Display Method:
Review
Dynamic Event-triggered Control and Estimation: A Survey
Xiaohua Ge, Qing-Long Han, Xian-Ming Zhang, Derui Ding
doi: 10.1007/s11633-021-1306-z
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Abstract:
The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components (e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms (ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms (DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables (ADVs) and dynamic threshold parameters (DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.
Research Article
A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines
Qiang Li, Yong-Sheng Qi, Xue-Jin Gao, Yong-Ting Li, Li-Qiang Liu
doi: 10.1007/s11633-021-1305-0
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Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed, which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a “W” structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect. Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference, and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.

Encoding-decoding Network With Pyramid Self-attention Module For Retinal Vessel Segmentation

Cong-Zhong Wu, Jun Sun, Jing Wang, Liang-Feng Xu, Shu Zhan
doi: 10.1007/s11633-020-1277-0
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Abstract:

Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researchers are committed to the research of automatic segmentation algorithms. With the development of convolution neural networks (CNNs), many tasks can be solved by CNNs. In this paper, we propose an encoding-decoding network with a pyramid self-attention module (PSAM) to segment retinal vessels. The network follows a U shape structure, and it comprises stacked feature selection blocks (FSB) and a PSAM. The proposed FSB consists of two convolution blocks with the same weight and a channel-wise attention block. At the head of the network, we apply a PSAM consisting of three parallel self-attention modules to capture long-range dependence of different scales. Due to the power of PSAM and FSB, the performance of the network improves. We have evaluated our model on two public datasets: DRIVE and CHASE_DB1. The results show the performance of our model is better than other methods. The F1, Accuracy, and area under curve (AUC) are 82.21%/80.57%, 95.65%/97.02%, and 98.16%/98.46% on DRIVE and CHASE_DB1, respectively.

Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration

Lu-Jie Zhou, Jian-Wu Dang, Zhen-Hai Zhang
doi: 10.1007/s11633-021-1298-8
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Abstract:

It is of great significance to guarantee the efficient statistics of high-speed railway on-board equipment fault information, which also improves the efficiency of fault analysis. Considering this background, this paper presents an empirical exploration of named entity recognition (NER) of on-board equipment fault information. Based on the historical fault records of on-board equipment, a fault information recognition model based on multi-neural network collaboration is proposed. First, considering Chinese recorded data characteristics, a method of constructing semantic features and additional features based on character granularity is proposed. Then, the two feature representations are concatenated and passed into the gated convolutional layer to extract the dependencies from multiple different subspaces and adjacent characters in parallel. Next, the local features are transmitted to the bidirectional long short-term memory (BiLSTM) to learn long-term dependency information. On top of BiLSTM, the sequential conditional random field (CRF) is used to jointly decode the optimized tag sequence of the whole sentence. The model is tested and compared with other representative baseline models. The results show that the proposed model not only considers the language characteristics of on-board fault records, but also has obvious advantages on the performance of fault information recognition.

DLA+: A Light Aggregation Network for Object Classification and Detection

Fu-Tian Wang, Li Yang, Jin Tang, Si-Bao Chen, Xin Wang
doi: 10.1007/s11633-021-1287-y
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An efficient convolution neural network (CNN) plays a crucial role in various visual tasks like object classification or detection, etc. The most common way to construct a CNN is stacking the same convolution block or complex connection. These approaches may be efficient but the parameter size and computation (Comp) have explosive growth. So we present a novel architecture called “DLA+”, which could obtain the feature from the different stages, and by the newly designed convolution block, could achieve better accuracy, while also dropping the computation six times compared to the baseline. We design some experiments about classification and object detection. On the CIFAR10 and VOC data-sets, we get better precision and faster speed than other architecture. The lightweight network even allows us to deploy to some low-performance device like drone, laptop, etc.

