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On-line modification of T-S fuzzy models based on gradient descent algorithm

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On-line modification of T-S fuzzy models based on gradient d...

第33届中国控制与决策会议

作者： FENG Haodong WEI Dong JIAO Huanyan School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission

The gradient descent algorithm is applied to the model parameter correction algorithm in view of the shortcoming that T-S fuzzy model cannot be used for real-time adaptive parameter *** this paper,an online model correction method combining T-S fuzzy model with gradient descent algorithm is proposed,and the operation data of a subway air-conditioning system in Wuhan is used for *** experimental results show that the T-S fuzzy model modified by gradient descent algorithm has better accuracy and higher operational efficiency.

关键词： T-S fuzzy model Gradient descent algorithm Air conditioning system model

Research on Energy Saving Control of Lighting Systems in Public Buildings

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Research on Energy Saving Control of Lighting Systems in Pub...

第33届中国控制与决策会议

作者： Shengyan Li Hongyan Ma Yongxue Ye Jiaming Dou Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

Most indoor users are unwilling to adjust the lighting equipment and shutters in daily life,resulting in increasing energy consumption of *** this paper,in order to reduce the energy consumption,using fuzzy control to solve this *** using fuzzy control,it could control the opening of indoor lamps,and let the shutters could determine the opening degree of the shutters according to the difference between the set illuminance values and the actual illuminance values on the working *** results show that the fuzzy control of classroom lighting system can reasonably control the running status of lamps according to the actual situation of the environment and reduce the energy consumption of the lighting *** with the single fuzzy controller,the double fuzzy controller of the shutter can make the difference between the illuminance value of indoor working surface,and the set illuminance value smaller,control the light sources in different areas of the room,and improve the uniformity of natural light in the *** method proposed in this paper can solve the energy consumption problem.

关键词： Lighting System Shutter Double Fuzzy Control System Fuzzy Control

Research on Human Pose Classification Based on Improved SVM

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Research on Human Pose Classification Based on Improved SVM

第33届中国控制与决策会议

作者： Yingda Zhang Hongyan Ma Yongxue Ye Jiaming Dou Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

With the development of intelligent buildings and aging of population,designing a high-accuracy alarm system is essential to prevent the elderly from accidentally falling *** address the problems of poor generalization and low accuracy of the SVM in the application of indoor elderly fall alarm systems,in this paper,the human keypoints detection algorithm,which increases the discrimination of the dataset,is proposed to pre-process the *** on this,the features of images are extracted from the histogram of the oriented gradient and the gray-level co-occurrence matrix,and the improved SVM algorithm is then used again for image *** simulation results show that the classification accuracy of using SVM to detect the dataset is around 91%,while the accuracy of the dataset classified as using improved SVM is around 97%.The method proposed in this paper is effective in increasing the accuracy of computer recognition of accidental falls by the elderly.

关键词： SVM Human keypoints Detection Image Classification Accidental Indoor Falls Aging of Population

Research on STF-DBSCAN based personal semantic location acquisition

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Research on STF-DBSCAN based personal semantic location acqu...

第33届中国控制与决策会议

作者： Zixuan Yao Dong Wei Yibing Ran School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission

With the development of technology and economy,the planning and construction of smart cities have generated new demands and *** intelligence-led technologies and applications are changing people's lives where location-based services are one of the most important technical *** recent years,the popularity of portable devices with GPS(Global Positioning System) function and the development of positioning technology make a large amount of individual track data ***,collecting personal semantic location by mining these tracklogs would be the application direction and research focus of location-based *** semantic location is a practical significant location that users will visit frequently,stay for a long time such as a home address,workplace,etc.).The acquisition of personal semantic location involves two steps,which are acquiring location and matching ***,most of the relevant research use density clustering-based algorithms to collect location *** the original DBSCAN algorithm is used to acquire location,all the trajectory points in the raw dataset need to be included in the clustering samples,which increases calculation and reduces the program *** paper addresses this problem and proposes an advanced density-based clustering algorithm ***,the trajectory points in the raw dataset are filtered to obtain the dwell ***,the dwell points are clustered and analyzed to achieve the purpose of improving the running *** the semantic recognition issue,this paper pre-defines the daily behavior pattern of individual and analyzes the temporal features of the trajectory points in each *** using the same trajectory data samples for location acquisition,the running time of the proposed algorithm in this paper is one-fifteenth of the original algorithm.

