Harmonic Minimization in Multilevel Converter Using an Adaptive Learning Algorithm

There is a wide use of multilevel converters as they can be used with high power and high voltage applications. A multilevel converter consists of large number of voltage levels in load  voltage and load current. A multilevel converter has number of various advantages like good quality of output voltage waveform, smaller values of  inductor and capacitor in passive filters. The output consists of less harmonics. Reduction in total harmonic distortion can be obtained  with the help of multilevel converter. Using learning algorithm like neural network, output voltage is controlled. Error in the reference voltage and output voltage is reduced. Neural network replaces the PI controller completely. A reduction in THD in output voltage and output current can be obtained by neural network by large margin as compared to PI controller. This increases the wide application of AC motor as load as it reduces torque pulsation and RF/EMI effect. It increases the efficiency by reducing power losses.    &nbsp

Harmonic Minimization in Multilevel Converter Using an Adaptive Learning Algorithm

There is a wide use of multilevel converters as they can be used with high power and high voltage applications. A multilevel converter consists of large number of voltage levels in load  voltage and load current. A multilevel converter has number of various advantages like good quality of output voltage waveform, smaller values of  inductor and capacitor in passive filters. The output consists of less harmonics. Reduction in total harmonic distortion can be obtained  with the help of multilevel converter. Using learning algorithm like neural network, output voltage is controlled. Error in the reference voltage and output voltage is reduced. Neural network replaces the PI controller completely. A reduction in THD in output voltage and output current can be obtained by neural network by large margin as compared to PI controller. This increases the wide application of AC motor as load as it reduces torque pulsation and RF/EMI effect. It increases the efficiency by reducing power losses.    &nbsp

Efficient segmentation and classification of the tumor using improved encoder-decoder architecture in brain MRI images

Primary diagnosis of brain tumors is crucial to improve treatment outcomes for patient survival. T1-weighted contrast-enhanced images of Magnetic Resonance Imaging (MRI) provide the most anatomically relevant images. But even with many advancements, day by day in the medical field, assessing tumor shape, size, segmentation, and classification is very difficult as manual segmentation of MRI images with high precision and accuracy is indeed a time-consuming and very challenging task. So newer digital methods like deep learning algorithms are used for tumor diagnosis which may lead to far better results. Deep learning algorithms have significantly upgraded the research in the artificial intelligence field and help in better understanding medical images and their further analysis. The work carried out in this paper presents a fully automatic brain tumor segmentation and classification model with encoder-decoder architecture that is an improvisation of traditional UNet architecture achieved by embedding three variants of ResNet like ResNet 50, ResNet 101, and ResNext 50 with proper hyperparameter tuning. Various data augmentation techniques were used to improve the model performance. The overall performance of the model was tested on a publicly available MRI image dataset containing three common types of tumors. The proposed model performed better in comparison to several other deep learning architectures regarding quality parameters including Dice Similarity Coefficient (DSC) and Mean Intersection over Union (Mean IoU) thereby enhancing the tumor analysis

Mapping Activity Area Localization in Functional MRI Imaging with Deep Learning based Automatic Segmented Brain Tumor for Presurgical Tumor Resection Planning

Functional Magnetic Resonance Imaging (fMRI) determines small blood flow variations that arise due to brain activity. fMRI major study is about functional anatomy which determines the area of the brain controlling vital functions such as hand and foot motor movements for both left and right, speech mantra, and speech word activities. For this instinctive localization of activity areas for specific tasks is very important. This paper appropriately describes the fMRI paradigm timeline with a modified fMRI paradigm timeline due to the hemodynamic response function (HRF).   Efficient activity area localization of thirty-three patients for fMRI data acquired from the hospital is achieved with dynamic thresholding. Dynamic thresholding is also effective in removing excess highlighted areas which helps in the reduction in expert efforts and time required to generate the patient report.  The localize activity area is further mapped with deep learning-based automatic segmented brain tumor regions to find overlapping regions. The exact location of the overlapping region is recovered which helps with preoperative counseling and tumor resection planning. All the results are verified and validated by two expert radiologists from the Hospital

Consumer Co-operation in Demand Side Load Management; an assessment

Power systems today are networked, and intelligent; thanks to the emergence of smart-grid infrastructure. This has opened up new frontiers in energy management paradigm. The transmission, distribution and dispatch systems have become smart and informed, leading to better services. A major concern in power systems operation is demand side load management; an issue affecting quality of service, and profitability simultaneously. This paper discusses a concept demand side load management (DSLM) system, which invokes consumer participation in DSLM, keeping consumer comfort intact. This system looks for change in time of use (TOU) of various appliances in the consumer premises, in agreement with consumer, and aims to attain peak shaving. The system limits the intervention, to peak hour and hence tries to keep consumer preferences least interfered. The performance assessment of the efficacy of system is done through simulation

Estimation of tuberculosis incidence at subnational level using three methods to monitor progress towards ending TB in India, 2015–2020

Objectives We verified subnational (state/union territory (UT)/district) claims of achievements in reducing tuberculosis (TB) incidence in 2020 compared with 2015, in India.Design A community-based survey, analysis of programme data and anti-TB drug sales and utilisation data.Setting National TB Elimination Program and private TB treatment settings in 73 districts that had filed a claim to the Central TB Division of India for progress towards TB-free status.Participants Each district was divided into survey units (SU) and one village/ward was randomly selected from each SU. All household members in the selected village were interviewed. Sputum from participants with a history of anti-TB therapy (ATT), those currently experiencing chest symptoms or on ATT were tested using Xpert/Rif/TrueNat. The survey continued until 30 Mycobacterium tuberculosis cases were identified in a district.Outcome measures We calculated a direct estimate of TB incidence based on incident cases identified in the survey. We calculated an under-reporting factor by matching these cases within the TB notification system. The TB notification adjusted for this factor was the estimate by the indirect method. We also calculated TB incidence from drug sale data in the private sector and drug utilisation data in the public sector. We compared the three estimates of TB incidence in 2020 with TB incidence in 2015.Results The estimated direct incidence ranged from 19 (Purba Medinipur, West Bengal) to 1457 (Jaintia Hills, Meghalaya) per 100 000 population. Indirect estimates of incidence ranged between 19 (Diu, Dadra and Nagar Haveli) and 788 (Dumka, Jharkhand) per 100 000 population. The incidence using drug sale data ranged from 19 per 100 000 population in Diu, Dadra and Nagar Haveli to 651 per 100 000 population in Centenary, Maharashtra.Conclusion TB incidence in 1 state, 2 UTs and 35 districts had declined by at least 20% since 2015. Two districts in India were declared TB free in 2020