Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning archite...
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This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emis...
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Accurate nodule detection with high sensitivity is essential for early lung cancer diagnosis. Focusing on small nodule detection, we propose an end-to-end framework, which includes a backbone, a candidate detection ne...
Accurate nodule detection with high sensitivity is essential for early lung cancer diagnosis. Focusing on small nodule detection, we propose an end-to-end framework, which includes a backbone, a candidate detection network, and a filter network. The backbone learns multi-layer features so that the region proposal network with feature pyramid structure detects nodules of various sizes, especially small ones. Moreover, the filter net is designed to further classify the proposals with low confidence, which utilizes the decoupled feature maps to make the features of nodules more discriminative. We validate our framework on the LUNA16 dataset. The results show that our framework detects more small nodules, and achieves a comparable performance with other CAD systems.
Accurate and timely estimation of sunflower yield is crucial for agricultural researchers, farmers, and breeders. Use of Unmanned Aerial Vehicles (UAVs) with multi-spectral sensors has been adopted to meet the need fo...
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ISBN:
(数字)9798331510503
ISBN:
(纸本)9798331510510
Accurate and timely estimation of sunflower yield is crucial for agricultural researchers, farmers, and breeders. Use of Unmanned Aerial Vehicles (UAVs) with multi-spectral sensors has been adopted to meet the need for precise sunflower seed yield predictions. This study proposes a combined approach that integrates 12 remotely sensed vegetation indices (VIs) with 3 key physiological traits - fresh weight, dry weight, and moisture factor - to capture the complex dynamics affecting sunflower crop. Predictive performance of VIs alone and in combination with physiological traits is evaluated using Root Mean Squared Error (RMSE). Our results show that incorporating these two sets of features, along with their temporal, spectral, and statistical characteristics, leads to consistent improvements in sunflower seed yield estimation that can reduce the root mean squared error to as low as 0.4075 ( $\text{kg} / \text{plot}$ ).
Secure system operations rely on reliable network structures. The loss of controllability may be the main reason to cause cascaded failures for complex network, e.g., Energy Internet (EI). However, the existing studie...
Secure system operations rely on reliable network structures. The loss of controllability may be the main reason to cause cascaded failures for complex network, e.g., Energy Internet (EI). However, the existing studies do not consider the network controllability to guide the system reconfiguration. To address this issue, the paper proposes a new structuring planning method for EI with consideration of controllability and economy. Firstly, the structure planning problem is modeled as a dynamic optimization problem with the tradeoff objectives of maximum social welfare and minimum driven nodes for long-term period. Then, a mixed maximum matching and deep deterministic policy gradient method is presented to obtain the approximate optimal planning solution with strong adaptability. Finally, simulation results demonstrate the effectiveness of the proposed method.
Climate change and geopolitics have led to the conception of plans for reducing greenhouse gas emissions and improving the sustainability of existing fossil-based energy systems. In this respect, district heating has ...
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Climate change and geopolitics have led to the conception of plans for reducing greenhouse gas emissions and improving the sustainability of existing fossil-based energy systems. In this respect, district heating has been identified as an indispensable player for its potential to integrate seamlessly environmentally-friendly heat sources. To improve the efficiency of these district heating systems, optimal operation schemes can be devised and enforced through control systems. To this end, we present a control-oriented nonlinear ODE-based model of temperature dynamics in a multi-producer district heating system. The model features a modular design and comprises the thermal dynamics of heat exchangers of producers and consumers interconnected by a distribution network of meshed topology. Then, we establish passivity properties and zero-state detectability for the modeled temperature dynamics that could be exploited for controller design and solving constrained optimization problems.
The work proposes the improvement of queue management priority-based Traffic engineering method. It is based on the interaction prediction principle to coordinate decisions at various levels. The lower level of calcul...
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Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory i...
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In the wet semiconductor process, abrasive nanoparticles are widely used in the chemical mechanical polishing (CMP) process to polish wafer surfaces for high planarization. During polishing, nanoparticles move with a ...
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Internet of Vehicles (IoV) frameworks integrate smart vehicles, roads, network infrastructures, and users into one system, enhancing environmental awareness, increasing efficiency, and reducing accidents. Although IoV...
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