arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 703 records · Page 39Linked to original sources

arXiv2024

HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit

Zero-shot coordination (ZSC) is a significant challenge in multi-agent collaboration, aiming to develop agents that can coordinate with unseen partners they have not encountered before. Recent cutting-edge ZSC methods have primarily focused on two-player video games such as OverCooked!2 and Hanabi. In this paper, we extend the scope of ZSC research to the multi-drone cooperative pursuit scenario, exploring how to construct a drone agent capable of coordinating with multiple unseen partners to capture multiple evaders. We propose a novel Hypergraphic Open-ended Learning Algorithm (HOLA-Drone) that continuously adapts the learning objective based on our hypergraphic-form game modeling, aiming to improve cooperative abilities with multiple unknown drone teammates. To empirically verify the effectiveness of HOLA-Drone, we build two different unseen drone teammate pools to evaluate their performance in coordination with various unseen partners. The experimental results demonstrate that HOLA-Drone outperforms the baseline methods in coordination with unseen drone teammates. Furthermore, real-world experiments validate the feasibility of HOLA-Drone in physical systems. Videos can be found on the project homepage~\url{https://sites.google.com/view/hola-drone}.

cs.RO↗

arXiv2024

DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation

Object detection is a fundamental task in various applications ranging from autonomous driving to intelligent security systems. However, recognition of a person can be hindered when their clothing is decorated with carefully designed graffiti patterns, leading to the failure of object detection. To achieve greater attack potential against unknown black-box models, adversarial patches capable of affecting the outputs of multiple-object detection models are required. While ensemble models have proven effective, current research in the field of object detection typically focuses on the simple fusion of the outputs of all models, with limited attention being given to developing general adversarial patches that can function effectively in the physical world. In this paper, we introduce the concept of energy and treat the adversarial patches generation process as an optimization of the adversarial patches to minimize the total energy of the ``person'' category. Additionally, by adopting adversarial training, we construct a dynamically optimized ensemble model. During training, the weight parameters of the attacked target models are adjusted to find the balance point at which the generated adversarial patches can effectively attack all target models. We carried out six sets of comparative experiments and tested our algorithm on five mainstream object detection models. The adversarial patches generated by our algorithm can reduce the recognition accuracy of YOLOv2 and YOLOv3 to 13.19\% and 29.20\%, respectively. In addition, we conducted experiments to test the effectiveness of T-shirts covered with our adversarial patches in the physical world and could achieve that people are not recognized by the object detection model. Finally, leveraging the Grad-CAM tool, we explored the attack mechanism of adversarial patches from an energetic perspective.

cs.CV↗

arXiv2024

An evolutionary approach for discovering non-Gaussian stochastic dynamical systems based on nonlocal Kramers-Moyal formulas

Discovering explicit governing equations of stochastic dynamical systems with both (Gaussian) Brownian noise and (non-Gaussian) Lévy noise from data is chanllenging due to possible intricate functional forms and the inherent complexity of Lévy motion. This present research endeavors to develop an evolutionary symbol sparse regression (ESSR) approach to extract non-Gaussian stochastic dynamical systems from sample path data, based on nonlocal Kramers-Moyal formulas, genetic programming, and sparse regression. More specifically, the genetic programming is employed to generate a diverse array of candidate functions, the sparse regression technique aims at learning the coefficients associated with these candidates, and the nonlocal Kramers-Moyal formulas serve as the foundation for constructing the fitness measure in genetic programming and the loss function in sparse regression. The efficacy and capabilities of this approach are showcased through its application to several illustrative models. This approach stands out as a potent instrument for deciphering non-Gaussian stochastic dynamics from available datasets, indicating a wide range of applications across different fields.

stat.ML↗

arXiv2024

Differential cross-section measurements for neutron-induced $α$ production reactions on carbon across neutron energy range of 6.2 to 76 MeV

