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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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State and Parameter Estimation of Nonlinear Systems Using Extended Kalman Filter Based sensor Fusion Approach

State and Parameter Estimation of Nonlinear Systems Using Ex...

引用

Emerging Technology (INCET), international conference for

作者： Tushar Dhiman Bharat Bhushan Sharma Department of Electrical Engineering NIT Hamirpur Hamirpur India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

the primary objective of this paper is to devise and execute sensor fusion employing the Extended Kalman Filter (EKF) for parameter estimation in nonlinear systems. sensor fusion tackles a data processing challenge where individual measurements from diverse sensors are conducted independently, yet the measured values exhibit correlation due to the system dynamics. Furthermore, these measurements are affected by various types of noise. Leveraging the Extended Kalman Filter (EKF) for sensor fusion enables the extraction of accurate information from imprecise measurements. the study employs the Extended Kalman Filter (EKF) algorithm for sensor fusion in the context of the Lorenz system and the Wilberforce Pendulum. Numerical simulations are conducted to validate the efficacy of the proposed methodology.

关键词： Parameter estimation System dynamics Service robots sensor fusion Robot sensing systems Numerical simulation Robustness

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Control Method of Dynamic Clustering Network in Wireless sensor Network

Control Method of Dynamic Clustering Network in Wireless Sen...

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Hailin Liu Chengguang Zhang Bingfu Hu Department of Information Engineering Weifang Engineering Vocational College Qingzhou Shandong Province China

To solve the routing holes in wireless sensor networks caused by high energy consumption of cluster head nodes and shortage of spectrum, a dynamic clustering based method for wireless sensor network networking control is presented. In the early stage of cluster networking, the communication efficiency index is constructed by collecting the signal-to-noise ratio and energy consumption of wireless sensors (nodes), and the cluster head node and working frequency are selected. To avoid the routing holes caused by cluster head node death, periodically check the communication efficiency index of the nodes to dynamically adjust the cluster head node and working frequency. A smooth transition between old and new cluster head nodes is achieved by substituting nodes. this control method ensures that a wireless sensor with the best performance always has a stable communication with the external terminal as the cluster head node in the wireless sensor network, and improves the performance of the entire wireless sensor network.

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Research on Detection Method of Foxing on Paper Cultural Relics Based on CNN and Hyperspectral 5

Research on Detection Method of Foxing on Paper Cultural Rel...

引用

5th international conference on Computer Information Science and Application Technology, CISAT 2022

作者： Hang, Liu Bin, Tang Zourong, Long Jianxu, Wang Qing, Chen Junfeng, Miao Jinfu, Zhang Mingfu, Zhao Nianbing, Zhong Huan, Tang Chongqing Key Laboratory of Fiber Optical Sensor and Photodetector Chongqing University of Technology Chongqing400054 China Key Scientific Research Base of Pest and Mold Control of Museum Collections of National Cultural Heritage Administration Chongqing China Three Gorges Museum Chongqing400054 China

ISBN: (纸本)9781510660076

Paper cultural relics are one of the most important cultural carriers of ancient human civilization and precious material materials. Due to age and human factors, a large amount of paper cultural heritage is being corroded by foxing, which seriously affects the safety of paper cultural heritage. At present, the detection and control of insect and mould diseases on paper cultural relics is still a recognized problem in the world. In order to solve the practical problem of foxing detection on paper cultural relics, this paper proposes a paper cultural relic foxing based on one-dimensional convolutional neural network based on the characteristics of nondestructive detection of hyperspectral imaging, aiming at the spectral characteristics difference between the foxing part and the healthy part. the spot detection method realizes the non-destructive detection of foxing on paper cultural relics. the experimental results show that the classification accuracy of the model in this paper is 99.17% for the foxing area and healthy area of paper cultural relics. Compared with traditional methods such as K nearest neighbor method and support vector machine, the convolutional neural network model can fully extract the deep spectral features of the foxing region, and can effectively avoid overfitting. the method of combining deep learning theory with hyperspectral provides a methodological basis for the protection of paper cultural relics. © 2022 SPIE.

