Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Free Predictive Current and Disturbance Rejection Control of Dual Three-Phase PMSM Drives Using Optimal Virtual Vector Modulation

Model-based predictive current control (MBPCC) relies heavily on adequate system modeling and accurate parameters. The detailed models of dual three-phase permanent magnet synchronous motors (PMSMs) contain mutual cross-coupling dynamics which make it difficult to extract accurate parameters. Also, some of the physical parameters of PMSMs are generally nonlinear functions of current and rotor position. In this article, a model-free predictive current control (MFPCC) based on an ultralocal model and an extended state observer is proposed for an asymmetrical dual three-phase (ADTP) PMSM. The MFPCC method is shown to provide superior current regulation when compared to the standard MBPCC approach under uncertain parameter conditions. Furthermore, the harmonic currents and the current ripple in the ADTP PMSM drive have been regulated to near-zero values by using optimized voltage vectors comprised of virtual vectors and null vectors. A generalized center-aligned pulsewidth modulation (PWM) scheme is presented to facilitate the synthesis of the optimal virtual voltage vectors for implementation on a low-cost digital signal processing platform. The performance improvements of the MFPCC with optimal virtual vector modulation are verified and compared with the conventional MBPCC method in both simulations and experiments on a surface-mount type dual three-phase PMSM.
Technological Empowerment: Applications of Machine Learning in Oral Healthcare

In the era of Artificial Intelligence the old paradigm of oral healthcare has got augmented with automation. Combining thinking abilities of human mind with the cutting edge technology of machine learning can aid the clinicians meet the growing needs and ensure cordial patient-doctor partnership. Advanced software and computing tools are being used to identify problem areas with lesser reporting time and appropriate clinical decision support system to track clinical outcomes. The perceptive abilities of machine learning is directly proportional to information obtained from patients, images, material applications and treatments done. The specialized algorithms are able to predict unexpected complications likely to be encountered and under-diagnosis of rare pathologies that otherwise might be missed due to limitations of clinicians expertise in that area. Today it is essential to embrace machine learning programmes to evolve age old working practices for greater performance and better outcomes by bridging the existing gap between diagnosis and treatment planning. The paper discusses and acknowledges the performance and futuristic applications of machine learning in various subareas of oral health and research.
Machine Learning Algorithms based Detection and Analysis of Stress - A Review

Now-a-days stress is one of the major issues in every individual’s life. It may cause many physiological and psychological problems. Many researchers have taken this into account and proposed various stress detection models. This paper mainly focuses on various stress detection models which are published in the latest years. The stress level of an individual is classified using machine learning algorithms like K-Nearest Neighbor (KNN), Naive Bayes (NB), Logistic regression (LR), Support Vector Machine (SVM) etc. It is observed that SVM produces a high accuracy when compared with other classifiers. The organization of the paper is as follows, Section I gives a brief introduction on stress and a basic idea about the datasets used by the researchers. Section II provides literature review. Section III contains analysis on collected literature.
Diverse Convolutional Neural Network Models for Feature Extraction from Brain Tumor Images

Medical imaging is important for both the doctor's precise diagnosis and the patient's subsequent treatment. Through several investigations, different algorithms have been incorporated into medical imaging. Medical picture feature extraction is investigated on a sizable amount of data to produce processing outputs that help doctors diagnose cases more precisely. To begin, the local binary pattern features will be extracted from the tumour image using rotation invariance. The rotation and movement of the image are altered, but its stability with respect to the coordinate system is maintained. By accurately describing the textural attributes of the tumour picture's surface layer, the approach increases the robustness of the image region description. The test results show how well the CNN algorithm retrieves features from photos of tumours. The techniques used by DenseNet, Xception, and Binary pattern CNN are compared in this work. With additional information, the CNN approach can precisely extract characteristics from tumour CT scans.
Performance analysis of various deep learning techniques for brain tumor classification

Brain MRI image tumors are classified using machine learning techniques in which features are extracted and given to the classifier for the classification task. The manual extraction of elements is time-consuming and leads to poor performance due to a poor selection of features. This paper describes the performance analysis of various deep-learning techniques for brain tumor classification. These methods were assessed using three different categorization performance indices. The logistic regression and hybrid approach discovered a maximum classification accuracy of 89% for small and 87% for large datasets.
Application of Tirofiban for Stent-Assisted Coiling in Acutely Ruptured Intracranial Aneurysms

