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检索条件"任意字段=International Conference on Computer Distributed Control and Intelligent Environmental Monitoring"

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Adaptive Service Function Chain Orchestration Algorithm Based on Deep Reinforcement Learning

Adaptive Service Function Chain Orchestration Algorithm Base...

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Signal Processing and intelligent Computing (SPIC), international conference on

作者： Wenbo Zhang Yangyang Zhang School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In traditional networks, service function chains consist of multiple network service devices that data flows traverse from source to destination, which leads to unnecessary resource waste due to issues like fixed network topology and deployments. SDN/NFV breakthroughs in decoupled control and hardware virtualization provide server virtualization techniques that lift limitations of traditional network bottom layer resources. This enables possibilities for reducing link latency and resource overhead to achieve adaptive *** propose DeepSFC, a distributed self-learning service chain orchestration method based on DRL. After offline training, the intelligent agents apply online with minimal overhead. Without requiring expert knowledge, they can make orchestration decisions by periodically observing environmental monitoring information. Each agent only observes its direct neighbors rather than requiring global knowledge. Compared to state-of-the-art heuristic and DRL algorithms, DeepSFC significantly improves throughput on real-world network topology and traffic traces (11% and 15%), while each online decision takes less time (1 ms).

关键词： Training Service function chaining Network topology Heuristic algorithms Signal processing algorithms Signal processing Throughput Deep reinforcement learning intelligent agents Virtualization

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An IoT-Edge Prototype for Wastewater Treatment Plant monitoring

An IoT-Edge Prototype for Wastewater Treatment Plant Monitor...

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international conference on Embedded & distributed Systems (EDiS)

作者： Abderrahmane Benhariga Lakhdar Loukil Houssam Eddine Zahaf Computer science Department University of Oran 1 Algeria IUT of Nantes Nantes University France

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519889

Wastewater treatment process presents significant challenges, particularly in achieving efficient treatment and realtime monitoring to ensure environmental safety. Traditional methods often fail to provide timely data, resulting in delays in identifying and addressing issues. This paper aims to address these challenges by employing Internet of Things (IoT) technology at the Edge level, specifically focusing on the physical layer with low-cost and low-power hardware solutions. We propose an IoT infrastructure for Mascara (a city in north-western Algeria) wastewater treatment plant, integrating various sensors to measure critical wastewater parameters such as pH, dissolved oxygen, conductivity, temperature, and turbidity. The infrastructure employs sensor kits equipped with real-time data displays and communication mediums to transfer data to a gateway for storage. The objective of this work is to demonstrate the effectiveness of IoT in enhancing the quality of wastewater treatment monitoring, thereby enabling plant operators to exercise better control over the treatment process. The successful implementation of the system underscores the significant potential of IoT technologies to revolutionize wastewater management, ultimately leading to more sustainable and efficient water resource management.

关键词： Temperature sensors Temperature measurement Wireless communication Real-time systems Sensors Wastewater Internet of Things Wastewater treatment monitoring Water resources

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EfficientNetB3 for High-Performance Insect Identification

EfficientNetB3 for High-Performance Insect Identification

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intelligent Cyber Physical Systems and Internet of Things (ICoICI), international conference on

作者： Jatin Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Efforts at conservation, pest control, and ecological monitoring depend on the accurate taxonomy of insects. Conventional approaches depending on professional identification are labour-intensive and prone to mistakes. The state-of-the-art convolutional neural network EfficientNetB3 model is applied in this work for automatic insect classification. The model obtained an amazing general accuracy of 98% using a dataset of 10,000 photos comprising ten insect classes: Bees, Beetles, Butterfly, Cicada, Dragonfly, Grasshopper, Moth, Scorpion, Snail, and Spider. Every class showed outstanding F1, recall, and accuracy; almost perfect accuracy came from Butterfly, Snail, and Grasshopper. Rare and with minimal effect on performance were minor misclassifications. These findings show the possibilities of EfficientNetB3 for correct and effective insect categorisation, therefore providing a strong instrument for entomological study and practical applications. Reducing misclassifications even more and raising the usefulness of the model for many bug species and environmental situations should be the main priorities of the next research.

关键词： Accuracy Biological system modeling Insects Instruments computer bugs Taxonomy Pest control Internet of Things Convolutional neural networks monitoring

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Adaptive Estimation for environmental monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Prediction of Quality and Lifetime of Fruit using Deep Learning

Prediction of Quality and Lifetime of Fruit using Deep Learn...

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international conference on ent Computing and control Systems (ICICCS)

作者： Gurram Sai Harshit Guntupalli Jothish L.Sujihelen Department of CSE Sathyabama Institute of Science & Technology Chennai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This proposed system focuses on developing a predictive model "Prediction of Quality and Lifetime of Fruits Using Deep Learning" is to create a reliable system for predicting fruit shelf life and evaluating fruit quality in real time. The system examines fruit photos to determine surface flaws, maturity levels, and general quality using Deep Learning-based object detection. It predicts how long fruits will last on the shelf by taking into account environmental factors like temperature and humidity. A Deep Learning model is trained using the preprocessed dataset, which comes from Roboflow, to ensure precise detection and classification. By decreasing fruit waste and empowering farmers, distributors, and retailers to make well-informed decisions, this technology aims to maximize supply chain management. In order to improve food quality monitoring and guarantee efficiency, sustainability in the agricultural industry. The suggested solution integrates computer vision and Deep Learning algorithms. The proposed work performs better when compared with the existing algorithms.

关键词： Deep learning computer vision Visualization Supply chain management Temperature Computational modeling Object detection Prediction algorithms Real-time systems Tuning

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Electrostatic Sensing-Based Human Activity Recognition

Electrostatic Sensing-Based Human Activity Recognition

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international conference on computer and Communications (ICCC)

作者： Qian Wang Zhenxing Wang School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Addressing issues such as compromised privacy, blind spots, strict environmental requirements, and high sensor costs in non-contact human activity recognition, this paper proposes a method that uses electrostatic induction to capture human activity. The method involves the analysis and processing of voltage signals induced by human activities via electrostatic induction, and achieves automatic recognition and monitoring of human actions using a 1D-CNN algorithm. The proposed method was tested in natural indoor environments, employing an 8 cm electrode to recognize human activity within a range of 1.5 meters. Experimental results demonstrate an identification accuracy of 80.8%, validating the effectiveness of the proposed approach.

关键词： Meters Electrodes Analytical models Accuracy Voltage Smart homes Electrostatic induction Data collection Sensors Human activity recognition

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Step-Wise Working Status for Next-Generation Smart Cities: Integrating Fog and Edge Computing Solutions

Step-Wise Working Status for Next-Generation Smart Cities: I...

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2025 international conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Beevi, L Sherin Nehru, V. Joe Prathap, P.M. Dani, W. Vinil R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India St. Peter's College of Engineering and Technology Department of Eee Avadi India

ISBN: (纸本)9798331521394

In the fast changing environment of smart cities, combining fog and edge computing with powerful IoT devices like Hikvision cameras is revolutionising urban administration and public safety. Hikvision, a video surveillance systems pioneer, offers a variety of IP and analogue cameras, thermal imaging, and intelligent software platforms used for traffic management, public safety, environmental monitoring, and infrastructure security. These technologies, which combine high-definition video quality, advanced image processing, and cognitive analytics, enable real-time monitoring, event detection, and quick responses. Traffic cameras collect real-time data for vehicle counts and congestion management, while environmental sensors monitor air, water, and noise levels to aid in health warnings and pollution control. Smart meters and wearable health gadgets monitor energy, water, and health indicators to optimise resource management. Parking sensors and waste management sensors boost parking and rubbish collection efficiency. The integration of fog and edge computing processes data locally, lowering latency and bandwidth utilisation, while fog nodes aggregate data for thorough analysis. Cloud storage enables long-term data analytics, which improves decision-making for city planners and emergency responders. Hikvision's software, which includes HikCentral and iVMS-4200, combines video surveillance, access control, and intelligent traffic management to create a comprehensive urban security framework. These systems are crucial to public safety because they provide real-time monitoring, facial recognition, and motion detection. For city planners, such innovations provide essential insights for urban growth and sustainability programs. The integration of Hikvision video surveillance with fog and edge computing is a game-changing approach to smart city management, improving urban safety and efficiency while improving inhabitants' quality of life. © 2025 IEEE.

关键词： Water pollution control

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Kaiser and Cumulative Proportion Principal Component Analysis for Temperature Compensation of Vibration in Reinforced Concrete Bridge 14

Kaiser and Cumulative Proportion Principal Component Analysi...

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14th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and control, Environment, and Management, HNICEM 2022

作者： Concepcion, Ronnie Cruz, Febus Reidj G. Carpio, Joy Bandala, Argel Relano, R-Jay De Leon, Joseph Aristotle Genevie Janairo, Adrian Guillermo, Marielet Galupino, Joenel Genoguin, Marvin Jade James Loresco, Pocholo De la Salle University Department of Manufacturing Engineering and Management Manila Philippines Mapua University School of Electrical Electronics and Computer Engineering Manila Philippines University of California Systems Engineering Department BerkeleyCA United States De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Civil Engineering Manila Philippines Eastern Visayas State University Department of Civil Engineering Tacloban City Philippines FEU Institute of Technology Electrical and Electronics Engineering Department Manila Philippines

ISBN: (纸本)9781665464932

Structural health monitoring (SHM) was developed to provide diagnosis of the state of civil structures, such as bridge and building, throughout their lifespan. The superstructure degrades over time mainly because of the continuous effect of environmental factors including temperature, wind, humidity, and traffic loading. Consequently, this study is concerned in reducing the masking effect of environmental factors, specifically on temperature. Principal component analysis (PCA), a supervised machine learning algorithm, was employed as the embedded statistical treatment for multidimensional reduction of feature matrix data to eliminate the temperature effect. Kaiser's criterion eliminated data variance of almost 20% that may result to poor data reconstruction. Cumulative proportion (CP) criterion eliminated data variance around 4%, which is a better choice for deciding the number of principal components to eliminate. Thus, the proposed experimental study addressed successful temperature compensation from reinforced concrete bridge vibration data by using PCA and CP criterion. © 2022 IEEE.

关键词： Structural health monitoring

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Adaptive Stream Processing Framework for Energy-Efficient Smart Greenhouses Using Neuromorphic Computing

Adaptive Stream Processing Framework for Energy-Efficient Sm...

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IoT Based control Networks and intelligent Systems (ICICNIS), international conference on

作者： Akey Sungheetha R Rajesh Sharma Sheila Mahapatra K Sasi Kala Rani A. Ezil Sam Leni R Tamilarasi Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This paper presents an innovative framework combining neuromorphic computing and stream processing analytics for energy-efficient smart greenhouse management. The proposed Adaptive Greenhouse Optimization and Resource Integration (AGORI) methodology addresses key challenges in real-time environmental monitoring, energy optimization, and sustainable agriculture. By leveraging Meta Spark Creator AR for visualization and implementing a novel hybrid algorithm combining Spiking Neural Networks (SNNs) with streaming data analytics, our system achieves 47% improved energy efficiency and 38% better crop yield prediction accuracy compared to traditional methods. The framework was validated across 14 international deployment sites, demonstrating robust performance under diverse climatic conditions. Results show significant improvements in resource utilization and operational sustainability, with particular emphasis on power stability and green energy integration.

关键词： Measurement Neuromorphic engineering Green products Prediction algorithms Energy efficiency Agriculture Sparks Resource management Optimization Crop yield

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Research on Modal Parameter Identification and Damage Diagnosis of Bridge Structure Based on Improved Genetic Algorithm

Research on Modal Parameter Identification and Damage Diagno...

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control, Electronics and computer Technology (ICCECT), IEEE international conference on

作者： Wenyu Xu Yin Wang School of Intelligent Manufacturing Nanjing University of Science and Technology Zijin College Nanjing China Nanjing Vocational University of Industry Technology Nanjing China

Large-scale bridge projects are an vital part of national infrastructure, and are directly related to the safety of people's lives and properties. The identification of modal parameters of bridge structures under working conditions is an vital part of bridge damage identification. Considering the practicability of bridge detection, bridge detection should generally be based on environmental excitation. The existing means of modal parameter identification under environmental excitation are not suitable for The identification accuracy of the modal frequency is high, while the identification of the displacement mode has a large error. The existing means of modal parameter identification under environmental excitation have relatively high identification accuracy for the modal frequency, while the identification of the displacement mode has a large error. Due to the complexity and large-scale bridge structure, the importance and necessity of bridge structure health monitoring are more and more recognized by people. The means used in this paper is to improve the genetic algorithm to simulate the phenomena of reproduction, crossover and gene mutation that occur in the spontaneous selection and spontaneous heredity process. Genetic algorithm is a global optimization adaptive probabilistic search algorithm, which has the advantages of intelligent optimization and robustness.

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