Research on Voiceprint Recognition of Camouflage Voice Based on Deep Belief Network

Nan Jiang, Ting Liu
doi: 10.1007/s11633-021-1283-2
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The problem of disguised voice recognition based on deep belief networks is studied. A hybrid feature extraction algorithm based on formants, Gammatone frequency cepstrum coefficients (GFCC) and their different coefficients is proposed to extract more discriminative speaker features from the original voice data. Using mixed features as the input of the model, a masquerade voice library is constructed. A masquerade voice recognition model based on a depth belief network is proposed. A dropout strategy is introduced to prevent overfitting, which effectively solves the problems of traditional Gaussian mixture models, such as insufficient modeling ability and low discrimination. Experimental results show that the proposed disguised voice recognition method can better fit the feature distribution, and significantly improve the classification effect and recognition rate.

Observer-based Multirate Feedback Control Design for Two-time-scale System
Ravindra Munje, Wei-Dong Zhang
doi: 10.1007/s11633-020-1268-6
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The use of a lower sampling rate for designing a discrete-time state feedback-based controller fails to capture information of fast states in a two-time-scale system, while the use of a higher sampling rate increases the amount of computation considerably. Thus, the use of single-rate sampling for systems with slow and fast states has evident limitations. In this paper, multirate state feedback (MRSF) control for a linear time-invariant two-time-scale system is proposed. Here, multirate sampling refers to the sampling of slow and fast states at different sampling rates. Firstly, a block-triangular form of the original continuous two-time-scale system is constructed. Then, it is discretized with a smaller sampling period and feedback control is designed for the fast subsystem. Later, the system is block-diagonalized and equivalently represented into a system with a higher sampling period. Subsequently, feedback control is designed for the slow subsystem and overall MRSF control is derived. It is proved that the derived MRSF control stabilizes the full-order system. Being the transformed states of the original system, slow and fast states need to be estimated for the MRSF control realization. Hence, a sequential two-stage observer is formulated to estimate these states. Finally, the applicability of the design method is demonstrated with a numerical example and simulation results are compared with the single-rate sampling method. It is found that the proposed MRSF control and observer designs reduce computations without compromising closed-loop performance.
Display Method:
Research Article
Dynamic System Identification of Underwater Vehicles Using Multi-output Gaussian Processes
Wilmer Ariza Ramirez, Juš Kocijan, Zhi Quan Leong, Hung Duc Nguyen, Shantha Gamini Jayasinghe
2021,  vol. 18,  no. 5, pp. 681-693,  doi: 10.1007/s11633-021-1308-x
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Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle (AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.
Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations
Ali Dolatshahi Zand, Kaveh Khalili-Damghani, Sadigh Raissi
2021,  vol. 18,  no. 5, pp. 694-717,  doi: 10.1007/s11633-021-1284-1
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In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.
STRNet: Triple-stream Spatiotemporal Relation Network for Action Recognition
Zhi-Wei Xu, Xiao-Jun Wu, Josef Kittler
2021,  vol. 18,  no. 5, pp. 718-730,  doi: 10.1007/s11633-021-1289-9
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Learning comprehensive spatiotemporal features is crucial for human action recognition. Existing methods tend to model the spatiotemporal feature blocks in an integrate-separate-integrate form, such as appearance-and-relation network (ARTNet) and spatiotemporal and motion network (STM). However, with blocks stacking up, the rear part of the network has poor interpretability. To avoid this problem, we propose a novel architecture called spatial temporal relation network (STRNet), which can learn explicit information of appearance, motion and especially the temporal relation information. Specifically, our STRNet is constructed by three branches, which separates the features into 1) appearance pathway, to obtain spatial semantics, 2) motion pathway, to reinforce the spatiotemporal feature representation, and 3) relation pathway, to focus on capturing temporal relation details of successive frames and to explore long-term representation dependency. In addition, our STRNet does not just simply merge the multi-branch information, but we apply a flexible and effective strategy to fuse the complementary information from multiple pathways. We evaluate our network on four major action recognition benchmarks: Kinetics-400, UCF-101, HMDB-51, and Something-Something v1, demonstrating that the performance of our STRNet achieves the state-of-the-art result on the UCF-101 and HMDB-51 datasets, as well as a comparable accuracy with the state-of-the-art method on Something-Something v1 and Kinetics-400.
