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Research on WNN Greenhouse Temperature Prediction Method Based on GA

Phyton-International Journal of Experimental Botany 2022年第10期91卷 2283-2296页

作者： Wenbin Dai Lina Wang Binrui Wang Xiaohong Cui Xue Li College of Mechanical and Electronic Engineering China Jiliang UniversityHangzhou310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China

Temperature in agricultural production has a direct impact on the growth of *** emergence of greenhouses has improved the impact of the original unpredictable changes in temperature,but the temperature modeling of greenhouses is still the main direction at *** network modeling relies on sufficient actual data to model greenhouses,but there is a widening gap in the application of different neural *** paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm(GA-WNN).With the simple network structure and the nonlinear adaptability of the wavelet basis function,wavelet neural network(WNN)improved model training speed and accuracy of prediction results compared with back propagation neural networks(BPNN),which was conducive to the prediction and control of short-term greenhouse temperature *** the same time,the genetic algorithm(GA)was introduced to globally optimize the initial weights of the original model,which improved the insensitivity of the model to the initial weights and thresholds,and improved the training speed and stability of the ***,simulation results for the greenhouse showed that the model training speed,prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.

关键词： Greenhouse temperature greenhouse modeling wavelet neural network genetic algorithm

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Hierarchical Scalable data Lake Design for Game Engine 8

Hierarchical Scalable Data Lake Design for Game Engine

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8th International Conference on Big data and Information Analytics, BigDIA 2022

作者： Yang, Mingbao Zhou, Peng Li, Shaobo Pu, Ruiqiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

With the rapid development of information technology, people are more and more inseparable from smart devices, and people and devices produce a large amount of data all the time. How to store and analyze massive data has become a research hotspot of the times. However, researchers mainly study the high reliability, high availability, high fault tolerance, high throughput, and high concurrent processing of big data systems, but there are few studies on designing hierarchical and scalable data lakes. In this study, a layered and extensible design of the data lake is carried out, which reduces the complexity of implementing business indicators during data analysis, and at the same time improves code reuse, scalability, development efficiency and code security. In addition, in order to avoid the problem of data accumulation or even job execution failure due to the skew of ip dimension table data, this paper designs the ip dimension table rowkey. This paper designs a set of real-time and offline big data analysis data lake architecture based on game engine and applies it to the actual production environment. © 2022 IEEE.

关键词： Big data

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Improving Cross-Domain Named Entity Recognition from the Perspective of Representation 28th

Improving Cross-Domain Named Entity Recognition from the P...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Jingyun Cai, Yi School of Software Engineering South China University of Technology Guangzhou510650 China The Key Laboratory of Big Data and Intelligent Robots South China University of Technology and the Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9783031306778

Recently, cross-domain named entity recognition (cross-domain NER), which can reduce the high data annotation costs faced by fully-supervised methods, has drawn attention. Most competitive approaches mainly rely on pre-trained language models like BERT to represent words. As such, the original chaotic representations may bring challenges (e.g., entity span detection errors and entity type misclassification) for them. Motivated by this, this proposal proposes to improve cross-domain NER by refining the original representations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Contrastive Learning Cross-domain Named Entity Recognition Representation

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Juan Xu Zhen Xu Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Hefei China

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings.

关键词： Training Vibrations Degradation Deep learning Adaptation models Transforms Predictive models

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Intelligent elevator system based on the Internet of Things: improving elevator safety and management efficiency

Intelligent elevator system based on the Internet of Things:...

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2024 International Conference on Smart Transportation and City engineering, STCE 2024

作者： Huang, Lingbo Chen, Yihong Zhao, Kun The Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong City China West Normal University Nanchong637002 China School of Electronic Information Engineering China West Normal University Nanchong637002 China

ISBN: (纸本)9781510690585

The number of elevators in China's urbanization process has increased significantly, yet they face safety problems such as aging and insufficient maintenance. This paper introduces an intelligent IoT elevator system, comprising a data acquisition terminal and a cloud platform, utilizing a hierarchical architecture and a variety of key technologies. The system can monitor elevator operations in real time, collect and analyze large amounts of data to predict failures, and provide maintenance plans. It also features real-time alarm, rescue management, and maintenance management functions. These improvements enhance rescue efficiency, reduce maintenance costs, ensure the safe operation of elevators, and improve the efficiency and transparency of safety management. © 2025 SPIE.

