Showing 28 results for Detection
Das P. Chennamsetty, Sravana K. Bali,
Volume 19, Issue 2 (6-2023)
Abstract
Symmetrical nature of mean of electrical signals during normal operating conditions is used in the fault detection task for dependable, robust, and simple fault detector implementation is presented in this work. Every fourth cycle of the instantaneous current signal, the mean is computed and carried into the next cycle to discover nonlinearities in the signal. A fault detection task is completed using a comparison of two sub cycle means, and the same concept is extended to faulty phase classification. Under various fault and system operating situations, the suggested technique is assessed for regular faults, remote end faults, high resistive faults, and high impedance arcing faults. This paper's extensive case studies illustrate the suggested scheme's simplicity, computational flexibility, speed, and reliability. The suggested approach yields 100% consistent results in 4-8 msec detection time.
S. Prasad Tiwari,
Volume 19, Issue 3 (9-2023)
Abstract
In spite of the numerous benefits over the traditional power distribution system, protection of the microgrid is a challenging and complex task. The varying fault resistances due to dissimilar grounding conditions can affect the performance of the protection scheme. Under such conditions, the magnitude of the fault current can vary from lower to higher level. In addition to the above, the dissimilar magnitude of fault current during grid connected and islanded mode demands a protection scheme that can easily discriminate the mode of operation. The magnitude of fault current in grid-connected and islanded modes needs a robust protection scheme. In this regard, an ensemble of subspace kNN based robust protection scheme has been proposed to detect the faulty conditions of the microgrid. The tasks of the mode detection, fault detection/classification as well as faulty line identification has been carried out in the proposed work. In the proposed protection scheme, discrete wavelet transform (DWT) has been used for processing of the data. After recording the voltage and current signals at bus-1, the protection scheme has been validated. The validation of the protection scheme in Section 6 reveals that the protection scheme is efficiently working.
Mohamed Hussien Moharam, Aya W. Wafik,
Volume 20, Issue 0 (12-2024)
Abstract
High peak-to-average power ratio (PAPR) has been a major drawback of Filter bank Multicarrier (FBMC) in the 5G system. This research aims to calculate the PAPR reduction associated with the FBMC system. This research uses four techniques to reduce PAPR. They are classical tone reservation (TR). It combines tone reservation with sliding window (SW-TR). It also combines them with active constellation extension (TRACE) and with deep learning (TR-Net). TR-net decreases the greatest PAPR reduction by around 8.6 dB compared to the original value.
Shankarshan Prasad Tiwari,
Volume 20, Issue 1 (3-2024)
Abstract
In recent years, due to the widespread applications of DC power-based appliances, the researchers attention to the adoption of DC microgrids are continuously increasing. Nevertheless, protection of the DC microgrid is still a major challenge due to a number of protection issues, such as pole-to-ground and pole-to-pole faults, absence of a zero crossing signal, magnitude of the fault current during grid-connected and islanded mode, bidirectional behaviour of converters, and failure of the converters due to enormous electrical stress in the converter switches which are integrated in the microgrid. Failure of the converter switches can interrupt the charging of the electrical vehicles in the charging stations which can affect transportation facilities. In addition to the above mentioned issues protection of the DC microgrid is more challenging when fault parameters are varying due to dissimilar grounding conditions and varying operational dynamics of the renewable sources of energy. Motivated by the above challenges a support vector machine and ensemble of k-nearest neighbor based protection scheme has been proposed in this paper to accurately detect and classify faults under both of the modes of operation. Results in the section 5 indicate that performance of the protection scheme is greater as compared to other algorithms.
Pampa Debnath, Diptadip Barai, Rajorshi Mandal, Ayeshee Sinha, Jeet Saha, Arpan Deyasi,
Volume 20, Issue 2 (6-2024)
Abstract
A novel architecture is proposed in the present paper for detection and monitoring of air pollution at real-time condition following industrial standard, embedded with gas sensors which are able to identify both organic as well as inorganic hazardous contents. A vis-à-vis comparative analysis is carried out with existing literature highlighting cons of most referred circuits, both in component, system and power consumption levels, and a generalized drawback is reported citing their inefficacy for real-time data collection and accuracy level. Detailed review is reported based on qualitative assessments also, and henceforth, justifies the significance of the proposed design; where not only higher ranges of detection are possible, however is also associated with lower power consumption (26.41% and 10.71% respectively compared to the two latest circuits) and finer detection of dust particles even at extremely low concentration. The architecture will help to implicate precautionary steps at real-time condition for controlling the harmful effect in Society.
