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An enhanced combined model for NEV sales prediction utilizing complexity self-awareness and sentiment score correction

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Energy 2025年 330卷

作者： Wan Dai Yuquan Liu Xinyi Huang Biyou Zou Jiaming Zhu School of Internet Anhui University Hefei 230039 Anhui China Science Island Branch of Graduate School University of Science and Technology of China Hefei 230026 Anhui China Stony Brook Institute at Anhui University Hefei 230039 Anhui China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei 230601 Anhui China

Accurately predicting new energy vehicle (NEV) sales not only contributes to promoting sustainable mobility but also provides support for resource allocation and infrastructure planning. To address the limitations of existing methods in integrating market sentiment and capturing multiscale data characteristics, this paper proposes a hybrid forecasting model that incorporates complexity self-awareness and sentiment score correction. First, a comprehensive time series dataset is constructed using historical sales data and the corresponding online reviews. Second, the market-oriented sentiment score self-correction (MSSC) algorithm is applied to dynamically optimize sentiment variables. Then, ensemble empirical mode decomposition (EEMD) is used to extract multifrequency components, which are evaluated and classified via the self-aware complexity-driven intelligent quantification (SCIQ) method for refined modelling. An empirical analysis based on BYD Tang NEV sales data demonstrates that the proposed model outperforms state-of-the-art benchmark models across four evaluation metrics. Moreover, after incorporating SCIQ and MSSC, the prediction accuracy improved by 30.04% and 22.34%, respectively, in terms of the RMSE, and by 2.04% and 1.39%, respectively, in terms of the first-order effectiveness. Furthermore, SHAP analysis is employed to interpret key influencing factors, enabling the formulation of consumer-oriented policy recommendations that support high-quality NEV industry development.

关键词： Sales

SATO: Straighten Any 3D Tubular Object

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IEEE Transactions on Medical Imaging 2025年

作者： Zhou, Yanfeng Zhou, Jiaheng Wang, Zichen Yang, Ge Shenzhen University School of Artificial Intelligence Shenzhen 518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Medical UltraSound Image Computing Lab Shenzhen 518060 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Chinese Academy of Sciences Institute of Automation Beijing China

3D tubular objects have complex spatial shapes. Direct volume visualization cannot intuitively display their morphological characteristics and surface abnormalities. Straightening reformation is an effective visualization method for tubular objects. It uses a swept frame to sample cross sections along the centerline of the tubular object to generate straightening result. So far, however, current methods cannot visualize the full 3D view and fail to interface with downstream morphological analysis. Furthermore, current swept frames impose strict restrictions on the shape of tubular objects and are computationally expensive. In this study, we propose a novel swept frame based on vector rotation and construct an automatic straightening reformation pipeline. Our method is applicable to various tubular objects and can be efficiently executed recursively while ensuring that the straightening results have no rotation bias. Extensive experiments on eight different tubular objects and quantitative evaluation on various downstream applications demonstrate the effectiveness and universality of our straightening pipeline. © 1982-2012 IEEE.

关键词： curved planar reformation Straighten swept frame tubular object visualization

Large Language Models for Generative Information Extraction: A Survey

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arXiv

arXiv 2023年

作者： Xu, Derong Chen, Wei Peng, Wenjun Zhang, Chao Xu, Tong Zhao, Xiangyu Wu, Xian Zheng, Yefeng Wang, Yang Chen, Enhong State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei230000 China Department of Data Science City University of Hong Kong Hongkong999077 Hong Kong Jarvis Research Center Tencent YouTu Lab Beijing100029 China Anhui Conch Information Technology Engineering Co. Ltd. Wuhu241000 China

Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks, in this study, we survey the most recent advancements in this field. We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques, and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on a thorough review conducted, we identify several insights in technique and promising research directions that deserve further exploration in future studies. We maintain a public repository and consistently update related works and resources on GitHub (LLM4IE repository). Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

Spintronic Terahertz Polarization Programmable System for Information Encoding

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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China Beijing Advanced Innovation Center for Big Data and Brain Computing China

Terahertz (THz) wave as an important electromagnetic wave has been increasingly applied in wireless communication and imaging sensing technology. It has been an increasingly practical application. Ultra-high data rates in communication technology push toward a broadband, controllable polarization, and multiple transmission channels signal sources. Here, we propose a programmable polarization spintronics terahertz source (PP-STS), in which contains a controllable polarization spintronic THz emitter and a microcontroller unit. The PP-STS not only can generate THz waves with wide frequency bandwidth, but also has the function of switching the orthogonal linear polarization state of the generated THz waves flexibly. By coding the microcontroller unit, the designed PP-SPS can directly program the orthogonal linear polarization states as a series of information to transmit. To achieve the interconversions between digital codes and the THz polarization information, an optimum matching THz polarization beam splitter (THz-PBS) is also designed via simulations and experiments. The THz-PBS, as a decoder, can accurately split the transverse electric (TE) and transverse magnetic (TM) modes into two THz detectors. At last, a coding and decoding prototype system of spintronic THz polarization is built by integrating the PP-STS, the THz-PBS and two THz detectors. The send and receive information with high speed and accuracy are verified in this prototype system as well. We believe that our work takes THz applications for multiplexing communications one step further and sheds light on commercializing the PP-STS with exciting manufacturing lines for efficient and economical THz polarization technology. © 2023, The Authors. All rights reserved.

