Design of a Controller for Simultaneous Control of Multiple Systems in Wireless Scenario

Wireless technology is becoming an ever-emerging part of human life with new services and products being released every month. Thus wireless communications brings huge benefits to the user or users. The used Radio Frequency (RF) Module is basically an Advanced Virtual RISC (AVR) microcontroller based communication system. The RF Module used in our project contains two units transmitter and receiver. The transmitter module converts parallel data into serial by using HT12E encoder prior to wireless transmission. The encoded data get received by receiver and converts or decodes the serial data into parallel by using HT12D decoder. After converting the data into parallel form which is made use by AVR16A micro controller to generate instructions for operation of relays connected to two different bulbs

Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation

AI Tool is a large language model (LLM) designed to generate human-like responses in natural language conversations. It is trained on a massive corpus of text from the internet, which allows it to leverage a broad understanding of language, general knowledge, and various domains. AI Tool can provide information, engage in conversations, assist with tasks, and even offer creative suggestions. The underlying technology behind AI Tool is a transformer neural network. Transformers excel at capturing long-range dependencies in text, making them well-suited for language-related tasks. AI Tool has 175 billion parameters, making it one of the largest and most powerful LLMs to date. This work presents an overview of AI Tool's responses on various sectors of industry. Further, the responses of AI Tool have been cross-verified with human experts in the corresponding fields. To validate the performance of AI Tool, a few explicit parameters have been considered and the evaluation has been done. This study will help the research community and other users to understand the uses of AI Tool and its interaction pattern. The results of this study show that AI Tool is able to generate human-like responses that are both informative and engaging. However, it is important to note that AI Tool can occasionally produce incorrect or nonsensical answers. It is therefore important to critically evaluate the information that AI Tool provides and to verify it from reliable sources when necessary. Overall, this study suggests that AI Tool is a promising new tool for natural language processing, and that it has the potential to be used in a wide variety of applications

Performance Enhancement of Active Power Filter using Robust Control Strategies

The prime focus of this thesis is to report control strategies to improve the performance of single phase shunt Active Power Filter (APF). Basically, Sliding Mode (SM) control strategy and Feedback Linearization based control strategy have been applied considering their ease of implementation and robustness under external disturbances. An low cost analog SM controller is presented to reduce the steady state current error. In this method a band pass filter is used for calculating the reference source current which makes source current Total Harmonic Distortion (THD) independent of source voltage THD. Multisim based simulation method and results are presented to report the method of low cost analog implementation. To overcome the drawbacks caused by varying switching frequency, a fixed switching frequency SM controller is presented, in which Artificial Neural Network (ANN) is used to generate the reference source current. In this control strategy, a proper combination of fixed frequency sliding mode current control, ANN based fundamental source current extraction circuit and unipolar PWM increases the dynamic response of APF system and makes it adaptive under variable load and source conditions. As feedback linearization based controller improves the performance of the power electronic systems by analysing stability of the complete system, a straight forward Partial Feedback Linearization (PFL) based control strategy is presented to reduce the source current THD of single phase shunt APF. The nonlinear system dynamics of the APF has been partially feedback linearized using its average dynamic model. New control input to the linearized system is obtained considering the stability of the complete APF system. After that, control input to APF is derived by nonlinear transformation. Stability of the internal dynamics of the system is analysed considering zero dynamics of the system. A prototype of the APF system is built and the proposed controller is implemented using dSPACE 1104. Both experimental and simulation results of the PFL based control strategy are compared with exact feedback linearization of APF via SM control for validation of performance improvement. Finally the application of PFL based control strategy is extended to three phase APF by considering it as Multiple Input Multiple Output (MIMO) system and MATLAB/Simulink based simulation results are presented to validate the theory

Performance Enhancement of Active Power Filter using Robust Control Strategies

The prime focus of this thesis is to report control strategies to improve the performance of single phase shunt Active Power Filter (APF). Basically, Sliding Mode (SM) control strategy and Feedback Linearization based control strategy have been applied considering their ease of implementation and robustness under external disturbances. An low cost analog SM controller is presented to reduce the steady state current error. In this method a band pass filter is used for calculating the reference source current which makes source current Total Harmonic Distortion (THD) independent of source voltage THD. Multisim based simulation method and results are presented to report the method of low cost analog implementation. To overcome the drawbacks caused by varying switching frequency, a fixed switching frequency SM controller is presented, in which Artificial Neural Network (ANN) is used to generate the reference source current. In this control strategy, a proper combination of fixed frequency sliding mode current control, ANN based fundamental source current extraction circuit and unipolar PWM increases the dynamic response of APF system and makes it adaptive under variable load and source conditions. As feedback linearization based controller improves the performance of the power electronic systems by analysing stability of the complete system, a straight forward Partial Feedback Linearization (PFL) based control strategy is presented to reduce the source current THD of single phase shunt APF. The nonlinear system dynamics of the APF has been partially feedback linearized using its average dynamic model. New control input to the linearized system is obtained considering the stability of the complete APF system. After that, control input to APF is derived by nonlinear transformation. Stability of the internal dynamics of the system is analysed considering zero dynamics of the system. A prototype of the APF system is built and the proposed controller is implemented using dSPACE 1104. Both experimental and simulation results of the PFL based control strategy are compared with exact feedback linearization of APF via SM control for validation of performance improvement. Finally the application of PFL based control strategy is extended to three phase APF by considering it as Multiple Input Multiple Output (MIMO) system and MATLAB/Simulink based simulation results are presented to validate the theory

