This paper introduces the UICVD Dataset, a novel resource fostering advancements in Robotic Process automation (RPA) and computer Vision. The paper focuses on recognizing UI (User Interface) components of a web applic...
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In this research work, Three non coupled dual servo electric cylinder synchronous motion control schemes based on PLC control have been designed, which are parallel synchronous control, master-slave synchronous contro...
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This research analyzes the distribution of a poultry plant, with defined processes and established areas. However, it has been proven that the poultry production process can optimize its processes by redistributing it...
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ISBN:
(数字)9783031563737
ISBN:
(纸本)9783031563720;9783031563737
This research analyzes the distribution of a poultry plant, with defined processes and established areas. However, it has been proven that the poultry production process can optimize its processes by redistributing it, applying the SLP (Systematic Layout Planning) methodology. In addition, the purpose is to take the process to a second level of improvement with process automation. Initially, the plant plans were analyzing in detail, these allowed us to visualize which areas should be improved. Then, the DAP (Process Activity Diagram) was analyzed, where the activities are identified for the plant process. Thereafter, the SLP method is applied, which is essential for the redistribution of the plant. Once a more efficient plant has been obtained, productivity is analyzed using a distance and load matrix. Furthermore, the idea of automation in important processes is shown in a cost and time table. Finally, a DAP is developed that gives validity to this proposal.
Firstly, analyze the development of electric vehicle energy storage systems, and then analyze the characteristics of common energy storage systems such as supercapacitors, lithium-ion batteries, and spring elastic ene...
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Taking the development of public computing service systems as the background requirement, combined with Digital Twin related technologies, the design of computing units is carried out. Research is carried out from the...
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Wavefront reconstruction is a powerful technique for accurately restoring all information about an object, with holography being the most effective method for achieving this. In this paper, we propose a reflective hol...
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Reinforcement learning in partially observable environments poses challenges due to limited and noisy observations. Traditional approaches like the Utile Suffix Memory (USM) algorithm suffer from inefficiencies and po...
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Vehicle detection plays a pivotal role in intelligent transportation systems and autonomous driving, ensuring safety, optimizing traffic flow, and supporting advanced driver assistance functions. However, dense vehicl...
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ISBN:
(纸本)9798350377040;9798350377033
Vehicle detection plays a pivotal role in intelligent transportation systems and autonomous driving, ensuring safety, optimizing traffic flow, and supporting advanced driver assistance functions. However, dense vehicle distributions, occlusions, and differing target sizes within complex road environments often challenge the performance of existing detection models. In response, this work introduces an improved vehicle detection framework founded on YOLOv8s. Our design integrates a dual attention mechanism-merging CBAM and SE modules-into the Backbone, thereby reinforcing feature extraction and enhancing detection for smaller targets. Additionally, a cross-layer multi-scale feature fusion strategy, built upon ReP-GFPN, is incorporated into the Neck to boost multi-scale information sharing and strengthen detection across various target dimensions. We further replace the traditional CIOU loss with Wise-IoU, enabling the model to better handle difficult samples and occlusion scenarios. Experiments on the UA-DETRAC dataset demonstrate a 4.8% improvement in mAP@0.5, underscoring the effectiveness of these enhancements in demanding traffic conditions. This work offers a promising avenue for advancing vehicle detection systems in intelligent transportation and autonomous driving contexts.
This paper proposes a deep learning-based sense of active control (SOA) identification and intervention system, which aims to identify the control perception of individuals in the process of performing tasks in real t...
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ISBN:
(纸本)9798350377040;9798350377033
This paper proposes a deep learning-based sense of active control (SOA) identification and intervention system, which aims to identify the control perception of individuals in the process of performing tasks in real time through deep learning models, and provide customized interventions based on the identification results to enhance the individual's sense of control experience. SOA is the self-perception of an individual's control over his or her own behavior, and its level plays an important role in cognitive psychology and behavioral decision-making. This study combines physiological signals (such as electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (EDA)) with behavioral data (such as hand movements and operation task performance) for feature extraction, and identifies SOA through DNN and CNN. Then, a personalized intervention mechanism is designed, and real-time feedback and dynamic adjustment strategies are used to optimize the sense of control experience. To verify the effectiveness of the system, this paper conducts multiple experimental simulations. The experimental results show that the SOA identification model based on deep learning can accurately identify different levels of control perception, with an accuracy rate of more than 90%. The SOA level of individuals has been significantly improved through the introduction of intervention strategies, especially under high-load task conditions. The intervention effect is more significant.
Diabetes mellitus poses a significant health challenge globally, emphasizing the importance of early prediction and intervention. In this information, we offer a novel approach for diabetes forecast in healthcare util...
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