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Robust Control of DFIG Based Wind Energy System Using an H? Controller
Wind Energy Conversion System (WECS) using a Doubly Fed Induction Generator (DFIG) is popular due to its control flexibility and higher conversion efficiency, but maintaining the operational stability and optimal efficiency under dynamic wind conditions is still a control challenge. In this paper, a nonlinear mathematical model for a DFIG based WECS was developed from fundamentals and its characteristics near the operating point were studied. A Proportional Integral (PI) controller and a Linear Quadratic Regulator (LQR) controller were designed to control the system and the behavior of the closed-loop system with these controllers was studied. While the designed PI controller failed to ensure stability, the LQR controller was giving stability but an LQR controller is vulnerable to loss of stability under uncertainties due to parameter variations or changes in operating points. A suboptimal H? controller was then synthesized to obtain robust control. The closed-loop system performance of the DFIG system with the proposed controller was found to be stable and superior to PI and LQR controllers in terms of performance. 2021, The Korean Institute of Electrical Engineers. -
Robust Bidirectional Long Short-Term Memory-Based Class Imbalance Handling in Dyslexia Prediction at its Early Stage
Dyslexia is a neurological condition that presents difficulties and obstacles in learning, particularly in reading. Early diagnosis of dyslexia is crucial for children, as it allows the implementation of appropriate resources and specialized software to enhance their skills. However, the evaluation process can be expensive, time-consuming, and emotionally challenging. In recent years, researchers have turned to machine learning and deep learning techniques to detect dyslexia using datasets obtained from educational and healthcare institutions. Despite the existence of several deep learning models for dyslexia prediction, the problem of handling class imbalance significantly impacts the accuracy of detection. Therefore, this study proposes a robust deep learning model based on a variant of long short-term memory (LSTM) to address this issue. The advantage of Bidirectional LSTM, which has the ability to traverse both forward and backward, improves the pattern of understanding very effectively. Still, the problem of assigning values to the hyper-parameters in BLSTM is the toughest challenge which has to be assigned in a random manner. To overcome this difficulty, the proposed model induced a behavioral model known as Red Fox Optimization algorithm (RFO). Based on the inspiration of red fox searching behavior, this proposed work utilized the local and the global search in assigning and fine-tuning the values of hyper-parameters to handle the class imbalance in dyslexia dataset. The performance evaluation is conducted using two different dyslexia datasets (i.e., dyslexia 12_14 & real-time dataset). The simulation results explore that the proposed robust Bidirectional Long Short-Term Memory accomplishes the highest detection rate with reduced error rate compared to other deep learning models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Robs algorithm
Sparse matrix is a matrix having a relatively large proportion (proportion - a ratio is a comparison of two numbers. We generally separate the two numbers in the ratio with a colon (:)) of zero elements. To store the elements of the matrix in computer memory, linear array concept of storing is used. When a sparse matrix is stored in full-matrix storage mode, all its elements, including its zero elements, are stored in an array, which is a wastage of memory. In order to avoid the memory and processing overhead many alternate forms are used. Each one has separate time and space complexities and performances. In this paper we are suggesting one way of representing the sparse matrix which has both time and space complexity O(2n) only, while all other methods work with complexity more than O(3n) where n is the total number of non-zero elements in the matrix .The implementation of this algorithm in applications may improve the performance especially in the area of adjacency matrix, tree representation, 3D representation to an object, network communication, electronics, mathematical calculations, picture storage/file storage, file compression, bioinformatics, and the computer performance. The proposed algorithm has a large scope not only in computing but also in different branches of science, electronics and graphics. 2006 Elsevier Inc. All rights reserved. -
Robotics: challenges and opportunities in healthcare
Today, healthcare services and systems are becoming very complex and include a large number of entities characterized by shared, distributed and heterogeneous devices, sensors, and information and communication technologies. Various artificial intelligence techniques have been implemented in various sectors like smart cities, energy, IT sectors, banking, agriculture, retails, and many more, but it has been always challenging to demonstrate this technique effectively in healthcare sector due to its sophisticated procedure and its handling. Data analytics research on healthcare data has grown significantly over the past 10-12 years, and the execution of data analytics algorithms and systems in healthcare has been progressing more quickly. The data analytics service section has gained considerable attention with the development of technology, especially artificial intelligence robots, in the healthcare sector. Robots can help people with cognitive, sensory, and motor disabilities, help the sick or injured, support caregivers, and assist the clinical workforce. The purpose of this study is to provide historical evolution of robotics in healthcare with an overview of the influence of robots in healthcare like clinical support, patient transfer in hospitals, to handle heavy surgical instruments, to transport medical waste, for drug delivery, patient management etc. Furthermore, this chapter also covered the challenges and opportunities in healthcare and also offers a comprehensive aspect at how robots are incorporate in various healthcare applications. 2025 Elsevier Inc. All rights reserved. -
