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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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WaveRFN-Nest: A Multi-Scale Feature Fusion Method Based on Wavelet Transform and Deep Learning

WaveRFN-Nest: A Multi-Scale Feature Fusion Method Based on W...

引用

software engineering and Computer Science (CSECS), international conference on

作者： Chuan Kou Yunxiao Zu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

Infrared and visible images fusion is a technique that combines two types of images and has broad application prospects. With the development of deep learning, various high-performance fusion models have emerged in this field, including STDFusionNet, DRF, RFN-Nest, and others. However, most of these models focus on optimizing the model architecture and fusion performance, without considering the key features that can support subsequent tasks such as classification and object detection in the images. To address these issues, this paper proposes a multi-scale feature fusion algorithm called WaveRFN-Nest. Firstly, the spatial attention module called WaveAtten is introduced to extract important detail information from visible light images. then, the WaveAtten module is embedded into the RFN-Nest model using a multi-scale feature fusion module, allowing the model to focus more on crucial features. the Experiments conducted on multiple datasets show that the composite model proposed in this paper ensures the quality of fused images with small parameter increments, improves the proportion of important detailed features in the images, and facilitates subsequent tasks.

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), international conference on

作者： Junran Wang Jixiao Wang Yunpeng Li Weisheng Zhang School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian China China Nuclear Power Engineering Co.Ltd Center for Nuclear Engineering Mechanics Beijing China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

this study investigates the application of adaptive loss weighting in the training of physics-informed neural networks (PINNs) to dynamically balance data, boundary, and physics losses. By employing cosine similarity of gradients, the proposed approach mitigates gradient dominance, ensuring more effective optimization. Numerical experiments on the Burgers equation demonstrate accelerated error convergence and enhanced training efficiency, underscoring its potential for tackling complex and high-dimensional physical systems.

关键词： Training Weight measurement Adaptation models Adaptive systems Neural networks predictive models Mathematical models Stability analysis Physics Optimization

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An Uncertainty Based predictive Analysis of Smart Water Distribution System Using Bayesian LSTM Approach 6

An Uncertainty Based Predictive Analysis of Smart Water Dist...

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6th IEEE international conference on Universal Village, UV 2022

作者： Zaman, Mostafa Islam, Maher Al Tantawy, Ashraf Abdelwahed, Sherif Virginia Commonwealth University Department of Electrical and Computer Engineering Richmond United States De Montfort University School of Computer Science and Informatics Leicester United Kingdom

ISBN: (纸本)9781665474771

A well-designed water distribution system is crucial for maintaining high service standards in any modern smart city. Moreover, as the population is sky-rocketing, the demand for energy and water is increasing more rapidly than a decade before. therefore, ensuring a steady clean water supply with optimized energy and water consumption has become necessary. To accurately monitor water distribution systems, the accuracy of input data plays a vital role in determining how accurate the system's status estimations are. there must be a way for system operators to know what is going on at any given time to make practical decisions about how reliable the data they are receiving is. the input data uncertainty can induce flow and pressure calculation inaccuracies, which can be fatal while planning for future demands and needs to be *** the degree of uncertainty in predicting the water distribution system's capacity or load can help people better prepare for future capacity or load predictions. Accurate uncertainty calculations are critical to time series forecasting. Probabilistic formulae are widely employed with classical time series models to estimate uncertainty. But incorporating new data and fine-tuning these models is a challenging task. this research paper presents a Bayesian LSTM network that computes both time series prediction and uncertainty assessment at the same time. In this paper, a real-time data set from VCU's OpenCity test bed is employed to evaluate the efficacy of the suggested strategy. © 2022 IEEE.

关键词： Water distribution systems

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proceedings of the 2021 6th international conference on Computing, Communication and Security, ICCCS 2021

Proceedings of the 2021 6th International Conference on Comp...

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6th international conference on Computing, Communication and Security, ICCCS 2021

ISBN: (纸本)9781665440394

the proceedings contain 19 papers. the topics discussed include: fake news detection using machine learning with feature selection;simulation and analysis of an improved quality multilevel inverter for linear types of load;recommendation engines: traditional vs deep learning approaches;climate change and covid-19 metaphors: environmental consciousness in social media;personalized engineering education model based on artificial intelligence for learning programming;ensemble transfer learning approach for breast cancer classification using thermograms;an efficient approach for load balancing in software-defined networks;diffusive nanoparticles and generation of repulsive effects by artificial electrical currents;understanding the global data of infections cases by covid-19 through gaussian and trapezoid models;and probabilities of risk due to random aerosol displacement and aleatory social distancing.

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Back Propagation Neural Network Model for Railway Risk Warning

Back Propagation Neural Network Model for Railway Risk Warni...

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Electronics and Communication, Network and Computer Technology (ECNCT), international conference on

作者： Baofu Duan Yunqian Yang Zhe Jing Yuguan Wei Anni Chu Liankai Bu College of Civil Engineering and Architecture Shandong University of Science and Technology Huangdao Qingdao Shandong China College of Civil Engineering and Architecture Shandong University of Science and Technology Qingdao Shandong China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

To effectively warn of railway risk accidents, a railway safety risk warning model based on neural network theory and the characteristics of railway safety risks was established. Taking the current situation of railway construction in a certain province as an example, the neural network model was trained and applied using expert evaluation data. By comparing with the measured values, it is found that the predicted results of the neural network model are close to the measured values.

