No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Enhanced solar systems efficiency and reduce energy waste by using IoT devices
... The optimal functioning of the system can be ensured with the help of real-time monitor values and analysis of the triggered values made from the micro-controller system in the IOT platform. The IoT-based web interface model can transmit the control signal and operation back to the solar panels and make the system automated for triggering the solar angle to increase the system efficiency [7]. An IoT system was developed based on the long-range wide area network for monitoring the air quality of an outdoor environment. ...
In this work, an indirect type solar dryer was developed using an Evacuated tube-based compound parabolic concentrator incorporated with PCM based latent heat thermal energy storage system. Without a tracking system, the absorber can collect direct and diffused ration by concentrating the solar radiation by assembling the two parabolas with compound rotation. The charging loop was built to maintain the proper heat collection from the solar collector. The heat transfer fluid pumped into the solar collector gains the heat by convection heat transfer mode. The heat transfer fluid flow inside the shell and tube Latent heat thermal energy storage system from the top transfer into the punch of tubes assembled parallel. Paraffin wax, due to its higher melting temperature and the latent heat of fusion, increases with an elongated hydrogen and carbon chain and is also a cost-effective, reliable, non-reactive material with self-nucleating properties. By implementing tubular encapsulation, direct contact between the PCM and the HTF is prevented, minimizing chemical reactions and enhancing PCM handling. Integrating a Thermal energy storage system into the solar dryer eliminates the fluctuation of solar radiation during non-sun shine hours, which helps prevent microbial growth. A power-full hard drive was developed especially for the energy-efficient IoT application. It has many features like low power, being a cost-effective platform, being integrated into Wi-Fi, containing a dual-core processor and having many input/output pins called GPIO (General Purpose Input/Output) to connect the sensor and control units. This system presented in this work assists in monitoring the system and controlling various vital parameters. This system would help small-scale farmers dry grains and monitor the system at a relatively cheaper cost.
Recently, the image reconstruction study on EIT plays a vital role in the medical application field for validation and calibration purpose. This research article analyzes the different types of reconstruction algorithms of EIT in medical imaging applications. Besides, it reviews many methods involved in constructing the electrical impedance tomography. The spatial distribution and resolution with different sensitivity has been discussed here. The electrode arrangement of various methods involved in the EIT system is discussed here. This research article comprises of adjacent drive method, cross method, and alternative opposite current direction method based on the voltage driven pattern. The assessment process of biomedical EIT has been discussed and investigated through the impedance imaging of the existent substances. The locality of the electrodes can be calculated and fixed for appropriate methods. More specifically, this research article discusses about the EIT image reconstruction methods and the significance of the alternative opposite current direction approach in the biomedical system. The change in conductivity test is further investigated based on the injection of current flow in the system. It has been established by the use of Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EDITORS) software, which is open-source software.
Low-level and medium-level leg injury patients can be operated wheelchair independently in the clinical region. The construction of an electric wheelchair is one of the solutions to operate electric wheelchair by the patients themselves. The motor is an essential part of an electrical wheelchair for driving from one place to another place. The response of the system is very important for the optimization of the system. The existing methods fail in gradual sensitivity during motion and lack of response time to the user. This article is consist of a design for optimizing the existing DC motor transfer function for the smart wheelchair. The perfect angular tuning of the derivative controller provides a better executing time for our proposed model. The smoother responses from the smart wheelchair are obtaining by the dynamic response of closed-loop control. The design of DC motors is to drive smart wheelchair as per the needs. Besides, the construction of a mathematical model for proposing a system involves the DC motor drive to the smart wheelchair arrangement. The proposed model gives independent mobility of smart wheelchair with less response time and better sensitivity. Here, the mathematical model provides the details of immediate response to the user and less execution time of the complex system process. Finally, the authors discussed future improvements to the current DC motor design in the proposed system.
Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall Journal of ISMAC (2021) high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.
Industry 4.0 promises reductions in maintenance costs through access to digital technologies such as the Internet of Things, cloud computing and data analytics. Many of the promised benefits to maintenance are, however, dependent on the quality of the data obtained through sensors and related technologies. In this work, we consider the effect of access to different levels of deterioration data quality, resulting in partial information about the underlying state of the system being monitored, by means of sensors, on condition-based maintenance policies. The sensors may be either internal company sensors, or more informative external sensors of which access is obtained at a cost. We analyze the structure of the optimal policy, where the actions are either to perform maintenance, to pay for external sensor information or to continue system operation with internal sensor information only. We show that the optimal policy consists of at most four regions based on the believed deterioration state of the system. The analysis allows us to numerically investigate the decision maker’s willingness to pay for more informative external sensor information with respect to the level of external sensor informativeness, when compared to that of the internal sensor, and the cost thereof.