Robotic dining delight unravelling the key factors driving customer satisfaction in service robot restaurants using PLS-SEM and ML
In the past few years there has been a remarkable surge in demand for robot service restaurants. However, as both the technology and the concept of such restaurants are relatively new, there is a limited understanding of how consumers would react to this new change in the service industry. This study focuses on the key factors influencing customer satisfaction and their intention to repeat the experience by using two staged hybrid PLS-SEM and Machine Learning approaches. The finding confirms that perceived enjoyment, speed, and novelty influence customer satisfaction, whereas perceived usefulness has no influence. Additionally, the study uncovers that customer satisfaction and trust positively mediate the relationship and establish the link with repeat experience. The machine learning models (Artificial Neural Network, Support Vector Machines, Random Forest, K-Nearest Neighbors, Elastic Net) predict the intention to repeat the experience of the service robot with an overall model fit of around 57%. We also discussed several new and useful theoretical and practical implications for enhancing the customer experience during the visit to the restaurants. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Robo-revolution: How automated financial advisors are reshaping global finance
Robo-advisors have the potential to revolutionise the financial service industry by making it more accessible and affordable. This study provides a comprehensive overview of robo-advisors in the arena of financial markets and investments and their gaining popularity in the fintech industry, particularly in emerging markets like India. It also discusses the changing landscape of the financial sector in India, benefits and challenges of fintech, and the legal and ethical implications of roboadvisors. The current study presents a comparative study between India and UK markets in terms of acceptance and penetration of robo-advisors. It highlights leading robo-advisory firms in India. The data visualisation is done with the help of Microsoft Power BI and Microsoft Excel on the statista survey data. The expected results of this study assist several stakeholders, such as academicians, researchers, investors, stock brokers, regulators, and policy makers. 2024, IGI Global. All rights reserved. -
Roadmap on ionic liquid crystal electrolytes for energy storage devices
The current organic liquid electrolytes used in electrochemical energy systems cause rapid performance degradation and even combustion. The advancement of new electrolytes with exceptional safety and electrochemical performance is crucial in addressing these challenges. Recently developed ionic liquid crystals (ILCs) offer promising opportunities for tailoring ion transport channels through modified nano segregated structures, thereby ensuring excellent operating safety and combining the advantageous properties of ionic liquids and liquid crystals. This review focuses on investigating the ion conductive properties and operational mechanisms of ILC electrolytes for energy storage and conversion devices, which play a pivotal role in the development of superior electrolytes. The review critically analyzes the recent development and fundamental properties electrochemical interaction framework of ILC electrolytes applied in energy storage devices. Particular attention is given to elucidating the mechanism of ILC and phase formation, past decade fabrication of energy storage device with ILC electrolytes, emphasizing their capacity for ion redistribution and exceptional stability. Additionally, the review addresses the drawback, limitation, commercialization, challenges and provides future perspective for the growth of ILC electrolytes in the field of energy storage. 2024 Elsevier B.V. -
Roadmap of effects of biowaste-synthesized carbon nanomaterials on carbon nano-reinforced composites
Sustainable growth can be achieved by recycling waste material into useful resources without affecting the natural ecosystem. Among all nanomaterials, carbon nanomaterials from biowaste are used for various applications. The pyrolysis process is one of the eco-friendly ways for synthesizing such carbon nanomaterials. Recently, polymer nanocomposites (PNCs) filled with bio-waste-based carbon nanomaterials attracted a lot of attention due to their enhanced mechanical properties. A variety of polymers, such as thermoplastics, thermosetting polymers, elastomers, and their blends, can be used in the formation of composite materials. This review summarizes the synthesis of carbon nanomaterials, polymer nanocomposites, and mechanical properties of PNCs. The review also focuses on various biowaste-based precursors, their nanoproperties, and turning them into proper composites. PNCs show improved mechanical properties by varying the loading per-centages of carbon nanomaterials, which are vital for many defence-and aerospace-related indus-tries. Different synthesis processes are used to achieve enhanced ultimate tensile strength and mod-ulus. The present review summarizes the last 5 years work in detail on these PNCs and their appli-cations. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Road-Traffic Congestion in Bengaluru : Psychological and Social Consequences