关键词： Efficiency

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Adaptive Detr: A Framework with Dynamic Sampling Points and Feature-Guided Adaptive Attention Updates

SSRN

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SSRN 2024年

作者： Li, Botao Yang, Huguang Xia, Chenglong Zheng, Han Zhang, Taohong Wulamu, Aziguli Beijing100083 China Guangxi546300 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

In recent years, models based on DETR (Detection Transformer) have made significant progress in the field of object detection. However, the original DETR model lacks adaptability in its encoder, performing well only on single datasets, which limits its performance in complex scenarios. To address this issue, we propose a new framework—Adaptive DETR—that features dynamic adjustment of the number of sampling points and feature-guided adaptive attention *** main innovations include: First, we introduce a new attention update strategy that dynamically calculates weights based on the features of the output image, determining the frequency of attention updates on the features, thereby enhancing detection accuracy. Second, to tackle the fixed sampling point issue in the deformable attention module of the DETR model, we implement dynamic adjustment of the number of sampling points, improving the model's generalization ability in complex scenes. Finally, we optimize the DETR decoder by performing attention calculations between queries responsible for bounding boxes and those responsible for semantic information during the query initialization phase, effectively injecting semantic information to enhance localization *** experimental results demonstrate that Adaptive DETR achieves the best performance on the UAVDT, VisDrone, and RSOD datasets, showcasing its efficiency and generalization capability. © 2024, The Authors. All rights reserved.

关键词： Object detection

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections 12

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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12th International Conference on Advanced Cloud and Big data, CBD 2024

作者： Wang, Yifan Zeng, Wei Li, Guanyu Xiong, Chenglong Wang, Zicheng Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (纸本)9798331511074

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate. © 2024 IEEE.

关键词： Markov processes

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Article reranking by memory-enhanced key sentence matching for detecting previously fact-checked claims 59

Article reranking by memory-enhanced key sentence matching f...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

ISBN: (纸本)9781954085527

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021 Association for Computational Linguistics

关键词： Semantics

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Quantum security analysis of SM4 algorithm 6

Quantum security analysis of SM4 algorithm

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6th International Conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Yang, Hao Wang, Chunhong Tang, Xueming Cui, Yongquan Lu, Songfeng Cai, Dunbo Qian, Ling Zhang, Runqing Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Maritime Communication Research Institute Wuhan430010 China Software Technology Co. Ltd. Suzhou215153 China

ISBN: (纸本)9781510682962

The security of commercial encryption algorithms has always been a focus of attention. The SM4 algorithm, a commercial cryptographic standard, has been recognized by ISO/IEC as an international standard and has shown excellent performance on classical computers. However, with the development of quantum computing, traditional encryption algorithms are facing new challenges. This paper investigates the quantum realization of SM4 algorithm. By applying the Grover algorithm to the SM4 algorithm, we conducted a quantum exhaustive attack and provided an estimation of the required quantum resources. This study offers a novel perspective on the application of quantum computing in breaking commercial encryption algorithms, which holds significant implications for the future development of quantum secure communication and cryptography. © 2024 SPIE.

关键词： Encryption algorithms

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Dynamic multi-objective optimization algorithm based on weighted differential prediction model 4

Dynamic multi-objective optimization algorithm based on weig...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Zhao, Yumeng Wang, Xianpeng Dong, Zhiming Wang, Yao Lou, Hangyu Hu, Tenghui Fu, Kai Tang, Lixin Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Shenyang110819 China Northeastern University Liaoning Engineering Laboratory of Operation Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this paper, a new algorithm for solving dynamic multi-objective optimization problems(DMOPs) is proposed. Most of the traditional dynamic multi-objective optimization algorithms will make predictions based on the overall average evolutionary direction of the population, which is hardly applicable to problems where the solution set and frontier do not vary with the environmental rules. In this paper, a dynamic multi-objective optimization algorithm based on weight difference prediction model is designed to solve such problems. The algorithm contains a weighted differential prediction strategy, and a differential model is built for each individual using the weights to predict the initial population after environmental changes. With this approach, each individual in the population can be made to respond quickly to environmental changes. We used three classical comparison algorithms to conduct experiments on a series of test problems. The experimental results show that the WD-MOEA/D algorithm can significantly improve the dynamic optimization performance and is effective in solving different types of dynamic problems. © 2022 IEEE.

关键词： Evolutionary algorithms

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