Objective: To investigate the use of Tirofiban for stent-assisted coiling (SAC) in patients with acutely ruptured intracranial aneurysms (ARICA). Methods: The clinical data of 46 patients with a total of 47 intracranial aneurysms treated in with SAC within 72 hours after subarachnoid hemorrhage in the Department of Neurointervention, Tiantan Hospital, Capital Medical University from January 2015 to January 2019 were retrospectively analyzed. Since oral antiplatelet agents cannot be taken preoperatively, these patients were intravenously infused with tirofiban hydrochloride (10 μg/kg, completed within 3 min) before stent deployment, followed by 0.10 μg/kg/min via continuous intravenous pumping for 12 hours. Aspirin enteric-coated tablets (300 mg) and clopidogrel tablets (300mg) were given orally or nasal feeding 4 hours before the discontinuation of tirofiban. After 24 hours, aspirin enteric-coated tablets (100 mg) were given on a daily basis, in combination with oral clopidogrel tablets (75mg) for 2 months, and aspirin enteric-coated tablets were administered as a monotherapy for 4 months.Results: The perioperative complications included intraoperative aneurysm re-rupture (n=1), postoperative intracranial hemorrhage (n=1), stent thrombosis (n=2), and upper gastrointestinal bleeding (n=2). No urinary tract or intra-abdominal bleeding was observed. After the surgery, 20 patients underwent lumbar puncture or continued lumbar cerebrospinal fluid drainage and 4 patients received ventricular puncture and drainage. No thrombocytopenia was noted.Conclusions: In patients with ARICA who are not able to receive prompt dual antiplatelet therapy, tirofiban hydrochloride is an effective alternative that can be used during SAC for ARICA.
Automatic Enlarged Lymph Node Detection by Volume Estimation from 3D Abdominal CT Images Based on Speed Up Robust Features and Maximum Intensity Projection

Lymph nodes parts of immune system, play important role in clinical practice, detection is challenging due to low contrast surrounding regions, irregular shapes, variable sizes, and sparsely distributed location. detected the lymph node manually, can be time-consuming, error-prone, and depend on the observer experience. I proposed a fully automatic method for 3D abdominal lymph node detection on CT scans. Detecting lymph node important cause, the main route for metastasis and deciding resection area, so it's important to identify location and structure of lymph node to make suitable surgical procedure. the proposed method consists of candidate lymph node detection procedure, and false positive reduction steps. First Maximum intensity projection (MIP) applied to produce algorithm with low computational time and to eliminate vessels, combination done between the speeded up robust features (SURF) and the blob detection with simplified features for extracting the predicted nodes, adding new calculation parameter to the system which is the volume of the LN, to add extra accuracy for the proposed method, which allow the system from verifying the candidate LN after apply projection between the three axis of the same CT bulk. the method is evaluated for pelvic LN detection on 49 volumes containing 142 LNs, yielding an 85.9%, detection rate with 0.734 false positive per volume, with an average running time of 12–35 seconds per volume.
The lower bound for the volume of a three-dimensional convex polytope

In this paper, we provide a lower bound for the volume of a three-dimensional smooth integral convex polytope having interior lattice points. Our formula has a quite simple form compared with preliminary results. Therefore, we can easily utilize it for other beneficial purposes. Firstly, as an immediate consequence of our lower bound, we obtain a characterization of toric Fano threefold. Besides, we compute the sectional genus of a three-dimensional polarized toric variety, and classify toric Castelnuovo varieties.
Presentation of Fuzzy Model to Compute the Edge Betweenness Centrality in Social Networks

Nowadays, we live in network area. The area through which the formation of various social network, new communicative and informing methods are introduced to the widespread social communications. A social network is a social structure which is made out of individuals and meanwhile, by the pass of time, the analyzing these social network will gain increasing primacy. In this research, one of the parameters of social network analysis called edge betweenness centrality is introduced. Edge betweenness is an edge to compute the shortest paths between pair of nodes in the network that passes through it most frequently. In this research, to detect the communities through edge betweenness centrality algorithm, a method is introduced in such a way that each edge by receiving one fuzzy membership degree in the interval [1, 0] the measure of its effect on the network will be different. One of the features of this algorithm that makes it distinguished from others is the application of fuzzy logic to detect the communities of social network. Then by introducing the density of each cluster the density measure of the communities graph is computed through considering the fuzzy detected structures. The finding of the implementation of algorithm indicated that introduced algorithm to compute the density of samples and to detect the number of mono-nodes while clustering has revealed more accuracy rather than the related works.
ZVT Interleaved High Step-Up Converter For Renewable Energy Systems

In this paper, a novel interleaved high step-up (HSU) converter is presented. The proposed converter features a large voltage gain and common input-output grounding. In this interleaved HSU converter, by using only one zero voltage transition (ZVT) auxiliary circuit with one auxiliary switch and low number of elements, soft switching (SS) performance for all the semiconductors over a wide range of load variations is achieved. This leads to advantages of high efficiency and low complexity, expense, and size. The characteristics of the proposed converter are compared to similar state of the art converters, and to confirm its effectiveness, the simulation results of the proposed converter are presented.
CIM/G-based pre-detection technology of power system graphical human-computer interaction script

Power grid dispatching and control system adopts CIM/G language to describe the human-computer interaction interface. With the development of a new generation of control systems, CIM/G language extension supports human-computer interaction scripts for human-computer interaction requirements of various complex business scenarios. Human-computer interaction scripts bring problems of writing and testing, which affect the development efficiency and picture quality. In this paper, we propose a CIM/G-based pre-detection technology of power system graphical human-computer interaction script, which pre-detects the script in the script writing stage, effectively reduces various problems when the scripts are running, and improves the application development efficiency.
Effective Machine Learning Techniques to Detect Fatty Liver Disease

Heart disease, lung disease, respiratory disease, etc. are currently the top killers. The majority of liver problems are difficult to detect early on. One of these is fatty liver disease, a common disorder brought on by a collection of too much liver's fat. Hepatic steatosis is an additional name for it. Alcoholism causes the cells of the liver to accumulate fat. The liver's ability to function is hampered by this. It could result in cancer of the liver and liver damage. Even if a person does not regularly consume alcohol, they can none the less get fatty liver disease. Blood tests, ultrasounds, and computerized tomography scans are the three main types of diagnostic tests. A more precise and dependable, for the early identification of fatty liver disease, an automated software is needed. To anticipate the disease, particular machine learning models are created for this purpose. To identify fatty liver disease with specificity, accuracy, and dependability, methods of Naive Bayes (NB), Random Forest (RF), and eXtreme Gradient Boosting with ANN are proposed in this study. A total of 70000 cases are included in the collection. This classification system is assessed for precision using a confusion matrix.
Mango Leaf disease Classification using deep learning Hybrid Model

Plant diseases are essential as they result in a severe reduction in the quality and quantity of agricultural products. Therefore, early detection and diagnosis of these diseases are imperative. To this end, we propose a deep learning-based approach that automates classifying mango leaf diseases. Deep learning-based classification methods like support vector machines classify various image databases and give the best performance in image segmentation. Here we proposed a way to extract deep qualities of Images by customizing the SVM and then applying the SVM and SGD (hybrid) method. We use the Basic Harumanis Mango Leaves 2021 Dataset for this research. Experimental results show that the suggested approach gives an accuracy of 97.7%.
Comparison of Single MDD and Series of MDDs in the Representation of Structure Function of Series-Parallel MSS

Series-parallel topology is one of the commonly encountered system topologies of Multi-State Systems (MSSs). We use the structure function which has a form of a Multiple-Valued logic (MVL) function to describe the system. The structure function of complex MSS can be considerably large. Therefore, we use Multi-valued Decision Diagram (MDD) for its representation. A straightforward way to represent the structure function is to use a single MDD. There exists an alternative approach that uses a series of MDDs to represent the structure function. Our present research suggests that the series approach is more efficient in terms of the number of nodes in the MDD when we consider series-parallel systems. The paper deals with a further experimental comparison of the two approaches by examining random initial variable ordering dynamically adjusted using the variable sifting heuristic. Results of the new experiment confirm that given an arbitrary series-parallel topology and arbitrary ordering of variables the approach that uses a series of diagrams is more efficient and never worse considering the number of nodes in the MDD. Furthermore, the new results also show that static ordering of variables by their indices is considerably better than any dynamic ordering in the case of series-parallel systems defined using the process introduced in our previous work.
IoT Based Energy Management Solution for Smart Green Buildings

Energy Management Systems (EMS) provide information on energy usage, especially which device is consuming how much energy for monitoring and control. These EMS can be substantially improved and enhanced through the use of Internet of Things (IoT) based energy monitoring technology to save more energy. This research proposes a real-time IoT based energy management system for smart green buildings. The proposed system contains three main phases, including measuring power consumption, forecasting power consumption, and face recognition. The method of forecasting used in this research is Short-Term Load Forecasting (STLF), based on the K-Nearest Neighbor (KNN) algorithm. There are six variables from Digital Power Meter (DPM) required as reference data to train the prediction methods, including Line Current A, Line Current B, Line Current C, Line Voltage A, Line Voltage B, and Line Voltage C. The forecasted result determines the power consumption of the smart building for the next hours of the same day. The active, reactive, and apparent powers are calculated based on the forecasted result. Face recognition in a smart building can prevent unauthorized persons from entering a certain area of a smart building. The method used in face recognition is based on the Viola-Johns algorithm. The results obtained from the accuracy of the Viola-Johns classifier based on Haar features indicate that the system can perfectly detect and recognize faces with a total accuracy of 90%. The True Negative Rate (TNR), Positive Predictive Value (PPV) and False Discovery Rate (FDR) were found to be 50%, 69.4%, and 30.5%, respectively.
A Low-cost IoT-based Health Monitoring System