关键词： Microcontrollers

Spintronic Terahertz Polarization Programmable System for Information Encoding

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SSRN 2023年

关键词： Microcontrollers

Spintronic Terahertz Polarization Programmable System for Information Encoding

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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng Beijing Advanced Innovation Center for Big Data and Brain Computing China School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China

关键词： Microcontrollers

Identification of Seed Maize Fields from Hyperspectral Imagery by Fusion of Spectral and Spatial Features

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SSRN 2024年

作者： Cheng, Jinpeng Yang, Hao Cao, Xiaoyu Wu, Qiang Zhang, Na Yan, Zhiyu Wang, Hongbin Huang, Linsheng Ma, Xinming Xiong, Shuping Yang, Guijun College of Agronomy Henan Agricultural University Zhengzhou450046 China Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Zhuhai Orbita Aerospace Science &Technology Co. Ltd Zhuhai519080 China Sinochem Modern Agriculture Co. Ltd. Beijing100031 China College of Geological Engineering and Geomatics Chang’an University Xi’an710054 China

Grasping the planting information of seed maize is utterly important for strengthening the macro-control of the seed industry market and ensuring the safe production of conventional maize. At present, the traditional ground survey is the main method for surveying the planting distribution of seed maize, which is inefficient and expensive. And in the existing remote sensing classification methods of seed maize, most of them use texture information, and seldom use hyperspectral information. Here, we studied the "Zhuhai-1" satellite hyperspectral data to construct the spectral and spatial features of seed maize identification, and then used the Support Vector Machine (SVM) classifier to prepare the planting pattern map of seed maize, and finally an efficient and economical identification model for seed maize based on hyperspectral satellite images was proposed. The classification uses a multi-layer mask method to classify seed corn and conventional corn in the scene of corn planting distribution. The spectral feature extraction method of the classification model compares the two methods of Class Means Matrix Clustering Feature (CMMCF) and PCA-LDA (Principal Component Analysis -Linear Discriminant Analysis), and the extraction of spatial features uses Multiscale Extended Morphological Profile method (MEMP). Then, four feature combinations of PCA-LDA, CMMCF, PCA-LDA-MEMP and CMMCF-MEMP were constructed for classification. The classification results use overall accuracy (OA), mapping accuracy (PA), and user accuracy (UA) to evaluate the model accuracy;Fisher discriminant ratio (FR) evaluates feature separability. The results of the model show that the FR value of the texture feature in the model is higher than that of the spectral feature, and the texture feature plays an important role in the extraction of seed maize. CMMCF-MEMP-SVM model was the best, OA reached 94.10%, PA and UA extracted from seed maize were 91.28% and 92.43%, respectively. In addition, the CMMCF-MEMP

关键词： Morphology

Multi-scale Graph Convolutional Neural Network for Object Recognition from Point Cloud data 2nd

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Multi-scale Graph Convolutional Neural Network for Object Re...

2nd International Conference on Urban Intelligence and Applications, ICUIA 2020

作者： Lu, Qiang Chen, Chao Teng, Jinfeng Zhang, Chunyuan Huang, Yi Xuan, Shanli Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China School of Computer and Information Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230009 China

ISBN: (纸本)9789813346000

How to make robots understand the point cloud data which is collected from the 3D sensor and complete the recognition has become a hot research direction in recent years. In this paper, we propose a new approach to improve the critical robotic capability, semantic understanding of the environment (i.e., 3D object recognition). The convolutional neural network (CNN) method has a very good recognition result in the 2D image domain, but it has certain difficulty in applying irregular and unordered 3D point clouds data. The network for point cloud data generally uses the convolution to realize the extraction of point cloud features by finding the neighborhood features on the point set. Due to the different neighborhood scales caused by the irregularity of 3D point cloud data, we propose a CNN structure that combines multi-scale features. By finding multiple neighborhoods of the point set and establishing local graph extraction features, the stable expression of the local neighborhood is obtained. At the same time, the key point calibration method is added, so that the network can dynamically focus on key point features to improve the recognition result. In a series of analytical experiments, we demonstrate competing results that demonstrate the effectiveness of the network structure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

Privileged graph distillation for cold start recommendation

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arXiv

arXiv 2021年

作者： Wang, Shuai Zhang, Kun Wu, Le Ma, Haiping Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui University China

The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems, warm users (items) have privileged collaborative signals of interaction records compared to cold start users (items), and these Collaborative Filtering (CF) signals are shown to have competing performance for recommendation. Many researchers proposed to learn the correlation between collaborative signal embedding space and the attribute embedding space to improve the cold start recommendation, in which user and item categorical attributes are available in many online platforms. However, the cold start recommendation is still limited by two embedding spaces modeling and simple assumptions of space transformation. As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD). The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links. The student model is composed of an entity-attribute graph without CF links. Specifically, the teacher model can learn better embeddings of each entity by injecting complex higher-order relationships from the constructed heterogeneous graph. The student model can learn the distilled output with privileged CF embeddings from the teacher embeddings. Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item. Finally, extensive experimental results on the real-world datasets clearly show the effectiveness of our proposed model on different types of cold start problems, with average 6.6%, 5.6%, and 17.1% improvement over state-of-the-art baselines on three datasets, respectively. © 2021, CC BY.

关键词： Embeddings