Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation a...
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Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation angle(θ)meet the condition ofθ+α=π/2,the intensity of radiation fields for the charged particle bunch(CPB)moving from left to right cannot be influenced by intrinsic losses,which means long-distance radiation can be ***,we observe an asymmetric CR when the CPB moves from the opposite *** addition,we select natural van der Waals(vd W)materialα-MoO3as an example,further confirming that the radiation field can reach the far field and the asymmetric CR radiation can also be *** wonderful properties with long-distance radiation will extend the application of CR to a certain extent for future design and fabrication.
Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data st...
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Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show t
Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offsho...
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Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offshore oil and gas processing plants and have been mandated in the design of such systems from national and international standards and *** paper has used actual ESDV operating data from four mid/late life oil and gas production platforms in the North Sea to research operational relationships that are of interest to those responsible for the technical management and operation of *** first of the two relationships is between the closure time(CT)of the ESDV and the time it remains in the open position,prior to the close *** has been hypothesised that the CT of the ESDV is affected by the length of time that it has been open prior to being closed(Time since the last stroke).In addition to the general analysis of the data series,two sub-categories were created to further investigate this possible relationship for CT and these are“above mean”and“below mean”.The correlations(Pearson's based)resulting from this analysis are in the“weak”and“very weak”*** second relationship investigated was the effect of very frequent closures to assess if this improves the *** operational records for six subjects were analysed to find closures that occurred within a 24 h period of each ***,no discriminating trend was apparent where CT was impacted positively or negatively by the frequent closure *** was concluded that the variance of ESDV closure time cannot be influenced by the technical management of the ESDV in terms of scheduling the operation of the ESDV.
Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and...
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In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an O(1/√T) convergence ...
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In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an O(1/√T) convergence rate for the SGD. We conduct a comprehensive implementation to demonstrate the efficiency of the newly proposed step size on the FashionMinst, CIFAR10, and CIFAR100 datasets. Moreover, we compare our results with nine other existing approaches and demonstrate that the new logarithmic step size improves test accuracy by 0.9% for the CIFAR100 dataset when we utilize a convolutional neural network (CNN) model.
Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(＞99％),cost-effectiveness,impressive mechanical properties,and chemical/thermal ***,its amphiphilic nature hinders sele...
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Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(＞99％),cost-effectiveness,impressive mechanical properties,and chemical/thermal ***,its amphiphilic nature hinders selective oil absorption in *** strategies to enhance hydrophobic-ity are reviewed,including synthetic methods and materials,with comprehensive explanations of the mechanisms driven by surface energy and *** performance indicators for MS in oil-water separation,including adsorption capacity,wettability,stability,emulsion separation,reversible wettability switching,flame retardancy,mechanical properties,and recyclability,are thoroughly *** conclu-sion,this review provides insights into the future potential and direction of functional melamine sponges in oil-water separation.
Cyberbullying remains a pressing issue in Thai social media, especially among teenagers. While many studies have explored deep learning approaches for sentiment analysis or toxicity detection, the detection of cyberbu...
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The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing...
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Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two ...
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Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave *** unsupervised learning results match well with those obtained through traditional numerical ***,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.
This research endeavors to scrutinize the influence of courses on students' final year project (FYP) scores and prognosticate FYP scores by applying methodologies such as clustering analysis, decision trees, logis...
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