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Research and Application of Automatic Tobacco Baking System Based on Multi-Source Fusion

Research and Application of Automatic Tobacco Baking System ...

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Electrical Automation and Artificial Intelligence (ICEAAI), international conference on

作者： Shuwei Chen Shuai Shi Shandong Youren Intelligent Technology Co. Ltd Jinan Shandong Province China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

With the rapid growth of tobacco market demand and the gradual aging of traditional roasting technology, automated tobacco roasting system has become an important means to improve production efficiency and guarantee tobacco quality. In this study, an automated tobacco roasting system based on multi-source data fusion is designed to collect environmental parameters such as temperature, humidity and airflow in the roasting process in real time through IoT devices and edge computing, and combine with deep learning and fuzzy control algorithms to achieve accurate regulation of the roasting curve. The system adopts a modular structure, including a data acquisition and monitoring module, an intelligent algorithm processing unit and a feedback control system, to ensure the efficient and stable operation of the roasting process. In the experimental process, through the comparison and verification of the automated process and traditional process, it is found that the automated system shows significant advantages in the proportion of top-grade tobacco, unit cost and error rate control: the proportion of top-grade tobacco is increased to 50.12%, the unit cost is reduced by 29%, and the error rate is reduced to 0.23%. The system not only greatly improves the appearance quality and market competitiveness of tobacco, but also realises the dynamic adaptation to different varieties of tobacco, providing theoretical basis and practical support for the intelligent development of the tobacco industry.

关键词： Deep learning Temperature distribution Costs Error analysis Process control Production Regulation Real-time systems Temperature control Monitoring

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Research on Lattice-Based Attribute-Based Encryption Schemes for Carbon data Sharing in Power systems under Carbon Neutrality

Research on Lattice-Based Attribute-Based Encryption Schemes...

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Limei Zhang Qianting Zhu Wenjing Xu School of Economics and Management China University of Petroleum Beijing Beijing China School of Business and Administration China University of Petroleum Beijing Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Driven by global carbon neutrality goals, carbon data sharing in power systems has become a critical component of low-carbon transformation. However, the high sensitivity and privacy requirements of carbon data pose significant challenges to traditional data security technologies. To address these challenges, this paper proposes an innovative lattice-based attribute encryption scheme designed to enhance the security and privacy of carbon data sharing in power systems. The scheme integrates the quantum-resistant properties of lattice-based cryptography with the fine-grained access control mechanisms of attribute-based encryption, enabling efficient encryption and secure sharing of carbon data. Through theoretical analysis, this paper demonstrates the scheme's superiority in resisting quantum computing attacks and supporting dynamic access policies. Experimental results indicate that the proposed scheme outperforms existing methods in terms of encryption efficiency (approximately 30% improvement), decryption success rate (over 99.5%), and resistance to attacks (withstanding 99.9% of simulated attacks). Furthermore, in large-scale power system environments, the scheme exhibits excellent scalability, supporting concurrent data sharing for over 10,000 nodes with a system latency of less than 200 milliseconds. This research not only provides an efficient and secure solution for carbon data sharing in power systems but also offers new insights into the application of lattice-based cryptography in real-world industrial scenarios, with significant theoretical and practical implications.

关键词： Resistance data privacy Sensitivity Scalability Heuristic algorithms Power system dynamics Quantum mechanics Carbon neutral Encryption Carbon

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advanced Fault Sleuth System Architectures for Prognostic Anomaly Detection and Autonomous Irregularity Mitigation in intelligent Power Networks 14

Advanced Fault Sleuth System Architectures for Prognostic An...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Kumar, Biresh Anurag, Arpit Anand, Sanyam Kumar, Ritesh Gupta, Kamaldeep Sharma, Binod Kumar Amity Inst. of Information Technology Amity University Jharkhand Ranchi India Cambridge Institute of Technology Tatisilwai Jharkhand Ranchi India St. Xavier’s College Jharkhand Ranchi India Dept. of Computer Sci. & Engg. BIT Mesra Jharkhand Ranchi India

ISBN: (纸本)9798331531935

Revenue losses, inconsistent service, delayed problem detection, and electricity theft are just a few of the major issues that power distribution networks deal with. The suggested Fault Sleuth System makes use of Internet of Things (IoT) technologies to facilitate remote system control, automatic anomaly detection, and real-time electrical parameter monitoring. The technology guarantees accurate fault localization and proactive theft prevention by combining sophisticated data analytics and feature extraction techniques. The technology greatly lowers maintenance costs and security threats while improving operating efficiency through thorough data collecting, preprocessing, and intelligent analysis. In addition to improving problem detection and theft mitigation, this strategy also raises customer happiness, power quality, and service reliability. The study's conclusions show how the Fault Sleuth System can revolutionize the way that important power distribution issues are handled. This technology opens the door for a more robust, safe, and effective energy infrastructure that can satisfy needs in the future by leveraging real-time analytics and state-of-the-art IoT technologies. © 2025 ieee.

