro mechanical system (MEMS) accelerometer, an MEMS gyroscope, an advanced RISC machines (ARM) CORTEX M3 microcontroller and an Ethernet PHY device.The remote node measures the yaw rate and the longitudinal velocity of an automobile and sends the measured values to the base node using Ethernet communication.The base node consists of an ARM CORTEX M3 microcontroller and an Ethernet PHY device.The base node receives the measured values and saves in a microSD card for further analysis.The characteristics of the network and the measurement system are studied and reported.
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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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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Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection
Sunil Nilkanth Pawar, Rajankumar Sadashivrao Bichkar
2015,  vol. 12,  no. 3, pp. 337-342,  doi: 10.1007/s11633-014-0870-x
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Grey Qualitative Modeling and Control Method for Subjective Uncertain Systems
Peng Wang, Shu-Jie Li, Yan Lv, Zong-Hai Chen
2015,  vol. 12,  no. 1, pp. 70-76,  doi: 10.1007/s11633-014-0820-7
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Recent Progress in Networked Control Systems-A Survey
Yuan-Qing Xia, Yu-Long Gao, Li-Ping Yan, Meng-Yin Fu
2015,  vol. 12,  no. 4, pp. 343-367,  doi: 10.1007/s11633-015-0894-x
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A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System
Kayode Owa, Sanjay Sharma, Robert Sutton
2015,  vol. 12,  no. 2, pp. 156-170,  doi: 10.1007/s11633-014-0825-2
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Cooperative Formation Control of Autonomous Underwater Vehicles: An Overview
Bikramaditya Das, Bidyadhar Subudhi, Bibhuti Bhusan Pati
2016,  vol. 13,  no. 3, pp. 199-225,  doi: 10.1007/s11633-016-1004-4
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An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers
Danasingh Asir Antony Gnana Singh, Subramanian Appavu Alias Balamurugan, Epiphany Jebamalar Leavline
2015,  vol. 12,  no. 5, pp. 511-517,  doi: 10.1007/s11633-014-0859-5
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Sliding Mode and PI Controllers for Uncertain Flexible Joint Manipulator
Lilia Zouari, Hafedh Abid, Mohamed Abid
2015,  vol. 12,  no. 2, pp. 117-124,  doi: 10.1007/s11633-015-0878-x
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Bounded Real Lemmas for Fractional Order Systems
Shu Liang, Yi-Heng Wei, Jin-Wen Pan, Qing Gao, Yong Wang
2015,  vol. 12,  no. 2, pp. 192-198,  doi: 10.1007/s11633-014-0868-4
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Robust Face Recognition via Low-rank Sparse Representation-based Classification
Hai-Shun Du, Qing-Pu Hu, Dian-Feng Qiao, Ioannis Pitas
2015,  vol. 12,  no. 6, pp. 579-587,  doi: 10.1007/s11633-015-0901-2
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Distributed Control of Chemical Process Networks
Michael J. Tippett, Jie Bao
2015,  vol. 12,  no. 4, pp. 368-381,  doi: 10.1007/s11633-015-0895-9
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Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses
Chandrasekaran Raja, Narayanan Gangatharan
2015,  vol. 12,  no. 4, pp. 393-401,  doi: 10.1007/s11633-014-0858-6
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Analysis of Fractional-order Linear Systems with Saturation Using Lyapunov s Second Method and Convex Optimization
Esmat Sadat Alaviyan Shahri, Saeed Balochian
2015,  vol. 12,  no. 4, pp. 440-447,  doi: 10.1007/s11633-014-0856-8
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Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking
David H. Owens, Chris T. Freeman, Bing Chu
2015,  vol. 12,  no. 3, pp. 243-253,  doi: 10.1007/s11633-015-0888-8
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Flexible Strip Supercapacitors for Future Energy Storage
Rui-Rong Zhang, Yan-Meng Xu, David Harrison, John Fyson, Fu-Lian Qiu, Darren Southee
2015,  vol. 12,  no. 1, pp. 43-49,  doi: 10.1007/s11633-014-0866-6
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Advances in Vehicular Ad-hoc Networks (VANETs): Challenges and Road-map for Future Development
Elias C. Eze, Si-Jing Zhang, En-Jie Liu, Joy C. Eze
2016,  vol. 13,  no. 1, pp. 1-18,  doi: 10.1007/s11633-015-0913-y
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Finite-time Control for a Class of Networked Control Systems with Short Time-varying Delays and Sampling Jitter
Chang-Chun Hua, Shao-Chong Yu, Xin-Ping Guan
2015,  vol. 12,  no. 4, pp. 448-454,  doi: 10.1007/s11633-014-0849-7
Abstract PDF SpringerLink
Backstepping Control of Speed Sensorless Permanent Magnet Synchronous Motor Based on Slide Model Observer
Cai-Xue Chen, Yun-Xiang Xie, Yong-Hong Lan
2015,  vol. 12,  no. 2, pp. 149-155,  doi: 10.1007/s11633-015-0881-2
Abstract PDF SpringerLink
Extracting Parameters of OFET Before and After Threshold Voltage Using Genetic Algorithms
Imad Benacer, Zohir Dibi
2016,  vol. 13,  no. 4, pp. 382-391,  doi: 10.1007/s11633-015-0918-6
Abstract PDF SpringerLink
A High-order Internal Model Based Iterative Learning Control Scheme for Discrete Linear Time-varying Systems
Wei Zhou, Miao Yu, De-Qing Huang
2015,  vol. 12,  no. 3, pp. 330-336,  doi: 10.1007/s11633-015-0886-x
Abstract PDF SpringerLink
Current Issue

2020 Vol.17 No.6

Table of Contents

ISSN 1476-8186

E-ISSN 1751-8520

CN 11-5350/TP

Editors-in-chief
Tieniu TAN, Chinese Academy of SciencesGuoping LIU, University of South WalesHuosheng HU, University of Essex
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