Search results for: track convergence.

Authors: R. Khaniki, M.B. Menhaj, H. Eliasi

Abstract:

This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.

Keywords: Generalized Predictive Control (GPC), TemperatureControl, Global Linearizing Control (GLC), Batch Reactor.

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Authors: Rinna Tanaka, Carlos Mendez Galindo, Toshiro Hayashikawa

Abstract:

The results show that the bridge equipped with seismic isolation bearing system shows a high amount of energy dissipation. The purpose of the present study is to analyze the overall performance of continuous curved highway viaducts with different bearing supports, with an emphasis on the effectiveness of seismic isolation based on lead rubber bearing and hedge reaction force bearing system consisted of friction sliding bearing and rubber bearing. The bridge seismic performance has been evaluated on six different cases with six bearing models. The effects of the different arrangement of bearing on the deck superstructure displacements, the seismic damage at the bottom of the piers, movement track at the pier-s top and the total and strain energies absorbed by the structure are evaluated. In conclusion, the results provide sufficient evidence of the effectiveness on the use of seismic isolation on steel curved highway bridges.

Keywords: Curved highway viaducts, non-linear dynamic response, seismic damage.

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Authors: Abdullah M. Almeshal, Mohammad R. Alenezi, Muhammad Moaz

Abstract:

In this paper, we discuss the performance of applying hybrid spiral dynamic bacterial chemotaxis (HSDBC) optimisation algorithm on an intelligent controller for a differential drive robot. A unicycle class of differential drive robot is utilised to serve as a basis application to evaluate the performance of the HSDBC algorithm. A hybrid fuzzy logic controller is developed and implemented for the unicycle robot to follow a predefined trajectory. Trajectories of various frictional profiles and levels were simulated to evaluate the performance of the robot at different operating conditions. Controller gains and scaling factors were optimised using HSDBC and the performance is evaluated in comparison to previously adopted optimisation algorithms. The HSDBC has proven its feasibility in achieving a faster convergence toward the optimal gains and resulted in a superior performance.

Keywords: Differential drive robot, hybrid fuzzy controller, optimization, path tracking, unicycle robot.

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

Abstract:

In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: Dynamic system modeling, neural network, normal equation, second order gradient descent.

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Authors: Aritras Roy, Rinku Mukherjee

Abstract:

The current study couples a quasi-steady Vortex Lattice Method and a camber correcting technique, ‘Decambering’ for unsteady post-stall flow prediction. The wake is force-free and discrete such that the wake lattices move with the free-stream once shed from the wing. It is observed that the time-averaged unsteady coefficient of lift sees a relative drop at post-stall angles of attack in comparison to its steady counterpart for some angles of attack. Multiple solutions occur at post-stall and three different algorithms to choose solutions in these regimes show both unsteadiness and non-convergence of the iterations. The distribution of coefficient of lift on the wing span also shows sawtooth. Distribution of vorticity changes both along span and in the direction of the free-stream as the wake develops over time with distinct roll-up, which increases with time.

Keywords: Post-stall, unsteady, wing, aerodynamics.

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Authors: Abdullah Bajelan, Adel Akbarimajd

Abstract:

Object manipulation techniques in robotics can be categorized in two major groups including manipulation with grasp and manipulation without grasp. The original aim of this paper is to develop an object manipulation method where in addition to being grasp-less, the manipulation task is done in a passive approach. In this method, linear and angular positions of the object are changed and its manipulation path is controlled. The manipulation path is a helix track with constant radius and incline. The method presented in this paper proposes a system which has not the actuator and the active controller. So this system requires a passive mechanical intelligence to convey the object from the status of the source along the specified path to the goal state. This intelligent is created based on utilizing the geometry of the system components. A general set up for the components of the system is considered to satisfy the required conditions. Then after kinematical analysis, detailed dimensions and geometry of the mechanism is obtained. The kinematical results are verified by simulation in ADAMS.

Keywords: Mechanical intelligence, Object manipulation, Passive mechanism, Passive non-prehensile manipulation.

