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As a major component of speech signal processing, speech emotion recognition has become increasingly essential to understanding human communication. Benefitting from deep learning, many researchers have proposed various unsupervised models to extract effective emotional features and supervised models to train emotion recognition systems. In this paper, we utilize semi-supervised ladder networks for speech emotion recognition. The model is trained by minimizing the supervised loss and auxiliary unsupervised cost function. The addition of the unsupervised auxiliary task provides powerful discriminative representations of the input features, and is also regarded as the regularization of the emotional supervised task. We also compare the ladder network with other classical autoencoder structures. The experiments were conducted on the interactive emotional dyadic motion capture (IEMOCAP) database, and the results reveal that the proposed methods achieve superior performance with a small number of labelled data and achieves better performance than other methods.
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Facial emotion recognition is an essential and important aspect of the field of human-machine interaction. Past research on facial emotion recognition focuses on the laboratory environment. However, it faces many challenges in real-world conditions, i.e., illumination changes, large pose variations and partial or full occlusions. Those challenges lead to different face areas with different degrees of sharpness and completeness. Inspired by this fact, we focus on the authenticity of predictions generated by different <emotion, region> pairs. For example, if only the mouth areas are available and the emotion classifier predicts happiness, then there is a question of how to judge the authenticity of predictions. This problem can be converted into the contribution of different face areas to different emotions. In this paper, we divide the whole face into six areas: nose areas, mouth areas, eyes areas, nose to mouth areas, nose to eyes areas and mouth to eyes areas. To obtain more convincing results, our experiments are conducted on three different databases: facial expression recognition + ( FER+), real-world affective faces database (RAF-DB) and expression in-the-wild (ExpW) dataset. Through analysis of the classification accuracy, the confusion matrix and the class activation map (CAM), we can establish convincing results. To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis. Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.
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This paper presents a novel five degrees of freedom (DOF) two-wheeled robotic machine (TWRM) that delivers solutions for both industrial and service robotic applications by enlarging the vehicle′s workspace and increasing its flexibility. Designing a two-wheeled robot with five degrees of freedom creates a high challenge for the control, therefore the modelling and design of such robot should be precise with a uniform distribution of mass over the robot and the actuators. By employing the Lagrangian modelling approach, the TWRM′s mathematical model is derived and simulated in Matlab/Simulink®. For stabilizing the system′s highly nonlinear model, two control approaches were developed and implemented: proportional-integral-derivative (PID) and fuzzy logic control (FLC) strategies. Considering multiple scenarios with different initial conditions, the proposed control strategies′ performance has been assessed.
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In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.
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This paper proposes an image encryption algorithm LQBPNN (logistic quantum and back propagation neural network) based on chaotic sequences incorporating quantum keys. Firstly, the improved one-dimensional logistic chaotic sequence is used as the basic key sequence. After the quantum key is introduced, the quantum key is incorporated into the chaotic sequence by nonlinear operation. Then the pixel confused process is completed by the neural network. Finally, two sets of different mixed secret key sequences are used to perform two rounds of diffusion encryption on the confusing image. The experimental results show that the randomness and uniformity of the key sequence are effectively enhanced. The algorithm has a secret key space greater than 2182. The adjacent pixel correlation of the encrypted image is close to 0, and the information entropy is close to 8. The ciphertext image can resist several common attacks such as typical attacks, statistical analysis attacks and differential attacks.
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Extracting the three-dimensional (3D) information including location and height of a pedestrian is important for vision-based intelligent traffic monitoring systems. This paper tackles the relationship between pixels′ actual size and pixels′ spatial resolution through a new method named pixel-resolution mapping (P-RM). The proposed P-RM method derives the equations for pixels′ spatial resolutions (XY-direction) and object′s height (Z-direction) in the real world, while introducing new tilt angle and mounting height calibration methods that do not require special calibration patterns placed in the real world. Both controlled laboratory and actual world experiments were performed and reported. The tests on 3D mensuration using proposed P-RM method showed overall better than 98.7% accuracy in laboratory environments and better than 96% accuracy in real world pedestrian height estimations. The 3D reconstructed images for measured points were also determined with the proposed P-RM method which shows that the proposed method provides a general algorithm for 3D information extraction.
