The goal of this project is to improve visually impaired people's quality of life by implementing an advanced system that is nevertheless easy to use. Our main approach is based on using machine learning, which is...
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Vehicle classification can potentially alter intelligent transportation systems following recent advances in computer vision. Applications of intelligent Transportation Systems improve traffic management by allowing u...
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Vehicle classification can potentially alter intelligent transportation systems following recent advances in computer vision. Applications of intelligent Transportation Systems improve traffic management by allowing us to make educated, safe, and smart decisions about transportation networks, resulting in increased safety and efficiency. advances in computer vision and deep learning have improved vehicle classification systems. This study uses real-time data from Hyderabad City to propose a deep-learning model for recognizing random vehicle classes, including Light commercial vehicles (LCVs), Light motor vehicles (LMVs), Oversized vehicles (OSVs) and Trucks. Real-time data includes a selected number of images for training and testing. This work uses images of classes. Among them, the largest is LMV, and the least is OSVs. Every class has 30% testing and 70% training data. Preprocessing begins with resizing, scaling, and augmentation of the featured images. The CNN model was the base model, while Python libraries were used to train the network architecture. The validation accuracy was 88%, as per the test results.
The research objects for evaluating the reliability of the real-time intelligent seismic processing system (RISP) in the Inner Mongolia seismic network were 31 earthquake events that occurred in the region within 30 d...
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The research objects for evaluating the reliability of the real-time intelligent seismic processing system (RISP) in the Inner Mongolia seismic network were 31 earthquake events that occurred in the region within 30 days before and after the Helingeer M-L 4.5 earthquake on March 30, 2020. The manual cataloging was compared to the output results of the real-time intelligent seismic processing system (automatic catalog). It was observed that the number of events identified by the RISP system was approximately 2.5 times that of the manual ones, with 30 automatic catalogs matching the manual catalogs (31). The event recall rate was as high as 96.8%. The automatic catalog had a small deviation from the manual catalog in terms of earthquake occurrence time, epicenter location, magnitude, and P-wave and S-wave phase arrival time. When the results of the automatic and manual catalogs are compared, it is evident that the cataloging of the same events in both catalogs is consistent, with the earthquake occurrence time and epicenter deviation usually settling within +/- 2 s and +/- 10 km, respectively. The automatic catalog meets the error range requirements of the manual catalog. The Real-time intelligent Seismic Processing System (RISP) produces data that meets expected goals and supports scientific research, such as rapid aftershock sequence production and earthquake swarm trend judgment post-earthquake.
In all position welding, the reasonable setting of welding process parameters at different positions and effective control of smooth transition are one of the difficulties in the automation of pipeline circumferential...
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As ship automation gradually advances, the automatic maintenance of ship propulsion systems becomes increasingly important, and currently, common maintenance often takes place after a breakdown. This paper, through re...
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Classification models can be learned from complicated multivariate temporal data and this is a topic we investigate in electronic health record systems. The difficulty is to come up with a collection of attributes tha...
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Airborne radar plays an important role in sea surface search and rescue and national defense, and the analysis of control variables in multiple dimensions of the sea conditions of the sea surface to be detected, the f...
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As intelligent systems and technologies mature, a shift in the modality of use is occurring, namely computer-based systems are no longer an assistive extension of the human operator but are an inherent part of human e...
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ISBN:
(纸本)9783031618567;9783031618574
As intelligent systems and technologies mature, a shift in the modality of use is occurring, namely computer-based systems are no longer an assistive extension of the human operator but are an inherent part of human endeavors. This, on the one hand, leads to augmented human performance, while on the other hand, presents challenges in managing the boundaries between human versus machine control, over- and under-reliance on automation, and artificial intelligence (AI). Social impacts such as dramatic changes in work paradigms, outsourcing, and human interactions are more and more pronounced. The presentation will explore the evolution of smart systems from fundamental models to advanced technologies today. It will discuss the human impact of the new advances on engineering, medicine, other endeavors, and the society at large. It will debate the question of "how much better off are we?" living in the technology driven universes and our interactions with the ubiquitous computer-based worlds.
作者：
Solainayagi, P.
Department Of Computer Science And Engineering Tamil Nadu Chennai India
Personalized care and continuous patient monitoring are signs of critical care units (ICUs), where the IoT may significantly enhance healthcare delivery. Optimal nutrition plans for ICU patients, taking into account t...
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As society advances, computer vision will play an increasingly crucial role in digital and intelligent transformations. Known as deep learning models, Convolutional Neural Networks (CNNs) have emerged as a key compone...
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As society advances, computer vision will play an increasingly crucial role in digital and intelligent transformations. Known as deep learning models, Convolutional Neural Networks (CNNs) have emerged as a key component of computer vision due to their superior performance in automatically detecting image features, handling high-dimensional data and performing large-scale classification tasks. This paper examines the development of CNNs, leveraging the strengths of current mainstream image recognition methods, and proposes a Self-Distillation and Attention-based Convolutional Neural Network (SDACNN) model to further enhance CNN accuracy. Experimental results demonstrate that the proposed model effectively accomplishes image recognition tasks.
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