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big data in support of the sustainable development goals(continued):a celebration of the establishment of the International Research center of big data for Sustainable development Goals(CBAS)

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big Earth data 2021年第4期5卷 443-444页

作者： Huadong Guo Heide Hackmann Ke Gong International Research Center of Big Data for Sustainable Development Goals World Federation of Engineering Organizations

The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable development Goals(SDGs).big data improves the extent to which scientific evidence and innova-tive technological solutions can be adopted to meet these ***,the tools and methods of big data are still somewhat of a novelty in this respect.

关键词： Sustainable continued enable

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Spatial distribution order parameter prediction of collective system using graph network

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Chinese Physics B 2023年第5期32卷 566-572页

作者：赵慧敏王瑞赵偲郑文 Institute of Public-Safety and Big Data College of Data ScienceTaiyuan University of TechnologyTaiyuan 030024China Center of Information Management and Development Taiyuan University of TechnologyTaiyuan 030024China Center for Healthy Big Data Changzhi Medical CollegeChangzhi 046000China

In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network(MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.

关键词： order parameter graph network collective system active matter

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Monitoring firn and wet snow on mountain glaciers:polarization and orbit effects

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地球空间信息科学学报（英文版） 2024年第3期27卷 761-772页

作者： Ying Huang Lei Huang Tobias Bolch Institute of Geology China Earthquake AdministrationBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina Institute of Geodesy Graz University of TechnologyGrazAustria

Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological *** Aperture Radar(SAR)images are often used to monitor the characteristics of glaciers based on the back-scattering ***,the influence of satellite orbit and polarization when collecting images for wide regions has not been well *** study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band SAR *** investigated regions have different climate *** compare backscatter coefficient distributions for wet snow and firn,derived from maximum likelihood classification under various polarizations,alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and ***,a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in *** furthermore show,by analyzing weather stations on glaciers,that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the *** contrast,firn ratios across different orbits show less variation in winter,and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic *** findings demonstrate glaciers'sensitivity to temperature fluctuations and the radar wave's responsiveness to surface ***,when employing SAR for glacier monitoring,it is crucial to consider the influence of orbit and polarization,in combination with temperature variations,and whether the season is winter or summer.

关键词： Continental glacier Synthetic Aperture Radar(SAR) wet snow firn radar glacier zones

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big earth data for achieving the sustainable development goals in the belt and road region

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big Earth data 2022年第1期6卷 1-2页

作者： Fang Chen Zhongchang Sun International Research Center of Big Data for Sustainable Development Goals

The Belt and Road(B&R)is an important region with historical,economic,cultural,and political significance,including 75%of the global population and numerous social ***,many countries along the B&R region are experiencing developmental challenges such as rapid urbanization,land degradation,water shortages,water and food security,frequent disasters,and large-scale ecosystem *** UN’s 17 Sustainable development Goals(SDGs)provide a universal call to action to end poverty,protect the planet,and ensure that all people enjoy peace and prosperity by 2030 and to achieve economic,social,and environmental sustainability at global,regional,and national scales.

关键词： planet sustainable goals

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Instance segmentation of high-resolution remote sensing images based on Bayesian classifier and discrete cosine transform 24

Instance segmentation of high-resolution remote sensing imag...

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4th International Conference on Artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： He, Jiangtao Wang, Licai Yu, Jintao You, Peihai Huang, Yixuan Big data research and development center North China Institute of Computing Technology China

ISBN: (纸本)9798400710049

The popularization and widespread application of high-resolution remote sensing imagery have made Instance Segmentation a hot research area. The difficulty of improving the instance segmentation accuracy of high-precision fine-grained remote sensing images has always been a research challenge. This paper proposes a new model, Oriented Bayes DCT, which effectively improves the accuracy of instance segmentation. The model utilizes a region proposal network to encode features, generate proposals, align the candidate boxes, and then uses Bayesian methods to predict class and box offsets. Compared to traditional methods of generating candidate boxes like RPN or Oriented RPN, the candidate boxes obtained using the Naive Bayes method are more accurate. Additionally, the model employs a mask prediction branch to predict segmentation. Through Discrete Cosine Transform (DCT), high-resolution binary grid masks are encoded into compact vectors, achieving precise instance segmentation of fine-grained remote sensing images. Compared to predicting the mask part using the Mask Head method, using the discrete cosine transform for encoding and decoding the ground truth masks results in lower error rates. Without tricks, Oriented Bayes DCT with ResNet50 achieves state-of-the-art segmentation accuracy on a commonly-used datasets iSAID for instance segmentation (37.7% mAP) and ResNet101 (38.5% mAP). © 2024 Copyright held by the owner/author(s).

关键词： Image segmentation

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Genetic Taboo Search Algorithm Optimized TCN-LSTM for Ultra-short-term Photovoltaic Power Prediction 3

Genetic Taboo Search Algorithm Optimized TCN-LSTM for Ultra-...

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3rd International Conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Wang, Honggang Zhang, Hao Li, Gaoyang Yin, Zenan Zhang, Haifeng Lin, Xiaojing Big Data Center of State Grid Corporation of China Application Development and Operation Center Beijing China

ISBN: (纸本)9798350352658

Solar energy is converted into electricity through photovoltaic power generation. This process is highly volatile and subject to a variety of factors. Characterizing this volatility is a crucial step in improving the accuracy of PV power prediction. This research suggests combining Genetic Taboo Search Algorithm (GATS) optimized Temporal Convolutional Networks (TCN) and Long Short-Term Memory Networks (LSTM) to optimize the ultra-short-term PV power forecast model in order to overcome this challenge. Firstly, the crucial meteorological factors affecting the PV power are selected for correlation analysis. Secondly, the optimal weight thresholds for the TCN and LSTM prediction models are optimized applying the genetic taboo search algorithm. In order to obtain the final optimized prediction results, the information entropy weight allocation method is used to linearly combine the two models of the TCN and the LSTM. The research results demonstrate that the prediction model is capable of accurately describing the ultra-short-term PV power volatility. Furthermore, the power prediction ac-curacy and reliability of the model outperform those of the conventional prediction approaches, demonstrating good adaptability and efficacy. © 2024 IEEE.