For early diagnosis of diseases, vital signs must be regularly checked. However, owing to the high cost, inadequacy, and complexity of monitoring devices, it is difficult for people to keep track of their vital signs at home. As a result, for health monitoring, a cost-effective, widely accessible, and simple-to-use system is required. In this study, the ESP32 micro-controller is used to create such a low-cost (approximately ${\$}$ 15), portable Internet-of-Things (IoT) based health monitoring system to make remote continuous monitoring and diagnostics feasible. Because of the low price, small size, and integrated Wi-Fi and Bluetooth module, the ESP32 is one of the greatest contenders for IoT-based health monitoring systems so far. In the proposed monitoring system, the lightweight Message Queue Telemetry Transport (MQTT) protocol was utilized. The data obtained through the system can be visualized in near real-time in a Graphical User Interface and also can be stored in a database for further analysis. The developed system has been tested and compared with medically certified equipment to validate the efficacy of the system.
A New Combination of Stator and Rotor Poles of Three-Phase Switched Reluctance Motor for Improved Performance

This paper proposes a new 12/20 three-phase switched reluctance motor (SRM). This SRM contains six connected C-cores in the stator and a rotor with 20 poles. Each C-core can be considered as two poles; a pole for passing the magnetic flux from the stator to the air gap and rotor and another pole for returning the magnetic flux from the rotor to the air gap and stator. Each pole is excited using a concentric winding. In order to achieve the highest torque, the suggested topology is optimized using the genetic algorithm (GA). Finally, the suggested SRM is simulated and analyzed. A comparative analysis was conducted between the designed motor and a similarly sized optimized typical SRM based on the finite element method (FEM) simulation results as a means of highlighting their respective characteristics. The suggested motor has been demonstrated to possess superior specifications in comparison to the typical SRM.
RTOSExtracter: Extracting user-defined functions in stripped RTOS-based firmware

In recent years, Real-Time Operating System (RTOS) has been widely used in the Internet of Things (IoT) devices in many fields. Meanwhile, IoT devices running RTOS are facing an increasing number of security vulnerabilities, which are caused mainly by user-defined functions. Therefore, researchers usually need to manually identify and analyze user-defined functions in the firmware to detect vulnerabilities. However, stripped RTOS-based firmware does not contain the debug symbols such as function names. There is no clear boundary between the system and user-defined functions, making it laborious and inefficient to identify user-defined functions from the thousands of functions.In this paper, we design and implement RTOSExtracter, an automated static analysis tool for identifying user-defined functions and their names in stripped RTOS-based firmware, which can be extended to support multiple RTOS types. This tool can disassemble the target firmware, recover the names of the task creation APIs, identify the parameter structure, and generate the parameter values that contain user-defined function addresses and function name addresses. To evaluate RTOSExtracter, we implemented a prototype of RTOSExtracter on IDA Pro with support for five common types of RTOS including FreeRTOS, LiteOS, RT-Thread, μC/OS-II, and μC/OS-III. We compiled 30 open-source projects covering these five RTOS types with 12 different compilers and optimizations and generated 275 firmware without the debug symbols to test RTOSExtracter. The experimental results show that RTOSExtracter identifies user-defined function addresses and function name addresses with high accuracy and low time cost. Furthermore, the case study shows that RTOSExtracter can effectively identify user-defined functions and their names in actual firmware.
HARMONIC COMPONENTS ESTIMATION IN POWER SYSTEM USING BACTERIAL FORAGING OPTIMIZATION ALGORITHM AND STOCHASTIC GRADIENT ALGORITHM WITH VARIABLE FORGETTING FACTOR

In this paper, a hybrid configuration algorithm called stochastic gradient method with variable forgetting factor (SGVFF) is proposed to better estimate unknown parameters in a power system such as amplitude and phase of harmonics using variable forgetting factor following the bacterial foraging optimization algorithm (BFO). It must be mentioned that harmonic estimation is a nonlinear problem and using linear optimization algorithms for solving this problem reduces the convergence speed. Thus, BFO algorithm is used for initial estimation. In this paper, first, using little information and by applying BFO algorithm in an off-line procedure initial value for SGVFF algorithm is achieved and then SGVFF algorithm is gained in an on-line procedure. In the hybrid algorithm applied in this paper, amplitudes and phases are estimated simultaneously. Simulation results indicate that the proposed method has faster convergence speed, better performance and higher accuracy in a noisy system in comparison with recursive least squares variable forgetting factors algorithm (RLSVFF). This proves the superiority of the proposed method.KEYWORDS: Power system harmonic; BFO algorithm; SGVFF method; RLSVFF method