The study investigated the commuting experiences of frequent travelers during congestion using a three-phase sequential exploratory design. Using semi-structured interviews, phase-1 explored the experiences of a sample of ten (4 women and 6 men) regular commuters on Bengaluru's congested roads. Thematic analysis revealed that psychological experiences due to travel adversities during congestion generated negative affect that narrowed thought-action repertoire of the commuters into fight or flight responses. Fight responses caused negative road occurrences that intensified travel adversities further, creating a vicious cycle showing a non-linear loop. Social consequences included challenges for personal time and activities, family time, health and health care activities, work, social, community, and recreational activities, increase of virtual socialization, and social Darwinism. In phase 2, a check-list of psychological consequences was developed based on the thematic analysis. Phase 3 statistically validated the vicious cycle in a sample of 190 (87 women and 103 men) commuters using structural equation modelling. The model substantiated the probability of the vicious cycle. Based on the model, a mathematical model was developed that could be used to test the non-linear relationship between the components of the vicious cycle. -
Road Accident Prediction using Machine Learning Approaches
Road accidents create a significant number of serious injuries reported per year and are a chief concern of the world, mostly in underdeveloped countries. Many people have lost their near and dear ones due to these road accidents. Hence a system that can potentially save lives is required. The system detects essential contributing elements for an accident or creates a link among accidents and various factors for the occurrence of accidents. This research proposes an Accident Prediction system that can help to analyze the potential safety issues and predict whether an accident will occur or not. A comparative study of various Machine Learning Algorithms was conducted to check which model can help predict accidents more accurately. The dataset used for this paper is the government record accidents that occurred in a district in India. Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, XGBoost, and Support Vector Machine are among the Machine Learning models used in this paper to predict accidents. The Random Forest algorithm gave the highest accuracy of 80.78% when the accuracies of the Machine Learning models were compared. 2022 IEEE. -
RNA-seq DE genes on Glioblastoma using non linear SVM and pathway analysis of NOG and ASCL5
Differentially Expressed genes related to Glioblastoma Multiforme as an output of RNASeq studies were further studied to conclude new research insights. Glioma is a type of intracranial tumor (within the skull), which can grow rapidly in its malignant stages. Gene expression in Grade II, III and IV Gliomas is analysed using non linear SVM models. The enriched GO terms were identified GOrilla. Pathways related to NOG and ASCL5 gene were studied using Reactome. 2020, Springer Nature Switzerland AG. -
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Risks and ethics of nanotechnology: an overview
Environmental nanotechnology is thought to be important to current environmental engineering and scientific techniques. The biomedical, textile, aerospace, manufacturing, cosmetics, oil, defense, agricultural, and electronics industries can all benefit from the use of nanotechnology to enhance a wide range of material properties, including physical, chemical, and biological properties. However, nanotechnology-based products or nanomaterials (e.g., nanofibers, nanowires, nanocomposites, and nanofilms) may be harmful to human health. Since nanomaterials are usually manufactured using novel manufacturing techniques and have a variety of sizes, shapes, and surface energies, there can also be uncertainties in their manufacture and handling. This chapter provides a detailed account of ethical issues related to nanotechnology, particularly environmental toxicity, risk management, health risk evolution, and environmental significance of nanomaterials. In addition, environmental challenges, toxic effect of nanoparticles on the environment, ethics of nanotechnology, and social, ecological, biological, and other legal issues are highlighted. The potential of nanomaterials in environmental remediation and their use in environmental protection is also emphasized. 2023 Elsevier Inc. All rights reserved. -
Risk management of technological accidents triggered by natural-hazards (Natech): A review of relevant indian legislation
The ill-effects of technological sites on the environment have been researched substantially across the world with particular reference to pollution. However, the threats posed by the environment to technological sites have rarely been studied. Such events, where natural forces trigger technological accidents, are called Natech accidents. It has been observed that developed countries are aware of this emerging hazard and they have responded to it by creating various legislative frameworks for managing Natech risk. In contrast, in developing countries, it has not yet received due attention. The present study has been done to understand the Indian perspective of the legal framework for Natech risk reduction. The study revealed that India has an elaborate legislative framework for disaster risk identification and management. Though there is attention to routine disaster risk, the risk from natural forces to the technological sites is rarely considered. Apart from recognizing natural forces as a threat, no specific legislation is available for Natech risk reduction. A developing country like India must manage the risk posed by natural forces to its technological infrastructure. There is a need for specific legislation to manage Natech risk, which will be an initiating force for the state of the art Natech risk management. 2021, World Research Association. All rights reserved. -
