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Investigating the impacts of spatial-temporal variation features of air density on assessing wind power generation and its fluctuation in China

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science China(Technological sciences) 2023年第6期66卷 1797-1814页

作者： REN GuoRui WANG Wei WAN Jie HONG Feng YANG Ke YU DaRen School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Xi’an Thermal Power Research Institute Co. Ltd.Xi’an 710054China School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China

Air density plays an important role in assessing wind *** density significantly fluctuates both spatially and *** literature typically used standard air density or local annual average air density to assess wind *** present study investigates the estimation errors of the potential and fluctuation of wind resource caused by neglecting the spatial-temporal variation features of air density in *** air density at 100 m height is accurately calculated by using air temperature,pressure,and *** spatial-temporal variation features of air density are firstly *** the wind power generation is modeled based on a 1.5 MW wind turbine model by using the actual air density,standard air densityρst,and local annual average air densityρsite,***ρstoverestimates the annual wind energy production(AEP)in 93.6%of the study *** significantly affects AEP in central and southern China *** more than 75%of the study area,the winter to summer differences in AEP are underestimated,but the intra-day peak-valley differences and fluctuation rate of wind power are ***ρsitesignificantly reduces the estimation error in *** AEP is still overestimated(0-8.6%)in summer and underestimated(0-11.2%)in *** for southwest China,it is hard to reduce the estimation errors of winter to summer differences in AEP by usingρ***ρsitedistinctly reduces the estimation errors of intra-day peak-valley differences and fluctuation rate of wind power,but these estimation errors cannot be ignored as *** impacts of air density on assessing wind resource are almost independent of the wind turbine types.

关键词： air density spatial-temporal variation power curve wind resource assessment fluctuation ERA-5

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Improving Multimodal Large Language Models through Combining Resampler and MLP Projections

Improving Multimodal Large Language Models through Combining...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, Zixing Bai, Yuting School of Computer Science and Technology Fudan University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

Current multimodal large language models (LLMs) achieve impressive performance through connecting visual encoders and LLMs by resampler or MLP projections. Although the MLP projections are effective and widely used in recent works when compared to resampler projections, it has the flaw of the inability to change the number of tokens, which limits the application scenarios. In this paper, we propose a new projection structure that inherits the efficiency of traditional MLP projections and has the ability to flexibly change the number of tokens. Furthermore, we propose a new multimodal LLM with the proposed projection applied. To validate the performance, we evaluate the proposed model on the first large-scale multimodal science question dataset scienceQA, and the first multimodal LLM evaluation benchmark MME. Our model achieves a new state-of-the-art (SoTA) result of 94.27% on scienceQA, which is higher than the previous SoTA result of 92.53% achieved by the LLaVA+GPT-4 judge model. On the MME benchmark, our model achieves better performance when compared to representative chatbot models BLIP-2 and InstructBLIP, with significantly lower training costs. © 2025 IEEE.

关键词： large language models Multimodal vision and language

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Dictionary-Assisted Chinese Nested Named Entity Recognition 38th

Dictionary-Assisted Chinese Nested Named Entity Recognition

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38th CCF National Conference of computer Applications, CCF NCCA 2023

作者： Wang, Ye Ding, Tongtong Li, Lijie College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9789819987634

Chinese Named Entity Recognition is a challenging task, made even more difficult by the presence of nested entity structures. Previous work on Nested Named Entity Recognition focused only on exploiting internal contextual information, while ignoring the use of external information. In this paper, we propose a dictionary-assisted Chinese Nested Named Entity Recognition model, called KBCNER. Our model uses the dictionary to obtain matching words, combines characters and phrases into character-phrase pairs, and integrates them into BERT. By doing so, we can extract richer semantic information from Chinese phrases than from a single character. The use of external information from the dictionary enhances the features of our model and obtains richer semantics. To avoid constraints from specific-length enumerations, we use bi-affine structures to obtain a global view of spans. We also model local interactions between spans using a Convolutional Neural Network (CNN), taking advantage of the spatial correlation between adjacent spans. Finally, we adopt the idea of contrastive learning based on R-drop to enhance the model's robustness. Experimental results demonstrate that our model achieves excellent performance on multiple datasets. By introducing external information, we improve the performance of the model, highlighting the significance of external information for Chinese Nested Named Entity Recognition. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Semantic segmentation of airborne LiDAR point cloud based on global geometric attention network

