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Found 14 papers, 0 papers with code
Analyzing and Overcoming Local Optima in Complex Multi-Objective Optimization by Decomposition-Based Evolutionary Algorithms
When addressing the challenge of complex multi-objective optimization problems, particularly those with non-convex and non-uniform Pareto fronts, Decomposition-based Multi-Objective Evolutionary Algorithms (MOEADs) often converge to local optima, thereby limiting solution diversity.
Pre-insertion resistors temperature prediction based on improved WOA-SVR
The pre-insertion resistors (PIR) within high-voltage circuit breakers are critical components and warm up by generating Joule heat when an electric current flows through them.
Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting
In recent developments, predictive models for multivariate time series analysis have exhibited commendable performance through the adoption of the prevalent principle of channel independence.
DeepEn2023: Energy Datasets for Edge Artificial Intelligence
We anticipate that DeepEn2023 will improve transparency in sustainability in on-device deep learning across a range of edge AI systems and applications.
TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss
Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time.
Poster: Real-Time Object Substitution for Mobile Diminished Reality with Edge Computing
Diminished Reality (DR) is considered as the conceptual counterpart to Augmented Reality (AR), and has recently gained increasing attention from both industry and academia.
Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices
In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices.
Confidence-based federated distillation for vision-based lane-centering
Machine learning-based steering angle prediction needs to consider the vehicle's limitation in uploading large amounts of potentially private data for model training.
EPAM: A Predictive Energy Model for Mobile AI
Predicting the energy consumption of these models, along with their different applications, such as vision and non-vision, requires a thorough investigation of their behavior using various processing sources.
Metamobility: Connecting Future Mobility with Metaverse
A Metaverse is a perpetual, immersive, and shared digital universe that is linked to but beyond the physical reality, and this emerging technology is attracting enormous attention from different industries.
LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality
In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources.
EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing
High definition (HD) map needs to be updated frequently to capture road changes, which is constrained by limited specialized collection vehicles.
Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications
In order to accurately measure the energy consumption on the smartphone and obtain the breakdown of energy consumed by each phase of the object detection processing pipeline, we propose a new measurement strategy.
Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles
These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications.
Networking and Internet Architecture
