In recent years, with the development of science and technology and its application in agricultural production, China's agricultural science and technology have made great progress, the concept of "agricultur...
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ISBN:
(数字)9781665454575
ISBN:
(纸本)9781665454575
In recent years, with the development of science and technology and its application in agricultural production, China's agricultural science and technology have made great progress, the concept of "agricultural processing"has been mentioned, and the research on agricultural processing has also achieved fruitful results. The combination of intelligent and automated machine learning algorithms with traditional industries can promote productivity improvement on the one hand, and realize industrial upgrading and transformation on the other hand. However, in practical production applications, machine learning algorithms are restricted by factors such as high cost, and the research and application of machine learning algorithms are greatly limited. With the development of virtual simulation technology in the field of machine learning algorithm research, it provides a new way for machine learning algorithm technology to be applied to agricultural product processing. Therefore, the research on machine learning algorithms has become a trend. The development of machine learning algorithms will drive the development of modern agriculture. It is very necessary for the research of machine learning algorithms to learn algorithms. The image is converted into a data matrix, and a computer used to replace the human brain is used to analyze the image, while completing a vision related task. China's agricultural development is facing severe challenges such as rising costs, continuous deterioration of the ecological environment and high tension of resource conditions. With the deepening of machine learning algorithm research and the rapid development of machine learning algorithm technology, machine learning algorithm simulation technology, as a safe and economic experimental tool in the application of machine learning algorithm technology, plays a more and more important role. In order to make full use of the latest research results abroad and narrow the gap with the advanced level ab
The proceedings contain 39 papers. The topics discussed include: quantum encoding of images using NEQR, FREI and LPIQE methods;the impact of digital technologies on the design and production of clothing;implementation...
ISBN:
(纸本)9781510692152
The proceedings contain 39 papers. The topics discussed include: quantum encoding of images using NEQR, FREI and LPIQE methods;the impact of digital technologies on the design and production of clothing;implementation of a genetic algorithm for two-dimensional arrays using the numpy library for python;comparative analysis of parallel discrete event simulation algorithms: time warp, window racer, and null messages;increasing the statistical signification of regression dependence in the problems of processing experimental samples of small volume;review of cell detection approaches in intelligent systems for antinuclear factor determination;the impact of gaussian filtering on image clustering accuracy;and quantifying the quality of responses generated by augmented large language models.
In this paper, we present a technique for extracting stoma outlines from 2.5D images acquired through smartphone-based 3D scanning. Accurate stoma outlining plays a crucial role in tailoring ostomy wafers, thereby min...
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When constructing a 3D surface grid using point cloud data closely matched with high-resolution satellite stereo images, holes often appear due to matching failures or limitations of triangulation algorithms, affectin...
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Structural health monitoring is crucial for ensuring the safety of civil infrastructure, and crack detection is an essential component of this process. Cameras provide high-resolution images of the structure's sur...
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ISBN:
(纸本)9781510660793;9781510660809
Structural health monitoring is crucial for ensuring the safety of civil infrastructure, and crack detection is an essential component of this process. Cameras provide high-resolution images of the structure's surface, which can be analyzed to detect and locate cracks. LiDAR sensors use laser beams to scan the surface of the structure and produce detailed 3D point clouds that can be used to detect cracks and measure their dimensions. The proposed approach aims to improve the accuracy and efficiency of crack detection in SHM by integrating the complementary strengths of cameras and LiDARs in a simulation environment. The approach involves the use of an intelligent algorithm that can automatically fuse the data from the cameras and LiDARs to produce a more comprehensive and accurate representation of surface cracks. The algorithm uses a machine learning-based crack detection technique that can accurately identify and locate cracks in real-time. Furthermore, a depth camera is used to provide a denser point cloud than LiDAR of the crack. The integration of cameras and LiDARs for crack detection in SHM offers several advantages, such as improved accuracy, faster data acquisition, and reduced costs compared to traditional methods. The proposed approach addresses the challenges of data fusion, imageprocessing, and intelligent algorithm development by offering a novel solution that leverages the strengths of both cameras and LiDARs. The findings of this study suggest that the proposed approach can significantly enhance the capabilities of SHM for crack detection. The approach offers a more accurate and efficient way of detecting cracks in real-time, which can help prevent further damage and ensure the safety of civil infrastructure.
Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intell...
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This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to dete...
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ISBN:
(数字)9798331506520
ISBN:
(纸本)9798331506537
This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes.
In order to solve the problem of fast processing for ultra-high-resolution images in embedded systems, this paper proposes a multi-mode tracking and recognition method based on embedded processing architecture, throug...
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ISBN:
(数字)9798331510138
ISBN:
(纸本)9798331510145
In order to solve the problem of fast processing for ultra-high-resolution images in embedded systems, this paper proposes a multi-mode tracking and recognition method based on embedded processing architecture, through the construction of a multi-GPU hardware processing system, the large-scale image parallel processing algorithm is studied, and a variety of imageprocessing modes such as full-frame, full-window tracking and tracking scanning are designed, and experimental verification is carried out on ultra-high-resolution images. The multi-GPU architecture and large-scale image parallel processing algorithm described in this paper have certain advantages over traditional processing methods.
Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL levera...
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ISBN:
(纸本)9781713871088
Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL leverages a learned dynamics model from the logged experience and augments the predicted state transition to extend the data distribution. For exploiting such benefit also on the image-based RL, we firstly propose a generative model, S2P (State2Pixel), which synthesizes the raw pixel of the agent from its corresponding state. It enables bridging the gap between the state and the image domain in RL algorithms, and virtually exploring unseen image distribution via model-based transition in the state space. Through experiments, we confirm that our S2P-based image synthesis not only improves the image-based offline RL performance but also shows powerful generalization capability on unseen tasks.
image colorization has always been a hot topic in computer vision. Since the emergence of deep learning and its excellent performance in many image-processing tasks, image colorization methods based on convolutional n...
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