In this work, MXene based piezo resistive stress sensor was fabricated using a freeze dry method. The device was fabricated to measure the mechanical stress, sensitivity, and tribo-electric measurements by converting ...
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Advancement in high-performance computing technology has paved way for development of Deep Learning algorithms for computer vision to provide unprecedented performance both in terms of accuracy and speed. Image recogn...
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In this research, various compositions of sensing layers have been studied for their use in humidity sensors. Materials used for the sensing layers were Polyvinyl Alcohol (PVA), Gelatin, and Tin Oxide (SnO2). The sens...
In this research, various compositions of sensing layers have been studied for their use in humidity sensors. Materials used for the sensing layers were Polyvinyl Alcohol (PVA), Gelatin, and Tin Oxide (SnO2). The sensor device was designed and fabricated using thick film technology through screen printing techniques on Alumina (Al2O3) substrates, with Silver-Palladium (AgPd) as the electrode material. The number of electrode fingers was varied to obtain optimum sensor resistance values. The PVA sensing layer was combined with gelatin and various compositions of SnO2 to study their effect on the sensor response to humidity. The sensor devices were tested in a custom chamber with varying humidity levels from 50% to 90%. The results showed that the sensor based on the PVAGelatin-SnO2 (PGS) sensing layer had higher resistance values and more sensitive responses than the sensor without gelatin. The hydrophilic nature of gelatin has been found to contribute significantly to the water sensitivity of the matrix. In addition, the difference in the number of fingers of the sensor electrode and the addition of SnO2 also affect the resistance value.
The research introduces the design of an AI-based disaster response system that subscribes to enhance real-time emergency management. Manual processing, slow communication and siloed data sources are common downsides ...
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ISBN:
(数字)9798331528140
ISBN:
(纸本)9798331528157
The research introduces the design of an AI-based disaster response system that subscribes to enhance real-time emergency management. Manual processing, slow communication and siloed data sources are common downsides of traditional-approach systems, which ultimately translate into slower response times or insufficient resource spend. The system deals with them by exploiting ML, predictive analytics, and automatic decision-making so that these can consolidate pre-existing real-time information from social media, satellite images or IoT sensors. Several experiments show better-than-previous: response times decrease from an average of 11 hours to 3.6 and prediction accuracy increased from 62.5% up to cover %85 Average efficiency also improved including +23% in resource allocation. The innovative solution shifts disaster response from being entirely reactive to proactive, creating faster and more informed responses during times of crisis, thereby assuring public safety for all in the face of calamities.
As the rate of garbage generation gradually increases, the past garbage disposal methods will be eliminated, so the classification of garbage has become an inevitable choice. The multi-category classification of garba...
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As the rate of garbage generation gradually increases, the past garbage disposal methods will be eliminated, so the classification of garbage has become an inevitable choice. The multi-category classification of garbage and the accuracy of recognition have also become the focus of attention. Aiming at the problems of single category, few types of objects and low accuracy in existing garbage classification algorithms. This paper proposes to use the improved YOLOV4 network framework to detect 3 categories, a total of 15 objects, and find that the average accuracy is 64%, Frame per second 92 f/s. It turns out that the improved YOLOV4 can better detect garbage categories and is suitable for embedded devices.
Autism Spectrum Disorder (ASD) children struggle with social interaction and communication, prompting the development of facial emotion recognition systems to aid communication with caretakers. Researchers have shown ...
Autism Spectrum Disorder (ASD) children struggle with social interaction and communication, prompting the development of facial emotion recognition systems to aid communication with caretakers. Researchers have shown interest in using non-invasive thermal imaging to recognize emotions. In this study, a wavelet-based technique was developed to detect changes in the thermal intensity values (TIV) of three regions of interest from frontal facial thermal image. The study analyzed thermal images of ASD children responding to audio-visual stimuli of five basic emotions and derived features from them. Wavelet coefficients were combined with thermal intensity values of each region of interest to form feature set, which were fed into a CNN model. The result showed the efficacy of the method with accuracy and precision attained at 91.81%% and 94.54% respectively. The study is useful in the development of robot assist training for the ASD children as part of early intervention program.
Rotary shears are common part of the material processing lines. During the operation, these shears are loaded with impact cutting torque, which is short in time, but reaches high values, which are comparable to motor ...
Rotary shears are common part of the material processing lines. During the operation, these shears are loaded with impact cutting torque, which is short in time, but reaches high values, which are comparable to motor rated torque. Therefore, it is a technical challenge to ensure the speed stability during the cut. The presented paper deals with the analysis of the rotary shears operation and material cutting process from the control point of view and presents a cutting torque compensation possibility. The designed control structures and compensations are verified by simulation with the simulation results included.
Laser beam machining (LBM) is the most widely used machining process and can be applied to almost all metallic and non-metallic range of materials. In this paper, the effect of process parameters such as cutting speed...
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Laser beam machining (LBM) is the most widely used machining process and can be applied to almost all metallic and non-metallic range of materials. In this paper, the effect of process parameters such as cutting speed, laser power, frequency, duty cycle and gas pressure have been investigated on hard die steel plate (EN-31, 10 mm thick) to determine the impact on taper angle, surface roughness (Ra) and Heat Affected Zone (HAZ). Second-order mathematical models have been developed using Response Surface Methodology (RSM) and compared with the experimental results. It has been observed from the main effect plot that cutting speed, laser power and frequency have a major impact on the taper angle. For getting better surface roughness, all the process parameters have been set to its mean value. Cutting speed, power and gas pressure were the significant parameters to control the HAZ. The bottom edge has been observed with larger HAZ than the top edge for all holes. Optimized parameters have been identified and industry desirable results were obtained for all the responses. A scanning electron microscope has been used to notice the striation pattern and surface damage on the top surface of the hole.
In this research study, we proposed a Distributed Average Integral (DAI) control-based Energy Management Model (EMM) to achieve the economic load dispatch and consensus within a distributed energy system. The proposed...
In this research study, we proposed a Distributed Average Integral (DAI) control-based Energy Management Model (EMM) to achieve the economic load dispatch and consensus within a distributed energy system. The proposed model incorporates the Laplacian graph theory for establishing communication among energy districts (acting as agents/nodes) and optimizing the power distribution while satisfying the load demand. In order to evaluate the effectiveness of the proposed model, simulations are performed using MATLAB. The simulation results illustrate that the DAI control mechanism ensures the optimized power distribution leading to enhanced resource utilization while effectively achieving economic consensus among energy districts. However, in this study ideal case scenario is assumed. Therefore, this research study may highlight the potential for future investigations on utilizing the DAI control for EMM in practical scenarios with real-world challenges such as system complexities, losses, and communication delays.
The development of the energy management systems (EMS) improves the energy efficiency of vehicular systems, control operational factors and behavior of electromechanical components by appropriating machine learning an...
The development of the energy management systems (EMS) improves the energy efficiency of vehicular systems, control operational factors and behavior of electromechanical components by appropriating machine learning and IoT techniques, making the systems more intelligent, sustainable, and interconnected. Specially, the inclusion of technology such as Industry 4.0 have allowed to improve the range of operation, control and optimization of autonomous vehicles. In fact, EMS have become relevant with the emergence of electrical vehicles (EV’s) and are proposed as an alternative to minimize dependence on fossil fuels, mitigation of negative ecological impacts, increase safety, also suggest efforts to achieve new energy sources, charge stations, decisions about final disposal of components and life cycle prediction which can be summed up in the need for more technological coverage. This article aims to show the framework about the EMS based on concepts, differences, advantages and disadvantages of the EV’s.
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