Display Method:
Review
A Survey on Modelling and Compensation for Hysteresis in High Speed Nanopositioning of AFMs: Observation and Future Recommendation
Maniza Armin, Priyo Nath Roy, Sajal Kumar Das
doi: 10.1007/s11633-020-1225-4
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This paper surveys the recent advances on the modeling and control of hysteresis of piezoelectric actuators (PTAs) in the context of high precision applications of atomic force microscopes (AFMs). The current states, findings, and outcomes on hysteresis modeling and control in terms of achievable bandwidth and accuracy are discussed in detailed. Future challenges and the scope of possible research are presented to pave the way to video rate atomic force microscopy.
Research Article
A Computational Model for Measuring Trust in Mobile Social Networks Using Fuzzy Logic
Farzam Matinfar
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.
Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance
Ze-Wen Wang, Jin-Hua She, Guang-Jun Wang
doi: 10.1007/s11633-020-1230-7
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This paper presents an adaptive equivalent-input-disturbance (AEID) approach that contains a new adjustable gain to improve disturbance-rejection performance. A linear matrix inequality is derived to design the parameters of a control system. An adaptive law for the adjustable gain is presented based on the combination of the root locus method and Lyapunov stability theory to guarantee the stability of the AEID-based system. The adjustable gain is limited in an allowable range and the information for adjusting is obtained from the state of the system. Simulation results show that the method is effective and robust. A comparison with the conventional EID approach demonstrates the validity and superiority of the method.
Integration of Facial Thermography in EEG-based Classification of ASD
Dilantha Haputhanthri, Gunavaran Brihadiswaran, Sahan Gunathilaka, Dulani Meedeniya, Sampath Jayarathna, Mark Jaime, Christopher Harshaw
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.
Binocular Vision Object Positioning Method for Robots Based on Coarse-fine Stereo Matching
Wei-Ping Ma, Wen-Xin Li, Peng-Xia Cao
doi: 10.1007/s11633-020-1226-3
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In order to improve the low positioning accuracy and execution efficiency of the robot binocular vision, a binocular vision positioning method based on coarse-fine stereo matching is proposed to achieve object positioning. The random fern is used in the coarse matching to identify objects in the left and right images, and the pixel coordinates of the object center points in the two images are calculated to complete the center matching. In the fine matching, the right center point is viewed as an estimated value to set the search range of the right image, in which the region matching is implemented to find the best matched point of the left center point. Then, the similar triangle principle of the binocular vision model is used to calculate the 3D coordinates of the center point, achieving fast and accurate object positioning. Finally, the proposed method is applied to the object scene images and the robotic arm grasping platform. The experimental results show that the average absolute positioning error and average relative positioning error of the proposed method are 8.22 mm and 1.96% respectively when the object′s depth distance is within 600 mm, the time consumption is less than 1.029 s. The method can meet the needs of the robot grasping system, and has better accuracy and robustness.
Novel Non-monotonic Lyapunov-Krasovskii Based Stability Analysis and Stabilization of Discrete State-delay System
Younes Solgi, Alireza Fatehi, Ala Shariati
doi: 10.1007/s11633-020-1222-7
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This paper proposes a novel less-conservative non-monotonic Lyapunov-Krasovskii stability approach for stability analysis of discrete time-delay systems. In this method, monotonically decreasing requirements of the Lyapunov-Krasovskii method are replaced with non-monotonic ones. The Lyapunov-Krasovskii functional is allowed to increase in some steps, but the overall trend should be decreasing. The model of practical systems used for stability analysis usually contain uncertainty. Therefore, firstly a non-monotonic stability condition is derived for certain discrete time-delay systems, then robust non-monotonic stability conditions are proposed for uncertain systems. Finally, a novel stabilization algorithm is derived based on the introduced non-monotonic stability condition. The Lyapunov-Krasovskii functional and the controller are obtained by solving a set of linear matrix inequalities (LMI) or iterative LMI based nonlinear minimization. The proposed theorems are first evaluated by some numerical examples, and then by simulation and implementation on the pH neutralizing process plant.
Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning
Fu-Qiang Liu, Zong-Yi Wang
doi: 10.1007/s11633-020-1221-8
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In industry, it is becoming common to detect and recognize industrial workpieces using deep learning methods. In this field, the lack of datasets is a big problem, and collecting and annotating datasets in this field is very labor intensive. The researchers need to perform dataset annotation if a dataset is generated by themselves. It is also one of the restrictive factors that the current method based on deep learning cannot expand well. At present, there are very few workpiece datasets for industrial fields, and the existing datasets are generated from ideal workpiece computer aided design (CAD) models, for which few actual workpiece images were collected and utilized. We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method. Included in our methods are three algorithms that we proposed: a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene; a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions; and a tangent vector based contour tracking and completion algorithm to get improved contour images. With our procedures, annotation information can be obtained using the algorithms proposed in this paper. Upon completion of the annotation process, a json format file is generated. Faster R-CNN (Faster R-convolutional neural network), SSD (single shot multibox detector) and YOLO (you only look once: unified, real-time object detection) are trained using the datasets proposed in this paper. The experimental results show the effectiveness and integrity of this dataset generation and annotation method.
Parabolic Trough Collector and Central Receiver Coupled with Fresnel Lens: Experimental Tests
Angelica Palacios, Dario Amaya, Olga Ramos
doi: 10.1007/s11633-019-1220-9
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Renewable energies have a high impact on power energy production and reduction of environmental pollution worldwide, so high efforts have been made to improve renewable technologies and research about them. This paper presents the thermal performance results obtained by simulation and experimental tests of a parabolic trough collector with central receiver coupled to Fresnel lens, under different configurations on the pipe. The simulation method was computational fluid dynamics (CFD) analysis in SolidWorks ® software tool, which works with Naiver-Stokes equations to converge on a solution. Experimental tests were formed with all configurations proposed and three observations for each one, a total of 12 observations were performed in all research. As a result, the best thermal performance in simulation was achieved with the Fresnel lens and black pipe collector, with a maximum temperature of 116 °C under 1 000 W/m2 radiation, the same system achieved in experimental tests a maximum temperature of 96 °C with a radiation of 983 W/m2.
Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm
Santanu Sahoo, Asit Subudhi, Manasa Dash, Sukanta Sabut
doi: 10.1007/s11633-019-1219-2
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Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram (ECG) signals. In a life-threatening situation, an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate. In this paper, we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals. The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database. The feature set comprises eight empirical mode decomposition (EMD) based features, three features from variational mode decomposition (VMD) and four features from RR intervals. In total, 15 features are ranked according to a ranker search approach and then used as input to the support vector machine (SVM) and C4.5 decision tree classifiers for classifying six types of arrhythmia beats. The proposed method achieved best result in C4.5 decision tree classier with an accuracy of 98.89% compared to cubic-SVM classifier which achieved an accuracy of 95.35% only. Besides accuracy measures, all other parameters such as sensitivity (Se), specificity (Sp) and precision rates of 95.68%, 99.28% and 95.8% was achieved better in C4.5 classifier. Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM. The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.
Remote Sensing Image Registration Based on Improved KAZE and BRIEF Descriptor
Huan Liu, Gen-Fu Xiao
doi: 10.1007/s11633-019-1218-3
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Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images. To solve this problem, this paper proposes a novel approach with regard to feature-based remote sensing image registration. There are two key contributions: 1) we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2) we design a gradually decreasing resolution of multi-scale pyramid space. And a binary code string is served as feature descriptors to improve matching efficiency. Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed. The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio, accuracy and computation efficiency.
