Vision is one of the important pathways for human perception of external information, with over 80% of perception being acquired through vision. How to enable computers to possess efficient and flexible visual systems...
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作者:
Elias, RitaIssa, Raja R. A.Univ Florida
Rinker Sch Construct Management Gainesville FL 32611 USA Univ Florida
Rinker Sch Construct Management Ctr Adv Construct Informat Modeling Gainesville FL 32611 USA
Designers usually assess only a few house design alternatives simulating their energy use, due to time and cost constraints. This study aims at developing a machine learning (ML)-based generative design (GD) framework...
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ISBN:
(数字)9780784485224
ISBN:
(纸本)9780784485224
Designers usually assess only a few house design alternatives simulating their energy use, due to time and cost constraints. This study aims at developing a machine learning (ML)-based generative design (GD) framework to automate the design process of Florida's detached residences while optimizing their energy performance. An artificial neural network (ANN) model was developed using the machine learning platform TensorFlow along with some Python-based Keras libraries based on a big dataset of around 17,000 newly constructed detached residences in Florida between the years 2009 and 2021. The GD framework was established using Autodesk Dynamo and Autodesk Revit internal generative design tool that uses the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The ANN model was created to predict the required capacities of the cooling and heating systems in detached houses considering 10 independent variables relating to the house geometry and its energy performance. The ANN was then integrated within the GD framework for performance evaluation purposes. The findings of this study included a fully automated design of energy-efficient detached houses greatly reducing the financial strain and time consumed by designers/developers using traditional techniques.
Cognitive radio networks (CRNs) are getting increasingly famous due to their potential to deliver excessive-bandwidth offerings to numerous users. Despite this, the signal satisfactory of those networks can be fantast...
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Diagnosing a brain tumor typically necessitates the expertise of a radiologist, whose skills and knowledge are essential to the process. Since the overall number of individuals has grown, so too has the quantity of me...
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The proceedings contain 65 papers. The special focus in this conference is on Mobile and Ubiquitous Systems: computing, Networking and Services. The topics include: High-Performance Features in Generalizable Fing...
ISBN:
(纸本)9783031639913
The proceedings contain 65 papers. The special focus in this conference is on Mobile and Ubiquitous Systems: computing, Networking and Services. The topics include: High-Performance Features in Generalizable Fingerprint-Based Indoor Positioning;SCORE: Scalable Contact Tracing over Uncertain Trajectories;faultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things;deepHeteroIoT: Deep Local and Global learning over Heterogeneous IoT Sensor Data;Federated Reinforcement learning for Automated LoRaWAN Management in Industrial IoT;a Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications;LOADHoC: Towards the Automatic Local Distribution of Computation Using Existing IoT Devices;e-Go Bicycle Intelligent Speed Adaptation System for Catching the Green Light;FedGCS: Addressing Class Imbalance in Long-Tail Federated learning;FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated learning;RADEAN: A Resource Allocation Model Based on Deep Reinforcement learning and Generative Adversarial Networks in Edge computing;a Stream Data Service Framework for Real-Time Vehicle Companion Discovery;KS-Autoformer: An Autoformer-Based SOC Prediction Framework for Electric Vehicles;deep Reinforcement learning-Based Multi-node Collaborative Task Offloading Optimization in 6G Space-Air-Ground Integrated Networks;securing Wireless Communication in Critical Infrastructure: Challenges and Opportunities;cross-User Activity Recognition via Temporal Relation Optimal Transport;selfAct: Personalized Activity Recognition Based on Self-Supervised and Active learning;a Novel Method for Wearable Activity Recognition with Feature Evolvable Streams;let’s Vibrate with Vibration: Augmenting Structural engineering with Low-Cost Vibration Sensing;research on Data Drift and Class Imbalance in Android Malware Detection;Reputation-Based Dissemination of Trustworthy Information in VANETs;reputation System
With the evolution of intelligent grid tech, the need for monitoring power quality has markedly risen, essential for the continuous functioning of the power grid. Addressing this matter, this research proposes a combi...
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Machine learning algorithms make a significant contribution to disease prediction. The main objective of this is to support researchers and practitioners in choosing the most suitable machine learning algorithm for th...
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It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use pr...
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The application of data mining in physical education experimental teaching guidance can situation and performance in experimental courses, and provide personalized teaching guidance and feedback. By analyzing students...
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With the development of information technology, the teaching of IoT technology has gradually become popular, but as an important part of hands-on training, the practical aspects of the course are restricted by various...
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