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Modular Digital Twin for Air Handling Units 29

Modular Digital Twin for Air Handling Units

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29th ieee international conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Facchinetti, Tullio Routhu, Thomas University of Pavia Dept. of Industrial Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350361230

In recent years, the escalating challenges of climate change and the imperative for sustainable development have catalyzed the need for more energy-efficient buildings. The use of digital twins has been accelerated by advances in Internet of Things (IoT) sensors, data analytics, and novel computational methods. A new digital twin system is introduced in this paper for the modeling of Air Handling Units (AHUs), designed to accurately simulate the energy behavior and consumption of the system. The digital twin, called AHUSim, leverages an accurate mathematical model of the AHU, a modular architecture, a simple organization of the code and the use of modern tools and approaches for the implementation. This paper details the development, implementation, and validation of AHUSim, underlining its potential impact on reducing energy consumption and operational costs in buildings, thereby contributing to the broader goals of energy efficiency and sustainability. Preliminary results show the usage of AHUSim to model the physical variables that are involved in the functioning of an AHU, and the results that can be obtained in terms of assessment of energy consumption under different working conditions. © 2024 ieee.

关键词： Energy utilization

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Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

引用

40th ieee international conference on Data engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 ieee.

关键词： Crowdsourcing

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Optimized Constrained Application Protocol for IoT Smart Home Data Security: A Novel Lightweight Approach

Optimized Constrained Application Protocol for IoT Smart Hom...

引用

2024 ieee international conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Subhashini, R. Jyothi, D.G. Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka India Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Department of Artificial Intelligence and Machine Learning Karnataka India

ISBN: (纸本)9798350388602

By increasing simplicity and efficiency, a smart environment aims to enhance human existence. The IoT paradigm has evolved into a crucial technology for creating smart buildings, and the majority of computer networks now have IoT technologies. Nevertheless, during this shift, IoT devices are susceptible to cyberattacks. Thus, in practical smart home applications, security and privacy are key issues. In order to improve data security in smart home settings while lowering computing costs, this publication proposes the Broken-Stick Regressive Lightweight Speck Ephemeral Cryptography-based Constrained Application Protocol (BRLSC-CoAP). Real-time data is gathered by IoT devices and sent to authorized users. BRLSC-CoAP includes processes for authentication, encryption, decryption, and registration to safeguard this process. Forbes indexive Broken-stick regression is used to verify user authentication, and a lightweight Speck ephemeral cryptography technique is utilized for encryption. The experimental findings demonstrate that BRLSC-CoAP performs better than traditional techniques by increasing confidentiality, accuracy, and precision in authentication while lowering computing overhead, delay, and cost.. © 2024 ieee.

关键词： Smart homes

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Towards Robust mmWave-based Human Activity Recognition using Large Simulated Dataset for Model Pretraining

Towards Robust mmWave-based Human Activity Recognition using...

引用

2024 ieee international conference on Big Data, BigData 2024

作者： Joshi, Vinay Xu, Shengkai Cao, Qiming Zhu, Yi Wang, Pu Xue, Hongfei Unc Charlotte Dept. of Computer Science Charlotte United States Purdue University Electrical and Computer Engineering West Lafayette United States Wayne State University Dept. of Computer Science Detroit United States

ISBN: (纸本)9798350362480

Human activity recognition (HAR) is crucial for real-world applications such as healthcare, surveillance, and smart homes. Among sensing technologies, millimeter wave (mmWave) sensors stand out due to their contactless nature, high sensitivity, and ability to operate in low-light environments while preserving privacy. However, the scarcity of mmWave sensing data limits the generalizability of mmWave-based HAR systems. To address this, we propose mmAP, a data augmentation and pretraining framework that synthesizes a large mmWave dataset using human mesh data, followed by pretraining a robust and general mmWave heatmap encoder using a multi-modal masked autoencoder framework using the synthesized data. We enhance the model's robustness with heatmap-specific data perturbations and perform task-specific fine-tuning on a small real-world dataset. The experiment results over the baseline demonstrate the effectiveness of the proposed mmAP framework. © 2024 ieee.