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Authors: A. Chebak, N. Barka, A. Menou, J. Brousseau, D. S. Ramdenee

Abstract:

This paper presents the study of hardness profile of spur gear heated by induction heating process in function of the machine parameters, such as the power (kW), the heating time (s) and the generator frequency (kHz). The global work is realized by 3D finite-element simulation applied to the process by coupling and resolving the electromagnetic field and the heat transfer problems, and it was performed in three distinguished steps. First, a Comsol 3D model was built using an adequate formulation and taking into account the material properties and the machine parameters. Second, the convergence study was conducted to optimize the mesh. Then, the surface temperatures and the case depths were deeply analyzed in function of the initial current density and the heating time in medium frequency (MF) and high frequency (HF) heating modes and the edge effect were studied. Finally, the simulations results are validated using experimental tests.

Keywords: Induction heating, simulation, experimental validation, 3D model, hardness profile.

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Authors: Y. Mohseniahouei, K. Abdella, M. Pollanen

Abstract:

In this paper, we explore the applicability of the Sinc- Collocation method to a three-dimensional (3D) oceanography model. The model describes a wind-driven current with depth-dependent eddy viscosity in the complex-velocity system. In general, the Sinc-based methods excel over other traditional numerical methods due to their exponentially decaying errors, rapid convergence and handling problems in the presence of singularities in end-points. Together with these advantages, the Sinc-Collocation approach that we utilize exploits first derivative interpolation, whose integration is much less sensitive to numerical errors. We bring up several model problems to prove the accuracy, stability, and computational efficiency of the method. The approximate solutions determined by the Sinc-Collocation technique are compared to exact solutions and those obtained by the Sinc-Galerkin approach in earlier studies. Our findings indicate that the Sinc-Collocation method outperforms other Sinc-based methods in past studies.

Keywords: Boundary Value Problems, Differential Equations, Sinc Numerical Methods, Wind-Driven Currents

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Authors: Sun Lim, Sewoong Jun, Il-Kyun Jung

Abstract:

This paper presents a wrap-around view system with 4 smart cameras module and remote motion mobile robot control equipped with smart camera module system. The two-level scheme for remote motion control with smart-pad(IPAD) is introduced on this paper. In the low-level, the wrap-around view system is controlled or operated to keep the reference points lying around top view image plane. On the higher level, a robot image based motion controller is utilized to drive the mobile platform to reach the desired position or track the desired motion planning through image feature feedback. The design wrap-around view system equipped on presents such advantages as follows: 1) a satisfactory solution for the FOV and affine problem; 2) free of any complex and constraint with robot pose. The performance of the wrap-around view equipped on mobile robot remote control is proven by experimental results.

Keywords: four smart camera, wrap-around view, remote mobile robot control

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Authors: Cheng-Chieh Chiang, Yi-Chia Chan, Greg C. Lee

Abstract:

This paper proposes a video-based framework for face recognition to identify which faces appear in a video sequence. Our basic idea is like a tracking task - to track a selection of person candidates over time according to the observing visual features of face images in video frames. Hence, we employ the state-space model to formulate video-based face recognition by dividing this problem into two parts: the likelihood and the transition measures. The likelihood measure is to recognize whose face is currently being observed in video frames, for which two-dimensional linear discriminant analysis is employed. The transition measure estimates the probability of changing from an incorrect recognition at the previous stage to the correct person at the current stage. Moreover, extra nodes associated with head nodes are incorporated into our proposed state-space model. The experimental results are also provided to demonstrate the robustness and efficiency of our proposed approach.

Keywords: 2DLDA, face recognition, state-space model, likelihood measure, transition measure.

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Authors: R. Sharma, M. Gopal

Abstract:

This paper presents a new problem solving approach that is able to generate optimal policy solution for finite-state stochastic sequential decision-making problems with high data efficiency. The proposed algorithm iteratively builds and improves an approximate Markov Decision Process (MDP) model along with cost-to-go value approximates by generating finite length trajectories through the state-space. The approach creates a synergy between an approximate evolving model and approximate cost-to-go values to produce a sequence of improving policies finally converging to the optimal policy through an intelligent and structured search of the policy space. The approach modifies the policy update step of the policy iteration so as to result in a speedy and stable convergence to the optimal policy. We apply the algorithm to a non-holonomic mobile robot control problem and compare its performance with other Reinforcement Learning (RL) approaches, e.g., a) Q-learning, b) Watkins Q(λ), c) SARSA(λ).

Keywords: Markov Decision Process (MDP), Mobile robot, Policy iteration, Simulation.

