In recent years, backdoor attack techniques on neural networks have been widely studied and researched. In this attack mode, the model implanted with a backdoor behaves normally when processing normal inputs, but once...
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Innovative solutions for sustainability and energy efficiency are crucial in green building management. This study presents a novel approach to optimizing air conditioning (AC) system operations in commercial building...
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Innovative solutions for sustainability and energy efficiency are crucial in green building management. This study presents a novel approach to optimizing air conditioning (AC) system operations in commercial buildings, with a focus on real-time control aimed at reducing energy consumption. We propose the Smart Visual Air Conditioning Controller (SVACC), which utilizes computer vision and deep learning-based human detection to intelligently manage AC operation, minimizing unnecessary runtime. By detecting human presence in meeting rooms, the system dynamically adjusts AC activation based on occupancy, thereby significantly reducing energy waste. A statistical analysis conducted over five months across ten conference rooms demonstrated that the SVACC reduced AC usage time by 33.60 %. We validate and optimize the SVACC across various building types, including commercial office spaces, industrial warehouses and laboratories, and residential apartments. The system achieved an optimal balance with 96.55 % precision and 93.33 % recall, resulting in an F1 score of 0.9492, demonstrating high performance across various environments. Our results underscore the effectiveness of the SVACC, which highlights the potential of integrating advanced deep learning models with HVAC systems to optimize energy consumption. This approach offers a promising solution for improving HVAC design and energy management across diverse building environments. Future work will focus on refining sensor technology and control algorithms to further optimize energy efficiency.
Artificial Intelligence (AI) is a rapidly developing discipline that concentrates on teaching computers to comprehend and scrutinize images, particularly in the medical field, with a specific emphasis on diagnosing Ca...
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Hump and pothole detection is essential for ensuring road safety and preventing damage to vehicles. In recent years, there has been a growing interest in developing automated methods for hump and pothole detection. Th...
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In light of the limited detection accuracy and susceptibility to missed detections exhibited by most algorithms under rainy conditions, a rain-day vehicle target detection model based on improved YOLOv8 is proposed. F...
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ISBN:
(纸本)9798350350227;9798350350210
In light of the limited detection accuracy and susceptibility to missed detections exhibited by most algorithms under rainy conditions, a rain-day vehicle target detection model based on improved YOLOv8 is proposed. Firstly, PIGWM is used to preprocess the original image for rain removal, and parameter importance-guided weight modification is employed to adjust network weights to address the performance degradation issue of deep learning models when processing incremental datasets, thereby improving the rain removal performance of images. Then, SlideLoss sliding loss function is introduced to enable the model to adaptively learn the threshold parameters of positive samples and negative samples, solving the imbalance problem between different samples and enhancing detection accuracy. Finally, CPCA attention mechanism is incorporated into the Neck feature fusion network to enhance the model's feature fusion capability. Experimental results on the self-built KITTI-RAIN dataset show that the improved algorithm achieves higher accuracy compared to the original model, with accuracy increasing from 92.6% to 94.5%, recall increasing from 82.9% to 87.6%, average precision increasing from 91.4% to 94.1%, and P, R, mAP increasing by 1.9%, 4.7%, and 2.7% respectively, demonstrating its effectiveness in adapting to vehicle detection tasks in rainy conditions.
This article presents the idea of a vision system allowing the analysis of images from multiple cameras for the needs of augmented reality systems. The presented solution allows for the effective determination of the ...
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The fake currency notes are detected using imageprocessing employing MATLAB in this paper. This project aims in providing the best techniques in image acquisition, and image segmentation. The work uses CANNY's al...
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A face has been used as a primary and unique attribute to authenticate individual users in emerging security approaches. Cybercriminals use the double-edged sword "imageprocessing" capabilities to deceive i...
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The enterprises digitalization determines the development trends of the industrial sector. There is a need to reduce the percent of human participation in processes associated with conveyor production, which requires ...
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In order to improve security measures across numerous domains, such as public safety, transportation, and critical infrastructure protection, the use of closed-circuit television (CCTV) systems for threat analysis has...
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