关键词： Large datasets

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A Position Sensorless Startup Method for DSEM Based on Pulse Injection

A Position Sensorless Startup Method for DSEM Based on Pulse...

引用

2024 ieee international conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and international Transportation Electrification conference, ESARS-ITEC 2024

作者： Xie, Xie Zhou, Bo Fang, Wenjing Yin, Yujie Wang, Huizhen College of Automation Engineering Nanjing University of Aeronautics and Astronautics Department of Electrical Engineering Jiangsu Province Nanjing China

ISBN: (纸本)9798350373905

This paper proposes a startup method for doubly salient electro-magnetic motor (DSEM) based on H-bridge converter. Firstly, the influence between three-phase currents and excitation current under the drive of H-bridge converter is analyzed. Then, a pulse injection-based method is proposed to estimate the position during the startup process by comparing the magnitude of the response current. This method fully utilizes the falling edge of the acceleration pulse, with only one test pulse injected to obtain the self-inductance relationship, it reduces the number of test pulse injections, thus improving the startup performance. Moreover, this method avoids the influence between three-phase currents and excitation current, and it does not depend on the accuracy of the previous position estimation. The injected test current does not generate negative torque. Finally, the feasibility and correctness of this method are verified through an 18/12 pole DSEM experimental platform. © 2024 ieee.

关键词： Sensorless control

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Consensus Protocol of T-S Fuzzy Positive Multi-Agent Systems 6

Consensus Protocol of T-S Fuzzy Positive Multi-Agent Systems

引用

6th international conference on Robotics, Control and automation engineering, RCAE 2023

作者： Fu, Renjie Shen, Han Yu, Chongxiang Wu, Yuanyuan School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9798350315301

This paper investigates the consensus problem of T-S fuzzy positive multi-Agent systems based on proportional-integral-derivative control protocol. To begin with, this paper utilizes the matrix decomposition technique to propose a proportional-integral-derivative control protocol. The consensus problem of the systems is transformed into a stability problem of augmented systems. By means of copositive Lyapunov functions and linear programming, some sufficient conditions for ensuring consensus and positivity of T-S fuzzy multi-Agent systems are established. The contributions of the paper lie in that: (i) A novel proportional-integral-derivative control protocol is proposed for T-S fuzzy positive multi-Agent systems and (ii) All presented conditions can be solved via linear programming. Finally, an example is provided to verify the validity of the proposed design. © 2023 ieee.

关键词： Multi agent systems

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Multi-Modal Emotion Recognition Network with Balanced Audio-Visual Feature Extraction 5

Multi-Modal Emotion Recognition Network with Balanced Audio-...

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5th international conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Chen, Zeyu Wu, Yiming Cao, Ronghui Hunan University College of Computer Science and Electronic Engineering Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331528911

Video and audio are ways for humans to perceive the world beyond language. It is meant to enable robots to imitate and recognize human emotional expressions. However, most current audio-visual emotion analysis models tend to extract deep features from only one modality. In contrast, the other modality plays a supporting role and cannot fully extract deep features from both modalities. This article proposes a balanced audio-visual emotion analysis model. Specifically, EfficientNet and Wav2vec 2.0 are used for visual and auditory modality feature extraction, respectively, ensuring that indepth features can be extracted from both modalities. Secondly, we use Transformer as the decision-level fusion operator, exchanging information between the two modalities. We verified our model on the RAVDESS dataset and achieved a Top1 accuracy of 88.54%, surpassing audio-visual emotion analysis models of the auxiliary type. © 2024 ieee.

关键词： Emotion Recognition

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Direct Multiple Prediction Intervals Construction of Wind Power Generation Based on LSTM and Differential Evolution Algorithm 5

Direct Multiple Prediction Intervals Construction of Wind Po...