Angle-differential cross sections for neutron-induced $α$ production in carbon were determined at thirty discrete neutron energy levels ranging from 6.2 to 76 MeV at the Back-n white neutron source of the China Spallation Neutron Source. Utilizing the $ΔE-E$ telescopes within the Light-charged Particle Detector Array spanning angular measurements from 24.5°to 155.5° in the laboratory frame, the $^{12}C(n,α)x$ reaction cross sections were obtained. These experimental findings exhibit a strong concordance with prior results and have been benchmarked against theoretical estimates from codes such as TALYS, Geant4, and assessments from the ENDF/B-VIII.0 database. Remarkably, distinct resonance-like features were observed at neutron energies of 13.7, 22.4, 29.5, and 61.8 MeV, marking their first-time identification in the literature.Furthermore, a comparative analysis involving the theoretical Distorted Wave Born Approximation was conducted.

nucl-ex↗

arXiv2024

Target-Aware Language Modeling via Granular Data Sampling

Language model pretraining generally targets a broad range of use cases and incorporates data from diverse sources. However, there are instances where we desire a model that excels in specific areas without markedly compromising performance in other areas. A cost-effective and straightforward approach is sampling with low-dimensional data features, which allows to select large-scale pretraining data for domain-specific use cases. In this work, we revisit importance sampling with n-gram features consisting of multi-granular tokens, which strikes a good balance between sentence compression and representation capabilities. We observed the sampled data to have a high correlation with the target downstream task performance while preserving its effectiveness on other tasks. This leads to the proposed data sampling paradigm where language models can be pretrained more efficiently on selected documents. On eight benchmarks we demonstrate with $\sim$1% of the data, pretrained models perform on par with the full RefinedWeb data and outperform randomly selected samples for model sizes ranging from 125M to 1.5B.

cs.CL↗

arXiv2024

TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings

Currently, inspired by the success of vision-language models (VLMs), an increasing number of researchers are focusing on improving VLMs and have achieved promising results. However, most existing methods concentrate on optimizing the connector and enhancing the language model component, while neglecting improvements to the vision encoder itself. In contrast, we propose Text Guided LLaVA (TG-LLaVA) in this paper, which optimizes VLMs by guiding the vision encoder with text, offering a new and orthogonal optimization direction. Specifically, inspired by the purpose-driven logic inherent in human behavior, we use learnable latent embeddings as a bridge to analyze textual instruction and add the analysis results to the vision encoder as guidance, refining it. Subsequently, another set of latent embeddings extracts additional detailed text-guided information from high-resolution local patches as auxiliary information. Finally, with the guidance of text, the vision encoder can extract text-related features, similar to how humans focus on the most relevant parts of an image when considering a question. This results in generating better answers. Experiments on various datasets validate the effectiveness of the proposed method. Remarkably, without the need for additional training data, our propsoed method can bring more benefits to the baseline (LLaVA-1.5) compared with other concurrent methods. Furthermore, the proposed method consistently brings improvement in different settings.

cs.CV↗

arXiv2024

Cross-Utterance Conditioned VAE for Speech Generation

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.

cs.SD↗

arXiv2024

Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms

Aiming at the problem that delay time is difficult to determine and prediction accuracy is low in building prediction model of SCR system, a dynamic modeling scheme based on a hybrid of multiple data-driven algorithms was proposed. First, processed abnormal values and normalized the data. To improve the relevance of the input data, used MIC to estimate delay time and reconstructed production data. Then used combined feature selection method to determine input variables. To further mine data information, VMD was used to decompose input time series. Finally, established NOx emission prediction model combining ELM and EC model. Experimental results based on actual historical operating data show that the MAPE of predicted results is 2.61%. Model sensitivity analysis shows that besides the amount of ammonia injection, the inlet oxygen concentration and the flue gas temperature have a significant impact on NOx emission, which should be considered in SCR process control and optimization.

eess.SP↗

arXiv2024

Data Driven based Dynamic Correction Prediction Model for NOx Emission of Coal Fired Boiler