关键词： Network security

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Big data Mining for Efficient Real-Time Traffic Management

Big Data Mining for Efficient Real-Time Traffic Management

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Priyanka Kaushik Priyanka Rawat Prakash Divakaran Vandana Mishra Chaturvedi Ranjeet Kumar Roy Manas Manglik Kaushik Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science and Engineering Chandigarh University Chandigarh India Himalayan University Itanagar India D Y Patil Deemed to be University Navi Mumbai India Dept. of Mathematics Lingayas Vidyapeeth Haryana India Dept. of Electrical and Electronics Manipal Institute of Technology MAHE Karnataka India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The application of big data mining for real-time traffic management aims to address the growing challenges of urban congestion, traffic accidents, and environmental pollution. As cities expand and vehicle numbers rise, traditional traffic management systems struggle to cope with the increasing complexity. Big data mining involves analysing vast amounts of heterogeneous data from sources such as GPS devices, traffic cameras, social media, and sensors. By employing advanced analytics, machine learning, and predictive modelling techniques, it is possible to extract valuable insights into traffic patterns, predict congestion, and identify potential incidents. These insights enable the development of dynamic and adaptive traffic management strategies, including real-time traffic signal optimization, dynamic routing, and incident response. The integration of big data mining with intelligent transportation systems (ITS) can significantly enhance traffic flow, reduce travel time, improve safety, and minimize environmental impact, paving the way for smarter and more sustainable urban mobility solutions.

关键词： Technological innovation Urban areas Machine learning Big data Traffic control Real-time systems data mining Vehicle dynamics Streams intelligent transportation systems

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Climatic Efficiency Analysis of Ten Leading UK Offshore Wind Farms: A data-Driven Approach 8th

Climatic Efficiency Analysis of Ten Leading UK Offshore Win...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Ma, Liyang Li, Chenyi Zhang, Long Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom

ISBN: (纸本)9789819602315

As wind energy becomes increasingly important in global energy systems, accurately evaluating the performance of wind farms is essential for understanding wind generation efficiency and fostering sustainable development. The aim of this paper is to assess wind power generation efficiency under different climatic wind conditions. A data-driven approach is used to simulate the power generation of ten leading wind farms in the UK, using real wind data and geographic information while considering wake effects. Furthermore, to evaluate whether these wind farms perform optimally when climate-related wind patterns change, we modify the layouts of the wind farms and compare their performance with the original designs. Extensive results from 15 years (2008 to 2023) of wind data show that the originally designed layouts of these ten leading wind farms will no longer be optimal when the wind resource changes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Offshore wind farms

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intelligent Fault Diagnosis in AC Microgrids Using Short Time Fourier Transform (STFT) and Kernel-Based Algorithms 4

Intelligent Fault Diagnosis in AC Microgrids Using Short Tim...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Pradhan, Rudranarayan Agarwal, Prem Prakash Behera, Jyotishree Sen, Sibani School of Electrical Sciences Odisha University of Technology and Research Bhubaneswar India

ISBN: (纸本)9798331530402

As the global energy landscape transitions towards sustainability, decentralized power generation through AC microgrids plays a crucial role in reducing transmission losses and integrating renewable energy sources (RES) like solar and wind. While RES offers environmental benefits, their intermittent nature presents significant challenges to the reliability and stability of microgrids. Accurate fault detection and classification are essential in addressing these challenges, but traditional threshold-based methods often fail to cope with the dynamic and non-linear behavior of microgrids. This paper introduces a novel fault detection and classification method for AC microgrids that combines Short Time Fourier Transform (STFT) for feature extraction with a kernel-based Support Vector Machine (SV Machine) classifier. By analyzing time-series data from phase currents and utilizing an SV Machine with a Radial Basis Function (RBF) kernel, the proposed method effectively identifies multiple fault types in real time. Simulation results demonstrate that the kernel-based SV Machine model with an RBF kernel achieves an impressive accuracy of 98.14%, along with high precision and recall, highlighting its robustness in practical applications. This study underscores the potential of kernel-based algorithms in enhancing the sustainability and reliability of modern energy systems. The innovative combination of STFT and SV Machine sets a new standard for advanced fault detection in decentralized power systems, making it a powerful tool for the future of energy management. © 2025 ieee.

关键词： Support vector machines

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Research of Simulation on Real-Time Transmission and Processing of High Orbit Remote Sensing Satellite data Based on Virtualization Technology

Research of Simulation on Real-Time Transmission and Process...