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Authors: Chien-Chun Kung, Feng-Lung Chiang, Kuei-Yi Chen, Hsien-Wen Wei, Ming-Yi Huang, Cai-Ming Huang, Sheng-Kai Wang

Abstract:

The aim of this paper is to present a new three-dimensional proportional-pursuit coupled (PP) guidance law to track highly maneuverable aircraft. Utilizing a 3-D polar coordinate frame, the PP guidance law is formed by collecting proportional navigation guidance in Z-R plane and pursuit guidance in X-Y plane. Feedback linearization control method to solve the guidance accelerations is used to implement PP guidance. In order to compensate the actuator time delay, the time delay compensated version of PP guidance law (CPP) was derived and proved the effectiveness of modifying the problem of high acceleration in the final phase of pursuit guidance and improving the weak robustness of proportional navigation. The simulation results for intercepting Max G turn situation show that the proposed proportional-pursuit coupled guidance law guidance law with actuator delay compensation (CPP) possesses satisfactory robustness and performance.

Keywords: Feedback linearization control, time delay, guidance law, robustness, proportional navigation guidance, pursuit guidance.

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Authors: Jhu-Wei Ji, Fa-Shian Chang, Lih-Tyng Hwang, Chih-Feng Liu, Jeng-Nan Lee, Shun-Min Wang, Kai-Yi Cho

Abstract:

This paper presents a tracked robot with blade arms powered to assist movement in difficult environments. As a result, the tracked robot is able to pass a ramp or climb stairs. The main feature is a pair of blade arms on both sides of the vehicle body working in collaboration with previously validated transformable track system. When the robot encounters an obstacle in a terrain, it enlists the blade arms with power to overcome the obstacle. In disaster areas, there usually will be terrains that are full of broken and complicated slopes, broken walls, rubbles, and ditches. Thereupon, a robot, which is instructed to pass through such disaster areas, needs to have a good off-road capability for such complicated terrains. The robot with crawling-assisting blade arms would overcome the obstacles along the terrains, and possibly become to be a rescue robot. A prototype has been developed and built; experiments were carried out to validate the enhanced crawling capability of the robot.

Keywords: Tracked robot, rescue robot, blade arm, crawling ability, control system.

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Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

Abstract:

Keywords: Structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames.}

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Authors: Ayodeji Adesina, Derek Molloy

Abstract:

Traditional higher-education classrooms allow lecturers to observe students- behaviours and responses to a particular pedagogy during learning in a way that can influence changes to the pedagogical approach. Within current e-learning systems it is difficult to perform continuous analysis of the cohort-s behavioural tendency, making real-time pedagogical decisions difficult. This paper presents a Virtual Learning Process Environment (VLPE) based on the Business Process Management (BPM) conceptual framework. Within the VLPE, course designers can model various education pedagogies in the form of learning process workflows using an intuitive flow diagram interface. These diagrams are used to visually track the learning progresses of a cohort of students. This helps assess the effectiveness of the chosen pedagogy, providing the information required to improve course design. A case scenario of a cohort of students is presented and quantitative statistical analysis of their learning process performance is gathered and displayed in realtime using dashboards.

Keywords: Business process management, cohort analytics, learning processes, virtual learning environment.

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Authors: Ying-jie Zhang, Li-ling Ge

Abstract:

This paper presents an improved image segmentation model with edge preserving regularization based on the piecewise-smooth Mumford-Shah functional. A level set formulation is considered for the Mumford-Shah functional minimization in segmentation, and the corresponding partial difference equations are solved by the backward Euler discretization. Aiming at encouraging edge preserving regularization, a new edge indicator function is introduced at level set frame. In which all the grid points which is used to locate the level set curve are considered to avoid blurring the edges and a nonlinear smooth constraint function as regularization term is applied to smooth the image in the isophote direction instead of the gradient direction. In implementation, some strategies such as a new scheme for extension of u+ and u- computation of the grid points and speedup of the convergence are studied to improve the efficacy of the algorithm. The resulting algorithm has been implemented and compared with the previous methods, and has been proved efficiently by several cases.

Keywords: Energy minimization, image segmentation, level sets, edge regularization.