Ubiquitous Networks powered by Wireless Sensor Networks (WSN) is cutting across many technologies assisting day-to-day human activities. This technology confers the ability to sense and surmise the external environmental factors of various ecologies. Interconnection of these sensing devices for Machine to Machine (M2M) communication leads to the origination of Internet-of-Things (IoT). Recent advancements in the technology of Internet-of-Things guides the production of smart objects that can accomplish location, identification, connection and measurement of external factors. This leads to a new type of communication paradigm between objects and humans. One of the important problem due to the population explosion that can be addressed by IoT is the Healthcare of individual human beings. Remote health monitoring is one of the greatest technology exploited in medical professionals to keep a check on the patient’s important health factors periodically. This was done in a smaller geographical area before the era of IoT. As IoT can communicate to other Internet, This remote healthcare monitoring can now be applied over a wider geographical topology. This paper extensively analyses the performance of 6LoWPAN and RPL IoT for healthcare applications. This paper especially focuses on monitoring an athlete's thermoregulation. Also, a new technique to identify and train marathon athletes to the race topography has been proposed. In this technique, each athlete is fitted with wearable sensors in their body in the training session to monitor and analyze the thermoregulation process occurring during training. After a detailed analysis of the athletes’ thermoregulation process, personal training schedules are charted down according to variation in the thermoregulation process in each athlete. This technique helps to monitor each athlete’s progress personally with less number of coaches and medical professionals leading to the prevention of unexpected death of a healthy athlete.
Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.
This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.
Fault detection in the transmission is a challenging task when examining the accuracy of the system. This fault can be caused by a man-made force or by using concurrent overvoltage in the power distribution line. This research focuses on two sections to handle the power transmission line problem and can be rectified as previously stated. An intelligent approach is utilized for monitoring and controlling line faults in order to improve the accuracy of the equipment in transmission line fault detection. After several iterations of the procedure, the combination of line and master unit improves the system's accuracy and reliability. The master unit identifies faulty poles in the network based on the variation of current and voltage of each node and calculates the distance between the station and the faulty node to reduce manual effort. In the proposed work, many sensors are used to detect the line fault in a network by placing the appropriate point. The pure information can be transferred to an authorized person or unit after many iterations due to knowledgeable devices. The faulty status of the pole information is displayed in the control unit by a display unit comprised of an alarm unit to alert the corresponding section using ZigBee techniques. The GSM unit provides the faulty status of an authorized person to rectify the problems immediately which further improve the reliability of the system. When compared to existing methods, our hybrid proposed method achieves a higher accuracy of 90%. This method aids to reduce the labor costs gradually to visit all-pole points instead of faulty pole points and thereby increasing the reliability of the electrical consumers.
This article focuses on evaluation of in-service power transformerphysical health condition using Health Index (HI) approach. The Health Index (HI) approach is applied by incorporating three key stages such as input for health index assessment, health index estimation and output health index for maintenance decision process of each transformer unit. The first stage is based on condition index, and importance index assessment and also through applying scoring and weight scheme for each test parameter. The condition index of a specific power transformer is evaluated by twelve different diagnostic tests and importance index is assessed from five different factors such as age, loading history, maintenance records, failure/faults, and its location, etc. Numerical weighting and scoring factors were assigned for every test/factor to determine the actual condition of the power transformers with regard to condition and importance aspects. In second stage, By combining the condition and importance index assessment evaluation, the numerical value called Health Index (HI) was estimated, which represent the overall health of a power transformer asset. In third stage, health index estimation results were used to plan for effective maintenance tasks. Through this approach, a case study was performed for 21 in-service power transformers belonging to Tamil Nadu electric utility. The HI results of 21 transformer units were ranked and classified into poor/failed, fair and good, which were further facilitated for Inspection, Repair, and Replacement (IRR) maintenance decisions. Thus, the study is very useful to utility maintenance engineers for better understanding the transformer physical health condition and required maintenance actions timely, which prevents unexpected failure and also reduce cost of maintenance in electric utilities.
