1,256 publications from this institution
This article studies the influence of pot temperature or electrolyte temperature in the aluminum reduction production. Specifically, these indexes reflect the distribution of the physical and energy field of the reduction cell, the current efficiency, and the lifespan of the aluminum reduction cell. Therefore, the pot temperature detection and identification are two critical and significant issues in the whole production process of aluminum electrolysis. However, due to the low measurement accuracy and high maintenance costs with the thermocouple sensor in the practical production process, the real-time measurement of pot temperature index is still a major challenge, which motivate us to develop a self-supervised soft sensor method based on deep long-short term memory (LSTM). Under the constraint of the limited samples, the proposed method achieves a competitive performance. First, the input variables are selected according to the expert experience. Then, a deep self-supervised model is built. Finally, the proposed self-supervised LSTM model is applied to real-time detection in an industrial electrolysis production case. The performance in the experiment outperforms other existing methods in terms of both accuracy and robustness aspects.
This paper presents an approach for data‐driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory‐scale three‐tank system is finally utilized. It shows that the proposed data‐driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large‐scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.
Notice of Violation of IEEE Publication Principles <br><br> After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. <br/> This paper studies the piecewise-affine memory H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering problem for nonlinear systems with time-varying delay in a delay-dependent framework. The nonlinear plant is characterized by a continuous-time Takagi-Sugeno fuzzy-affine model with parametric uncertainties. The purpose is to develop a new approach for filter synthesis procedure with less conservatism. Specifically, by constructing a novel Lyapunov-Krasovskii functional, together with a Wirtinger-based integral inequality, reciprocally convex inequality and S-procedure, an improved criterion is first attained for analyzing the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance of the filtering error system, and then via some linearization techniques, the piecewise-affine memory filter synthesis is carried out. It is shown that the existence of desired filter gains can be explicitly determined by the solution of a convex optimization problem. Finally, simulation studies are presented to reveal the effectiveness and less conservatism of the developed approaches. It is anticipated that the proposed scheme can be further extended to the analysis and synthesis of continuous-time fuzzy-affine dynamic systems with integrated communication delays in the networked circumstance.
In this chapter, the relevance and potential of micro-sized wind turbines (μSWTs) have been discussed and the following facts have been stated In wide areas of developing countries with a poor grid power supply, μSWTs can be an important factor of social, economic and technological development. From an educational point of view, μSWTs can be a valuable source of motivation, helping to provide a practical presentation of basic and advanced mechatronical principles. Moreover, μSWTs can also be an excellent field for cooperative and project-based learning, allowing to define a wide variety of experimental projects with a direct real-life application. From a research perspective, μSWTs constitute a unique opportunity to perform full-scale physical experimentation on advanced research topics at a very low cost and with very limited resources. To provide a practical demonstration of the μSWTs potential in academic and research experimentation, a low-cost platform for active magnetic bearing vibration control has been presented.
This paper deals with the performance analysis of a vibration-isolation system for Michelangelo Buonarroti’s famous Ronadanini Pietà statue based on the monitoring and analysis of vibration signals. A tuned mass-damper–inerter is introduced in order to increase the effectiveness of the isolator in the horizontal direction. Specifically, a multi-degree-of-freedom (MDOF) model for the system, including non-linear terms, is proposed. The monitoring data of the structure inside the museum were utilized to update the MDOF model of this structure. Then, the effect of different parameters was analysed, and some modifications proposed to enhance the efficiency of the isolation system in its working condition. A combination of tuned mass-dampers (TMD) with an inerter is proposed to attain a considerable increase in the performance of the isolation system based on the main feature of the tuned mass-damper–inerter (TMDI), which can apply high apparent mass to a system without adding considerable real mass to the original system. Various performance functions are used to illustrate the efficiency of the proposed TMDI. It is shown that a significant effect of this passive method is to reduce the level of vibration in the updated model of this sensitive and valuable object.
