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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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Cloud Removal in Satellite Imagery using Adversarial Network and RGB-Optical Data Fusion 5

Cloud Removal in Satellite Imagery using Adversarial Network...

引用

IEEE 5th international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Ghildiyal, Sushil Goel, Neeraj Saini, Mukesh Indian Inst Technol Ropar Dept Comp Sci & Engn Rupnagar Punjab India

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

Many earth observation activities rely on optical remote sensing data. the optical remote sensing imagery is exploited in various applications like farmland monitoring, land use, land cover, 3D city models, vegetation growth, and disaster mitigation. Despite all, cloud cover significantly impacts on spatial and temporal accessibility of the earth observation. Since the first observation, it has been one persistent difficulty for optical remote sensing. For decades, researchers have been studying to remove clouds from optical images. the procedure of clearing the clouds becomes more difficult as they thicken. In such instances, it is customary to reconstruct utilizing additional images such as synthetic aperture radar (SAR) or near-infrared (NIR). In this paper, we propose a two-stage architecture-based cloud removal framework. the first stage of our network translates SAR and optical cloudy images to synthetic optical (RGB) image using the conditional Generative Adversarial Network (cGAN) and the second stage reconstructs the cloud-free image by fusing the synthetic optical (RGB) and cloudy optical image. the network was tested on the real cloudy images and the proposed method was compared with the state-of-the-art models and showed better results for cloud removal.

关键词： Earth Satellites Clouds Optical fiber networks Optical imaging Adaptive optics Optical sensors

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Multi-SourceWeak Supervision Fusion for Disaster Scene Recognition in Videos 5

Multi-SourceWeak Supervision Fusion for Disaster Scene Recog...

引用

IEEE 5th international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Presa-Reyes, Maria Tao, Yudong Ma, Rui Chen, Shu-Ching Shyu, Mei-Ling Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Univ Miami Dept Elect & Comp Engn Coral Gables FL 33124 USA

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

Images or video recordings assist emergency responders in quickly inspecting the damage after a disaster event. New techniques are needed to help responders organize and find important information at the right time. However, most existing methods do not meet public safety standards due to a lack of training data. We propose a multi-source weak supervision fusion technique to train on a highly imbalanced dataset annotated with noisy labels. Using a Confident Learning technique, we reduce the noise effect while boosting the class labels' quality. We combine the predictive power from models trained on large-scale visual datasets using Differential Evolution. this research demonstrates a fully-automatic approach with great potential to reduce the required time and resources while delivering exceptional results. In the TRECVID2021 Disaster Scene Description and Indexing (DSDI) Challenge, our technique achieved the top score among all the submitted runs, independent of the training data utilized.

关键词： damage assessment deep learning convolutional neural networks

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Efficient Underwater Object Detection Using Deep Neural networks 5

Efficient Underwater Object Detection Using Deep Neural Netw...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Rasool, Shaik Atheeq Annamalai, R. Natarajan, B. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India

ISBN: (纸本)9798350383522

the increasing amount of underwater visual data and deep sea research have led to the rise of marine animal identification as a major field for data processing and analysis. the need to protect the ecosystem emphasizes how important this endeavor is, yet it's still difficult because of things like the complexities of underwater backdrops, poor picture and video quality, and the variety of movements that marine life exhibits. this research used deep learning techniques to identify certain marine species and mark their presence with bounding boxes in uploaded images and videos in order to overcome these difficulties. the main goal is to provide workable methods for detecting marine life, so providing crucial backing for further studies in this area. By using the core deep learning frameworks, YOLOv8 and Faster-RCNN, the research was able to improve the efficiency and accuracy of marine life identification by making significant progress in detection time and mean average precision (mAP). this research work not only solves the current problems with finding marine animals but also establishes the groundwork for future research into comprehending and protecting the fragile undersea ecology. © 2024 IEEE.

关键词： Convolutional Neural Net- work Faster R-CNN Object Detection

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Embedded System Based on KL25Z Development Version: an Intelligent Audible and Visual Alarm Communication System 5

Embedded System Based on KL25Z Development Version: an Intel...

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5th international conference on Energy, Electrical and Power Engineering, CEEPE 2022

作者： Li, Xiaoyu College of Electrical Engineering and Automation Nanjing Normal University Nanjing China

ISBN: (纸本)9781665479059

Based on the idea of the smart home, this article design an intelligent security housekeeper system combining two-way communication technology with acoustic-optic control technology to reduce the home security risks, Meanwhile, this system utilizes the low energy consumption equipment and write the program for controlling sound and light to solve the problem of serious power loss. this paper briefly introduces the function, hardware design and software debugging of the system, with emphasis on the implementation of each module in FRDM- KL25Z hardware which is based on the ARM micro-controller. through experiments and code debugging of each module, the automatic control of light and two-way information transmission is finally realized. © 2022 IEEE.