关键词： Convolutional neural networks

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Gearbox Fault Diagnosis Using Stacked Multisensor Image Fusion and Self-Normalizing Convolutional Neural networks

Gearbox Fault Diagnosis Using Stacked Multisensor Image Fusi...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Boubker Najdi Mohammed Benbrahim Mohammed Nabil Kabbaj Systems Engineering Modeling and Analysis Laboratory Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the accurate diagnosis of gearbox faults in industrial machinery is essential for ensuring operational efficiency and preventing costly downtime. this paper introduces a novel diagnostic framework that employs advanced signal processing and deep learning techniques to elevate fault detection accuracy and reliability in gearbox systems. By leveraging stacked Synchrosqueezing Wavelet Transforms (SSWT) for multi-sensor image fusion, our approach begins with transforming raw vibration signals from multiple sensors into high-resolution Time-Frequency (TF) representations, essential for detecting subtle mechanical anomalies. these images are then processed using Self-Normalizing Convolutional Neural networks (SCNNs), which exploit their inherent normalization capabilities to maintain consistency across network layers and mitigate the vanishing gradient problem. the spatial features extracted are further analyzed through Gated Recurrent Units (GRUs), adept at modeling long-term dependencies and capturing fault progression dynamics. Enhancement of this process with a Multihead Attention layer allows selective focusing on salient features, significantly improving diagnostic precision. Validated on two public gearbox datasets, our method achieved 100% accuracy on both, advancing gearbox fault diagnostics and setting a new benchmark for industrial fault detection methods.

关键词： Fault diagnosis Deep learning Wavelet transforms Vibrations Accuracy Fault detection Signal processing Feature extraction Convolutional neural networks Image fusion

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Design and Development of energy efficient Relay-Based Cooperative Routing Protocol (RBCRP) for WSNs 5

Design and Development of energy efficient Relay-Based Coope...

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5th international conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)

作者： Patil, Raghavendra G. E. thippeswamy, M. N. SDM Inst Technol Dept Comp Sci & Engn Ujire India Nitte Meenakshi Inst Technol Dept Comp Sci & Engn Bengaluru India

ISBN: (纸本)9781665432726

A spatially distributed sensors nodes are contained in the "Wireless sensor networks"(WSNs) for monitoring environmental and physical situations. For the operations of WSNs, Energy efficiency is a very crucial problem. For increasing the network lifetime as well as for reducing the utilization of energy of sensor, routing plays a crucial part in WSN and is also handled wisely. the main focus of this proposed work is a relay-based cooperative routing protocol that estimates a path for routing based on the Channel qualities (CQI) and energy consumption whereas quality of the channel are computed depending on the relay nodes. the proposed work is compared with existing routing protocols LEACH and Selection Election Protocol (SEP). the experimental results of the the proposed work RBCRP show that it is providing better performance compared to the other two existing protocols.

关键词： wireless sensor networks CQI LEACH SEP Energy cooperative protocol

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Predictive Maintenance and Real Time Monitoring using IoT and Cloud Computing

Predictive Maintenance and Real Time Monitoring using IoT an...

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Image processing and Capsule networks (ICIPCN), international conference on

作者： Aaryan Suthar Kishor Kolhe Vitthal Gutte Dhanashri Patil Department of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Industrial Revolution 4.0 describes the rapid transformation that industry is going through due to Artificial Intelligence (AI), Machine Learning (ML) and IoT (Internet of things). Industrial IoT (IIOT) combines traditional industrial processes with modern technology such as sensors, data analytics, and machine learning to optimize and automate processes. Like the Industrial Revolution, IIoT is transforming the way that goods are produced, but with some significant differences. IoT enables machines and devices to communicate with one another in real-time, providing valuable insights into the performance of industrial processes. this allows for predictive maintenance, where machines can be repaired or replaced before they fail, reducing downtime and maintenance costs. OEE (Overall Equipment Efficiency), an industry standard for gauging production efficiency, is raised with the use of predictive maintenance. By employing IoT, cloud computing, machine learning and real-time monitoring to identify abnormalities, malfunctions, and errors in manufacturing equipment, this paper seeks to boost OEEas a result of predictive maintenance. sensors are employed in equipment to identify motion and other crucial data, which is subsequently transmitted to the cloud for analysis. As a result of this work, equipment dependability and efficiency are increased while downtime and maintenance costs are decreased.

关键词： Cloud computing Data analysis Data visualization Machine learning Real-time systems sensor systems sensors

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Agriculture Crop Yield Forecasting using Deep Learning Techniques

Agriculture Crop Yield Forecasting using Deep Learning Techn...

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Image processing and Capsule networks (ICIPCN), international conference on

作者： D Vaishnavi R Bavithra M Rufina Marssha S Sowmiya Dept. of CSE SRC SASTRA Deemed to be University Kumbakonam Tamilnadu India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Crop yield predictions offer valuable insights for various peoples such as farmers, policymakers, and commodity traders, guiding them in making strategic decisions concerning food security and market entry. the complex dynamics between crop-specific attributes, environmental conditions, and management practices significantly impact crop yields. this complexity not only poses a challenge in understanding crop yields. Hence, this work proposes a crop yield prophecy model by comparing the deep learning algorithms such as Long ShortTerm Memory (LSTM) and 1-Dimensional Convolutional Neural Network (1DCNN) to find the optimal model for overcoming such difficulties.