关键词： Personal Semantic Location DBSCAN Spatio-temporal Features

Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction

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arXiv 2023年

作者： Zhang, Hao Liu, Yang Liu, Xiaoyan Liang, Tianming Sharma, Gaurav Xue, Liang Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The *** HangZhou 310000 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. Specifically, we propose a graph view of sentence bags referring to an entity pair, which enables message-passing based aggregation of information related to the entity pair over the sentence bag. The proposed framework alleviates the common problem of noisy labeling in distantly supervised relation extraction and also effectively incorporates inter-dependencies between sentences within a bag. Extensive experiments on two large-scale biomedical relation datasets and the widely utilized NYT dataset demonstrate that our proposed framework significantly outperforms the state-of-the-art methods for biomedical distant supervision relation extraction while also providing excellent performance for relation extraction in the general text mining domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations

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arXiv 2023年

作者： Pei, Qizhi Zhang, Wei Zhu, Jinhua Wu, Kehan Gao, Kaiyuan Wu, Lijun Xia, Yingce Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China China University of Science and Technology of China China Huazhong University of Science and Technology China Microsoft Research United States Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose BioT5, a comprehensive pretraining framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. BioT5 utilizes SELFIES for 100% robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, BioT5 distinguishes between structured and unstructured knowledge, leading to more effective utilization of information. After fine-tuning, BioT5 shows superior performance across a wide range of tasks, demonstrating its strong capability of capturing underlying relations and properties of bio-entities. Our code is available at https://***/QizhiPei/BioT5. Copyright © 2023, The Authors. All rights reserved.

关键词： Integration

Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Building Cooling load prediction based on LightGBM

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IFAC-PapersOnLine 2022年第11期55卷 114-119页

作者： RuoChen Zhao Dong Wei YiBing Ran Guang Zhou YuChen Jia ShiLun Zhu YouQuan He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Xicheng District Beijing 100032 China Beijing Municipal Science and Technology Commission Key Laboratory of Intelligent Processing for Building Big Data Xicheng District Beijing 100032 China Siemens Ltd. China 100020 China Zhongkai University of Agriculture and Engineering Guangzhou Tevo Energy Management Co. Ltd.

In building Heating Ventilation and Air Conditioning (HVAC) control systems, the HVAC must be controlled in real-time according to the actual building cooling load demand while minimizing energy consumption. Given that the building cooling load is affected by several factors and that historical data are sparse and have outliers due to sensor errors, existing prediction methods cannot accurately calculate the building's cooling load demand. Therefore, this paper first pre-processes the historical building cooling load data, fills in the missing data utilizing the KNNImputer algorithm, and then locates and replaces the outlier data through the 3-sigma method, solving the poor data availability problem. Moreover, to address the problem of small feature variety in historical data, this paper analyzes the time features and the climatic features separately. Specifically, our method splits the time features into several independent features for describing the impact of pedestrian flow on the building cooling load and expands the climatic features to increase the variety of the features that can be used for training and improving the model's prediction accuracy. Finally, the LightGBM algorithm is exploited to train the processed data and build a building cooling load prediction model. The proposed data pre-processing method and model training algorithm are validated through Python programming, with the results highlighting that the proposed method effectively improves the prediction accuracy of building cooling loads.

关键词： Building cooling load prediction LightGBM data pre-processing KNN 3Sigma

ER: Equivariance Regularizer for Knowledge Graph Completion

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arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the "size" of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods. © 2022, CC BY.

关键词： Knowledge graph