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In this paper, the problem of load transportation and robust mitigation of payload oscillations in uncertain tower-cranes is addressed. This problem is tackled through a control scheme based on the philosophy of Active-Disturbance-Rejection. Here, a general disturbance model built with two dominant components: polynomial and harmonic, is stated. Then, a disturbance observer is formulated through state-vector augmentation of the tower-crane model. Thus, better performance of estimations for system states and disturbances is achieved. The control law is then formulated to actively reject the disturbances but also to accommodate the closed-loop system dynamics even under system uncertainty. The proposed control schema is validated via experimentation using a small-scale tower-crane, and compared with other relevant ADRC-based techniques. The experimental results show that the proposed control scheme is robust under parametric uncertainty of the system, and provides improved attenuation of payload oscillations even under system uncertainty.
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Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian Combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
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The aim of this work is to model and analyze the behavior of a new smart nano force sensor. To do so, the carbon nanotube has been used as a suspended gate of a metal-oxide-semiconductor field-effect transistor (MOSFET). The variation of the applied force on the carbon nanotube (CNT) generates a variation of the capacity of the transistor oxide-gate and therefore the variation of the threshold voltage, which allows the MOSFET to become a capacitive nano force sensor. The sensitivity of the nano force sensor can reach 0.124 31 V/nN. This sensitivity is greater than results in the literature. We have found through this study that the response of the sensor depends strongly on the geometric and physical parameters of the CNT. From the results obtained in this study, the increase in the applied force has as a consequence an increase in the value of the threshold voltage VTh of the MOSFET. 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.
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The objective of this paper is to propose a reduced-order observer for a class of Lipschitz nonlinear discrete-time systems. The conditions that guarantee the existence of this observer are presented in the form of linear matrix inequalities (LMIs). To handle the Lipschitz nonlinearities, the Lipschitz condition and the Young′s relation are adequately operated to add more degrees of freedom to the proposed LMI. Necessary and sufficient conditions for the existence of the unbiased reduced-order observer are given. An extension to \begin{document}$\mathcal{H}_\infty$\end{document} performance analysis is considered in order to deal with \begin{document}$\mathcal{H}_\infty$\end{document} asymptotic stability of the estimation error in the presence of disturbances that affect the state of the system. To highlight the effectiveness of the proposed design methodology, three numerical examples are considered. Then, high performances are shown through real time implementation using the ARDUINO MEGA 2560 device.
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This paper investigates the necessity of feasibility considerations in a fault tolerant control system using the constrained control allocation methodology where both static and dynamic actuator constraints are considered. In the proposed feasible control allocation scheme, the constrained model predictive control (MPC) is employed as the main controller. This considers the admissible region of the control allocation problem as its constraints. Using the feasibility notion in the control allocation problem provides the main controller with information regarding the actuator′s status, which leads to closed loop system performance improvement. Several simulation examples under normal and faulty conditions are employed to illustrate the effectiveness of the proposed methodology. The main results clearly indicate that closed loop performance and stability characteristics can be significantly degraded by neglecting the actuator constraints in the main controller. Also, it is shown that the proposed strategy substantially enlarges the domain of attraction of the MPC combined with the control allocation as compared to the conventional MPC.
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2019,  vol. 16,  no. 3,   pp. 261-273 ,  doi: 10.1007/s11633-018-1161-8
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This paper describes the development and modeling of a remotely operated scaled multi-wheeled combat vehicle (ROMWCV) using system identification methodology for heading angle tracking. The vehicle was developed at the vehicle dynamics and crash research (VDCR) Lab at the University of Ontario Institute of Technology (UOIT) to analyze the characteristics of the full-size model. For such vehicles, the development of controllers is considered the most crucial issue. In this paper, the ROMWCV is developed first. An experimental test was carried out to record and analyze the vehicle input/output signals in open loop system, which is considered a multi-input-single-output (MISO) system. Subsequently, a fuzzy logic controller (FLC) was developed for heading angle tracking. The experiments showed that it was feasible to represent the dynamic characteristics of the vehicle using the system identification technique. The estimation and validation results demonstrated that the obtained identified model was able to explain 88.44% of the output variation. In addition, the developed FLC showed a good heading angle tracking.
