Detecting and delineating brain tumors from MRI images using artificial intelligence presents a complex challenge in medical AI. Recent progress has seen a variety of techniques employed to assist medical professional...
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Cloud resource providers in a market face dynamic and unpredictable consumer behavior. The way, how prices are set in a dynamic environment, can influence the demand behavior of price sensitive customers. A Cloud reso...
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The extensive use of computers and networks for exchange of information has also had ramifications on the growth and spread of crime through their use. Law enforcement agencies need to keep up with the emerging trends...
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The safety of construction workers is a paramount concern in the modern construction industry. A significant proportion of injuries and fatalities on construction sites are attributed to a lack of adherence to safety ...
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ISBN:
(纸本)9798350359015
The safety of construction workers is a paramount concern in the modern construction industry. A significant proportion of injuries and fatalities on construction sites are attributed to a lack of adherence to safety regulations, often resulting from the non-utilization of Personal Protective Equipment (PPE) by workers. Additionally, effective monitoring of construction areas by site supervisors can be challenging due to the vastness and complexity of construction sites. Manual monitoring, while a crucial aspect of construction safety management, is often hindered by time constraints and associated costs. To address these challenges, the application of computer vision and Convolutional Neural Network(CNN) techniques has led to the development of automated helmet and jacket detection systems. This research employs the YOLOv8 object detection algorithm to explore the efficacy of safety helmet detection, aiming to enhance the accuracy of automated safety helmet detection systems. The YOLOv8 object detection algorithm is a robust tool for recognizing objects in images. Our dataset comprises a total of 4,200 images, including 4,000 images of hard hats and jackets obtained from Kaggle [25], supplemented by 200 images depicting foggy, rainy, and nighttime conditions from our custom collection sourced from the internet. The dataset was meticulously divided into training, testing, and validation sets, following a ratio of 60%, 20%, and 20%, respectively. The experimental results revealed that the YOLOv8m architecture achieved an accuracy of 92%, demonstrating its effectiveness in detecting safety helmets under various lighting conditions, including low-light environments. This performance highlights the potential of the proposed approach for real-world applications in construction site safety monitoring. This developed model primarily focuses on detecting helmets and jackets in real-time in adverse weather conditions such as rainy, foggy, and low-light environments. It trigg
Distributed Real-Time Embedded (DRE) systems that ad-dress safety and mission-critical system requirements are applied in a variety of domains today. Complex, integrated systems like managed satellite clusters expose ...
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Distributed Real-Time Embedded (DRE) systems that ad-dress safety and mission-critical system requirements are applied in a variety of domains today. Complex, integrated systems like managed satellite clusters expose heterogeneous concerns such as strict timing requirements, complexity in system integration, deployment, and repair;and resilience to faults. Integrating appropriate modeling and analysis techniques into the design of such systems helps ensure predictable, dependable and safe operation upon deployment. This paper describes how we can model and analyze applications for these systems in order to verify system properties such as lack of deadline violations. Our approach is based on (1) formalizing the component operation scheduling using Colored Petri nets (CPN), (2) modeling the abstract temporal behavior of application components, and (3) integrating the business logic and the component operation scheduling models into a concrete CPN, which is then analyzed. This model-driven approach enables a verification-driven workflow wherein the application model can be refined and restructured before actual code development.
During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for in-c...
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Two new methods for the computation of cyclomatic complexity especially for decomposable representations are introduced. Building software by integration is a developing paradigm, especially enabled by the emerging co...
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Two new methods for the computation of cyclomatic complexity especially for decomposable representations are introduced. Building software by integration is a developing paradigm, especially enabled by the emerging component technologies. Decomposition of the design for a top-down approach is a prerequisite for this paradigm. Cubic flowgraphs are instrumental in providing formalisms for decomposition and integration. Cyclomatic complexity analysis of a design representation that is decomposable is the goal of this research. In addition to introducing cyclomatic complexity computation using cubic flowgraphs, preservation of cyclomatic complexity in the decomposition of the cubic flowgraph is also presented.
Cardiovascular diseases persist as a prominent global cause of mortality, emphasizing the importance of precise prediction techniques for timely identification and intervention. This research explores the efficacy of ...
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This paper proposes a hybrid quasi-ARMAX modeling and identification scheme for nonlinear systems. The idea is to incorporate a group of certain nonlinear nonparametric models (NNMs) into a linear ARMAX structure. Par...
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ISBN:
(纸本)0780335902
This paper proposes a hybrid quasi-ARMAX modeling and identification scheme for nonlinear systems. The idea is to incorporate a group of certain nonlinear nonparametric models (NNMs) into a linear ARMAX structure. Particular effort is made to find a better compromise to the trade-off between the model flexibility and the simplicity for estimation by using knowledge information efficiently. As the result, we obtain a model equipped with a linear ARMAX structure, flexibility and simplicity. The effectiveness and usefulness of the proposed hybrid model are examined by applying it to identification and control of nonlinear systems.
We describe several observations regarding the completeness and the complexity of bounded model checking and propose techniques to solve some of the associated computational challenges. We begin by defining the comple...
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