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Authors: Chunshien Li, Jhao-Wun Hu, Tai-Wei Chiang, Tsunghan Wu

Abstract:

Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybrid learning (HL) approach for the neuro-fuzzy system, the prediction performance is excellent and the speed of learning convergence is much faster than other compared approaches. In the experiments, we use the well-known Mackey-Glass chaos time series. According to the experimental results, the prediction performance and accuracy in time series forecasting by the proposed approach is much better than other compared approaches, as shown in Table IV. Excellent prediction performance by the proposed approach has been observed.

Keywords: forecasting, hybrid learning (HL), Neuro-FuzzySystem (NFS), particle swarm optimization (PSO), recursiveleast-squares estimator (RLSE), time series

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Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

Abstract:

As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: Apartment housing, machine learning, multi-objective optimization, performance prediction.

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Authors: Samia Sadouki Chibani, Abdelkamel Tari

Abstract:

Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence.

Keywords: Elephant herding optimization, web service composition, bio-inspired algorithms, QoS optimization.

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Authors: Khairullah Khan, Baharum B. Baharudin, Aurangzeb Khan, Fazal_e_Malik

Abstract:

Opinion extraction about products from customer reviews is becoming an interesting area of research. Customer reviews about products are nowadays available from blogs and review sites. Also tools are being developed for extraction of opinion from these reviews to help the user as well merchants to track the most suitable choice of product. Therefore efficient method and techniques are needed to extract opinions from review and blogs. As reviews of products mostly contains discussion about the features, functions and services, therefore, efficient techniques are required to extract user comments about the desired features, functions and services. In this paper we have proposed a novel idea to find features of product from user review in an efficient way. Our focus in this paper is to get the features and opinion-oriented words about products from text through auxiliary verbs (AV) {is, was, are, were, has, have, had}. From the results of our experiments we found that 82% of features and 85% of opinion-oriented sentences include AVs. Thus these AVs are good indicators of features and opinion orientation in customer reviews.

Keywords: Classification, Customer Reviews, Helping Verbs, Opinion Mining.

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Authors: Xinhua Zhang, Kelin Li

Abstract:

In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.

Keywords: Exponential stability, stochastic fuzzy cellular neural networks, time-varying delays, impulses, reaction-diffusion terms.

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Authors: Ming Guan

Abstract:

This paper proposes a simple model of economic geography within the Dixit-Stiglitz-Iceberg framework that may be used to analyze migration patterns among three cities. The cost–benefit tradeoffs affecting incentives for three types of migration, including echelon migration, are discussed. This paper develops a tractable, heterogeneous-agent, general equilibrium model, where agents share constant human capital, and explores the relationship between the benefits of echelon migration and gross human capital. Using Chinese numerical solutions, we study the manifestation of echelon migration and how it responds to changes in transportation cost and elasticity of substitution. Numerical results demonstrate that (i) there are positive relationships between a migration-s benefit-and-wage ratio, (ii) there are positive relationships between gross human capital ratios and wage ratios as to origin and destination, and (iii) we identify 13 varieties of human capital convergence among cities. In particular, this model predicts population shock resulting from the processes of migration choice and echelon migration.

Keywords: Dixit-Stiglitz-Iceberg framework, elasticity , echelonmigration, trade-off

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Authors: Tao Dai, Andy Adler, Behnam Shahrrava

Abstract:

This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projection matrix of pseudo-inverse to estimate system deviation. This method introduces temporal weights into the projection matrix, which is typically a better model of the real error's behavior than homogeneous temporal weights. The regularization overcomes the ill-conditioning introduced by both the forgetting process and the increasing size of the input matrix. This algorithm is tested by independent trials with coloured input signals and various parameter combinations. Results show that the proposed algorithm is superior in terms of convergence rate and misadjustment compared to existing algorithms. As a special case, a variable step size NLMS with forgetting factor is also presented in this paper.

Keywords: Adaptive signal processing, affine projection algorithms, variable step-size adaptive algorithms, regularization.

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Authors: Chakkrit Snae, Michael Brueckner

Abstract:

In this paper the authors present the framework of a system for assisting users through counseling on personal health, the Personal Health Assistance Service Expert System (PHASES). Personal health assistance systems need Personal Health Records (PHR), which support wellness activities, improve the understanding of personal health issues, enable access to data from providers of health services, strengthen health promotion, and in the end improve the health of the population. This is especially important in societies where the health costs increase at a higher rate than the overall economy. The most important elements of a healthy lifestyle are related to food (such as balanced nutrition and diets), activities for body fitness (such as walking, sports, fitness programs), and other medical treatments (such as massage, prescriptions of drugs). The PHASES framework uses an ontology of food, which includes nutritional facts, an expert system keeping track of personal health data that are matched with medical treatments, and a comprehensive data transfer between patients and the system.