2019,  vol. 16,  no. 3,   pp. 274-285 ,  doi: 10.1007/s11633-018-1147-6
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This paper presents a redundantly actuated and over-constrained 2RPU-2SPR parallel manipulator with two rotational and one translational coupling degrees of freedom. The kinematics analysis is firstly carried out and the mapping relationship of the velocity, acceleration and the independent parameters between the actuator joint and the moving platform are deduced by using the vector dot product and cross product operation. By employing d′Alembert′s principle and the principle of virtual work, the dynamics equilibrium equation is derived, and the simplified dynamics mathematical model of the parallel manipulator is further derived. Simultaneously, the generalized inertia matrix which can characterize the acceleration performance between joint space and operation space is further separated, and the performance indices including the dynamics dexterity, inertia coupling characteristics, energy transmission efficiency and driving force/torque balance are introduced. The analysis results show that the proposed redundantly actuated and over-constrained 2RPU-2SPR parallel manipulator in comparison with the existing non-redundant one has better dynamic comprehensive performance, which can be demonstrated practically by the successful application of the parallel kinematic machine head module of the hybrid machine tool.
2019,  vol. 16,  no. 3,   pp. 286-296 ,  doi: 10.1007/s11633-019-1171-1
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Research has demonstrated a significant overlap between sleep issues and other medical conditions. In this paper, we consider mild difficulty in falling asleep (MDFA). Recognition of MDFA has the potential to assist in the provision of appropriate treatment plans for both sleep issues and related medical conditions. An issue in the diagnosis of MDFA lies in subjectivity. To address this issue, a decision support tool based on dual-modal physiological feature fusion which is able to automatically identify MDFA is proposed in this study. Special attention is given to the problem of how to extract candidate features and fuse dual-modal features. Following the identification of the optimal feature set, this study considers the correlations between each feature and class and evaluates correlations between the inter-modality features. Finally, the recognition accuracy was measured using 10-fold cross validation. The experimental results for our method demonstrate improved performance. The highest recognition rate of MDFA using the optimal feature set can reach 96.22%. Based on the results of current study, the authors will, in projected future research, develop a real-time MDFA recognition system.
2019,  vol. 16,  no. 3,   pp. 297-309 ,  doi: 10.1007/s11633-018-1133-z
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Due to the frequent occurrence of various emergencies in recent years, people have put forward higher requirements on the emergency supply chain management. It is of great significance to explore the key management indicators of emergency supply chain for its management and efficient operation. In order to reveal the essence of emergency supply chain management, production, procurement, distribution, storage, use, recycling and other emergencies, supply chain links are considered to establish an emergency supply chain management index system to identify the key influencing factors in the emergency supply chain. The emergency supply chain involves many management elements and the traditional qualitative analysis and comprehensive evaluation methods have their shortcomings in practice. In order to get a more suitable method, a novel evaluation model is proposed, based on Rough set – house of quality method. In this paper, Rough set is used to filter the indexes, eliminate redundant indicators, and simplify many management indicators of the emergency supply chain system to a few core indicators. Then, the house of quality is used to analyze and sort the core index to get the key management index of emergency supply chain. The effectiveness of the proposed evaluation model is validated through a series of numerical experiments. The experimental results also show that the proposed evaluation model can assist decision makers in optimizing the emergency supply chain procedure and improving the efficiency of accident rescue.
