Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method...
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Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.
Person re-identification (re-ID) has wide applications in surveillance and security. It is also challenging due to viewpoint, occlusion and illumination variations across different cameras. One solution to unsupervise...
Person re-identification (re-ID) has wide applications in surveillance and security. It is also challenging due to viewpoint, occlusion and illumination variations across different cameras. One solution to unsupervised person re-ID problems is synthetic data augmentation. Generative neural networks have been used to translate images from the source domain into the target domain. In this paper, we introduce a new virtual-human image dataset that can be used as the source domain for person re-ID. This new dataset has images labeled by person identity, background, viewpoint and illumination intensity. We also explore GAN-based and Diffusion-based generative methods for unpaired image-to-image translation and provide qualitative and quantitative evaluation for the synthetic results.
The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information ...
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This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimiz...
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Unmanned Aerial Vehicles (UAVs) have the ability to acquire high resolution RGB images from plant fields. A field used for plant research is usually divided into smaller groups of plants known as "plots" to ...
Unmanned Aerial Vehicles (UAVs) have the ability to acquire high resolution RGB images from plant fields. A field used for plant research is usually divided into smaller groups of plants known as "plots" to evaluate varieties or management practices. In this paper, we propose an optimization-based, rotation-adaptive approach for extracting plots in a UAV RGB orthomosaic image. From the experiments, our proposed method is robust against range/plot rotation and achieves higher segmentation accuracy compared with existing plot extraction approaches.
Human action recognition has been one of the hot topics in computervision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size ...
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The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based ob...
The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-consuming and not feasible for real application. In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning. Results show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10% of original training data.
This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking ...
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ISBN:
(数字)9798350348187
ISBN:
(纸本)9798350348194
This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking and unlocking the workstation is transparent to the user. Since workstations and smartphones have built-in microphones and speakers, the system does not require additional hardware. The uniqueness of the solution is being based on acoustic signals. These signals are transmitted by the user's smartphone and received by the workstation microphone. The system is “pure play acoustic” since no wiring or radio transmission is used. The system configuration supports multiple users in the same area. Eavesdropping prevention is provided by sequentially generated random one-time keys. Acoustic communication can be applied either in the audible range or beyond the human hearing range depending on the sampling rate of the smartphone and the workstation.
Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretatio...
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In medical image analysis, the long-range spatial features are often not accurately obtained by the traditional convolutional neural networks. Hence, we propose a TransClaw U-Net network structure. The transformer par...
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