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Found 14 papers, 2 papers with code
Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework
In this study, we propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development.
Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry
The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models.
Pathway toward prior knowledge-integrated machine learning in engineering
Despite the digitalization trend and data volume surge, first-principles models (also known as logic-driven, physics-based, rule-based, or knowledge-based models) and data-driven approaches have existed in parallel, mirroring the ongoing AI debate on symbolism versus connectionism.
Application of attention-based Siamese composite neural network in medical image recognition
Aiming at the problem of few-shot samples, a Siamese neural network suitable for classification model is proposed.
A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation
The framework is integrated with a feedforward loop that embedded a dynamic building environment with leading and lagging system information involved: The simulation combined with system characteristic information is imported to the ML predictive algorithms.
Utilizing Domain Knowledge: Robust Machine Learning for Building Energy Prediction with Small, Inconsistent Datasets
In this study, component-based machine learning (CBML) as the knowledge-encoded data-driven method is examined in the context of energy-efficient building engineering.
How Many Grid-Forming Converters Do We Need? A Perspective From Small Signal Stability and Power Grid Strength
Based on our analysis, we further study the problem of how to configure GFM converters in the grid and how many GFM converters we will need.
Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX
This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals.
A Thin Format Vision-Based Tactile Sensor with A Micro Lens Array (MLA)
Vision-based tactile sensors have been widely studied in the robotics field for high spatial resolution and compatibility with machine learning algorithms.
Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning
Third, we evaluate explainability by qualitative and quantitative methods demonstrating the matching of preliminary knowledge and data-driven derived strategies and show correctness of activations at component interfaces compared to white-box simulation results (envelope components: R2 = 0. 92.. 0. 99; zones: R2 = 0. 78.. 0. 93).
PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection
Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure.
PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding
We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation.
ActiveHNE: Active Heterogeneous Network Embedding
To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).
Prediction-Tracking-Segmentation
We introduce a prediction driven method for visual tracking and segmentation in videos.