2019,  vol. 16,  no. 3,   pp. 310-328 ,  doi: 10.1007/s11633-018-1165-4
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In this paper, we report on the identification and modeling of unknown and higher order processes into first order plus dead time (FOPDT) plants based on the limit cycle information obtained from a single relay feedback test with an online fractional order proportional integral (FOPI) controller. The parameters of the test processes are accurately determined by the state space method while the FOPI controller settings are re-tuned to achieve enhanced performance based on the identified model parameters based on the balanced-tuning method. A new performance index, integral time fractional order absolute error (ITFIAE) is introduced in this paper for balanced tuning of fractional order (FO) controllers. It requires minimum design specifications without a-priori knowledge of gain and phase crossover frequencies and is done non-iteratively without disrupting the closed loop. Four test processes and experimental analysis on a coupled tank system (CTS) validate the theory proposed.
2019,  vol. 16,  no. 3,   pp. 329-340 ,  doi: 10.1007/s11633-016-0955-9
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The H state estimation problem for a class of stochastic neural networks with Markovian jumping parameters and leakage delay is investigated in this paper. By employing a suitable Lyapunov functional and inequality technic, the sufficient conditions for exponential stability as well as prescribed H norm level of the state estimation error system are proposed and verified, and all obtained results are expressed in terms of strict linear matrix inequalities (LMIs). Examples and simulations are presented to show the effectiveness of the proposed methods, at the same time, the effect of leakage delay on stability of neural networks system and on the attenuation level of state estimator are discussed.
2019,  vol. 16,  no. 3,   pp. 341-353 ,  doi: 10.1007/s11633-018-1167-2
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The choice of fulcrums for control of socio-economic systems represented by directed weighted signed graphs is a topic of current interest. This article proposes a new method for identifying nodes of impact and influential nodes, which will provide a guaranteed positive system response over the growth model. The task is posed as an optimization problem to maximize the ratio of the norms of the accumulated increments of the growth vector and the exogenous impact vector. The algorithm is reduced to solving a quadratic programming problem with nonlinear restrictions. The selection of the most effective vertices is based on the cumulative gains of the component projections onto the solution vector. Numerical examples are provided to illustrate the effectiveness of the proposed method.
2019,  vol. 16,  no. 3,   pp. 354-368 ,  doi: 10.1007/s11633-018-1166-3
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The growth of environmental energy harvesting has been explosive in wireless computing systems especially when replacing or recharging batteries manually is impracticable. This work investigates the scheduling of periodic weekly hard real-time tasks under energy constraints. Based on this motivation, we proposed a real-time scheduling algorithm, namely energy guarantee dynamic voltage and frequency scaling (EG-DVFS), that utilizes the earliest deadline-harvesting (ED-H) scheduling algorithm combined with dynamic voltage and frequency scaling. This one is qualified as real-time since tasks must satisfy their timing constraints. We assume that the preemptable tasks receive dynamic priorities according to the earliest deadline first (EDF) rule. EG-DVFS adjusts the processor′s behavior by characterizing the properties of the energy source module, capacity of the stored energy as well as the harvested energy in a future duration. Specifically, tasks are executed at full processor speed if the amount of energy in the battery is enough to finish its execution. Otherwise, the processor slows down task execution to the lowest possible processor speed while still guaranteeing to meet all the timing constraints. EG-DVFS mainly depends on the on-line computation of the slack time and the slack energy with dynamic voltage and frequency selection in order to achieve an improved system performance. Experimental results show that EG-DVFS can achieve capacity savings up of up to 33% when compared to ED-H.
2019,  vol. 16,  no. 3,   pp. 369-388 ,  doi: 10.1007/s11633-016-1021-3
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This paper proposes fuzzy model predictive control (FMPC) strategies for nonlinear interconnected systems based mainly on a system decomposition approach. First, the Takagi-Sugeno (TS) fuzzy model is formulated in such a way to describe the behavior of the nonlinear system. Based on that description, a fuzzy model predictive control is determined. The system under consideration is decomposed into several subsystems. For each subsystem, the main idea consists of the decomposition of the control action into two parts: The decentralized part contains the parameters of the subsystem and the centralized part contains the elements of other subsystems. According to such decomposition, two strategies are deflned aiming to circumvent the problems caused by interconnection between subsystems. The feasibility and e–ciency of the proposed method are illustrated through numerical examples.