STEP AP 242 Managed Model-based 3D Engineering: An Application Towards the Automation of Fixture Planning
Remil George Thomas, Deepak Lawrence K., Manu R.
2021,  vol. 18,  no. 5, pp. 731-746,  doi: 10.1007/s11633-020-1272-x
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Fixture design and planning is one of the most important manufacturing activities, playing a pivotal role in deciding the lead time for product development. Fixture design, which affects the part-quality in terms of geometric accuracy and surface finish, can be enhanced by using the product manufacturing information (PMI) stored in the neutral standard for the exchange of product model data (STEP) file, thereby integrating design and manufacturing. The present paper proposes a unique fixture design approach, to extract the geometry information from STEP application protocol (AP) 242 files of computer aided design (CAD) models, for providing automatic suggestions of locator positions and clamping surfaces. Automatic feature extraction software “FiXplan”, developed using the programming language C#, is used to extract the part feature, dimension and geometry information. The information from the STEP AP 242 file is deduced using geometric reasoning techniques, which in turn is utilized for fixture planning. The developed software is observed to be adept in identifying the primary, secondary, and tertiary locating faces and locator position configurations of prismatic components. Structural analysis of the prismatic part under different locator positions was performed using commercial finite element method software, ABAQUS, and the optimized locator position was identified on the basis of minimum deformation of the workpiece. The area-ratio (base locator enclosed area (%)/work piece base area (%)) for the ideal locator configuration was observed as 33%. Experiments were conducted on a prismatic workpiece using a specially designed fixture, for different locator configurations. The surface roughness and waviness of the machined surfaces were analysed using an Alicona non-contact optical profilometer. The best surface characteristics were obtained for the surface machined under the ideal locator positions having an area-ratio of 33%, thus validating the predicted numerical results. The efficiency, capability and applicability of the developed software is demonstrated for the finishing operation of a sensor cover – a typical prismatic component having applications in the naval industry, under different locator configurations. The best results were obtained under the proposed ideal locator configuration of area-ratio 33%.
A 2D Mapping Method Based on Virtual Laser Scans for Indoor Robots
Xu-Yang Shao, Guo-Hui Tian, Ying Zhang
2021,  vol. 18,  no. 5, pp. 747-765,  doi: 10.1007/s11633-021-1304-1
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The indoor robots are expected to complete metric navigation tasks safely and efficiently in complex environments, which is the essential prerequisite for accomplishing other high-level operation tasks. 2D occupancy grid maps are sufficient to support the robots in avoiding all obstacles in the environments during navigation. However, the maps based on normal laser scans only reflect a horizontal slice of the environment, which may cause the problem of some obstacles missing or misinterpreting their exact boundaries, thereby threatening the safety and efficiency of robot navigation. This paper presents a 2D mapping method based on virtual laser scans to provide a more comprehensive representation of obstacles for indoor robot navigation. The resulting maps can accurately represent the top-down projected contours of all obstacles no matter where their vertical positions are. The virtual laser scans are initially generated from raw data of an RGB-D camera based on the filtering, projection, and polar-coordinate scanning. The scans are fed directly to the laser-based simultaneous localization and mapping (SLAM) algorithms to update the current map and robot position. Two auxiliary strategies are proposed to further improve the quality of maps by reducing the impact of the narrow field of view and the blind zone of the RGB-D camera on the observations. In this paper, the improved virtual laser generation method makes the extracted 2D observations fit the laser-based SLAM algorithms, and two auxiliary strategies are novel ways to improve map quality. The generated maps can reflect the comprehensive obstacle information in indoor environments with good accuracy. The comparative experiments are carried out based on four simulation scenarios and three real-world scenarios to prove the effectiveness of our 2D mapping method.
Behavior-based Autonomous Navigation and Formation Control of Mobile Robots in Unknown Cluttered Dynamic Environments with Dynamic Target Tracking
Nacer Hacene, Boubekeur Mendil
2021,  vol. 18,  no. 5, pp. 766-786,  doi: 10.1007/s11633-020-1264-x
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While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots (TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: “target tracking”, “obstacle avoidance”, “dynamic wall following” and “avoid robots”. The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.
Robust Observer-based Control of Nonlinear Multi-omnidirectional Wheeled Robot Systems via High Order Sliding-mode Consensus Protocol