Vibrations is an extremely important issue to consider when designing various systems. It may lead to discomfort and malfunction or in some cases collapse of structures. To compensate for these vibrations different types of damping devices can be applied. The main focus which the following work addresses has been to look at standard methodology which enables determining the hysteresis from defined range of measurements which special focus on the MR damper.The mathematical equations that lie behind the Bingham, Dahl, Lugre and Bouc-Wen have been studied to describe the behavior of the MR damper. The hysteresis equations of Bouc-Wen, Lugre, and Dahl have been modeled and simulated in Matlab/Simulink. We have manipulated the different parameters in the models and analyzed their effects on the outcome. The hysteresis models of Bouc-Wen, Dahl and LuGre have been analyzed and compared analytically to really show the difference in the models. At last the Bouc-Wen model was implemented together with the SAS(Semi Active Suspension) system in the laboratory. The model parameters were tuned manually to try to fit the response of the system. This paper shows that the methodology flowchart can be implemented and generalized for any kind of dampers and used to find behavior of MR damper with different mathematical models. The Bouc-Wen model was found to be model to both illustrate the MR damper and recreate the behavior of the SAS system.
This paper reports on a study undertaken within the CEPT SE42 project team, with the objective of deriving regulatory radiation limits which mitigate the impact of adjacent-channel interference from fixed/mobile communication network base stations (BSs) in the Digital Dividend spectrum (790-862 MHz) to digital terrestrial television (DTT) services below 790 MHz. A novel stochastic approach for the calculation of BS block-edge mask (BEM) out-of-block (or baseline) limits is presented. Results indicate that, for a typical BS in-block EIRP of 59 dBm/(10 MHz) or greater, the fraction of locations in which a DTT receiver would suffer unacceptable levels of interference does not improve significantly with a reduction in the BS BEM baseline EIRP limit below 0 dBm/(8 MHz). This is due to the finite frequency selectivity of the DTT receivers. It is also shown that for in-block EIRPs below 59 dBm, the baseline EIRP limit should be reduced proportionately in order for the DTT location failure rate to be broadly independent of the communication network deployment geometry.
Graph partitioning, a classical NP-hard combinatorial optimization problem, is widely applied to industrial or management problems. In this study, an approximated solution of the graph partitioning problem is obtained by using a deterministic annealing neural network algorithm. The algorithm is a continuation method that attempts to obtain a high-quality solution by following a path of minimum points of a barrier problem as the barrier parameter is reduced from a sufficiently large positive number to 0. With the barrier parameter assumed to be any positive number, one minimum solution of the barrier problem can be found by the algorithm in a feasible descent direction. With a globally convergent iterative procedure, the feasible descent direction could be obtained by renewing Lagrange multipliers red. A distinctive feature of it is that the upper and lower bounds on the variables will be automatically satisfied on the condition that the step length is a value from 0 to 1. Four well-known algorithms are compared with the proposed one on 100 test samples. Simulation results show effectiveness of the proposed algorithm.
Social media, e.g. Twitter, has become a widely used medium for the exchange of information, but it has also become a valuable tool for hackers to spread misinformation through compromised accounts. Hence, detecting compromised accounts is a necessary step toward a safe and secure social media environment. Nevertheless, detecting compromised accounts faces several challenges. First, social media activities of users are temporally correlated which plays an important role in compromised account detection. Second, data associated with social media accounts is inherently sparse. Finally, social contagions where multiple accounts become compromised, take advantage of the user connectivity to propagate their attack. Thus how to represent each user's network features for compromised account detection is an additional challenge. To address these challenges, we propose an End-to-End Compromised Account Detection framework (E2ECAD). E2ECAD effectively captures temporal correlations via an LSTM (Long Short-Term Memory) network. Further, it addresses the sparsity problem by defining and employing a user context representation. Meanwhile, informative network-related features are modeled efficiently. To verify the working of the framework, we construct a real-world dataset of compromised accounts on Twitter and conduct extensive experiments. The results of experiments show that E2ECAD outperforms the state of the art compromised account detection algorithms.
This article explores a new framework of distributed state and fault estimation (DSFE) for the state-saturated systems over sensor networks. To this aim, the upper bound on estimation error covariance (EEC) is ensured and the explicit expression of the corresponding estimator gains is given with both quantization effects and state saturations. Further, a feasible upper bound is located on EEC and minimized by parameterizing the estimator gain. The matrix simplification technique is adopted to deal with the sensor network topology's sparseness problem. Additionally, the estimation performance is first analyzed and then ensured by conducting a sufficient condition. At last, experiments are carried out to verify the feasibility of the developed DSFE method.
Introduction: Recent studies suggest that treatment should be begun immediately in children who have recently started to stutter. The purpose of this study was to design a telehealth application for parents of young children who stutter. It is an evidence-based treatment that can be administered from an early age compared to the current “wait and see” approaches.
 Materials and Methods: This research involved a qualitative content analysis. At first, a comprehensive review was performed on different well-established therapeutic programs, and their main therapeutic components were extracted via several sessions held by our focused group. Subsequently, six independent stuttering experts and five parents of stutter children were asked to rate the program’s items regarding its content and face validities by a 5-point Likert questionnaire. Finally, the entire program was used to form an easy to use, family- friendly software.
 Results: Seven therapeutic principles and five common factors were extracted from all available well-established stuttering treatment programs. They were designed in an easy to use software program. The final telehealth program was found to have a high face and content validities.
 Conclusion: This program might be used in future clinical practice for stuttering children under the age Four. However, its efficacy has yet to be examined.
This paper investigates the problem of model reduction for a class of continuous-time Markovian jump linear systems with incomplete statistics of mode information, which simultaneously considers the exactly known, partially unknown and uncertain transition rates. By fully utilising the properties of transition rate matrices, together with the convexification of uncertain domains, a new sufficient condition for performance analysis is first derived, and then two approaches, namely, the convex linearisation approach and the iterative approach, are developed to solve the model reduction problem. It is shown that the desired reduced-order models can be obtained by solving a set of strict linear matrix inequalities (LMIs) or a sequential minimisation problem subject to LMI constraints, which are numerically efficient with commercially available software. Finally, an illustrative example is given to show the effectiveness of the proposed design methods.
This paper studies the fixed-order piecewise-affine (PWA) output-feedback control of PWA systems in an H∞ setup. In particular, the conventional output-feedback closed-loop system is first augmented with the introduction of the input vector, and a descriptor presentation of PWA system is acquired. Then, a bounded real lemma is derived for the resulting PWA system, which is realized by the construction of a smooth piecewise Lyapunov function and application of the S-procedure. Furthermore, by availing oneself of the descriptor formulation, the fixed-order PWA output-feedback controller synthesis is carried out in a unified framework. An illustrative example is provided to show the efficacy and impact of the proposed control design methodology in a Chua's circuit.
This paper is concerned with the problem of load-dependent H2 control for vehicle semi-active suspension. A quarter-car model equipped with a magnetorheological (MR)-damper, which captures essential features of a real car suspension, is considered in this study. H2-norm measures the root-mean-square (RMS) value of output to white noise input. Considering the fact that road roughness is often modelled as white noise, H2-norm is used to quantify control objectives of ride comfort and safety as well as suspension deflection and control effort. To guarantee system performance against parameter variations, a linear matrix inequality (LMI)-based design framework has been utilised and a linear parameter-varying (LPV) controller is synthesised. The design procedure of the semi-active suspension requires the inverse dynamics of MR damper, which is obtained through a locally linear neuro-fuzzy (LLNF) network. To illustrate the effectiveness of the proposed approach, the system outputs for both impulse and real road inputs are compared with H∞ controller in terms of performances.