关键词： Deep learning Training Time series analysis Predictive models Prediction algorithms Convolutional neural networks Reliability

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Mask R-CNN with Multi-Backbones - A Comparative Analysis 5

Mask R-CNN with Multi-Backbones - A Comparative Analysis

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5th international conference on Signal processing and Information Security, ICSPIS 2022

作者： Rubin, Rufus Jacob, Chinnu Anzar, S.M. Panthakkan, Alavikunhu TKM College of Engineering Centre for Artificial Intelligence Kollam691005 India TKM College of Engineering Department of Electronics and Communication Kollam691005 India University of Dubai College of Engineering and IT United Arab Emirates

ISBN: (纸本)9781665492652

Instance segmentation is a computer vision task for detecting and localizing an object in an image. It involves identifying, segmenting, and categorizing each object in an image. the idea behind instance segmentation is to categorize objects that belong to the same class into different instances. With instance segmentation, one can locate the bounding boxes of each instance as well as the object segmentation maps for each instance, allowing one to determine the number of instances in the image. Mask Region-based convolutional neural network is the cutting-edge method for instance segmentation. the backbone architecture of the mask R-CNN consists of a feature pyramid network, a region proposal network, and a region of interest alignment network. In this paper, three CNN models such as ResNet101, ResNet50, and MobileNetV1 are used as backbone network structures to compare the mask R-CNN architecture. the performance of these models is compared with the Penn- Fudan dataset using performance metrics such as mean Average Precision, mean Average Recall, and F1-score. the ResNet-101 output achieves a mean Average Precision of 98.3% over the other two models. the performance scores and losses of the three models were also discussed. Experimental studies show that ResNet-101 outperforms the other two models. © 2022 IEEE.

关键词： Convolutional neural networks

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Temporal-Frequency-Spatial Features Fusion for Multi-channel Informed Target Speech Separation 5

Temporal-Frequency-Spatial Features Fusion for Multi-channel...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Zhang, Wen Lin, Bin Ma, Li Zhou, Aolong Wu, Guoli Strategic Evaluation and Consultation Center Academy of Military Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China College of Meteorology and Oceanography National University of Defense Technology Changsha China

ISBN: (纸本)9781665485890

Our goal is to make full use of time-frequency domain features and spatial domain features of the multichannel speech signal, and we propose an end-to-end multichannel target speech separation method based on temporal-frequency-spatial feature fusion, called the cTFS model. For the target speech separation task, the cTFS model takes the angel feature of the target speech signal as the prior knowledge, then predicts the complex ideal ratio mask target with a complex U-shaped network. We achieve the reconstruction of the target speech signal by signal approximation. Furthermore, a multi-channel target speaker separation dataset is constructed based on the WSJ0-2mix dataset based on the signal reverberation model. the performance of each target speaker separation model is evaluated on this dataset using the evaluation metrics SDR, SI-SNR, PESQ, and STOI. Experimental results show the effectiveness of the proposed method as well as the benefit of incorporating angle feature information in multichannel speech separation. © 2022 IEEE.

关键词： Complex networks

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An Improved Content Based Image Retrieval System using Unsupervised Deep Neural Network and Locality Sensitive Hashing 5

An Improved Content Based Image Retrieval System using Unsup...

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5th international conference on Computer, Communication, and Signal processing, ICCCSP 2021

作者： Balasundaram, Prabavathy Muralidharan, Sriram Bijoy, Sruthi SSN College of Engineering Department of CSE Chennai India

ISBN: (纸本)9781665432771

Nowadays, many types of digital devices generate data in the form of text, image and video. Hence, it is essential to have a data archival system that maintains and accesses the data. In this context, Content Based Image Retrieval (CBIR) is the problem of searching for images in a large database of images. the CBIR involves feature extraction and identification of similar images. Existing works have utilized supervised deep neural networks to learn the features and used exhaustive searching technique to identify the similar images. However, since real datasets of images are unlabelled and large in size, the usage of supervised deep neural networks and exhaustive searching technique will be a time-consuming process. thus, this paper proposes an Improved CBIR system that first utilizes an unsupervised deep learning approach to extract the features. Further, it utilizes Locality Sensitive Hashing (LSH) to reduce search space and K Nearest Neighbor (KNN) algorithm on the reduced search space to find the required number of similar images. © 2021 IEEE.

关键词： Deep neural networks

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