Kinematic Analysis of an Under-actuated, Closed-loop Front-end Assembly of a Dragline Manipulator
Muhammad A. Wardeh, Samuel Frimpong
doi: 10.1007/s11633-019-1217-4
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Dragline excavators are closed-loop mining manipulators that operate using a rigid multilink framework and rope and rigging system, which constitute its front-end assembly. The arrangements of dragline front-end assembly provide the necessary motion of the dragline bucket within its operating radius. The assembly resembles a five-link closed kinematic chain that has two independent generalized coordinates of drag and hoist ropes and one dependent generalized coordinate of dump rope. Previous models failed to represent the actual closed loop of dragline front-end assembly, nor did they describe the maneuverability of dragline ropes under imposed geometric constraints. Therefore, a three degrees of freedom kinematic model of the dragline front-end is developed using the concept of generalized speeds. It contains all relevant configuration and kinematic constraint conditions to perform complete digging and swinging cycles. The model also uses three inputs of hoist and drag ropes linear and a rotational displacement of swinging along their trajectories. The inverse kinematics is resolved using a feedforward displacement algorithm coupled with the Newton-Raphson method to accurately estimate the trajectories of the ropes. The trajectories are solved only during the digging phase and the singularity was eliminated using Baumgarte′s stabilization technique (BST), with appropriate inequality constraint equations. It is shown that the feedforward displacement algorithm can produce accurate trajectories without the need to manually solve the inverse kinematics from the geometry. The research findings are well in agreement with the dragline real operational limits and they contribute to the efficiency and the reduction in machine downtime due to better control strategies of the dragline cycles.
Controller Optimization for Multirate Systems Based on Reinforcement Learning
Zhan Li, Sheng-Ri Xue, Xing-Hu Yu, Hui-Jun Gao
doi: 10.1007/s11633-020-1229-0
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The goal of this paper is to design a model-free optimal controller for the multirate system based on reinforcement learning. Sampled-data control systems are widely used in the industrial production process and multirate sampling has attracted much attention in the study of the sampled-data control theory. In this paper, we assume the sampling periods for state variables are different from periods for system inputs. Under this condition, we can obtain an equivalent discrete-time system using the lifting technique. Then, we provide an algorithm to solve the linear quadratic regulator (LQR) control problem of multirate systems with the utilization of matrix substitutions. Based on a reinforcement learning method, we use online policy iteration and off-policy algorithms to optimize the controller for multirate systems. By using the least squares method, we convert the off-policy algorithm into a model-free reinforcement learning algorithm, which only requires the input and output data of the system. Finally, we use an example to illustrate the applicability and efficiency of the model-free algorithm above mentioned.
The Propagation Background in Social Networks: Simulating and Modeling
Kai Li, Tong Xu, Shuai Feng, Li-Sheng Qiao, Hua-Wei Shen, Tian-Yang Lv, Xue-Qi Cheng, En-Hong Chen
doi: 10.1007/s11633-020-1227-2
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Recent years have witnessed the booming of online social network and social media platforms, which leads to a state of information explosion. Though extensive efforts have been made by publishers to struggle for the limited attention of audiences, still, only a few of information items will be received and digested. Therefore, for simulating the information propagation process, competition among propagating items should be considered, which has been largely ignored by prior works on propagation modeling. One possible reason may be that, it is almost impossible to identify the influence of propagation background from real diffusion data. To that end, in this paper, we design a comprehensive framework to simulate the propagation process with the characteristics of user behaviors and network topology. Specifically, we propose a propagation background simulating (PBS) algorithm to simulate the propagation background by using users′ behavior dynamics and out-degree. Along this line, an ICPB (independent cascade with propagation background) model is adapted to relieve the impact of propagation background by using users′ in-degree. Extensive experiments on kinds of synthetic and real networks have demonstrated the effectiveness of our methods.
Item Ownership Relationship Semantic Learning Strategy for Personalized Service Robot
Hao Wu, Zhao-Wei Chen, Guo-Hui Tian, Qing Ma, Meng-Lin Jiao
doi: 10.1007/s11633-019-1206-7
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In order to satisfy the robotic personalized service requirements that can select exclusive items to perform inference and planning according to different service individuals, the service robots need to have the ability to independently obtain the ownership relationship between humans and their carrying items. In this work, we present a novel semantic learning strategy for item ownership. Firstly, a human-carrying-items detection network based on human posture estimation and object detection model is used. Then, the transferred convolutional neural network is used to extract the characteristics of the objects and the back-end classifier to recognize the object instance. At the same time, the face detection and recognition model are used to identify the service individual. Finally, on the basis of the former two, the active learning of ownership items is completed. The experimental results show that the proposed ownership semantic learning strategy can determine the ownership relationship of private goods accurately and efficiently. The solution of this problem can improve the intelligence level of robot life service.