关键词： Backpropagation Current measurement Computational modeling Neural networks Railway safety predictive models Prediction algorithms Rail transportation Data models Communication networks

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Improved Tomato Flower Visual Inspection Based on YOLOv8

Improved Tomato Flower Visual Inspection Based on YOLOv8

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Jipeng Yao Musha Yasenjiang Jie Zhang School of Mechanical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

A YOLOv8-based improved pollination detection method is provided for complex tomato flowers in a smart greenhouse environment. By using GSConv with Slim-Neck technique, the parameters and computational complexity are reduced to ensure the stability of its performance. In order to adapt more accurately to targets of different scales, SIoU is chosen to be used instead of CIoU to improve the accuracy of the detection assessment. the experimental results show that compared with the base model, our method improves the detection accuracy by 4.7%, while the FLOPs are reduced by 0.9G, and outperforms other models in terms of both detection speed and accuracy.

关键词： Performance evaluation Visualization Accuracy Smoothing methods Computational modeling predictive models Signal processing Feature extraction Stability analysis Computational complexity

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Automatic Navigation and Voice Cloning Technology Deployment on a Humanoid Robot

Automatic Navigation and Voice Cloning Technology Deployment...

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international conference on Control and Robots (ICCR)

作者： Dongkun Han Boyuan Shao Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

Mobile robots have great potential and are expected to be widely used in the service industry. this study aims to develop a control algorithm for the automatic navigation of Cruzr, a humanoid mobile robot. Initially, a virtual environment is created using SLAM in Gazebo simulation software, allowing for global path planning through local path tracking. the two-wheel differential chassis kinematics model ensures autonomous obstacle avoidance. the mapping and trajectory generation algorithms are successfully implemented on the real robot Cruzr. Performance of automatic navigation is compared between the Dynamic Window Approach (DWA) and Model predictive Control (MPC) algorithms. Additionally, a mobile application for voice cloning, based on a Hidden Markov Model, is created and tested on Cruzr.

关键词： Simultaneous localization and mapping Navigation Heuristic algorithms software algorithms Cloning Humanoid robots Virtual environments software Trajectory Mobile robots

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Maximum Power Point Tracking Using Model predictive Control

Maximum Power Point Tracking Using Model Predictive Control

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international conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Srishti Prabin K Padhy Marco Rivera Dip Prakash Samajdar Department of ECE PDPM IIITDM Jabalpur Jabalpur India PEMC Research Institute University of Nottingham Nottingham UK

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

this the development of photovoltaic (PV) systems has increased as a direct consequence of the growing need for electricity on a global scale. Because of the non-linear nature of PV systems, specialized procedures are required to maximize the amount of energy that can be produced. To solve this problem, this study proposed a straightforward, yet highly effective Fuzzy model predictive control (FMPC). the system model is utilized to estimate the current, while the ideal operating point is discovered by a specific cost function. this method uses predictive equations to do away with the requirement for extra sensors. this study also presents a comparative analysis of the proposed fuzzy model predictive control (FMPC) with existing MPC techniques. Under rapidly changing atmospheric circumstances, the predictive control method offers both improved steady-state stability and faster dynamic reaction, demonstrating higher performance in an ideal system. MATLAB simulation results demonstrate the benefits of this Fuzzy Model predictive Control.

关键词： Maximum power point trackers Photovoltaic systems Simulation Instruments Mathematical models Stability analysis Steady-state Sensors MATLAB predictive control

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Research on PV Power Prediction Method Based on LDBO-BP

Research on PV Power Prediction Method Based on LDBO-BP

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Wenxuan Ji Qipeng Zheng Feng Li Wei Quan dept. School of Computer & Communication Lanzhou University of Technology Lanzhou China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

the traditional neural network method has some defects in the prediction process and still needs further optimisation and enhancement. therefore, in this paper, a Logistic Chaos Mapping improved Dung Beetle Optimization (DBO) algorithm is proposed to optimise the photovoltaic (PV) power prediction model for Back Propagation (BP) neural networks. Experimental results on the Sanyo dataset show that the LDBO-BP method is an effective PV power prediction method.

关键词： Chaos Photovoltaic systems Computational modeling Neural networks Human factors predictive models Prediction algorithms

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Construction Method of Transmission Line Cost Prediction Model Based on ISM

Construction Method of Transmission Line Cost Prediction Mod...

引用

Energy Systems and Electrical Power (ICESEP), international conference on

作者： Qingshen Geng Junyan Xiao Shandong Electric Power Engineering Consulting Institute Coit Ltd. Jinan China Shandong Institute For Product Quality Inspection Jinan China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

thereare many factors that affect the cost of transmission lines. In order to achieve the prediction of transmission line cost, a method for constructing transmission line cost prediction model based on ISM is proposed. Firstly, identify several influencing factors of transmission line cost and analyze their mutual influence relationships; then, construct a interpretative structural model for the relationship among these factors; Finally, provide transmission line cost prediction models based on source influencing factors, intermediate influencing factors, and direct influencing factors. this provides a new method for predicting the cost of transmission lines.

关键词： Analytical models Power transmission lines Costs Transmission line matrix methods predictive models Data models

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