2019,  vol. 16,  no. 3,   pp. 389-397 ,  doi: 10.1007/s11633-016-1008-0
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The optimal test sequence design for fault diagnosis is a challenging NP-complete problem. An improved difierential evolution (DE) algorithm with additional inertial velocity term called inertial velocity difierential evolution (IVDE) is proposed to solve the optimal test sequence problem (OTP) in complicated electronic system. The proposed IVDE algorithm is constructed based on adaptive difierential evolution algorithm. And it is used to optimize the test sequence sets with a new individual fltness function including the index of fault isolation rate (FIR) satisfled and generate diagnostic decision tree to decrease the test sets and the test cost. The simulation results show that IVDE algorithm can cut down the test cost with the satisfled FIR. Compared with the other algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA), IVDE can get better solution to OTP.
2019,  vol. 16,  no. 3,   pp. 398-412 ,  doi: 10.1007/s11633-018-1113-3
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A Fourier kernel based time-frequency transform is a proven candidate for non-stationary signal analysis and pattern recognition because of its ability to predict time localized spectrum and global phase reference characteristics. However, it suffers from heavy computational overhead and large execution time. The paper, therefore, uses a novel fast discrete sparse S-transform (SST) suitable for extracting time frequency response to monitor non-stationary signal parameters, which can be ultimately used for disturbance detection, and their pattern classification. From the sparse S-transform matrix, some relevant features have been extracted which are used to distinguish among different non-stationary signals by a fuzzy decision tree based classifier. This algorithm is robust under noisy conditions. Various power quality as well as chirp signals have been simulated and tested with the proposed technique in noisy conditions as well. Some real time mechanical faulty signals have been collected to demonstrate the efficiency of the proposed algorithm. All the simulation results imply that the proposed technique is very much efficient.
2010,  vol. 7,  no. 3,   pp. 324-329, doi: 10.1007/s11633-010-0510-z
2011,  vol. 8,  no. 3,   pp. 326-332, doi: 10.1007/s11633-011-0588-y
2015,  vol. 12,  no. 3,   pp. 229-242, doi: 10.1007/s11633-015-0893-y
2011,  vol. 8,  no. 2,   pp. 215-220, doi: 10.1007/s11633-011-0576-2
2013,  vol. 10,  no. 4,   pp. 281-287, doi: 10.1007/s11633-013-0722-0
2012,  vol. 9,  no. 3,   pp. 237-247, doi: 10.1007/s11633-012-0640-6
2012,  vol. 9,  no. 4,   pp. 358-368, doi: 10.1007/s11633-012-0656-y
2012,  vol. 9,  no. 3,   pp. 331-336, doi: 10.1007/s11633-012-0652-2
2011,  vol. 8,  no. 2,   pp. 147-153, doi: 10.1007/s11633-011-0567-3
2011,  vol. 8,  no. 2,   pp. 244-253, doi: 10.1007/s11633-011-0579-z
2015,  vol. 12,  no. 2,   pp. 134-141, doi: 10.1007/s11633-015-0880-3
2014,  vol. 11,  no. 3,   pp. 299-307, doi: 10.1007/s11633-014-0792-7
2013,  vol. 10,  no. 3,   pp. 181-193, doi: 10.1007/s11633-013-0711-3
2011,  vol. 8,  no. 3,   pp. 280-285, doi: 10.1007/s11633-011-0583-3
2011,  vol. 8,  no. 2,   pp. 262-268, doi: 10.1007/s11633-011-0581-5
2011,  vol. 8,  no. 3,   pp. 348-356, doi: 10.1007/s11633-011-0591-3
2012,  vol. 9,  no. 5,   pp. 555-560, doi: 10.1007/s11633-012-0679-4
2013,  vol. 10,  no. 4,   pp. 288-295, doi: 10.1007/s11633-013-0723-z
2012,  vol. 9,  no. 5,   pp. 487-491, doi: 10.1007/s11633-012-0671-z
2013,  vol. 10,  no. 1,   pp. 85-90 , doi: 10.1007/s11633-013-0700-6
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2019 Vol.16 No.3