نشریه مهندسی برق
سال پنجاه و سوم شماره 3 (پیاپی 105، پاییز 1402)

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can have significant effects on people's quality of life and daily functioning. polysomnography is the gold standard for diagnosing sleep apnea which cannot provide the expectations of a fast and economical diagnosis by analyzing several signals simultaneously. In this regard, the development of automatic, reliable and cost-effective diagnosis algorithms is important. Therefore, in this study, with the aim of diagnosing obstructive sleep apnea events, an automatic diagnostic algorithm based on single-lead Electrocardiogram (ECG) signal has been proposed. For this purpose, a new adaptive method based on Empirical Fourier Decomposition (EFD) and extraction of statistical and fractal dimension features from the Fourier Intrinsic Band Functions (FIBF) of the signal along with the ReliefF selection algorithm and Random Forest classification has been used. Empirical Fourier Decomposition can be a new tool for signal decomposition, which provides a suitable capability in extracting oscillations related to non-stationary components of the signal. In order to evaluate the proposed algorithm, the Apnea-ECG database, which contains 70 recordings of single-channel ECG signals, has been used. The results have shown that the proposed algorithm is able to detect obstructive sleep apnea events with %88.03 accuracy, %83.44 sensitivity, and %90.84 specificity. The high accuracy of the obtained results along with the number of suitable features indicates a compromise between the accuracy and the number of extracted features, which leads to a suitable computational load for the proposed algorithm, which makes it possible to use it in clinical applications.

One of the most important decisions in power systems is the optimal management of assets. Asset management in power distribution companies is mainly based on improving system reliability. Nevertheless, the concerns of distribution companies are not be limited to improving the reliability of the network. Other important criteria related to the risks that threaten their business can influence decision to plan maintenance programs, which are defined as intangible risks. These risks mainly include safety, environmental effects and company reputation. Hence, it is important to include the intangible risk index in the asset management decision-making problem, along with improving the tangible risk index (reliability index), which is not studied in research studies. In this paper, in order to achieve the application of risk management in asset management in electrical energy distribution systems, a risk-based preventive maintenance planning model is proposed from the perspective of tangible and intangible risk indices. The proposed model is applied to a sample feeder from the North Khorasan distribution network in Iran. Risk-based preventive maintenance planning for a 5-year period for the studied feeder and its impact on the company's asset management are presented. The results showed that, using the proposed model, the maintenance costs are decreased by 23 percent compared to existing methods.

In recent years, due to the increase in internet usage, the energy consumption of communication networks' infrastructures has been significantly increased, resulting in more monetary costs and carbon pollution. To address this challenge, green communication is investigated in order to reduce economic costs and environment pollution of ICT section. In this paper, a distributed sleep-scheduling method is proposed that switches off some network links and nodes in order to save energy during low traffic periods. The proposed approach selects switching-off nodes based on vertex cover problem and the occurrence of their related links. Then, some low-occurrent links in the new topology are switched off if the network performance remains satisfied. The proposed approach does not depend on any central controller and imposes minimum computing and network traffic overload since it does not use the network matrix and profits from the information already provided by link-state protocols. Simulation results performed on real network scenarios show that our proposed technique allows higher network performance compared to other green alternatives, while a significant amount of energy conservation is achieved.

This paper designs an adaptive improved dynamic surface controller for a class of uncertain nonlinear systems in the presence of input hysteresis and uncertain control direction. It is assumed that nonlinear functions in the system dynamics and parameters of the input nonlinearity are uncertain. At first, uncertain dynamics of the system is identified by the fuzzy system as a linear approximator. Then, the proposed improved dynamic surface controller is designed. In the proposed controller, a Nussbaum-type function is used to deal with the uncertain control direction problem. Also, for eliminating the “explosion of complexity” problem in the backstepping approach and sensitivity to the time constant of the filters in the dynamic surface control approach, a nonlinear tracking differentiator is used to obtain the virtual inputs derivative. Furthermore, by considering the norm of the adjustable parameters as an adaptive parameter, number of adaptive parameters and consequently computational burden are decreased considerably. Stability analysis of the proposed controller guarantees that all of the closed-loop signals are uniformly ultimately bounded. Also, adaptive laws for online tuning of adjustable parameters are proposed based on the Lyapunov method. Simulation results on two numerical and application examples verify the effectiveness and proper performance of the proposed controller.

One of the advantages of electric vehicles is their mass storage capability, which can help to change the hourly generation portfolio and reduce the operating costs of the SCUC scheduling problem. On the other hand, the uncertainty in the power system can lead to an imbalance in production and consumption, and as a result, unpredictable blackouts. Therefore, studies of the flexibility of the power system have gained particular importance. In this paper, the effect of Vehicle-to-grid (V2G) on the flexibility index and operating cost of the power system has been investigated as it’s considered a quick response source. The uncertainty of electric vehicles (EVs) has been modeled using the Z-number method. In fact, this method describes the number of v2g capable charging stations in each parking lot as a probabilistic–possibilistic variable. Improving flexibility is reasonable when operating costs are at the lowest possible level. For this reason, in order to reach the expected level of flexibility, the SCUC problem has been solved, considering security and flexibility, and the numerical analysis shows the improvement of the level of flexibility with the minimum cost of operation. In order to demonstrate the effectiveness of the proposed method, the IEEE 24-bus test system has been used.

The diagnosis of neurodevelopmental diseases, such as attention deficit/hyperactivity disorder (ADHD), has gained great attention in clinical studies due to its effect on the quality of human life. This disorder is caused by genetic factors, and anatomical and functional brain abnormalities, which can lead to timing deficits, working memory impairments, and inattention. Since the investigation of symmetry between activities of different brain regions may play an important role in the early diagnosis of this disorder, similarity quantification between brain signals is one of the existing challenges in the field of ADHD detection. The goal of this study is to compute symmetry between certain cortical areas from inter-hemispheric or intra-hemispheric channel pairs. For this purpose, a new algorithm based on dynamic time warping as a bivariate feature extraction step and support vector machine (SVM) classifier has been proposed. The proposed method's ability in distinct brain regions has also been explored.The proposed methods have been evaluated on electroencephalogram (EEG) recordings of 14 ADHD children and 19 age-matched healthy controls performing a time-reproduction task. It has achieved high average accuracy rates of 94.38±0.007 in discriminating between healthy controls and patients with ADHD. The experimental results have also demonstrated the superior performance of the proposed method in comparison with previous ADHD detection methods using EEG signals.

This paper presents a Transimpedance Amplifier (TIA) for high sensitivity Near Infrared Spectroscopy (NIRS). The proposed TIA is based on the Regulated Cascode (RGC) structure with an extra transistor employed to implement additional feed-forward path and achieve higher gain values. The extra transistor senses a partially amplified input signal, available in the conventional circuit, and conveys an additional ac current into the load, which provides a higher gain. In addition, a bandwidth extension method is introduced using a capacitor and resistor, which can improve amplifier’s bandwidth by 40%. The proposed TIA is designed in 0.18µm CMOS technology and achieves a transimpedance gain of 101.9dB with a -3dB bandwidth of 91.2 MHz considering 2pF of photodiode capacitance at the TIA input. The input referred noise is 4.4pA/√Hz while dissipating 151µW power.