关键词： Human computer interaction Visualization Power engineering Power demand Wireless networks Smart homes Information processing

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A small-sample fault diagnosis approach for RV reducer with RP-SST parallel CNN-SVM model 5

A small-sample fault diagnosis approach for RV reducer with ...

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5th international conference on Mechanical Engineering and Materials, ICMEM 2024

作者： Pan, Jiahao Liu, Chuanmeng Jin, Nan Peng, Yulong Qiu, Guangqi School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology 1958 Hakka Avenue Jiangxi Ganzhou China

ISBN: (数字)9781510688971

ISBN: (纸本)9781510688964

For addressing the problem of low accuracy in the available diagnostic methods due to the insufficient fault-sample, this article proposes a small-sample fault diagnosis method with Recurrence plot (RP), Synchrosqueezed transform (SST) and parallel convolutional neural network (CNN) and support vector machine (SVM). this method first utilizes RP and SST techniques to extract the multimodal features into image processing. then, the parallel CNN is used for multimodal feature fusion, enabling it to utilize more fault information within a limited number of samples. Finally, SVM is used for fault classification. the fault experiments for RV reducer were performed to verify the effectiveness of the proposed fault diagnosis method. the complementarity of RP-SST in multimodal feature extraction was verified for solving the problem of insufficient feature extraction due to sample size limitations effectively. Further, the SOTA fault diagnosis model showed that the proposed method achieved a fault diagnosis accuracy of 96.26% in small sample situations, significantly better than CNN-BIGRU, CNN-LSTM, and Resnet model. © 2025 SPIE.

关键词： Convolutional neural networks

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5th international conference on Information Systems and Management Science, ISMS 2022

5th International Conference on Information Systems and Mana...

引用

5th international conference on Information Systems and Management Science, ISMS 2022

ISBN: (纸本)9783031311529

the proceedings contain 49 papers. the special focus in this conference is on Information Systems and Management Science. the topics include: Efficient Approach for Virtual Machine and Resource Allocation in Cloud Computing;a Novel Approach for Service Selection and Ranking in Federated Cloud;a Review on IoT Based Wireless sensor Network and Security;automated Spoken Language Identification Using Convolutional Neural networks & Spectrograms;a Software Quality Characteristics Optimization Model to Reduce Evaluation Overhead;smart Home Using Internet of things;ADAPT- Automated Defence TrAining PlaTform in a Cyber Range;an Improved Recommender System for Dealing with Data Sparsity Using Autoencoders and Neural Collaborative Filtering;Neural Network Based Algorithm to Estimate the Axial Capacity of Corroded RC Columns;application of IoT for Proximity Analysis and Alert Generation for Maintaining Social Distancing;ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers;Indian Sign Language Digit Translation Using CNN with Swish Activation Function;Prognosis of Viral Transmission in Naturally Ventilated Office Rooms Using ML;impact of Organization Justice on Organizational Citizenship Behavior and Employee Retention;adopting Metaverse as a Pedagogy in Problem-Based Learning;design of Fuzzy Based Controller for Temperature Process in Fire Tube Boiler;Accelerating the Prediction of Anti-cancer Peptides Using Integrated Feature Descriptors and XGBoost;intelligent Door Locking System;automated Generation of Multi-tenant Database Systems Based upon System Design Diagrams;multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis;analysis and Optimization of Fault - Tolerant Behaviour of Motors in Electric Vehicular Systems;plant Disease Detection Using Deep Learning Techniques.

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Fine-tuned LSTM-Based Model for Efficient Honeypot-Based Network Intrusion Detection System in Smart Grid networks 5

Fine-tuned LSTM-Based Model for Efficient Honeypot-Based Net...