The real-time prediction of NOx emissions is of great significance for pollutant emission control and unit operation of coal-fired power plants. Aiming at dealing with the large time delay and strong nonlinear characteristics of the combustion process, a dynamic correction prediction model considering the time delay is proposed. First, the maximum information coefficient (MIC) is used to calculate the delay time between related parameters and NOx emissions, and the modeling data set is reconstructed; then, an adaptive feature selection algorithm based on Lasso and ReliefF is constructed to filter out the high correlation with NOx emissions. Parameters; Finally, an extreme learning machine (ELM) model combined with error correction was established to achieve the purpose of dynamically predicting the concentration of nitrogen oxides. Experimental results based on actual data show that the same variable has different delay times under load conditions such as rising, falling, and steady; and there are differences in model characteristic variables under different load conditions; dynamic error correction strategies effectively improve modeling accuracy; proposed The prediction error of the algorithm under different working conditions is less than 2%, which can accurately predict the NOx concentration at the combustion outlet, and provide guidance for NOx emission monitoring and combustion process optimization.

eess.SY↗

arXiv2024

Short-term power load forecasting method based on CNN-SAEDN-Res

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.

eess.SP↗

arXiv2024

Critical link identification of power system vulnerability based on modified graph attention network

With the expansion of the power grid and the increase of the proportion of new energy sources, the uncertainty and random factors of the power grid increase, endangering the safe operation of the system. It is particularly important to find out the critical links of vulnerability in the power grid to ensure the reliability of the power grid operation. Aiming at the problem that the identification speed of the traditional critical link of vulnerability identification methods is slow and difficult to meet the actual operation requirements of the power grid, the improved graph attention network (IGAT) based identification method of the critical link is proposed. First, the evaluation index set is established by combining the complex network theory and the actual operation data of power grid. Secondly, IGAT is used to dig out the mapping relationship between various indicators and critical links of vulnerability during the operation of the power grid, establish the identification model of critical links of vulnerability, and optimize the original graph attention network considering the training accuracy and efficiency. Thirdly, the original data set is obtained through simulation, and the identification model is trained, verified and tested. Finally, the model is applied to the improved IEEE 30-node system and the actual power grid, and the results show that the proposed method is feasible, and the accuracy and speed are better than that of traditional methods. It has certain engineering utilization value.

eess.SY↗

arXiv2024

Secondary frequency control of islanded microgrid considering wind and solar stochastics

This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reduce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios.

eess.SY↗

arXiv2024

Low carbon optimal scheduling of integrated energy system considering waste heat utilization under the coordinated operation of incineration power plant and P2G

In order to improve energy utilization and reduce carbon emissions, this paper presents a comprehensive energy system economic operation strategy of Incineration power plant Power-to-gas (P2G) with waste heat recovery. First, consider the coordinated operation of Incineration power plant - P2G, introduce the refined Power-to-gas two-stage operation process, add Hydrogen fuel cells on the basis of traditional Power-to-gas to reduce the energy ladder loss, and recycle the Methanation reaction heat; Secondly, in order to improve the energy utilization efficiency of Incineration, it is considered to install a waste heat recovery device containing a water source heat pump to recover the waste heat of flue gas and consume some electric energy, sourced from wind power, and add a CO2 separation device to combine the recovered CO2 with P2G to synthesize CH4 to achieve carbon recycling. Finally, within the framework of a tiered carbon trading mechanism an IES optimization model for electricity-heat with the goal of minimizing the system operating cost is constructed, and the GUROBI modeling optimization engine is used to solve this model. The results verify the effectiveness of the model.

eess.SY↗

arXiv2024

Prim2Room: Layout-Controllable Room Mesh Generation from Primitives

We propose Prim2Room, a novel framework for controllable room mesh generation leveraging 2D layout conditions and 3D primitive retrieval to facilitate precise 3D layout specification. Diverging from existing methods that lack control and precision, our approach allows for detailed customization of room-scale environments. To overcome the limitations of previous methods, we introduce an adaptive viewpoint selection algorithm that allows the system to generate the furniture texture and geometry from more favorable views than predefined camera trajectories. Additionally, we employ non-rigid depth registration to ensure alignment between generated objects and their corresponding primitive while allowing for shape variations to maintain diversity. Our method not only enhances the accuracy and aesthetic appeal of generated 3D scenes but also provides a user-friendly platform for detailed room design.