M. R. Rahimi Khoygani, R. Ghasemi, P. Ghayoomi
2021,  vol. 18,  no. 5, pp. 787-801,  doi: 10.1007/s11633-020-1254-z
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This paper presents a novel observer-based controller for a class of nonlinear multi-agent robot models using the high order sliding mode consensus protocol. In many applications, demand for autonomous vehicles is growing; omnidirectional wheeled robots are suggested to meet this demand. They are flexible, fast, and autonomous, able to find the best direction and can move on an optional path at any time. Multi-agent omnidirectional wheeled robot (MOWR) systems consist of several similar or different robots and there are multiple different interactions between their agents, thus the MOWR systems have complex dynamics. Hence, designing a robust reliable controller for the nonlinear MOWR operations is considered an important obstacles in the science of the control design. A high order sliding mode is selected in this work that is a suitable technique for implementing a robust controller for nonlinear complex dynamics models. Furthermore, the proposed method ensures all signals involved in the multi-agent system (MAS) are uniformly ultimately bounded and the system is robust against the external disturbances and uncertainties. Theoretical analysis of candidate Lyapunov functions has been presented to depict the stability of the overall MAS, the convergence of observer and tracking error to zero, and the reduction of the chattering phenomena. In order to illustrate the promising performance of the methodology, the observer is applied to two nonlinear dynamic omnidirectional wheeled robots. The results display the meritorious performance of the scheme.
Robust Optimal Higher-order-observer-based Dynamic Sliding Mode Control for VTOL Unmanned Aerial Vehicles
Yashar Mousavi, Amin Zarei, Arash Mousavi, Mohsen Biari
2021,  vol. 18,  no. 5, pp. 802-813,  doi: 10.1007/s11633-021-1282-3
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This paper investigates the precise trajectory tracking of unmanned aerial vehicles (UAV) capable of vertical take-off and landing (VTOL) subjected to external disturbances. For this reason, a robust higher-order-observer-based dynamic sliding mode controller (HOB-DSMC) is developed and optimized using the fractional-order firefly algorithm (FOFA). In the proposed scheme, the sliding surface is defined as a function of output variables, and the higher-order observer is utilized to estimate the unmeasured variables, which effectively alleviate the undesirable effects of the chattering phenomenon. A neighboring point close to the sliding surface is considered, and as the tracking error approaches this point, the second control is activated to reduce the control input. The stability analysis of the closed-loop system is studied based on Lyapunov stability theorem. For a better study of the proposed scheme, various trajectory tracking tests are provided, where accurate tracking and strong robustness can be simultaneously ensured. Comparative simulation results validate the proposed control strategy′s effectiveness and its superiorities over conventional sliding mode controller (SMC) and integral SMC approaches.
Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network
Lu-Jie Zhou, Jian-Wu Dang, Zhen-Hai Zhang
2021,  vol. 18,  no. 5, pp. 814-825,  doi: 10.1007/s11633-021-1291-2
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The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience, is characterized by one-sidedness and low efficiency. In the process of high-speed train operation, numerous text-based on-board logs are recorded by on-board computers. Machine learning methods can help technicians make a correct judgment of fault types using the on-board log reasonably. Therefore, a fault classification model of on-board equipment based on attention capsule networks is proposed. This paper presents an empirical exploration of the application of a capsule network with dynamic routing in fault classification. A capsule network can encode the internal spatial part-whole relationship between various entities to identify the fault types. As the importance of each word in the on-board log and the dependencies between them have a significant impact on fault classification, an attention mechanism is incorporated into the capsule network to distill important information. Considering the imbalanced distribution of normal data and fault data in the on-board log, the focal loss function is introduced into the model to adjust the imbalanced data. The experiments are conducted on the on-board log of a railway bureau and compared with other baseline models. The experimental results demonstrate that our model outperforms the compared baseline methods, proving the superiority and competitiveness of our model.
Global FLS-based Consensus of Stochastic Uncertain Nonlinear Multi-agent Systems
Jia-Xi Chen, Jun-Min Li
2021,  vol. 18,  no. 5, pp. 826-837,  doi: 10.1007/s11633-021-1279-y