Performance Improvement of Discrete-time Linear Control Systems Subject to Varying Sampling Rates Using the Tikhonov Regularization Method
Fernando Agustín Pazos, Anibal Zanini, Amit Bhaya
doi: 10.1007/s11633-019-1205-8
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Methods to stabilize discrete-time linear control systems subject to variable sampling rates, i.e., using state feedback controllers, are well known in the literature. Several recent works address the use of the Tikhonov regularization method, originally designed to attenuate the noise effects on ill-posed problems, with the aim of improving performance and stabilizing approximately controllable dynamical systems. Inspired by these works, we propose the use of a feedback controller designed using the Tikhonov method to regularize discrete-time linear systems subject to varying sampling rates. The goal is to minimize an error function, thus improving the performance of the closed loop system and reducing the possibility of instability. Illustrative examples show the effectiveness of the proposed method.
HDec-POSMDPs MRS Exploration and Fire Searching Based on IoT Cloud Robotics
Ayman El Shenawy, Khalil Mohamed, Hany Harb
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
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.
A Practical Approach to Representation of Real-time Building Control Applications in Simulation
Azzedine Yahiaoui
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.
Hybrid Dynamic Neural Network and PID Control of Pneumatic Artificial Muscle Using the PSO Algorithm
Mahdi Chavoshian, Mostafa Taghizadeh, Mahmood Mazare
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.
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
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
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
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
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
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
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
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
doi: 10.1007/s11633-016-0968-4
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Display Method:
Review
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Han Xu, Yao Ma, Hao-Chen Liu, Debayan Deb, Hui Liu, Ji-Liang Tang, Anil K. Jain
2020,  vol. 17,  no. 2, pp. 151-178,  doi: 10.1007/s11633-019-1211-x
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Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.
Electronic Nose and Its Applications: A Survey
Diclehan Karakaya, Oguzhan Ulucan, Mehmet Turkan
2020,  vol. 17,  no. 2, pp. 179-209,  doi: 10.1007/s11633-019-1212-9
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In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.
Research Article
Text-mining-based Fake News Detection Using Ensemble Methods
Harita Reddy, Namratha Raj, Manali Gala, Annappa Basava
2020,  vol. 17,  no. 2, pp. 210-221,  doi: 10.1007/s11633-019-1216-5
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Social media is a platform to express one′s views and opinions freely and has made communication easier than it was before. This also opens up an opportunity for people to spread fake news intentionally. The ease of access to a variety of news sources on the web also brings the problem of people being exposed to fake news and possibly believing such news. This makes it important for us to detect and flag such content on social media. With the current rate of news generated on social media, it is difficult to differentiate between genuine news and hoaxes without knowing the source of the news. This paper discusses approaches to detection of fake news using only the features of the text of the news, without using any other related metadata. We observe that a combination of stylometric features and text-based word vector representations through ensemble methods can predict fake news with an accuracy of up to 95.49%.
Spectral-spatial Classification of Hyperspectral Images Using Signal Subspace Identification and Edge-preserving Filter
Negin Alborzi, Fereshteh Poorahangaryan, Homayoun Beheshti
2020,  vol. 17,  no. 2, pp. 222-232,  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.
Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network
Kittinun Aukkapinyo, Suchakree Sawangwong, Parintorn Pooyoi, Worapan Kusakunniran
2020,  vol. 17,  no. 2, pp. 233-246,  doi: 10.1007/s11633-019-1207-6
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This paper proposes a solution to localization and classification of rice grains in an image. All existing related works rely on conventional based machine learning approaches. However, those techniques do not do well for the problem designed in this paper, due to the high similarities between different types of rice grains. The deep learning based solution is developed in the proposed solution. It contains pre-processing steps of data annotation using the watershed algorithm, auto-alignment using the major axis orientation, and image enhancement using the contrast-limited adaptive histogram equalization (CLAHE) technique. Then, the mask region-based convolutional neural networks (R-CNN) is trained to localize and classify rice grains in an input image. The performance is enhanced by using the transfer learning and the dropout regularization for overfitting prevention. The proposed method is validated using many scenarios of experiments, reported in the forms of mean average precision (mAP) and a confusion matrix. It achieves above 80% mAP for main scenarios in the experiments. It is also shown to perform outstanding, when compared to human experts.
Orientation Measurement for Objects with Planar Surface Based on Monocular Microscopic Vision
Ying Li, Xi-Long Liu, De Xu, Da-Peng Zhang
2020,  vol. 17,  no. 2, pp. 247-256,  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.
Tracking Registration Algorithm for Augmented Reality Based on Template Tracking
Peng-Xia Cao, Wen-Xin Li, Wei-Ping Ma
2020,  vol. 17,  no. 2, pp. 257-266,  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.
Smooth-optimal Adaptive Trajectory Tracking Using an Uncalibrated Fish-eye Camera
Zhao-Bing Kang, Wei Zou, Zheng Zhu, Hong-Xuan Ma
2020,  vol. 17,  no. 2, pp. 267-278,  doi: 10.1007/s11633-019-1209-4
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Abstract:
This paper presents a two-stage smooth-optimal trajectory tracking strategy. Different from existing methods, the optimal trajectory tracked point can be directly determined in an uncalibrated fish-eye image. In the first stage, an adaptive trajectory tracking controller is employed to drive the tracking error and the estimated error to an arbitrarily small neighborhood of zero. Afterwards, an online smooth-optimal trajectory tracking planner is proposed, which determines the tracked point that can be used to realize smooth motion control of the mobile robot. The tracked point in the uncalibrated image can be determined by minimizing a utility function that consists of both the velocity change and the sum of cross-track errors. The performance of our planner is compared with other tracked point determining methods in experiments by tracking a circular trajectory and an irregular trajectory. Experimental results show that our method has a good performance in both tracking accuracy and motion smoothness.
Modeling of a Smart Nano Force Sensor Using Finite Elements and Neural Networks
Farid Menacer, Abdelmalek Kadr, Zohir Dibi
2020,  vol. 17,  no. 2, pp. 279-291,  doi: 10.1007/s11633-018-1155-6
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Abstract:
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.
Image Encryption Algorithm Based on Compressive Sensing and Fractional DCT via Polynomial Interpolation
Ya-Ru Liang, Zhi-Yong Xiao
2020,  vol. 17,  no. 2, pp. 292-304,  doi: 10.1007/s11633-018-1159-2
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Abstract:
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.
Hidden Markov Model Approach for Software Reliability Estimation with Logic Error
R. Bharathi, R. Selvarani
2020,  vol. 17,  no. 2, pp. 305-320,  doi: 10.1007/s11633-019-1214-7
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Abstract:
To ensure the safe operation of any software controlled critical systems, quality factors like reliability and safety are given utmost importance. In this paper, we have chosen to analyze the impact of logic error that is one of the contributors to the above factors. In view of this, we propose a novel framework based on a data driven approach known as software failure estimation with logic error (SFELE). Here, the probabilistic nature of software error is explored by observing the operation of a safety critical system by injecting logic fault. The occurrence of error, its propagations and transformations are analyzed from its inception to end of its execution cycle through the hidden Markov model (HMM) technique. We found that the proposed framework SFELE supports in labeling and quantifying the behavioral properties of selected errors in a safety critical system while traversing across its system components in addition to reliability estimation of the system. Our attempt at the design level can help the design engineers to improve their system quality in a cost-effective manner.
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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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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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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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
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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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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
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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
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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
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Current Issue

2020 Vol.17 No.2

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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