In today's society, technological breakthroughs have led to the automation of most industries in order to ensure smooth operations and minimize human interaction. The automation has led to challenges in an importa...
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The potential of optical wireless communication (OWC) systems for high-speed data transfer, especially over extended distances, is being investigated more and more. However, problems including signal attenuation, disp...
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At present, ensuring reliability is an integral part of maintaining the high efficiency of operation of electric power equipment. The reliability factor can be considered and considered at different stages of solving ...
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control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control. Some of these applications use measurement ...
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control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control. Some of these applications use measurement devices that sample equidistantly in the amplitude domain. The aim of this paper is to develop a non-parametric estimator of the impulse response of continuous-time systems based on such sampling strategy, known as Lebesgue-sampling. To this end, kernel methods are developed to formulate an algorithm that adequately takes into account the output intersample behavior, which ultimately leads to more accurate models and more efficient output sampling compared to the standard approach. The efficacy of this method is demonstrated through a mass-spring damper case study.
The growing demand for energy-saving lamps highlights critical limitations in traditional LED secondary optics, which reduce energy efficiency and complicate heat dissipation - a key factor in LED lamp longevity. This...
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Pneumatic muscle actuators (PMAs) are efficient and versatile, mimicking natural muscle-Tendon systems. They are widely used in manufacturing, automotive, and aerospace industries due to their lightweight design, high...
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Coverage control with a multi-robot system requires the robot team to be optimally distributed across some environment. This environment may have multiple, distinct areas of interest, each of which associated with a s...
Coverage control with a multi-robot system requires the robot team to be optimally distributed across some environment. This environment may have multiple, distinct areas of interest, each of which associated with a specific task that requires robots with particular capabilities. In such scenarios, the first problem to be solved is that of assigning each robot in the team to one of the importance areas, while accounting for the robots' capabilities and the task/area requirements. This paper investigates this problem and proposes a novel multi-robot task allocation (MRTA) scheme in the context of coverage control. More specifically, the proposed method employs integer linear programming (ILP) to solve the constrained robot-to-task allocation problem. The resulting algorithm allocates each robot in the heterogeneous team to one of the importance areas, subject to a number of realistic constraints such as the tasks' requirements, and the robots' capabilities and energy levels. Our approach introduces elements of fault tolerance and robustness to situational changes, since it can also be executed periodically during the coverage process, to reassign the robots in case of robot faults, changes in the tasks/areas, and other factors that alter the allocation solution. The proposed scheme also has the advantage of being separate from the coverage control algorithm, and therefore supports a modular framework. Finally, a set of realistic simulated scenarios are used to validate the task allocation scheme being proposed.
The impact of information dissemination on epidemic control is essentially subject to individual behaviors. Vaccination is one of the most effective strategies against the epidemic spread, whose correlation with the i...
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The impact of information dissemination on epidemic control is essentially subject to individual behaviors. Vaccination is one of the most effective strategies against the epidemic spread, whose correlation with the information dissemination should be better understood. To this end, we propose an evolutionary vaccination game model in multiplex networks by integrating an information-epidemic spreading process into the vaccination dynamics, and explore how information dissemination influences vaccination. The spreading process is described by a two-layer coupled susceptible-alert-infected-susceptible(SAIS) model, where the strength coefficient between two layers characterizes the tendency and intensity of information *** find that the impact of information dissemination on vaccination decision-making depends on not only the vaccination cost and network topology, but also the stage of the system evolution. For instance, in a two-layer BA scale-free network, information dissemination helps to improve vaccination density only at the early stage of the system evolution, as well as when the vaccination cost is smaller. A counter-intuitive conclusion that more information transmission cannot promote vaccination is obtained when the vaccination cost is larger. Moreover, we study the impact of the strength coefficient and individual sensitivity on the fraction of infected individuals and social cost, and unveil the role of information dissemination in controlling the epidemic.
Computer-aided diagnosis (CAD) helps physicians in tumors' identification into various biomedical tissues, breast is one. Through the last few years, image classification techniques based on deep learning (DL), ha...
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ISBN:
(纸本)9798350335569
Computer-aided diagnosis (CAD) helps physicians in tumors' identification into various biomedical tissues, breast is one. Through the last few years, image classification techniques based on deep learning (DL), have obtained noticeable success in differentiating breast ultrasound images automatically. More researches, concerned with breast cancer detection, are representing the output as a black and white segmented image (mask) or a selected region on the breast ultrasound image which will in role submitted to physicians to help them in making a better diagnosis decision. Our paper represents the second step. In this paper: ten pre-trained Convolutional neural networks (CNNs) classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify segmented breast ultrasound images by transfer learning (TL). A dataset of 375 breast ultrasound images' masks (ground truths), divided as 125 normal, 125 benign, and 125 malignant, has been utilized in training and validation. A dataset of 255 breast ultrasound images' ground truths (masks), divided as 85 normal, 85 benign, and 85 malignant, has been utilized to evaluate the classification accuracy of each CNN model after TL process. Each CNN model's evaluation (over the 255 breast ultrasound images' masks) has represented different accuracy values evaluated. The best accuracy value was for ResNet50 with an accuracy of 97.25 %. The proposed classification scheme for segmented ultrasound images can be regarded as a step that may help in an automatic breast cancer diagnosis system. The produced trained ten CNNs models in this study (input image size: 128 by 128 by 3) are not in their optimum case but it can be considered as a start to any researcher interests in studying the classification of segmented binary images of breast cancers, the ten produced CNNs models are available to researchers at: https://***/
Problem: People with Down Syndrome must be served special because they have an intellectual disability with abnormality in memory and learning, so, creating a model for DS recognition may provide safe services to them...
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