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Reliability analysis for Electronic and control system and providing a model to express the relationship between the various components have crucial importance. This study proposes a methodic approach to analyze the electronic and control system of mechanical system reliability in early design phase with deficiency of the sufficient field data available. In this method, first the different components of the system are identified. The function configuration and relate to each other are considered. Using generic data available through various different standards such as MIL-HDBK-217F and EPRD, failure rate and mean time between failures (MTBF) are calculated for each component. Applying this information, the system reliability model is developed and the relations between components in terms of reliability are explained. In this context, the computation is completed to analyze the reliability of the system. The importance measure technique is utilized to determine the importance of various components of the system. Also using reliability allocation method, reliability of each component is calculated to evaluate the modification required to upgrade the system to the specified reliability goal. Finally, considering the maintenance data of each component, availability estimations and related properties are obtained. As an application, this method is applied on the electrical and control system of a horizontal drilling machine. The results are discussed with concluding remarks in suitability of the proposed methodology.

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Optical interconnects as appropriate alternatives for electrical interconnects in the computer chips and boards can be realized by CMOS-based integrated silicon photonics. Dielectric slot waveguide, as one of the newest optical waveguide structures, can form the infrastructure of the passive and active components in these integrated circuits. The passive components have the linear behavior. In order to realize the all-optical active components such as laser, amplifier, and modulator we can use the nonlinear effects in the silicon photonics waveguides. On the other hand, Si-nc:SiO2 as a new material, has a stronger nonlinear property than Si. The results of the full-wave analyzes of the slot waveguide in the linear and nonlinear regimes show that the slot region of this waveguide can be filled with the Si-nc:SiO2 and also realize a high optical intensity. Therefore, this waveguide intensifies the nonlinear behaviors by two factors.

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Epilepsy is the most common serious brain disorder that characterized by recurrent seizures. Epilepsy affects 65 million people worldwide today and about two million new cases occur each year. The most negative aspect of seizure that causes the patient couldn’t have normal life, is its sudden and incontrollable features. So, the achievement of an algorithm that is capable to predicting the occurrence of seizures would help sufferers to live a normal life safe, and they can move out of harm's way. In this study, we proposed a prediction method for absence seizures based on the time-frequency analysis and complexity measure in EEG signals of WAG/Rij rats as a valid animal model of human absence epilepsy. We investigated the changes of permutation Entropy and the wavelet power of theta frequency range, simultaneously. The proposed seizures prediction algorithm was applied to long-term EEG recordings of WAG/Rij rats. The results indicate that the algorithm successfully detected the pre-ictal state prior to onset of seizures in 210 out of 298 seizures.The dependence of accuracy, sensitivity and anticipation time of prediction algorithm on program settings and attributes of EEG recordings are discussed.In this study, we found that the measure of PE reduced in pre-ictal and ictal states of EEG signals in these rats. The reduction of complexity of EEG signals prior to onset of seizures that was demonstrated by means of PE might be indicating the neural synchronization of brain networks in WAG/Rij rats.

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In this paper, we investigate the design method for interval type-2 (IT2) T-S fuzzy controller based on IT2 T-S fuzzy observer for nonlinear systems along with uncertainty parameters. In order to analyze the stability and synthesis the control methods conveniently, an IT2 (T–S) fuzzy model is applied through representing the dynamic of nonlinear systems and dynamic of observer. Uncertainty parameters are captured by IT2 membership function characterized by the lower and upper membership functions. In this paper, for IT2 fuzzy controller, the membership functions and number of rules can be freely chosen different from the IT2 T–S fuzzy model and IT2 T-S fuzzy observer. This method is known non- Parallel Distributed Compensation. To reduce the conservativeness of stability analysis, a fuzzy Lyapunov function candidate is applied. The stability conditions in term of linear matrix inequlities (LMIs) are obtained.

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In this paper, a finite-time stabilized guidance law is addressed in presence of some measurement noises. The measurement noise would effect on the guidance system stability and or performances. Hence, in presence of measurement noise, the guidance law must be modified such a way that the noise effect on the guidance system response would be reduced. By using the stochastic stability theory, a modified guidance law, depended on the measurement noises variance, will be proposed such that the line of sight angle rate is stabilized in a finite time. After such a finite-time, no force would be applied to the vehicle actuators. Then the line of sight angle would be a constant one. The proposed method would be used in a two-dimensional numerical example. The effectiveness of the suggested method is shown in the simulation results.

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With the increasing expansion of power systems, random factors affecting the performance of these systems have also increased. Rising demand for electrical energy, along with the aforementioned random factors, has led to uncertainty analysis methods being of particular importance in analyzing the small signal stability of the power systems. In this paper, a method based on polynomial approximation for probabilistic small signal stability analysis of the power systems is presented. Since the correct determination of unknown coefficients has a direct effect on the accuracy of the polynomial approximation method, this paper presents a method that is able to determine these coefficients with more coverage on the probable input space of the problem and in addition, is able to maintain its efficiency even by increasing the number of random input variables. After determining unknown coefficients, the load flow results and system state matrix are determined for random changes of all loads and based on Hermit's polynomial approximation. Then, the eigenvalues ​​of the system are determined and the stability of the small signal of the system is probabilistically studied. In order to evaluate the accuracy and effectiveness of the proposed method, the IEEE 14-bus benchmark system is simulated in MATLAB software and the results of the proposed method is compared with the results of the two conventional methods of Point Estimation and Monte Carlo. Examination of the results has shown that the proposed method in this paper, in addition to validity, has good accuracy and high computational speed.
 

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Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities.