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High-Temperature Dual-Rail Contactless MEMS Logic for Industrial Edge computing

High-Temperature Dual-Rail Contactless MEMS Logic for Indust...

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ieee international conference on Micro Electro Mechanical systems

作者： Aleksandra Marković Adrian Laborde Nicolas Mauran Hervé Fanet Gaël Pillonnet Bernard Legrand LAAS-CNRS Université de Toulouse CNRS Toulouse France CEA LETI Univ. Grenoble Alpes Grenoble France

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

Sensors are advancing to integrate smart functionalities within the same package to enhance data sensing capabilities. However, implementing in-sensor computing at high temperatures is challenging due to CMOS limitations. To overcome this, we introduce the first demonstration of electromechanical computing using a process flow similar to conventional MEMS sensors, enabling seamless in-MEMS-sensor computing at elevated temperatures. Our approach uses a contactless mechanism, addressing the reliability issues of existing digital circuits based on MEMS relays and ensuring durability under significant temperature fluctuations. We demonstrate NOT logic operation as well as state propagation through a cascade of 10 pipelined gates, paving the way for digital processing within the MEMS sensor’s physical layer. Additionally, our devices operate at temperatures up to at least 473 K, opening new possibilities for in-sensor computing in extreme environments.

关键词： Micromechanical devices Temperature sensors Temperature distribution Semiconductor device reliability Logic gates Physical layer Robustness Relays intelligent sensors Electromechanical sensors

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Drone-Based intelligent Bridge Damage Detection and Maintenance Decision-Making System

Drone-Based Intelligent Bridge Damage Detection and Maintena...

引用

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

作者： Jie Lin Ao Huang Sida Lei Hubei Jiaotou Intelligent Testing Co. Ltd Wuhan China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

With the rapid advancement of technology, drone technology has demonstrated significant potential in the field of bridge inspection and maintenance. However, traditional bridge inspection methods, which rely on visual inspections by human inspectors, suffer from issues such as time consumption, high costs, and difficulty in achieving comprehensive monitoring. To address these challenges, this paper proposes and implements an intelligent bridge damage detection and maintenance decision-making system based on drones. The system integrates high-resolution cameras, advanced communication technologies, and deep learning-based damage detection algorithms to enable efficient and accurate monitoring of bridge structures. By optimizing and combining the ShuffleNetV2 and YOLOv8 algorithms, the system maintains high detection accuracy under various lighting and environmental conditions. Additionally, the system employs big data analysis and machine learning techniques to provide scientifically informed maintenance decision support. Experimental results demonstrate that the proposed system excels in bridge damage identification and assessment, significantly improving detection efficiency and accuracy. This research provides an effective technological approach for bridge health management and holds substantial engineering application value.

关键词： Bridges Deep learning Accuracy Decision making Inspection Big data Cameras Maintenance Monitoring Drones

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Short-Term Load Forecasting Using Hybrid Distance Correlation and LSTM 8th

Short-Term Load Forecasting Using Hybrid Distance Correlatio...

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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： Li, Zhongpeng Wang, Xiaoliang Cheng, Hanjing Zhu, Yehui Cao, Ke-Cai Wu, Hongjie Gu, Juping School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center Suzhou China

ISBN: (纸本)9789819602247

In the field of short-term electricity load forecasting for microgrids, accurate forecasting is crucial for the stable operation of electricity grids and the improvement of energy efficiency. The selection of suitable features is a crucial step in the forecasting process as it significantly influences the accuracy and efficiency of the model’s predictions. To improve the selection of features, the study proposes the DC-ATTN-LSTM model, which includes the distance correlation (DC), the attention mechanism (ATTN) and the Long Short-Term Memory (LSTM) to improve the accuracy of short-term load forecasts. First, the DC method philtres the multivariate time series data of the microgrid to identify important features related to the electrical loads. An attention mechanism then refines these features and focuses the model on forecast-relevant information. These prioritised features are proposed to a newly designed LSTM network, which is designed to learn the temporal dependencies and non-linear patterns in the load data using advanced deep learning techniques. Energy consumption data from Tetouan, Morocco, is used for empirical validation to compare the DC-ATTN-LSTM model with traditional LSTM models and other prediction techniques. The results show that the DC-ATTN-LSTM model outperforms the benchmark models on various assessment measures, confirming its effectiveness in short-term electrical load forecasting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microgrids

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Research on a Decision Support System for Land Resource Management Based on Remote Sensing and GIS

Research on a Decision Support System for Land Resource Mana...

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Digital Analysis and Processing, intelligent Computation (DAPIC), international conference on

作者： Cheng Wang Shanxi University of Finance and Economics Taiyuan Shanxi China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the rapid increase in population and the acceleration of urbanization, the complexity and significance of land resource management are becoming more pronounced. Remote sensing technology and Geographic Information systems (GIS) serve as fundamental spatial information tools, offering precise data support and advanced analytical methods for land management. This paper first introduces the foundational principles of remote sensing and GIS, emphasizing their relevance to land resource governance. It then explores the development of a decision support framework that integrates remote sensing and GIS, detailing key aspects such as data acquisition and preprocessing, spatial data analysis and modeling, and system architecture design. The system's effectiveness and applicability are assessed through real-world case studies. The integration of modern surveying technologies plays a crucial role in enhancing mapping efficiency. The combined application of total stations, UAV-based oblique photogrammetry, CAD mapping, and GIS spatial analysis has demonstrated promising results and substantial practical value. This system significantly improves land resource utilization efficiency, streamlines management workflows, and provides robust technical support for informed decision-making. Finally, this paper examines existing technological challenges and discusses the future trajectory of remote sensing and GIS in the domain of land resource management.

关键词： Decision support systems Solid modeling data analysis Accuracy Spatial databases data models Resource management Reliability Remote sensing Geographic information systems

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Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions

Deep Learning Enabled Secure IoT Module for Smart Agricultur...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Shaharyar Alam Ansari Salman Ali Mohammad Luqman Shahnawaz Ahmad School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Aligrah Muslim University Aligarh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The Internet of Things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. The research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. The proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. The initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). The system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance.

关键词： Smart agriculture Ciphers Recurrent neural networks Elliptic curve cryptography Sensor systems Encryption Internet of Things data communication intelligent sensors Monitoring

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Dear participants of the 9th international conference 'intelligent data acquisition and advanced computing systems'!

Proceedings of the 2017 IEEE 9th International Conference on...

引用

Proceedings of the 2017 ieee 9th international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2017 2017年 1卷 vi页

作者： Costoiu, Mihnea University 'Politehnica' of Bucharest Romania

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From Legacy to intelligent IIoT systems: Automation, Scalability and Elasticity

From Legacy to Intelligent IIoT Systems: Automation, Scalabi...

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Working ieee/IFIP conference on Software Architecture

作者： Gianluca Caiazza Teodors Lisovenko Pietro Ferrara Fabio Berti Francesca Ferrari Alessandro Zaupa Guangzheng Zhang Ca’ Foscari University Venice Italy Zamperla Vicenza Italy

ISBN: (数字)9798331520908

ISBN: (纸本)9798331520915

The Internet of Things (IoT) revolution is reshaping how physical devices embedded with software connect to the Internet, facilitating seamless data exchange and driving automation. Industrial IoT (IIoT) extends these capabilities to industrial devices and Cyber-Physical systems (CPS), driving intelligent Manufacturing. This integration supports advanced applications like remote monitoring, predictive maintenance, machine learning (ML), and artificial intelligence (AI) optimization, enhancing production efficiency, adaptability, and decisionmaking. However, managing the vast amounts of data generated requires scalable, automated software architectures. Many small and medium-sized enterprises (SME) face challenges in building such systems due to limited resources and expertise, often starting with manual data collection and basic *** paper presents a solution: a fully automated, configurable, and scalable software architecture for intelligent Manufacturing. Our system has been operational for almost one and a half years on 21 plants, processing about 17K tasks, amounting to more than three months of computations. The experimental results show that automation and elasticity have been needed by such systems since the beginning, while scalability is not required during an initial experimentation phase.

关键词： Automation Software architecture Scalability Computer architecture Production Manuals Elasticity Software Manufacturing Industrial Internet of Things

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Improved Multilabel Land Cover Identification Using Machine Learning and data Enhancement

Improved Multilabel Land Cover Identification Using Machine ...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Landage, Rajshree Nagalakshmi, K. Hussein, Ramy Riad Pavithra, Avvari Keerthana, S. Nithya, R. Cmr College of Engineering & Technology Department of Cse Telangana Hyderabad India Sethu Institute of Technology Department of Computer Science Engineering Pulloor Tamil Nadu Kariapatti626115 India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Saveetha institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Bannari Amman Institute of Technology Computer Science and Engineering Sathyamangalam Erode India

ISBN: (纸本)9798331527495

The categorization of arrive cover could be a burgeoning subject of think about in farther detecting. Conventional strategies generally concentrate on the pixel-based calculations that are incapable to handle high-resolution pictures viably., or on the less difficult single-label situation. In any case., the issue of multilabel arrive cover scene classification is still not well caught on. Indeed whereas convolutional neural systems and profound learning have appeared an extraordinary capacity to handle troublesome machine learning issues like picture classification., their execution is disillusioning when prepared with a little number of explained tests. This research proposes a information enlargement strategy which will significantly extend a little information set to gigantic extents in arrange to induce around this issue. Our tests on a multilabel adaptation of the UC Merced Arrive Utilize information set appear the guarantee of the proposed method., which accomplishes an F -score metric enhancement of over 60ve the state of the craftsmanship at this time. For multilabel arrive cover scene classification-a significant job in inaccessible detecting and natural monitoring-deep learning has appeared to be a strong device. In arrange to extend the accuracy and flexibility of arrive cover categorization., this work examines the utilize of profound learning strategies., more particularly convolutional neural systems (CNNs)., in conjunction with information expansion approaches. Since a few land cover types inside a single scene are complicated and cover., multilabel classification is particularly troublesome. In arrange to overcome this., we utilized a CNN engineering that was prepared on a wide run of adj. picture datasets. We too utilized a few information enlargement strategies., such turn., scaling., and flipping., to progress the model's generalization aptitudes. © 2025 ieee.

关键词： Environmental monitoring

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Leveraging AI to Personalize HR Marketing Campaigns: A data-Driven Approach 3

Leveraging AI to Personalize HR Marketing Campaigns: A Data-...

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3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Alshar, Moath Mahmoud Sao, Ameet Sharma, Manish Kadyan, Sunil Rao, Vuda Sreenivasa Anitha Vijayalakshmi, B. Jadara University Faculty of Business Business Administration Department Jordan National Institute of Construction Management and Research Haryana Bahadurgarh India Maharaja Agrasen Institute of Management Studies Department of Business Administration Delhi India Manav Rachna University School of Management and Commerce Faridabad India Koneru Lakshmaiah Education Foundation Department of CSE A.P. Vaddeswaram India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of ECE Chennai India

ISBN: (纸本)9798331523893

The research proposes an AI-driven personalization framework for enhancing the efficiency and engagement of HR marketing campaigns. The system integrates real-time data processing, collaborative filtering, and feedback loops to dynamically recommend job opportunities tailored to individual candidate preferences. Using an HR-related dataset from Kaggle, the framework preprocesses data by handling missing values, scaling numerical features via Min-Max Scaling, and reducing dimensionality using Principal Component Analysis (PCA). Collaborative Filtering is employed to predict candidate preferences through matrix factorization, dynamically updating recommendations based on real-time user interactions. The experimental evaluation highlights the framework's superior performance compared to traditional methods. Click-Through Rate (CTR) improved significantly, with AI-driven personalization achieving 30% compared to 18% from rule-based methods and 12% from the baseline. Accuracy analysis revealed that Collaborative Filtering outperformed random and content-based methods, achieving the highest precision (85%), recall (88%), and F1-score (86%). These results underscore the effectiveness of AI in aligning candidate preferences with recruitment goals while ensuring dynamic adaptation through feedback. The proposed system also seamlessly integrates personalized insights into HR marketing channels such as job portals, email campaigns, and social media advertisements, optimizing recruitment processes and enhancing user engagement. Future research aims to extend the dataset to include diverse candidate profiles, incorporate multi-modal data sources, and leverage deep learning for improved recommendation accuracy. Additionally, explainability techniques will be explored to ensure transparency and build trust among users. This framework demonstrates the transformative potential of AI-driven personalization in reshaping HR marketing strategies. © 2025 ieee.

关键词： Convolutional Neural Network Cyber Security Multi-Layer Perceptron Network Anomaly detection Tuna Swarm Optimization

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Graph-Based Modeling for Multi-input Temperature Prediction in Power Plant 8th

Graph-Based Modeling for Multi-input Temperature Prediction...

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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： Gang, Yusen Peng, Chen Cheng, Chuanliang Shanghai University Shangda Road Baoshan District Shanghai99 China

ISBN: (纸本)9789819602247

The operational efficiency and safety of thermal power generation units are intricately dependent on the precise control of combustion chamber temperatures. Accurate temperature prediction is indispensable for optimizing boiler combustion efficiency and swiftly identifying abnormal operational conditions. In this research, we propose an innovative end-to-end data-driven model designed to address the inherent complexity of multi-input multi-output temperature prediction. By fusing temperature data with a graph-based framework and harnessing spatiotemporal attention mechanisms, our model, underpinned by Graph Convolutional Network and Transformers, adeptly captures intricate spatial relationships among output variables. Empirical validation using real-world plant data attests to the model’s exceptional predictive performance. The results highlight its potential to significantly enhance temperature prediction accuracy in power plant operations. Notably, the model achieves a root mean squared error of 2.257 and a mean absolute error of 1.803 as the best prediction scores. Overall, our proposed approach presents a promising solution for the accurate prediction of temperature, with potential implications for improving operational efficiency and safety in thermal power generation units. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Learning intelligent computing Signal Analysis Signal Processing systems Modelling

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