Xiang Pan
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View article: CNN-based state prediction for a varying number of storage in economic dispatch
CNN-based state prediction for a varying number of storage in economic dispatch Open
Economic dispatch (ED) is essential for power system operations. However, the large-scale energy storage (ES) integration introduces numerous binary state variables into ED formulations. Although relaxation-based methods and machine learni…
View article: Rest-Wake Training: A Comprehensive Study with Theoretical and Experimental Innovations on CIFAR-10
Rest-Wake Training: A Comprehensive Study with Theoretical and Experimental Innovations on CIFAR-10 Open
Training deep neural networks is resource intensive. In this paper, we introduce a novel Rest-Wake training paradigm that alternates between active gradient updates (wake phase) and parameter consolidation (rest phase). Our method is enhan…
View article: Exploring Diversity-Aware Augmented Learning for Multi-Solution Optimization
Exploring Diversity-Aware Augmented Learning for Multi-Solution Optimization Open
Machine learning has proven highly effective in addressing constrained optimization problems by approximating the mapping from hyperparameters to solutions. However, standard supervised learning methods often fall short due to the presence…
View article: Bridging Domains with Approximately Shared Features
Bridging Domains with Approximately Shared Features Open
Multi-source domain adaptation aims to reduce performance degradation when applying machine learning models to unseen domains. A fundamental challenge is devising the optimal strategy for feature selection. Existing literature is somewhat …
View article: Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin
Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin Open
This study focuses on the application of deep learning for transforming semantic point clouds into semantic Building Information Models (BIM) to create a Heritage Digital Twin, centering on Taoping Village, a site of historical and cultura…
View article: Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations
Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations Open
Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains und…
View article: Research on Classification of Rod-Shaped Ground Objects in Road Scene Based on Multi-Feature Associated Network
Research on Classification of Rod-Shaped Ground Objects in Road Scene Based on Multi-Feature Associated Network Open
Vehicular LiDAR technology provides powerful technical support for the accurate acquisition of rod-shaped ground objects' spatial information in road scenes. However, how to solve the accurate extraction and classification of rod-shaped gr…
View article: Accelerating Dataset Distillation via Model Augmentation
Accelerating Dataset Distillation via Model Augmentation Open
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones. Existing DD methods based on gradient matching achieve leading performance; however, they are ext…
View article: Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens Open
The term `spurious correlations' has been used in NLP to informally denote any undesirable feature-label correlations. However, a correlation can be undesirable because (i) the feature is irrelevant to the label (e.g. punctuation in a revi…
View article: Task Transfer and Domain Adaptation for Zero-Shot Question Answering
Task Transfer and Domain Adaptation for Zero-Shot Question Answering Open
Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning methods to new domains, labeled data may not always be available.…
View article: DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings
DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings Open
The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes. As the training dataset may contain a mixture of data points corresponding to different load…
View article: Intrusion Detection Model for Wireless Sensor Networks Based on MC‐GRU
Intrusion Detection Model for Wireless Sensor Networks Based on MC‐GRU Open
A crucial line of defense for the security of wireless sensor network (WSN) is intrusion detection. This research offers a new MC‐GRU WSN intrusion detection model based on convolutional neural networks (CNN) and gated recurrent unit (GRU)…
View article: Task Transfer and Domain Adaptation for Zero-Shot Question Answering
Task Transfer and Domain Adaptation for Zero-Shot Question Answering Open
Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks.However, when applying machine learning methods to new domains, labeled data may not always be available.T…
View article: Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens Open
The term ‘spurious correlations’ has been used in NLP to informally denote any undesirable feature-label correlations. However, a correlation can be undesirable because (i) the feature is irrelevant to the label (e.g. punctuation in a revi…
View article: Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems Open
Ensuring solution feasibility is a key challenge in developing Deep Neural Network (DNN) schemes for solving constrained optimization problems, due to inherent DNN prediction errors. In this paper, we propose a ``preventive learning'' fram…
View article: Calculating Question Similarity is Enough: A New Method for KBQA Tasks
Calculating Question Similarity is Enough: A New Method for KBQA Tasks Open
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base. The core idea is to find the link between the internal knowledge behind questions and known triples of the know…
View article: DeepOPF-V: Solving AC-OPF Problems Efficiently
DeepOPF-V: Solving AC-OPF Problems Efficiently Open
AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) …
View article: Hash Self-Attention End-to-End Network for Sketch-Based 3D Shape Retrieval
Hash Self-Attention End-to-End Network for Sketch-Based 3D Shape Retrieval Open
To improve the feature clustering of sketches and 3D models, a feature extraction network with self attention and hash regularization is proposed. Firstly, the 3D model is rendered to obtain the different views. Secondly, the self attentio…
View article: DeepOPF-V: Solving AC-OPF Problems Efficiently
DeepOPF-V: Solving AC-OPF Problems Efficiently Open
AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) …
View article: DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility Open
Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the feasibility of the predicted solutions to physical system co…
View article: DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems Open
High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems. It requires grid operators to solve alternative current optimal power flow (AC-OPF) problems more frequently for economical …
View article: DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow
DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow Open
We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for reliable and cost-effective power system operation.DeepOPF is inspire…
View article: CCDC 1887453: Experimental Crystal Structure Determination
CCDC 1887453: Experimental Crystal Structure Determination Open
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available …