Raheel Jawad, Rawaa Jawad,
Volume 20, Issue 3 (9-2024)
Abstract
Fire accidents are a disaster that can cause loss of life, property damage and permanent disability to the affected victim. Firefighting is a very important and dangerous job. Firefighters must extinguish the fire quickly and safely to prevent further damage and destruction. Detecting and extinguishing fires is a dangerous task that always puts the lives of firefighters at risk. One of the most effective tools for early fire extinguishing is the firefighting robot. Fire sensing in most industries is absolutely essential to prevent catastrophic losses. Robots with this type of embedded system can save the lives of engineers in industrial sites with hazardous conditions. This project aims to design and implement a solar-powered with artificial intelligent of mobile fire detection robot to detect fires in disaster-prone areas and thus reduce human work effort and level of destruction. Design a robot capable of moving using a rotary motor, finding a flame using a flame sensor, and extinguishing a fire using a water spray using a pump, all of which is controlled by an Arduino Uno microcontroller and programmed using an artificial intelligence (fuzzy) logic technology) using MATLAB, the inputs It has two variations:: flame and gas with three organic functions, each of which has a gas variable (low, medium, high), flame sensor (small, normal, large), and the output is a pump, (pump off , pump on ) with 9 rules. In addition to the experimental setup of the proposed system which demonstrates the performance of sensors (gas, flame) using fuzzy and implemented logic tools. The performance of the solar panels was first tested using MATLAB software as well as experimentally under different weather conditions. The pump's performance is being tested experimentally, and the robot is also being tested to detect and extinguish fires. The process of designing and implementing robotics involves creating mechanical and electrical systems. The results showed the effect of temperature change on the solar panel, as when it increases, the panel’s production capacity decreases, as well as the effect of decreased solar radiation resulting from clouds and other things, and the extent of its effect. Impact on the performance efficiency of solar panels, and observing the pump performance in terms of flow rate and height. Hence, it can be noted that the robot designed in the project is capable of discovering fire sources and extinguishing them using fire-fighting systems equipped with a water tank and a controllable pump to spray the water necessary for the process. From this study, can be concluded that the designed model is able to work according to its initial design with artificial intelligence with the least amount of errors, and therefore it can be applied in industrial applications, avoiding fire damage and extinguishing it when it occurs for the first time.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi,
Volume 20, Issue 4 (11-2024)
Abstract
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
𝐒𝐢𝐫𝐚𝐣𝐮𝐬 𝐒𝐚𝐥𝐞𝐡𝐢𝐧, Shakila Rahman, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐍𝐮𝐫, 𝐀𝐡𝐦𝐚𝐝 𝐀𝐬𝐢𝐟, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐁𝐢𝐧 𝐇𝐚𝐫𝐮𝐧, Jia Uddin,
Volume 20, Issue 4 (11-2024)
Abstract
Abnormal activity detection is crucial for video surveillance and security systems, aiming to identify behaviors that deviate from normal patterns and may indicate threats or incidents such as theft, vandalism, accidents, and aggression. Timely recognition of these activities enhances public safety across various environments, including transportation hubs, public spaces, workplaces, and homes. In this study, we focus on detecting violent and non-violent activities of humans using a YOLOv9-based deep learning model considering the above issues. A diverse dataset has been built of 9,341 images from various platforms, and then the dataset has been pre-processed, i.e., augmentation, resizing, and annotating. After pre-processing, the proposed model has been trained which demonstrated strong performance, achieving an F1 score of 95% during training for 150 epochs. It was also trained for 200 epochs, but early stopping was applied at 148 epochs as there was no significant improvement in the results. Finally, the results of the YOLOv9-based model have been analyzed with other baseline models (YOLOv5, YOLOv7, YOLOv8, and YOLOv10) and it performed better compared with others.
