Hierarchical Stochastic Frequency Constrained Micro-Market Model for Isolated Microgrids

With the developments of isolated microgrids (IMGs) and prosumers in remote areas, energy trading has emerged as a critical aspect of IMGs. However, the lack of an upstream network and the low inertia of the system may threaten the secure operation of these networks. This paper proposes a Micro-Market (lM) model for IMGs that includes a precise hierarchical control structure. To address the IMGs low inertia and high intermittency of renewable energy sources (RES), the proposed lM manages the active-reactive power and schedules primary and secondary active reserves to maintain the frequency within in a predefined range. Additionally, a bidirectional linearized AC power flow is established to schedule the reactive reserve and the proposed model is formulated as a two-stage stochastic mixed-integer linear problem (MILP) to maximize social welfare (SW) over the next 24 hours. To validate the effectiveness of the proposed model, the lM is tested on an IMG based on a CIGRE medium-voltage benchmark system, and different operational cases are simulated. The results demonstrate that the proposed model, which takes into account hierarchical control levels and technical issues of the IMG, is a cost-effective way to maximize social welfare while ensuring the secure operation of the IMG.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioimaton|en=nonPeerReviewed

A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach

The main idea of this article is to provide a numerical diagnostic method for breast cancer diagnosis of the MRI images. To achieve this goal, we used the region's growth method to identify the target area. In the area's growth method, based on the similarity or homogeneity of the adjacent pixels, the image is subdivided into distinct areas according to the criteria used for homogeneity analysis to determine their belonging to the corresponding region. In this paper, we used manual methods and use of FCM as the function of genetic algorithm fitness. The presented algorithm is performed for 212 healthy and 110 patients. Results show that GA-FCM method have better performance than hand method to select initial points. The sensitivity of presented method is 0.67. The results of the comparison of the fuzzy fitness function in the genetic algorithm with other technique show that the proposed model is better suited to the Jaccard index with the highest Jaccard values and the lowest Jaccard distance. Among the techniques, the presented works well because of the similarity of techniques and the lowest Jaccard distance. Values close to 0.9 are close to 0.8

A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature

Sleep is an essential process for the body that helps to maintain its health and vitality. The first stage in the diagnosis and treatment of sleep disorders is sleep staging. Due to the complications in manual sleep staging by the physician, computer-aided sleep stage classification algorithms are gaining attention. In this study, a novel approach was introduced to extract distinctive representations from single-channel EEG signal for automatic sleep staging. Standard deviation as a single feature was extracted from the frequency subbands of EEG, which gave a comprehensive understanding of the signal and its activity within various frequency ranges for different sleep stages. The features formed the input space of the proposed two-stream convolutional neural network (CNN) for classification and two-stage learning was used to train the model that achieved improvements in terms of accuracy, reliability and robustness against traditional classifiers and conventional training method of the neural networks. For the performance evaluation, three well-known benchmark datasets including Sleep EDF, Sleep EDFx and DREAMS Subject were used. The proposed algorithm by utilizing simple and effective methods improved sleep stage classification results by achieving an overall accuracy of 93.48%, 93.14% and 83.55%, respectively. The introduced framework in this study has great potential for practical implementation on a home-based sleep staging device

Performance analysis of OFDM channel estimation under IQ imbalance

This study was done in attempt to investigate the effect of In-Phaseand- Quadrature Imbalance (IQI) presence in Orthogonal Frequency Division Multiplexing system (OFDM). Although OFDM system is widely used in the communication system, it is prone and sensitive to non-idealities such as IQI. This issue causes serious performance degradation in the system. Channel estimation plays an important part in an OFDM system. Thus, this study will investigate the effect of IQI in OFDM system channel estimation. There are two types of channel estimation scheme used in this paper. They are the Least Square (LS) and Linear Minimum Mean Square Error (LMMSE). The outcome for this channel estimation scheme will be compared with their theoretical values based on the channel’s Mean Square Error (MSE). To obtain the result, LS and LMMSE channel estimation was developed and simulated using MATLAB Simulink software. Then, the corresponding output was analyzed. From the analysis, the performance of these two-channel estimation schemes was affected after the addition of IQI. However, comparing both schemes, LMMSE has better performance compared to LS in terms of MSE

Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2–4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID 19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection studies. The results based on the proposed network were very promising in terms of accuracy, precision, sensitivity, and specificity compared to the other deep transfer learning approaches. Depending on the high performance of the proposed method, it can be used during the treatment of patients

Cytotoxic and Apoptotic Activities of Methanolic Subfractions of Scrophularia oxysepala

Herbs have played a positive role in medicine for thousands of years. In the current study, we investigated the cytotoxicity effects of Scrophularia oxysepala methanolic subfractions and the underlying mechanism responsible for cell death in human breast carcinoma (MCF-7 cells) and mouse fibrosarcoma (WEHI-164 cells). From 60% and 80% methanolic fractions, four subfractions (Fa, Fb, Fc, and Fd), yielded from size exclusion by Sephadex-LH20 column chromatography, were chosen. MTT assay revealed that all subfractions significantly reduced cell viability after 24 h and 36 h in a dose-dependent manner; it is worth noting that Fa and Fb subfractions had the highest cytotoxicity, with IC50 values of 52.9 and 61.2 μg/mL in MCF-7 at 24 h, respectively. ELISA, TUNEL, and DNA fragmentation assay revealed that antiproliferative effects of all subfractions were associated with apoptosis on cancer cells, without any significant effect on L929 normal cells. qRT-PCR data showed that, after 24 h treatment with IC50 concentrations of the subfractions, caspase-3 expression was increased in cancer cells while the expression of Bcl-2 was decreased. S. oxysepala methanolic subfractions induce apoptosis in MCF-7 and WEHI-164 cells and could be considered as a source of natural anticancer agents

Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network

In recent years, detecting driver fatigue has been a significant practical necessity and issue. Even though several investigations have been undertaken to examine driver fatigue, there are relatively few standard datasets on identifying driver fatigue. For earlier investigations, conventional methods relying on manual characteristics were utilized to assess driver fatigue. In any case study, such approaches need previous information for feature extraction, which could raise computing complexity. The current work proposes a driver fatigue detection system, which is a fundamental necessity to minimize road accidents. Data from 11 people are gathered for this purpose, resulting in a comprehensive dataset. The dataset is prepared in accordance with previously published criteria. A deep convolutional neural network–long short-time memory (CNN–LSTM) network is conceived and evolved to extract characteristics from raw EEG data corresponding to the six active areas A, B, C, D, E (based on a single channel), and F. The study’s findings reveal that the suggested deep CNN–LSTM network could learn features hierarchically from raw EEG data and attain a greater precision rate than previous comparative approaches for two-stage driver fatigue categorization. The suggested approach may be utilized to construct automatic fatigue detection systems because of their precision and high speed