For a clean energy source, renewable energy plays a vital role. Biomass, wind energy and solar energy used to power up the cities and large scale industries. It shows there is a gap between the generation of power and usage. Solar panel based energy generation system is familiar in many countries over a year. The adjustment of photo voltaic cell to the inclination angle of the sun is a major crisis. The sun sets an angular movement throughout the day. From dawn to set, we calculate the position of the sun. So that automatic panel adjustment can be made to increase the efficiency of the panel on generations. Another factor to note is the efficiency of the panel depends on the deposition of dust particles. The performance of the panel can improve with automatic cleaning. Internet of things devices that can sense the dust deposition on the panel and it is cleans them in a semi- automated system. With the electrostatic precipitator methodology the solar cells are isolated. The power deployment found in many countries and globally enormous demand for energy arise in this era. With the smart meter installed wecan control the energy lost and miss handled. This paper describes about positioning of the panel and removal of dust particles and a smarter way to identify the energy lost.
In the present-day competitive environment, industries are facing with a new crisis of shrinking profit margins. Organizations’/companies cannot ill afford quality, safety, poor environment and productivity issues. There is thus the requirement of an integrated approach towards management of maintenance. The aim is to present a framework for a programme for an effective continuous improvement of issues related to maintenance. Maintenance undoubtedly plays a key role in an organization’s long-term profitability. In this article, there is a proposal for an integrated maintenance management. The suggested proposal is based on maintenance management, maintenance operation and equipment management (predictive maintenance, preventive maintenance, total productive maintenance). This article explores the benefits of integrated maintenance management compared with the traditional maintenance approach and discusses some of the latest tools in this area.
The objective of this work is to design a sensor-fault detection and diagnosis system for the Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this objective within the limited computation, memory, and energy resources of the sensors. More importantly, the detection of faults is time-sensitive, whereas the diagnosis does not necessarily need to be fast. We propose a distributed sensor-fault detection and diagnosis system based on machine learning algorithms where the fault detection block is implemented in the sensor in order to achieve output immediately after data collection. This block consists of an auto-encoder to transform the input signal into a lower-dimensional feature vector, which is then provided to a Support Vector Machine (SVM) for classification as normal or faulty. Once detected, fault diagnosis is performed at a central node, such as a network server, to reduce the computational load on the sensor. In this work, a Fuzzy Deep Neural Network (FDNN) is used for diagnosis to provide further information, such as the type of fault. Here, the input propagates through a deep neural network and a fuzzy representation process. The output of these two components is then fused through densely connected layers. This multi-modal technique learns high-level representations in the data that are missed by conventional methods. To assess the performance of the proposed model, we utilize data obtained from a healthy temperature-to-voltage converter that are then injected with five types of fault: drift, bias, precision degradation, spike, and stuck faults. The performance from fault detection is analyzed in terms of detection accuracy, area under the ROC curve (AUC-ROC), false positive rate, and F1 score. Furthermore, the efficiency of fault diagnosis is shown by the classification accuracy parameter. The experimental results show the efficiency of the proposed fuzzy learning-based model over classic neuro-fuzzy and non-fuzzy learning approaches.
The demand of highway lighting system is ubiquitous but its operation contributes to extensive financial cost and concerning environmental implications. For this reason, recent researches have investigated possible solutions to boost the efficiency of the existing lighting system. However, the ultimate guide for green energy enabled smart highway lighting system is still lacking in terms of quality and comprehensiveness. The purpose of this paper is to discuss divergent proceedings in the literature to establish procedures of designing and developing energy efficient green highway lighting system, taking into account performance and environmental impact perspectives. A complete taxonomy is presented to identify and organize the literature into several categories, including fundamental design principles with their advantages, disadvantages and research challenges. This paper also intends to give a possible framework to the readers to bridge the gaps among the existing studies. These findings are anticipated to inform researchers and policymaker on perceiving the benefits of the ameliorated energy efficiency in the highway lighting set-up. Furthermore, open issues identified in this paper will pave the way on achieving future highway lighting systems that are not only facilitating safe and seamless driving experience, but also energy-efficient for environmental sustainability.
Analysis of Soil Nutrients based on Potential Productivity Tests with Balanced Minerals for Maize-Chickpea Crop
- Koresh