This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificia...
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ISBN:
(纸本)9783031734199;9783031734205
This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificial intelligence (AI) and advanced imageprocessing techniques to prevent firing by activating the safety pin in various situations. Through an integrated camera module and a minicomputer from the Jetson family, animals can be detected in real-time, and various data analysis techniques, along with object recognition algorithms, allow for ethical intervention in many situations that are invisible to the human eye and could be exploited by hunters. Particularly in various national parks, the frequent reports of accidental or uninformed hunting of rare and endangered species, especially during their breeding seasons, underline the importance of this technology. Furthermore, hunters' preference for the most dominant animal in a herd as a trophy poses a significant threat in evolutionary terms. This technology contributes significantly to the conservation of wildlife while promoting ethical and sustainable practices in hunting tourism. The study addresses the development, testing, and implementation of this technology and adds a technological dimension to wildlife conservation strategies.
This paper aims to study the design of deep learning adaptive algorithms for different types of inertial navigation systems. Underwater inertial navigation plays a crucial role in fields such as ocean exploration, but...
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ISBN:
(纸本)9798400707032
This paper aims to study the design of deep learning adaptive algorithms for different types of inertial navigation systems. Underwater inertial navigation plays a crucial role in fields such as ocean exploration, but improving its accuracy faces challenges, and combined navigation methods are often adopted. Deep learning is widely applied in inertial navigation, which can be used for sensor data processing, positioning, attitude estimation, and can also be combined with other navigation technologies. Based on previous research, this paper delves into the characteristics of different inertial navigation devices, combines with large language models, and proposes an LLM-based adaptive parameter selection expert system. This system can select appropriate deep learning network parameters based on parameters such as the material, accuracy, and service life of the inertial navigation device to ensure the matching between the model and the inertial navigation device. The network design is divided into a displacement prediction module and a heading angle prediction module, and different targeted designs are adopted. The experiment was conducted in the Yellow Sea, equipped with strapdown fiber optic inertial navigation systems and strapdown laser inertial navigation systems, and targeted training was carried out according to the characteristics of the two sets of systems. The fiber optic inertial navigation system has an earlier factory time and a large data scale;the laser inertial navigation system has a recent factory time and high accuracy. The experimental results show that the error is significantly suppressed after adopting the deep learning algorithm, and it is feasible to use the expert system to drive the setting of targeted parameters, which has the value of promotion and research.
With the continuous progress of computer technology, distributed intelligent systems become more and more popular. Based on this background, this paper discusses the theory of image recognition based on distributed in...
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Soysauce-like aroms based wine needs to be stored in a dark place during long-term storage. When recognizing images of the base wine cellar, the quality of the collected images is greatly affected by environmental lig...
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ISBN:
(纸本)9798350351040;9798350351033
Soysauce-like aroms based wine needs to be stored in a dark place during long-term storage. When recognizing images of the base wine cellar, the quality of the collected images is greatly affected by environmental light, and the imaging quality is poor and there are many noise points in low light environments. The influence of ambient light poses significant challenges to computer vision. To address the issues of poor image visibility and high interference in low illuminance environments, this article proposes an improved low-illuminance image enhancement method based on Retinex-Net network. Convert the input image from RGB domain to IISV color space for processing, introduce denoising convolutional neural network Deam-Net network into the reflection image of V component for denoising, enhance the color of the illumination image of V component through spatial attention module and channel attention module, and perform bilateral filtering and contrast stretching on the S component. Finally, fuse all components and convert to RGB for obtain the enhanced image. Prove through verification have shown that the low -illumination images enhanced by the algorithm proposed in this article have improved brightness, prominent details, minimal image distortion, and are realistic and natural. They are superior to other algorithms from subjective feelings and objective evaluation indicators.
Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are of...
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image captioning is essential in many fields including assisting visually impaired individuals, improving content management systems, and enhancing human-computer interaction. However, a recent challenge in this domai...
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Leveraging the spatio-spectral modulation and sophisticated reconstruction algorithms, the colorful compressive spectral imaging (CCSI) method can reconstruct a three-dimensional spectral image from a single compressi...
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Biometric systems, even with high accuracies, commonly suffer from various attacks. In practical applications, some systems directly display the matching scores, or attackers can obtain the matching scores. This paper...
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Current image dehazing algorithms often encounter issues of contrast reduction and color distortion in the shadow regions of images. To address this challenge, this paper proposes a comprehensive atmospheric model tha...
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Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to dis...
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