引用

5th international conference on Clean Energy and Electric Power engineering, ICCEPE 2024

作者： Tong, Licong Quan, Hao Han, Xiao Peng, Fuming Ma, Xiang School of Automation Nanjing University of Science and Technology Department of Electrical Engineering Nanjing210094 China School of Automation Nanjing University of Science and Technology Nanjing210094 China Sintef Industry POB 124 Blindern Oslo0314 Norway

ISBN: (纸本)9798350390315

The integration of renewable energy sources like wind power into power systems is expected to steadily increase. Therefore, accurate and reliable wind power forecasting plays a crucial role in optimizing the operation of the power system. Traditional methods for point forecasting fail to properly handle the uncertainties associated with datasets. In this paper, probabilistic interval forecasting offers a unique solution to preliminarily estimate and quantify the potential impacts and risks faced by system operations. This paper proposes a hybrid intelligent algorithm based on long short-term memory (LSTM) and differential evolution Algorithm (DE) for hyperparameter optimization, and constructs the multiple prediction intervals (PIs) of wind power directly through the lower upper bound estimation (LUBE) method. Furthermore, the paper introduces evaluation indices of PIs to establish a multi-objective optimization function, which comprehensively optimizes the reliability and sharpness of the PIs. The algorithm was tested using real wind farm data in the case study and compared with benchmark models. The results indicate that the proposed hybrid intelligent algorithm demonstrates higher reliability and effectiveness. © 2024 ieee.

关键词： Wind forecasting

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An Identification Technique for Diseases of Medicinal Plants 7

An Identification Technique for Diseases of Medicinal Plants

引用

7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Pandey, Bihari Nandan Pandey, Mahima Shanker Pandey, Brijesh Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Uttar Pradesh Ghaziabad India Galgotias College of Engineering & Technology Greater Noida Department of Computer Science and Engineering Greater Noida India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (纸本)9798350350067

Disease detection in medicinal plants is critical to ensuring the quality and efficacy of herbal drug treatments. Early and accurate diagnosis can prevent disease spread to other vegetation and keep species' genetic range. numerous strategies for detecting illnesses in medicinal plants have been advanced in recent years, consisting of morphological observation, histological evaluation, molecular biology techniques, and imaging methods. The purpose of this abstract is to review the cutting-edge state of the art in disease detection in medicinal plants and to highlight the benefits and boundaries of numerous strategies. The emphasis is on the usage of modern-day technology to enhance the speed, accuracy, and performance of disorder detection, which includes high-throughput DNA sequencing, real-time PCR, and non-invasive imaging strategies. The capacity of these techniques to improve our understanding of plant disorder mechanisms and aid in the development of effectively managed measures is also discussed. universal, this abstract emphasizes the significance of ongoing work to ensure the long-term viability of medicinal plant manufacturing and the availability of herbal medicines. © 2024 ieee.

关键词： Plant diseases

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LSTM Based Character Recognition for Text Extraction in Real-Time Natural Scenes Images 2

LSTM Based Character Recognition for Text Extraction in Real...

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2nd ieee international conference on Data science and Network Security, ICDSNS 2024

作者： Kant Soni, Vishnu Chouksey, Praveen Tandan, S.R. Pimpalkar, Amit Miri, Rohit Nema, Neetesh Kumar Dr. C. V. Raman University Department of Computer Science and Engineering C.G. Bilaspur India Government R. V. R. S. Kanya Mahavidyalaya Department of Computer Science C.G. Kawardha India Nagpur India Utd Csvtu Department of Computer Science and Engineering C.G. Bhilai India

ISBN: (纸本)9798350373110

Extracting text from complex real-world images poses a significant challenge in computer vision due to cluttered backgrounds, diverse fonts, and varying orientations. Traditional methods struggle with accuracy in such scenarios. This research introduces a novel text extraction model, Deep Convolutional Neural Networks (DCNNs), for precise character recognition. By combining Optical Character Recognition (OCR) with Long Short-Term Memory (LSTM) networks, the model effectively handles sequential data and enhances text extraction performance. By integrating OCR with LSTM, the proposed approach aims to overcome the limitations of traditional methods and enhance text extraction performance in challenging scenarios. Evaluation of ICDAR datasets yielded a high average confidence score of 95.50%, highlighting the model's accuracy in challenging text extraction tasks. Real-time testing in Bilaspur, India, demonstrated the model's robustness and adaptability, surpassing human annotations. These results highlight the potential of deep learning, particularly the OCR-LSTM fusion, for accurate text extraction in diverse natural scene images. © 2024 ieee.

关键词： Convolutional neural networks

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