Semantic segmentation of airborne LiDAR point cloud based on...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Li, Han Men, Chaoguang Liu, Yongmei Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798331527624

Deep learning-based semantic segmentation of airborne laser scanning (ALS) point clouds receives extensive attention from researchers in the field of remote sensing and computing. However, due to the special properties of ALS point cloud data, it makes difficulties in the semantic segmentation task. Therefore, a new global geometric attention network (GGAN) for ALS point cloud data semantic segmentation is proposed in this paper. Firstly, a local geometric attention convolution module is proposed to embed local geometric feature information. Then, a global relationship aware attention module was inserted into the network to improve the global perception of the geometric information. The experimental results of semantic segmentation show that the GGPN method can achieve satisfactory classification results. Semantic segmentation experiments are conducted on the ISPRS benchmark point cloud dataset to evaluate the performance of the GGAN network, and the method in this paper achieves an overall accuracy of 81.6 and the average F1 of 71.4 which is better than most of the existing popular methods. © 2024 IEEE.

关键词： Semantic Segmentation

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A Speech Spoofing Detection Method Based on Feature Fusion and Residual Attention 24

A Speech Spoofing Detection Method Based on Feature Fusion a...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lan, Haiyan Dong, Linfan Wang, Yuhua Li, Yike Jiang, Dongxu Han, Zhiying College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710865

The emergence of various types of deception attacks presents a substantial risk to Automatic Speaker Verification (ASV) systems, and an increasing number of experts and scholars are focusing on the research of voice deception detection. This paper introduces an innovative approach for detecting voice deception that addresses the accuracy and generalization limitations of traditional methods. The introduced method is based on spectral-temporal feature fusion, which complements the acoustic features captured by different channels to obtain more comprehensive speaker information. It then utilizes multi-channel fused spectrogram features, applying a residual attention learning mechanism to assign varying levels of attention to the spectrogram features from different channels. The method further leverages an improved ResNet network to learn more refined acoustic information for classification. The method attained an EER of 3.60 and a t-DCF score of 0.0931 on the LA logical access corpus of ASVspoof 2019, which represent improvements of 55.5% and 56.0%, respectively, over the best performing baseline system. © 2024 Copyright held by the owner/author(s).

关键词： Spectrographs

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Polyp Segmentation Achieved Through Multi-Scale Feature Fusion Based on Attention Mechanism 11

Polyp Segmentation Achieved Through Multi-Scale Feature Fusi...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Xia, Mingze Zhang, Liguo Yin, Guisheng Dong, Yuxin College Of Computer Science And Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798331531904

Given the high prevalence of colon diseases, it is crucial to utilize computer vision technology for precise polyp segmentation during colonoscopy. However, manual detection becomes increasingly challenging due to the similarity in texture and color between polyp tissue and surrounding normal tissue, leading to higher risks of false positives and missed detections. This study introduces a novel network called CabaNet that prioritizes boundary awareness through coordinate attention. The network consists of four main modules: an encoder utilizing pyramid vision transformer PVT, a cascade part decoder employing Parallel hierarchical aggregation decoder (PHAD) for feature fusion and obtaining a comprehensive semantic global map. Additionally, the refinement pyramid module (RP) enhances feature fusion through a lightweight multi-scale learning strategy. The reverse coordinate attention module (Coo-A) combines features from PHAD and RP to achieve precise and complete edge prediction mapping. Experimental results on five commonly used datasets demonstrate significant improvement in image segmentation accuracy by our proposed model. © 2024 IEEE.