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Using graph theory, matrix theory, adaptive control, fuzzy logic systems and other tools, this paper studies the leader-follower global consensus of two kinds of stochastic uncertain nonlinear multi-agent systems (MAS). Firstly, the fuzzy logic systems replaces the feedback compensator as the feedforward compensator to describe the uncertain nonlinear dynamics. Secondly, based on the network topology, all followers are divided into two categories: One is the followers who can obtain the leader signal, and the other is the follower who cannot obtain the leader signal. Thirdly, based on the adaptive control method, distributed control protocols are designed for the two types of followers. Fourthly, based on matrix theory and stochastic Lyapunov stability theory, the stability of the closed-loop systems is analyzed. Finally, three simulation examples are given to verify the effectiveness of the proposed control algorithms.
An Immersive Virtual Reality System for Rodents in Behavioral and Neural Research
Li Liu, Zi-Yang Wang, Yu Liu, Chun Xu
2021,  vol. 18,  no. 5, pp. 838-848,  doi: 10.1007/s11633-021-1307-y
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Context cognition involves abstractly deriving meaning from situational information in the world and is an important psychological function of higher cognition. However, due to the complexity of contextual information processing, along with the lack of relevant technical tools, little remains known about the neural mechanisms and behavioral regulation of context cognition. At present, behavioral training with rodents using virtual reality techniques is considered a potential key for uncovering the neurobiological mechanisms of context cognition. Although virtual reality technology has been preliminarily applied in the study of context cognition in recent years, there remains a lack of virtual scenario integration of multi-sensory information, along with a need for convenient experimental design platforms for researchers who have little programming experience. Therefore, in order to solve problems related to the authenticity, immersion, interaction, and flexibility of rodent virtual reality systems, an immersive virtual reality system based on visual programming was constructed in this study. The system had the ability to flexibly modulate rodent interactive 3D dynamic experimental environments. The system included a central control unit, virtual perception unit, virtual motion unit, virtual vision unit, and video recording unit. The neural circuit mechanisms in various environments could be effectively studied by combining two-photon imaging and other neural activity recording methods. In addition, to verify the proposed system′s performance, licking experiments were conducted with experimental mice. The results demonstrated that the system could provide a new method and tool for analyzing the neural circuits of the higher cognitive functions in rodents.
Simultaneous Stabilization of Port-Hamiltonian Systems Subject to Actuation Saturation and Input Delay
Liang-Cheng Cai
2021,  vol. 18,  no. 5, pp. 849-854,  doi: 10.1007/s11633-015-0928-4
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This paper investigates the simultaneous stabilization of Port-Hamiltonian (PH) systems subject to actuation saturation (AS) and input delay. Firstly, two parallel connecting PH systems subject to the AS and input delay are proposed. Secondly, a simultaneous stabilization control law is designed by a difference between the two feedback control laws containing the input delay. Thirdly, computing a Lyapunov-Krasovskii function assures the simultaneous stabilization of the above systems. Finally, simulation is given to show the correctness of the proposed contents.
Correction to: A Survey on 3D Visual Tracking of Multicopters
Qiang Fu, Xiang-Yang Chen, Wei He
2021,  vol. 18,  no. 5, pp. 855-855,  doi: 10.1007/s11633-019-1213-8
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Correction to: Transfer Hierarchical Attention Network for Generative Dialog System
Xiang Zhang, Qiang Yang
2021,  vol. 18,  no. 5, pp. 856-856,  doi: 10.1007/s11633-020-1223-6
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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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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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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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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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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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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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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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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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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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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
A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks
Jin Xie, San-Yang Liu, Jia-Xi Chen
Abstract PDF
Abstract:
This paper aims to propose a framework for manifold regularization (MR) based distributed semi-supervised learning (DSSL) using single layer feed-forward neural network (SLFNN). The proposed framework, denoted as DSSL-SLFNN is based on the SLFNN, MR framework, and distributed optimization strategy. Then, a series of algorithms are derived to solve DSSL problems. In DSSL problems, data consisting of labeled and unlabeled samples are distributed over a communication network, where each node has only access to its own data and can only communicate with its neighbors. In some scenarios, DSSL problems cannot be solved by centralized algorithms. According to the DSSL-SLFNN framework, each node over the communication network the initial parameters of the SLFNN with the same basis functions for semi-supervised learning (SSL). All nodes calculate the global optimal coefficients of the SLFNN by using distributed datasets and local updates. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that DSSL-SLFNN based algorithms work in a fully distributed fashion and are privacy preserving methods. Finally, several simulations are presented to show the efficiency of the proposed framework and the derived algorithms.
A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization
Qian-Long Dang, Wei Xu, Yang-Fei Yuan
Abstract PDF
Abstract:
Many isolation approaches, such as zoning search, have been proposed to preserve the diversity in the decision space of multimodal multi-objective optimization (MMO). However, these approaches allocate the same computing resources for subspaces with different difficulties and evolution states. In order to solve this issue, this paper proposes a dynamic resource allocation strategy (DRAS) with reinforcement learning for multimodal multi-objective optimization problems (MMOPs). In DRAS, relative contribution and improvement are utilized to define the aptitude of subspaces, which can capture the potentials of subspaces accurately. Moreover, the reinforcement learning method is used to dynamically allocate computing resources for each subspace. In addition, the proposed DRAS is applied to zoning searches. The experimental results demonstrate that DRAS can effectively assist zoning search in finding more and better distributed equivalent Pareto optimal solutions in the decision space.
A Model of Spray Tool and a Parameter Optimization Method for Spraying Path Planning
Ru-Xiang Hua, Wei Zou, Guo-Dong Chen, Hong-Xuan Ma, Wei Zhang
Abstract PDF
Abstract:
The digital camouflage spraying of special vehicles carried out by robots can greatly improve the spraying efficiency, spraying quality, and rapid adaptability to personalized patterns. The selection of spray tool and the accuracy of the adopted mathematical spray tool model has a great impact on the effectiveness of spray path planning and spraying quality. Since traditional conical spray tool models are not suitable for spraying rectangular digital camouflage, according to the characteristics of digital camouflage, the coating thickness cumulative distribution model of strip nozzle spray tool for 2D plane spraying and 3D surface spraying is derived, and its validity is verified by simulation. Based on the accumulation velocity model of the coating thickness on the curved surface (ASCT) and aiming at spraying path planning within the same surface and different surfaces, a path parameter optimization method based on coating uniformity evaluation of adjacent path overlapping area is proposed. Combined with the vehicle surface model, parameters such as path interval, spray tool angle and spray tool motion velocity can be calculated in real-time to ensure uniform coating. Based on the known local three-dimensional model of vehicle surface and the comprehensive spraying simulation, the validity of the purposed models: the coating thickness on the adjacent path area (APCT), the coating thickness on the intersection of two surfaces (CCCT), the coating thickness on the intersection of a plane and a surface (PSCT), and the optimization method of path parameters are verified. The results show that compared with the traditional spray tool, the strip nozzle can better ensure the uniformity of the coating thickness of digital camouflage spray. Finally, according to a practical spraying experiment, the results prove that the proposed models not only are effective but also meet the practical industrial requirements and are of great practical value.
Improved Network for Face Recognition Based on Feature Super Resolution Method
Ling-Yi Xu, Zoran Gajic
 doi: 10.1007/s11633-021-1309-9
Abstract PDF
Abstract:
Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resolution images, perform poorly in recognizing low-resolution faces. In this work, an improved multi-branch network is proposed by combining ResNet and feature super-resolution modules. ResNet is for recognizing high-resolution facial images and extracting features from both high- and low-resolution images. Feature super-resolution modules are inserted before the classifier of ResNet for low-resolution facial images. They are used to increase feature resolution. The proposed method is effective and simple. Experimental results show that the recognition accuracy for high-resolution face images is high, and the recognition accuracy for low-resolution face images is improved.
Current Issue

2021 Vol.18 No.5

Table of Contents

ISSN 1476-8186

E-ISSN 1751-8520

CN 11-5350/TP

CiteScore: 5.4, Top 9% (Q1)

Editor-in-chief
Tieniu TAN, Chinese Academy of Sciences
Global Visitors