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5th international conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Albaseer, Abdullatif Abdallah, Mohamed Hamad Bin Khalifa Univ Coll Sci & Engn Div Informat & Comp Technol Doha Qatar

ISBN: (纸本)9781665482370

Honeypot is considered a powerful complement to the Network Intrusion Detection System (NIDS) in smart grid (SG) systems, which minimizes the workload of NIDSs while providing access to information about the attacker's actions. this assists in further tracing the attack surface and, in return, enables the NIDSs to prevent such behaviors. Machine learning (ML) has recently attracted considerable attention in the SG security domain as a stringent technique for designing and implementing algorithms to predict security threats. However, large data sets collected by honeypots require more effort for faster response, real-time processing, and decision-making, especially for limited resources SG's devices. thus, this paper proposes an approach to address this challenge, including feature extraction, oversampling and weak label combinations. We demonstrate that all classic ML algorithms cannot maintain the desired performance level when reducing the number of selected features ( i.e., using only 25% of the features). As a result, we resort to the Deep Learning approach and propose an LSTM-based model that outperforms the state-of-the-art in terms of accuracy, precision, recall, and f1-score. We conduct extensive simulations using a realistic dataset that includes large log files. the proposed approach can employ just 25% of the features from each collected network packet while attaining 99.8% testing accuracy with a 13% improvement compared to the benchmarks.

关键词： Smart Grid Security Honeypot Network Intrusion Detection System (NIDS) Deep Learning Based Detector

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Supervised Neural networks for Fruit Identification 5th

Supervised Neural Networks for Fruit Identification

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5th international conference on Recent Trends in Image processing and Pattern Recognition (RTIP2R)

作者： Bandyopadhyay, Ahana Ghosh, Sourodip Bose, Moinak Kessi, Louisa Gaur, Loveleen Appl AI Res Lab Vermillion SD 57069 USA ORPALIS A PSPDFKit Co Auvergne Rhone Alpes France Amity Univ Amity Int Business Sch Noida 201313 Uttar Pradesh India

ISBN: (纸本)9783031235986;9783031235993

thinking about the quick progression of humankind, a critical concern is given to the nourishments that we devour. In the proposed research, we propose a fruit classification system using two popular deep learning frameworks, CNN and ResNet50. With a total data size of 28, 283 fruit images (41 different categories), both the models performed exceptionally well on the network architectures, where the test accuracy achieved by CNN and ResNet50 V2 was 97.48% and 98.89% respectively, therefore obtaining a state of the art results when compared to research findings in accordance to the same dataset. the training directory was augmented, and a validation set of 12% was set to monitor the consistency and reliability of the results achieved. A number of evaluation parameters like Precision, Sensitivity, F-Scores, and ROC were calculated to analyze the results obtained. Furthermore, we took advantage of a predictor model to visualize the results on a test set with 3, 615 images. Our code can be found in the mentioned link: https://***/Sourodip-ghosh123/Fruits-360.

关键词： Fruit classification Convolutional Neural networks Deep neural networks ResNet

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Mapping-aware Biased Training for Accurate Memristor-based Neural networks 5

Mapping-aware Biased Training for Accurate Memristor-based N...

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5th IEEE international conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Diware, Sumit Gebregiorgis, Anteneh Joshi, Rajiv V. Hamdioui, Said Bishnoi, Rajendra Delft University of Technology Computer Engineering Lab Delft Netherlands Ibm Research Division Yorktown HeightsNY United States

ISBN: (纸本)9798350332674

Memristor-based computation-in-memory (CIM) can achieve high energy efficiency by processing the data within the memory, which makes it well-suited for applications like neural networks. However, memristors suffer from conductance variation problem where their programmed conductance values deviate from the desired values. Such variations lead to computational errors that result in degraded inference accuracy in CIM-based neural networks. In this paper, we present a mapping-aware biased training methodology to mitigate the impact of conductance variation on CIM-based neural networks. We first determine which conductance states of the memristor are inherently more immune to variation. the neural network is then trained under the constraint that important weights can only take numeric values which directly get mapped to such favorable states. Simulation results show that our proposed mapping-aware biased training achieves up to 2.4× hardware accuracy compared to the conventional training. © 2023 IEEE.

关键词： Memristors

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Differential Image-based Fast and Compatible Convolutional Layers for Multi-core Processors 5

Differential Image-based Fast and Compatible Convolutional L...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Hong, Sunghoon Park, Daejin Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea

ISBN: (纸本)9781665456456

Convolutional neural networks with powerful visual image analysis for artificial intelligence are gaining popularity in many research fields, leading to the development of various high-performance algorithms for convolution operators present in these networks. One of these approaches is implemented with general matrix multiplication (GEMM) using the well-known im2col transform for fast convolution operations. In this paper, we propose a multi-core processor-based convolution technique for high-speed convolutional neural networks (CNNs) using differential images. the proposed method improves the convolutional layer's response speed by reducing the computational complexity and using multi-thread technology. In addition, the proposed algorithm has the advantage of being compatible with all types of CNNs. We use the darknet network to evaluate the convolutional layer's performance and show the best performance of the proposed algorithm when using 4-thread parallel processing.

关键词： Convolutional neural networks Convolution techniques Deep learning Fast convolution Multicore processors

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