关键词： Prediction models

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Legal Event Detection Model Based on ERNIE 3.0-CRF and Ensemble Learning 5

Legal Event Detection Model Based on ERNIE 3.0-CRF and Ensem...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Wang, Siyu Wang, Licai Zhang, Ke North China Institute of Computing Technology Big Data Research and Development Center Beijing China

ISBN: (纸本)9798350385557

Recognizing events is a prerequisite for making judgments, so discovering events in legal documents is important for the analysis of legal case. Legal Event Detection aims to extract legal event trigger words from legal documents and categorize events and is the basis of many judicial AI applications. Oriented to the needs of understanding and analyzing legal events in the judicial trial process, and in response to the low accuracy of event detection caused by the many types of events and little sample data in the legal domain, this paper constructs a pre-training model oriented to event detection in the legal domain, which makes full use of ERNIE 3.0's powerful semantic representation learning capability and CRF's effective modeling capability for sequence annotation tasks. In addition, we further enhance the effectiveness of the model through data augmentation and ensemble learning. The experimental results show that the method works better compared to the baseline model and improves the prediction accuracy of legal event detection. © 2024 IEEE.

关键词： Learning systems

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Research on large model table question answering method based on thought of chain and vector matching filtering 3

Research on large model table question answering method base...

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3rd International Conference on Electronic Information Engineering and data Processing, EIEDP 2024

作者： Tang, Haichao Wang, Licai Hu, Xun Luo, Qibin Big data research and development center North China Institute of Computing Technology Beijing China

ISBN: (纸本)9781510680531

Implementing table-based question answering can fully leverage the rich information in tabular data, and the development of large language models has provided strong natural language processing and understanding capabilities. Current research on table-based question answering with large language models mainly focuses on how to improve text-to-SQL abilities and the development of table pre-training models, lacking in studies on feasible technical approaches for applying general large language models to engineering applications involving large-scale database table question answering. In response, combining techniques such as prompt learning and chain of thought,this paper constructs a database question answering chain of thought based on large language model, proposes a table filtering scheme based on vectorized matching of table structural information, specifically addressing poor table filtering effects, and constructs a database optimization chain of thought in the context of de-identification scenarios. Experimental verification shows that after adopting the chain of thought construction and vector matching filtering scheme, the database question answering capability of the large model has been effectively improved. © 2024 SPIE.

关键词： Information filtering

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RIF-Proto: A Few-Shot Relation Extraction for Prototype Networks Based on Relational Information Features 5

RIF-Proto: A Few-Shot Relation Extraction for Prototype Netw...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Zhang, Shuai Wang, Licai Huang, Yangchen North China Institute of Computing Technology Big Data Research and Development Center Beijing China

ISBN: (纸本)9798350385557

Relation extraction stands as a core task within the realm of information extraction, focusing on identifying the target relations between entities in text. While traditional relation extraction has been extensively researched, certain relations lack sufficient labeled data, rendering traditional models ineffective in practical research. Hence, the research focus in this field has shifted towards few-shot learning methods that leverage limited labeled data for training. Significant progress has been made in few-shot relation extraction, with prototype networks garnering attention from scholars for their outstanding performance in few-shot tasks. Prototype networks utilize the clustering idea to achieve relationship classification simply and efficiently, and their training process is mainly divided into three parts: generation of relationship prototypes, relationship classification, and loss calculation. The majority of current research is centered around leveraging external information to enhance the generation of prototypes and loss functions, but due to the difficulty of obtaining external data and the high complexity of data processing, it greatly increases the time cost and the difficulty of implementation. Given the aforementioned challenges, this paper introduces a method for prototype refinement leveraging relationship similarity among existing data. This approach aims to effectively address the issue of significant deviations in relationship prototypes. Initially, extract relationship vectors from the instances in both the support set and query set, followed by computing the relationship similarity between samples based on these vectors, and finally, this paper constructs a prototype correction module, which utilizes the relationship similarity to assign weights to the samples in the support set, to achieve the purpose of prototype correction. In the experiments against the standard dataset FewRel1.0., we observe that the method substantially improves the ex

关键词： Query processing

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Large Language Model Based on Full-Text Retrieval for Temporal Knowledge Q&A Approach 5

Large Language Model Based on Full-Text Retrieval for Tempor...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Li, Zhidong Wang, Licai Luo, Qibin Qiao, Silong North China Institute of Computing Technology Big Data Research and Development Center Beijing China

ISBN: (纸本)9798350385557

Knowledge Q&A is one of the hot research topics in the field of natural language processing, and temporal knowledge Q&A is a difficult area of Q&A reasoning because it also needs to consider the temporal relationship of knowledge. Today's research usually focuses on the word vector similarity between knowledge and questions as an important basis for answering, while ignoring the sentence granularity semantic information embedded in the knowledge. In this paper, we propose a method of temporal knowledge Q&A for large language models based on full-text retrieval, firstly, the sentence granularity knowledge recall is performed by Elasticsearch so that large language models can learn the knowledge that is highly relevant to the problem, and then verify the temporal knowledge Q&A ability of large language models under Zero-shot, One-shot and Few-shot. The experiments were conducted on the ICEWS05-15 dataset, and the accuracy of answers was significantly improved, demonstrating the effectiveness of the temporal knowledge Q&A method for large language models based on Elasticsearch. © 2024 IEEE.

关键词： Semantics

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