cs.CV↗

arXiv2024

Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks

Combinatorial optimization (CO) is the fundamental problem at the intersection of computer science, applied mathematics, etc. The inherent hardness in CO problems brings up challenge for solving CO exactly, making deep-neural-network-based solvers a research frontier. In this paper, we design a family of non-autoregressive neural networks to solve CO problems under positive linear constraints with the following merits. First, the positive linear constraint covers a wide range of CO problems, indicating that our approach breaks the generality bottleneck of existing non-autoregressive networks. Second, compared to existing autoregressive neural network solvers, our non-autoregressive networks have the advantages of higher efficiency and preserving permutation invariance. Third, our offline unsupervised learning has lower demand on high-quality labels, getting rid of the demand of optimal labels in supervised learning. Fourth, our online differentiable search method significantly improves the generalizability of our neural network solver to unseen problems. We validate the effectiveness of this framework in solving representative CO problems including facility location, max-set covering, and traveling salesman problem. Our non-autoregressive neural solvers are competitive to and can be even superior to state-of-the-art solvers such as SCIP and Gurobi, especially when both efficiency and efficacy are considered. Code is available at https://github.com/Thinklab-SJTU/NAR-CO-Solver

cs.LG↗

arXiv2024

TG-LMM: Enhancing Medical Image Segmentation Accuracy through Text-Guided Large Multi-Modal Model

We propose TG-LMM (Text-Guided Large Multi-Modal Model), a novel approach that leverages textual descriptions of organs to enhance segmentation accuracy in medical images. Existing medical image segmentation methods face several challenges: current medical automatic segmentation models do not effectively utilize prior knowledge, such as descriptions of organ locations; previous text-visual models focus on identifying the target rather than improving the segmentation accuracy; prior models attempt to use prior knowledge to enhance accuracy but do not incorporate pre-trained models. To address these issues, TG-LMM integrates prior knowledge, specifically expert descriptions of the spatial locations of organs, into the segmentation process. Our model utilizes pre-trained image and text encoders to reduce the number of training parameters and accelerate the training process. Additionally, we designed a comprehensive image-text information fusion structure to ensure thorough integration of the two modalities of data. We evaluated TG-LMM on three authoritative medical image datasets, encompassing the segmentation of various parts of the human body. Our method demonstrated superior performance compared to existing approaches, such as MedSAM, SAM and nnUnet.

cs.CV↗

arXiv2024

DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios

Accurate real-time object detection enhances the safety of advanced driver-assistance systems, making it an essential component in driving scenarios. With the rapid development of deep learning technology, CNN-based YOLO real-time object detectors have gained significant attention. However, the local focus of CNNs results in performance bottlenecks. To further enhance detector performance, researchers have introduced Transformer-based self-attention mechanisms to leverage global receptive fields, but their quadratic complexity incurs substantial computational costs. Recently, Mamba, with its linear complexity, has made significant progress through global selective scanning. Inspired by Mamba's outstanding performance, we propose a novel object detector: DS MYOLO. This detector captures global feature information through a simplified selective scanning fusion block (SimVSS Block) and effectively integrates the network's deep features. Additionally, we introduce an efficient channel attention convolution (ECAConv) that enhances cross-channel feature interaction while maintaining low computational complexity. Extensive experiments on the CCTSDB 2021 and VLD-45 driving scenarios datasets demonstrate that DS MYOLO exhibits significant potential and competitive advantage among similarly scaled YOLO series real-time object detectors.

cs.CV↗

arXiv2024

Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane

We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass. Unlike existing methods that output a single, unified 3D shape, Frankenstein simultaneously generates multiple separated shapes, each corresponding to a semantically meaningful part. The 3D scene information is encoded in one single tri-plane tensor, from which multiple Singed Distance Function (SDF) fields can be decoded to represent the compositional shapes. During training, an auto-encoder compresses tri-planes into a latent space, and then the denoising diffusion process is employed to approximate the distribution of the compositional scenes. Frankenstein demonstrates promising results in generating room interiors as well as human avatars with automatically separated parts. The generated scenes facilitate many downstream applications, such as part-wise re-texturing, object rearrangement in the room or avatar cloth re-targeting. Our project page is available at: https://wolfball.github.io/frankenstein/.

cs.CV↗