关键词： Image enhancement

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Flare Forecast Model Based on DS-SMOTE and SVM with Optimized Regular Term

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Research in Astronomy and Astrophysics 2023年第6期23卷 38-46页

作者： Jie Wan Jun-Feng Fu Ren-Qing Wen Ke Han Meng-Yao Yu Peng E School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin 150001China School of Computer and Information Engineering Harbin University of CommerceHarbin 150028China

The research of flare forecast based on the machine learning algorithm is an important content of space *** order to improve the reliability of the data-driven model and weaken the impact of imbalanced data set on its forecast performance,we proposes a resampling method suitable for flare forecasting and a Particle Swarm Optimization(PSO)-based Support Vector Machine(SVM)regular term optimization *** the problem of intra-class imbalance and inter-class imbalance in flare samples,we adopt the density clustering method combined with the Synthetic Minority Over-sampling Technique(SMOTE)oversampling method,and performs the interpolation operation based on Euclidean distance on the basis of analyzing the clustering space in the minority *** the same time,for the problem that the objective function used for strong classification in SVM cannot adapt to the sample noise,In this research,on the basis of adding regularization parameters,the PSO algorithm is used to optimize the hyperparameters,which can maximize the performance of the ***,through a comprehensive comparison test,it is proved that the method designed can be well applied to the flare forecast problem,and the effectiveness of the method is proved.

关键词： Sun:flares Sun:magnetic fields Sun:X-rays gamma-rays (Sun:)sunspots

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Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

Neural network approaches for rumor stance detection: Simula...

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作者： Li, Hao Yang, Wu Wang, Wei Wang, Huanran College of Computer Science and Technology Harbin Engineering University Harbin China

This research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain-like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field. © 2024 John Wiley & Sons Ltd.

关键词： Neural network models

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SSD-MIR: Towards Medical Image Restoration With Structured Visual State Space Duality

SSD-MIR: Towards Medical Image Restoration With Structured V...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Binghong Zhang, Zhengqian Wang, Haiyu Yu, Tianshuo Wang, Dong Wang, Jialiang Ren, Junjun Liu, Yongzhuang Jiang, Wei Harbin Institute of Technology School of Mathematics School of Computer Science and Technology Harbin China Harbin Institute of Technology Department of Fundamental Education Harbin China University of Glasgow James Watt School of Engineering Glasgow United Kingdom Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology School of Mathematics Harbin China

ISBN: (纸本)9798350386226

In the field of Medical image processing, Transformers and CNNs bring filter-based local sensing and attention-based global modelling, which strengthened their dominant position. However, the scales required in different applications vary, and improper usage or selection on models can lead to performance decrease, resulting in more serious consequences in downstream tasks. The recent advanced Structured State Space Model has shown its potential as a baseline model, and is further proved to maintaining implicit duality with the attention mechanism, The corresponding model Mamba2 shows its long-range modelling ability with optimized long-term memory and linear computational *** this paper, we propose SSD-MIR, a novel architecture leveraging the advantages of state space duality mechanism. SSD-MIR includes three parts: input projection and feature representation, deep feature transformation, output projection and feature inversion. With the three components sequentially connected to form our model, it is able to model long-range dependencies and learn the mapping between low-quality input and high-quality output simultaneously. In the main deep feature transformation part, depthwise convolution is used for additional feature mapping as an alternative of 1D causal convolution, and an optimized quad-direction scanning mechanism is implemented to form the overall 2D state space dual module. The corresponding wrapper module is built in MetaFormer style with the usage of residual connection and LayerNorm. We perform extensive experiments on multiple medical image restoration tasks, and the results are superior to the state-of-the-arts, demonstrating the strength and efficacy of our model. © 2024 IEEE.

关键词： Mapping

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Refinement modeling and verification of secure operating systems for communication in digital twins

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Digital Communications and Networks 2024年第2期10卷 304-314页

作者： Zhenjiang Qian Gaofei Sun Xiaoshuang Xing Gaurav Dhiman School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India

In traditional digital twin communication system testing,we can apply test cases as completely as possible in order to ensure the correctness of the system implementation,and even then,there is no guarantee that the digital twin communication system implementation is completely *** verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented *** this paper,we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture,and to model the related assembly *** verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states,indicating that the system meets the design expectations.

关键词： Theorem proving Isabelle/HOL Formal verification System modeling Correctness verification

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