The current study uses heart rate variability (HRV) signal processing to investigate changes in the multifractal dimension in congestive heart failure (CHF) patients and predict sudden cardiac death (SCD). In this regard, HRV signals are first extracted, and their four sub-signals are determined using the Local Characteristic Decomposition (LCD) method. In the next step, using the Teager Energy method, the instant amplitudes of each sub-signal obtained in the previous step are calculated; thus, new signals are generated based on these instant amplitudes. Employing multifractal detrended fluctuation analysis (MF-DFA), the modified fractal dimensions of each new signal are then obtained. With the t-test method, appropriate features are selected and input into the support vector machine (SVM) classifier. By detecting subtle changes in HRV signals, this method can detect SCD in CHF patients.  The results indicate that the proposed algorithm can distinguish the signals of SCD subjects with an accuracy of 84.76% 26 minutes before the event. In addition, after passing each 5-minute interval, the proposed method can update and determine how much time is left before SCD occurs

Wind energy is one of the renewable energy sources. Using the maximum power available in the wind is necessary to achieve the performance of the wind turbine at maximum pow-er. One of the ways to control the voltage at the connection point of the wind turbine to the power grid is to use flexible ac transmission systems (FACTS) controller. In this paper, the effect of static synchronous compensator (STATCOM) in a distribution system with induc-tion generator wind farm is analyzed and simulated. The studied system consists of a 9 MW wind farm connected to a 25 kV distribution system that delivers power to the grid through a 25 kV feeder. The results show that the STATCOM can, in addition to providing active power in short-circuit fault conditions, adjust the voltage changes at the common connection point between the wind farm and the grid in normal and fault conditions

Accurate working length measurement plays a key role in the success of root canal treatment. In this paper, a novel system is proposed for predicting root canals working length from dental radiographs. The system uses image processing techniques to detect a tooth midline and estimate its length in pixels. The estimated length is then used to predict the working length (in mm) by a weighted linear regression model. The system’s performance was evaluated using a database of single- and double-rooted teeth. The mean working length prediction error was 7.3% for single-rooted teeth, and 6.7% and 5.6% for the mesio-buccal and the distal canals of double-rooted teeth, respectively. The system was also successfully used to predict the working length of double-rooted teeth’s mesio-lingual canal, which is invisible in the radiographs. The mean prediction error was 6.9% in this case. The accuracy of these working length predictions indicates that the proposed solution could potentially be used to develop practically efficient working length measurement tools that can overcome some problems of the traditional radiographical measurements such as time-consuming repeated measurements and subjective manual adjustments

The goal is to create a speech recognition system that is able to recognize Persian speech. Pro-sodic speech is attributed to the hierarchical structure from speech rhythm and tonal expression to the smallest syllable components and provides important information about trans segmental features such as F0 (fundamental frequency), intensity, and duration, which are crucial for natu-ral sound. Prosodic features are highly language dependent, however, the relationship between linguistic features and prosodic data is not well understood in some languages. While relatively high-performance prosodic generators have been developed for many languages, very limited work has been done on prosodic generators in Farsi. In this article, we first use a simple four-layer RNN to extract prosodic information, then we investigate the hybrid ANN/HMM model for Persian speech recognition. 210 samples of the speech of a male person were collected and after removing the noise, 47 of the samples were manually labeled phonetically. Then, the remaining training samples were automatically labeled and new neural networks (ANN) were created for the final recognition of the three-layer MLP type. Four methods including MEL, MEL derivative, energy, and energy derivative were used to extract features, and the values of each of these four methods were combined and given to the neural network. Then we use the neural network to classify these feature vectors and get the most similar vowels. We give the order of vowels as "observations" to HMMs (which are created based on pronunciations) and then find the most probable HMM (or in other words, the most words) to the input sound and output it. By applying recognition on 99.4% of test data, we even reached 100% accuracy in one case, which is a very favorable result considering the small number of speech data

In recent years, energy efficiency and related solutions to enhance the efficiency of power plants have been the focus of attention. For this purpose, the combined heat and power (CHP) plants are increasingly used to generate the required electrical and thermal powers, simultaneously. On the other hand, the reliability of the power system has become one of the important studies of the power system, so that today the consumers expect the electricity provided them with least possible interruption. For this purpose, in this paper, the reliability of the power system including CHP plants from adequacy point of view is evaluated. To study the effect of the CHP units on the adequacy indices of the power system, a reliability model is developed for CHP units that considers both the failure of composed components and the effect of this unit from thermal power generation point of view. To consider the heat generation of the CHP units in the related reliability model, a four-state reliability model is developed for each unit based on the electrical and thermal power of the CHP plant. In this paper, the reliability modeling of different CHP technologies including the CHP units based on the gas turbine, reciprocating engine, micro-turbine, steam turbine, and fuel cell is studied. To investigate the effectiveness of the proposed model, the adequacy assessment of the Roy Billinton test system (RBTS) and IEEE reliability test system (IEEE-RTS) including the CHP units is performed and the impact of the CHP units on the reliability indices is evaluated

In recent years, in developed countries, the extraction of electricity and heat in municipal sewerage refineries has become very common. This technology generates significant amounts of energy in the form of electricity and heat. In this article, we will get acquainted with a CHP system with an average capacity of 1 MW of electrical energy and 1.2 MW of thermal energy which is used in South Tehran sewerage refinery. Due to the significant sensitivity of the economic results to the parameter of revenue from the sale of electricity to the grid and a significant effect on reducing greenhouse gas emissions, a slight increase in the purchase rate of electricity generated from biogas digesters can make this investment more attractive to many domestic and foreign investors. Also, due to the environmental importance of the anaerobic digestion of sludge in municipal sewerage refineries, such projects can also be considered from an environmental point of view. If biogas is used as fuel for this CHP power plant, the efficiency will be 44.9%, and the reduction in heating and electricity costs also will be 8299.25 and 66895.38 , respectively. In the CHP cycle of this treatment plant, we hypothetically added a PEMFC and studied this cycle, Due to the addition of this system in the cycle, the electrical efficiency of the cycle increased and also, the emission of polluting gases from the CHP system is reduced due to the consumption of the biomass source