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Wireless Sensors and wireless sensor networks have been used broadly in academic communities. That is owing to increasingly development of small scale equipment in engineering that prepares enormous applications. Numerous researches have been done in order to using of these sensors as well as establishing them as a network and large variety of solutions have been suggested. One heuristic method to modeling wireless sensor networks is distributed adaptive modeling. Processing is done in fully distributed manner at this way. An adaptive network contains a number of nodes those are capable to learn and adapt. Every node exchanges its data with neighbor nodes so as to the network could finally solve an estimation or interference problem. In this paper, at first, the position of distributed adaptive processing will be explained, then we review some proposed strategies for adaptive modeling and after that simulation results will be compared.

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This paper proposes direct adaptive fuzzy control with less restriction on control gain for siso nonlinear systems and is presented a simplified type-2 fuzzy system. Adaptation law is derived based on Lyapunuv stability analysis that assures adaptive parameters and tracking error to be bonded. Since in addition to consequent parameters,width and centers of the membership functions are tuned then the estimation error is very small so as to be negligible.Furthermore, the number of membership functions required is seen to be less than that needed with type-1 fuzzy sets. The simulation results that are conducted on inverted pendulum and magnetic levitation systems confirm the efficacy of the proposed scheme. In the presence of noise the reference input is tracked very well and tracking error is very small.

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Abstract— In OFDM systems, it is necessary to estimate the channel to overcome the distortion caused by channel fading’s which can be induced by many phenomena such as: delay spread, mobility and Doppler shift. Most of the channel estimation techniques are proposed in frequency domain using the pilot symbols. One of them that is less complicated is least-squares (LS) method which is widely used in channel estimation but it is more sensitive to noise respected to the other reported techniques. In this paper, a new threshold based method using wavelet decomposition will be proposed which is based on an initial LS estimation technique. The reported simulation results show that the proposed method has better performance compared to the other methods such as Lee Method that has been published recently.

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This paper describes load torque estimation (LTE) issue in induction motors with uncertainty, using dynamic sliding mode control (DSMC). In DSMC the chattering is removed due to the integrator which is placed before the input control of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system and then, the plant model should be completely known. To solve this problem, a new nonlinear observer called integral-chain observer (ICO) has been used. The advantage of the proposed approach is to have the system controlled as well as its main task i.e. LTE. Moreover, we assume that only output of system is accessible which is important in practical implementation. Simulation results are presented to demonstrate the approach.

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The development of renewable energy sources, distributed generation, energy storage and nonlinear controllable loads in modern distribution networks has led to the consideration of the state estimation in intelligent and active distribution networks. The performance of the Energy Management Central's distribution network depends on the results of the state estimation. In this paper, two-stage estimation with the network reduction process is proposed. The number of distribution network meter is low, so obtaining accurate initial information from network conditions improves performance state estimation. The initial state estimation with the network reduction process to obtain accurate initial data is performed. The initial data are used as a measure to improve the performance of secondary estimation. This method solves the problem of scarcity of accurate measurements and improves the accuracy of the state estimation in distribution network. The results of the proposed methodology are demonstrated on the 69 nodes of IEEE  standard distribution network.

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Downlink channel estimation of Massive MIMO is an important challenge is 5G wireless communication.  A classic method for this purpose is training-based method which leads to decreasing in transmitted information rate. To cope with this problem, recently, a blind channel estimation method has been presented in which by assuming that the value of large scale fading coefficient is known, the channel gain is estimated in multiuser Massive MIMO system. In this paper, we propose a new method that simultaneously estimates both channel gain and large scale fading coefficient by applying two different power control gain in the coherence interval. The proposed method is applicable for ZF and MR precoding. The proposed method has higher transmitted information rate (does not need to transmit the large scale fading coefficient) and lower MSE in high SNR values with respect to reference method.

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3D human pose estimation is one of the most significant tasks in computer vision with wide range of applications. The works for estimating human pose initialized from 2D skeletal estimation from multiple data and has proceeded toward 3D skeletal estimation from minimum input information. In this paper, 3D human pose estimation from a single RGB image is investigated. The proposed work is considered as the ones which firstly estimate 2D pose and then lift the estimated 2D configuration to 3D space. Since most of the errors in this attitude are originated by inaccurate 2D pose inference, we have proposed a method for predicting more accurate 2D poses to obtain 3D poses with less errors. The proposed approach for estimating 2D pose has leveraged deep learning along with the information of the edge map. In other words, we have made use of edge features, which are hand-designed features, in order to guide the deep neural network in training and in learning the features in accordance with the defined objective. Experimental results have demonstrated less errors in 2D and consequently 3D pose estimation in Human3.6M and HumanEva-I benchmarks.

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 In this paper an observer-based robust fault estimation scheme is proposed for a special class of Lipchitz nonlinear systems where the disturbances and faults are assumed to be coupled with the main system states. In the considered model of system, fault is assumed to enter both of the state and output equations as an unmeasured nonlinear function and coupled with the states. The disturbances and the uncertainties are considered as nonlinear functions coupled with the states. To the best of the authors’ knowledge these conditions have not been previously considered in related papers. In the proposed approach, a Luenberger observer is designed for the estimation of faults and states of system simultaneously. The effect of system disturbances is attenuated with the L2  norm. The necessary conditions for the existence of such observer is expressed in the form of Linear Matrix Inequality. The Lipchitz constant of the nonlinear function is obtained by solving the proposed Linear Matrix Inequality. Finally, the performance of the proposed method is simulated on a three-phase induction motor. The results indicate good performance of the proposed method.
 

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This paper presents a new online scheme of estimating power system transient voltage instability of interconnected synchronous generators using intelligent Bayesian theory based on wide area signals from WAMS data. For this purpose, by using online measurement of the system oscillatory signals gathered from WAMS technology and developing them as a series of input-output pairs data, the system dynamic category (stable/unstable) are achieved and used for Bayesian training. The proposed scheme is an online non-model-based technique with the ability of estimating binary decisions among the power system dynamic signals. In the case of evaluating effectiveness of the developed algorithm, by using an IEEE 39 test system, different fault events with the potential of transient voltage instabilities are investigated. In this case, considering two sampling data at pre-fault and post-fault occurrence moments, the system dynamic statues are estimated. Results present ability of the proposed scheme for fast and secure estimations of the system transient voltage instability.

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Estimating the state of charge of lithium- ion batteries is of great importance not only for optimal energy management, but also for ensuring safe operation, preventing charging and discharging, and as a result reducing the life of the battery. However, this parameter cannot be measured directly from the battery terminals. Therefore, there is a need to estimate it. In this paper an improved auxiliary marginal particle filter is presented to estimate the state of charge of lithium-ion batteries. In the proposed method, unlike the particle filter, sampling is done on the marginal distribution and the sampling dimensions do not increase with the passage of time. In addition, genetic operators and M-H algorithm have been used in the proposed method to increase diversity among particles. The use of genetic operators and the M-H algorithm causes the resampled particles to asymptotically approximate the samples from the posterior probability density function of the true state and increases the compatibility. The performance of the proposed method for estimating the state of charge of the battery has been compared with the estimation of the state of charge based on the developed particle filter and traceless particle filter. The results show the effective performance of the proposed method in comparison with other methods. The proposed method to obtain the same estimation accuracy as the particle filter requires far fewer particles and the amount of calculations is low. The root mean square error in the proposed method with different particles is close to 0.007, while in other methods, the root mean square error increases with the decrease of particles.