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Yuming Zhou Feng Xiong Yuanyuan Liu Xiaobing Yin Beijing Institute of Remote Sensing Information Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Efficient collaborative operation of high orbit remote sensing satellite data transmission and processing equipment is an effective guarantee for the efficient production, quality and cost of remote sensing data image products. In order to improve the operational efficiency of remote sensing data transmission and processing system equipment, optimize equipment configuration schemes, save equipment cost and accelerate data processing efficiency, this paper builds a network transmission simulation model based on virtualization technology in response to the high real-time and large data volume characteristics of high orbit satellite observation. Based on Arena software, simulation research was conducted on the operational efficiency of system processing equipment. Combined with the specific engineering example of Gaofen-4 satellite, model parameter setting and simulation are carried out, and the operational efficiency under different equipment configuration schemes are analyzed. The study provides a reference for decision-making of the actual operation of high orbit remote sensing satellite data transmission and processing.

关键词： Satellites systems operation data processing Orbits Real-time systems data models Software data communication Virtualization Remote sensing

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Optimizing Task Estimation Accuracy in Agile Projects: A data-Driven Study 17th

Optimizing Task Estimation Accuracy in Agile Projects: A Da...

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17th Asian conference on intelligent Information and database systems, ACIIDS 2025

作者： Hnatkowska, Bogumila Grębski, Radoslaw Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 Wroclaw50-750 Poland IT Radoslaw Grębski Wroclaw Poland

ISBN: (纸本)9789819660070

Accurate task estimation is critical in any project management. Effective estimation allows teams to allocate resources efficiently, set realistic expectations, and manage project risks. Therefore, understanding the factors influencing the estimate’s accuracy is essential to improve project management practices and achieve better results. The study aimed to identify the optimal task time ranges to minimize estimation errors. The analysis focused on task durations estimated based on Story Points, examining whether shorter times lead to more accurate estimates. In addition, the study considers the potential improvement in the estimate’s accuracy with the project’s progress. Based on a publicly available dataset, the analysis examined estimation errors in different projects and issues that lasted less than an hour to more than eight days. The results show that tasks estimated to take 4 h to 1 day and 1–2 days had the smallest estimation errors. The latter was confirmed for user stories, which do not need to be split into shorter tasks. Tasks estimated at 0.5 h were consistently underestimated, and those with durations greater than eight days were overestimated, leading to more significant errors. Furthermore, the study found that the estimate accuracy did not improve as the project advanced, possibly due to unpredictable tasks such as bug fixing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Project management

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Research on Simulation Model of Urban Environment Design and Renewal Using Virtual Reality Technology

Research on Simulation Model of Urban Environment Design and...

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Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Wei Ma Changchun University of Architecture and Civil Engineering Changchun China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes a research method of urban environment design and renewal simulation model based on virtual reality technology, aiming to improve the efficiency and accuracy of urban planning and renewal process. This method takes point cloud data processing as the core technology, combines advanced 3D modeling and virtual reality technology, and constructs a high-precision urban environment simulation system. The system includes key modules such as data acquisition, optimization algorithm design, model construction, simulation verification and interactive operation. Among them, through the improved point cloud data processing algorithm, the rapid reconstruction of complex urban spatial structure is realized, and efficient 3D rendering and large-scale scene simulation are supported. data analysis of multiple actual urban scenes shows that this method has excellent performance in various environments. The simulation system supports accurate visual comparison of different urban design schemes, interactive scene adjustment through real-time dynamic operation, and quantitative evaluation of design results. Experimental data show that the system can control the simulation error within 5% in multiple urban renewal scenarios; the data processing efficiency is improved by about 30% compared with traditional methods, and the 3D reconstruction time is shortened to 70% of the original.

关键词： Point cloud compression Solid modeling Accuracy Three-dimensional displays Heuristic algorithms Urban planning Virtual reality data processing Rendering (computer graphics) data models

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intelligent Career Advisory System: Enhancing Career Matching with BERT and Graph Neural Networks

Intelligent Career Advisory System: Enhancing Career Matchin...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee international conference on

作者： Min Hua Mideth Abisado College of Computing and Information Technologies National University Manila Philippine School of Information Engineering Xinyang Agriculture and Forestry University Xinyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Traditional career recommendation systems often fail to capture dynamic skill demands, subtle query semantics, and potential ethical and bias issues. In this paper, we propose a hybrid model that integrates BERT for semantic understanding and Graph Neural Networks (GNN) for relational reasoning to construct a dynamically updated career knowledge graph. The graph is built by fusing multi-source data from LinkedIn, O*NET, and synthetic data generated via GPT-4, and a cross-modal attention mechanism is used to fuse text and graph features. Experiments on the CareerQA dataset, consisting of 50,000 annotated queries, demonstrates that our model improves the Fl-score by 18.7% and achieves an NDCG@5 of 0.921. A user study involving 1,200 participants further validates the system's practical effectiveness with an 89.2 % positive feedback rate.

关键词： Ethics Engineering profession Social networking (online) Fuses Semantics Knowledge graphs Graph neural networks Real-time systems Recommender systems Synthetic data

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