Risk management of future of Defi using artificial intelligence as a tool
This chapter explores AI's pivotal roles in managing risks within DeFi, emphasizing strategic implementation to enhance risk assessment, management, and decisionmaking processes for a better user experience. The convergence of AI and DeFi presents unprecedented opportunities, fostering transparency and decentralization. Drawing from diverse sources, the study evaluates AI's effectiveness, particularly in machine learning, in addressing emerging risks. It focuses on how AI can guide DeFi's future while managing market and credit risks through tasks like data preparation, modeling, stress testing, and validation. Additionally, AI aids in data quality assurance, text mining, and fraud detection. Emphasis is placed on identifying and managing risks that could hinder DeFi's future, highlighting key AI techniques. Given the financial industry's ongoing transformation, these insights are increasingly vital. 2024, IGI Global. All rights reserved. -
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Risk Factor Based Stage Advancement Prediction of Cataract Using Deep Learning Techniques
In modern world, Cataract is the predominant causative of blindness. Treatment and detection at the early stage can reduce the number of cataract sufferers and prevent surgery. Two types of images are generally used for cataract related studies- Retinal Images an Slit lamp Images. The quality of Retinal images is selected by utilizing the hybrid naturalness image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is and Deep newlinelearning convolutional neural network (DCNN) categorizes the images based on quality newlinescore. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid GMHE-HF) is utilized for enhanced noise filtering. The Slit lamp image quality selection is done using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) model. Further a new algorithm Normalization based Contrast limited adaptive histogram equalization (NCLAHE) is used for image enhancement. Images are pre-processed utilizing the wiener filtering (WF) with Convolutional neural network (CNN) with adaptive atom search optimization (CNN-AASO) for removing the noise. Further, the denoised image is enhanced by Gaussian mixture based contrast enhancement (GMCE) for contrast enhancement. The cataract detection and classification is performed using two phases. In phase I, the cataract is detected using Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model. Phase II uses slit lamp images and detects the type and grade of cataracts through proposed Batch Equivalence ResNet-101 (BE_ResNet101) model.This work also proposes the risk factors for cataracts and classify the cataracts risk using deep learning models. The dataset is pre-processed by missing values and the string values are converted into numeric values. -
Risk Behavior Among Emerging Adults: The Role of Adverse Childhood Experiences (ACE), Perceived Family and Interpersonal Environment
Background: Evidence demonstrates that ambiance provided during childhood and the interactions of children with different social agents during childhood have an impact on their adult behaviour. Objective: The current research tries to explore the role of adverse childhood experiences and perceived family and interpersonal interactions in their resultant adult risk behaviour. Method: Around 613 emerging adults (1824 years; Male 343 and Female 270) from the northern districts of Kerala, India took part in the study. The participants were selected using multistage sampling techniques. A Semi-structured Questionnaire was used to understand the perceived family and interpersonal environment. In addition, a checklist (adopted from the risk behaviour scale and youth risk behaviour survey) was also employed. The checklist assisted to understand the presence of actual risk behaviours. Results: Hierarchical Logistic Regression analysis is used to test the hypotheses. The results revealed that 87.2 % of the participants were engaged in at least one type of risk behaviour. Socio-demographic variables (gender and family type) and items of perceived family and interpersonal relationships and adverse childhood experiences were found to be significant predictors of emerging adult risk behaviour. Conclusion: The results further highlight the significance of childhood experiences and the current family environment of emerging adults in understanding their behaviour, and in designing evidence-based intervention program for emerging adults. 2023 The Author(s). -
Risk Assessment Model for Quality Management System
The ecological and economic risk assessment system and its cost were also factored into the document. The distribution of workplace challenges and hazards, represented by quantitative or subjective occupational risk metrics, was typical in the areas of building safety and environmentally responsible workers. Environmental risk assessment refers to the identification & evaluation of risks, the formulation & application of managerial decisions to lessen the chance of unfortunate conditions, and also the substantial decrease of materials or other damages. Risk assessment facilitates the transition from an area of uncertainty to one where outcomes are more or less expected. The Deming-Shewhart cycle, which would be fully linked to the policy process and performance measurement system, appears to be the implementation technique of the ecological and economic structure under consideration. It would be a cyclical sequence of the associated effective measures. A high degree of adaptability to any internally or externally stressful conditions would be ensured by the synthesis of the fundamentals of the management system & mechanisms for controlling environmental potential costs. This also guarantees the rapid identification of expert hazards, optimization and efficiency gains. 2022 IEEE. -