Keywords: Personal health assistance service, expert system, ontologies, knowledge management, information technology.

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

Abstract:

Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: Long-short-term memory, re-identification, security critical application, spatial-temporal awareness.

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Authors: Eugen Pop, Mihai Barbos, Razvan Lupu

Abstract:

The client server systems using mobile communications networks for data transmission became very attractive for many economic agents, in the purpose of promoting and offering electronic services to their clients. E-services are suitable for business developing and financial benefits increasing. The products or services can be efficiently delivered to a large number of clients, using mobile Internet access technologies. The clients can have access to e-services, anywhere and anytime, with the support of 3G, GPRS, WLAN, etc., channels bandwidth, data services and protocols. Based on the mobile communications networks evolution and development, a convergence of technological and financial interests of mobile operators, software developers, mobile terminals producers and e-content providers is established. These will lead to a high level of integration of IT&C resources and will facilitate the value added services delivery through the mobile communications networks. In this paper it is presented a client server system, for e-services access, with Smartphones and PDA-s mobile software applications, installed on Symbian and Windows Mobile operating systems.

Keywords: Client server system, e-services access, mobile communications, PDA, Smartphone.

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Authors: H. Shayeghi, M. Mahdavi, A. Kazemi

Abstract:

Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, transmission expansion planning considering network adequacy restriction has not been investigated. Thus, in this paper, STNEP problem is being studied considering network adequacy restriction using discrete particle swarm optimization (DPSO) algorithm. The goal of this paper is obtaining a configuration for network expansion with lowest expansion cost and a specific adequacy. The proposed idea has been tested on the Garvers network and compared with the decimal codification genetic algorithm (DCGA). The results show that the network will possess maximum efficiency economically. Also, it is shown that precision and convergence speed of the proposed DPSO based method for the solution of the STNEP problem is more than DCGA approach.

Keywords: DPSO algorithm, Adequacy restriction, STNEP.

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Authors: C. Treerattanaphan, P. Boonpramuk, P. Singla

Abstract:

Frequent, continuous speech training has proven to be a necessary part of a successful speech therapy process, but constraints of traveling time and employment dispensation become key obstacles especially for individuals living in remote areas or for dependent children who have working parents. In order to ameliorate speech difficulties with ample guidance from speech therapists, a website has been developed that supports speech therapy and training for people with articulation disorders in the standard Thai language. This web-based program has the ability to record speech training exercises for each speech trainee. The records will be stored in a database for the speech therapist to investigate, evaluate, compare and keep track of all trainees’ progress in detail. Speech trainees can request live discussions via video conference call when needed. Communication through this web-based program facilitates and reduces training time in comparison to walk-in training or appointments. This type of training also allows people with articulation disorders to practice speech lessons whenever or wherever is convenient for them, which can lead to a more regular training processes.

Keywords: Web-Based Remote Training Program, Thai Speech Therapy, Articulation Disorders.

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Authors: J. Soltani, H. Abootorabi Zarchi, Gh. R. Arab Markadeh

Abstract:

In this paper a sliding-mode torque and flux control is designed for encoderless synchronous reluctance motor drive. The sliding-mode plus PI controllers are designed in the stator-flux field oriented reference frame which is able to track the mentioned reference signals with a minimum pulsations in the state condition. In addition, with these controllers a fast dynamic response is also achieved for the drive system. The proposed control scheme is robust subject to parameters variation except to stator resistance. To solve this problem a simple estimator is used for on-line detecting of this parameter. Moreover, the rotor position and speed are estimated by on-line obtaining of the stator-flux-space vector. The effectiveness and capability of the proposed control approach is verified by both the simulation and experimental results.

Keywords: Synchronous Reluctance Motor, Direct Torque and Flux Control, Sliding Mode, Field-Oriented Frame, Encoderless.

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

Abstract:

Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: Classification, classifier fusion, CNN, Deep Learning, prediction, SNR.

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