Robust Short-term Operation of AC Power Network with Injection Uncertainties

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution is essential for ACOPF with short-term injection uncertainties. Usually, applying Robust Optimization (RO) for conventional non-linear ACOPF results in computationally intractable Robust Counterpart (RC), which is undesirable as ACOPF is an operational problem. Hence, this paper proposes a single-stage non-integer non-recursive RC of ACOPF, using a dual transformation, for short-term injection uncertainties. The proposed RC is convex, tractable, and provides base-point active power generations and terminal voltage magnitudes (setpoints) of conventional generators that satisfy all constraints for all realizations of defined injection uncertainties. The non-linear impact of uncertainties on other variables is inherently modeled without using any affine policy. The proposed approach also includes the budget of uncertainty constraints for low conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based participation factored AC power flows validate the robustness of the obtained setpoints on NESTA and case9241pegase systems for different injection uncertainties. Comparison with previous approaches indicates the efficacy of the proposed approach in terms of low operational cost and computation time.Comment: 16 pages, 5 figures, 5 table

QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach

As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum QoS estimates that fulfilling a customer’s expectations becomes a complicated and demanding task. Several different metaheuristics are presented as potential solutions to this problem. However, most of them are unable to strike a healthy balance between exploring new territory and capitalizing on existing resources. A novel approach is suggested to balance exploration and exploitation via the use of Genetic Algorithms (GA) and the Eagle Strategy algorithm. Cloud computing provides clients with capabilities that are enabled by information technology by using services that are available on demand. To circumvent difficulties such as a delayed convergence rate or an early convergence, this technique allows for the establishment of a healthy equilibrium between exploratory and exploitative activities. The result of the experiment shows that the Eagle Strategy algorithm (ESA) and GA are better than other conventional algorithms at making a globally QoS-based Cloud Service Selection System faster

QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach

As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum QoS estimates that fulfilling a customer’s expectations becomes a complicated and demanding task. Several different metaheuristics are presented as potential solutions to this problem. However, most of them are unable to strike a healthy balance between exploring new territory and capitalizing on existing resources. A novel approach is suggested to balance exploration and exploitation via the use of Genetic Algorithms (GA) and the Eagle Strategy algorithm. Cloud computing provides clients with capabilities that are enabled by information technology by using services that are available on demand. To circumvent difficulties such as a delayed convergence rate or an early convergence, this technique allows for the establishment of a healthy equilibrium between exploratory and exploitative activities. The result of the experiment shows that the Eagle Strategy algorithm (ESA) and GA are better than other conventional algorithms at making a globally QoS-based Cloud Service Selection System faster

Dengue fever coinfection in COVID‐19 era: A public health concern

Abstract Background and Aim Dengue and SARS‐CoV‐2 coinfection is commonly encountered and constantly reported in particularly the dengue‐endemic regions thus posing a co‐epidemic threat. Coinfection is also significantly associated with morbidity and mortality. Comorbidity risk during a coinfection is of a greater concern. Although the pathophysiologies of the two infections vary, their identical clinical symptoms during coinfection result in diagnostic and therapeutic complexities. Methods A literature search for the current relevant reports was carried out. The searched databases were Scopus, PubMed, Google Scholar and the Web of Science, with health agencies like the WHO. Based on the selection criteria, the most recent and pertinent reports published in English language were included for the ease of understanding, deciphering and analysing the secondary data. Results A delay in proper diagnosis of coinfection could result in serious complications with poor patient outcome. Whether it is a standalone dengue or COVID‐19 infection or a coinfection, specific biomarkers may be utilized for its foolproof diagnosis. This article highlights the various diagnostic techniques and immune responses from the perspective of prompt and appropriate public health management for patients suffering from COVID‐19 and dengue viral coinfections, both being independently or collectively capable of damaging a human body. Conclusion As coinfection poses significantly large burden on an already‐fragile healthcare facility, constant monitoring of a coinfected patient is needed for prompt and suitable therapeutics. Also, to maintain high vigilance and invoke appropriate preventive measures particularly in dengue endemic regions, the government, healthcare authority and the general public need to collaborate and cooperate