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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Mohamadi, Fred Tialinx Inc. Irvine United States

ISBN: (纸本)9798350363289

This paper introduces a novel approach leveraging the U-Net algorithm to generate silhouette images of objects using sparse data generated by computers or obtained from mmWave radar. Through this method, exceptional replication of previously unseen images is achieved. The implementation of this algorithm holds significant potential for applications such as collision avoidance on roads or runway incursion. By harnessing sparse data and the power of deep learning, this approach offers promising prospects for enhancing safety measures in various domains. © 2024 IEEE.

关键词： Deep Neural Network encoder-decoder mmWave Object Detection Obscured Environment

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NL-DSE: Non-Local Neural Network with decoder-Squeeze-and-Excitation for Monocular Depth Estimation 48

NL-DSE: Non-Local Neural Network with Decoder-Squeeze-and-Ex...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tsai, Tsung-Han Wan, Wei-Chung National Central University Department of Electrical Engineering Taoyuan City Taiwan

ISBN: (纸本)9781728163277

Monocular Depth Estimation is a popular and challenging problem for many years. IR CNNs (Convolutional Neural Networks)-based method with encoder-decoder architecture is proposed and shows a reasonable result. In this paper, we propose a SE-Net-based module for the decoder part in the encoder-decoder architecture to improve the result. We proposed a DSE (decoder-Squeeze-and-Excitation) module to deal with the whole up-sampling process globally for the decoder part. We also include the Non-local Network space attention method to design the Non-Local decoder-Squeeze-and-Excitation (NL-DSE) module. The proposed NL-DSE module is installed and evaluated on the NYU Depth V2 dataset and achieves higher accuracy. Moreover, the design is independent of the encoder-decoder architecture and can be applied in the other encoder-decoder networks to have a more accurate network. © 2023 IEEE.

关键词： CNNs encoder-decoder monocular depth estimation SE-Net up-sampling recalibration

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An Infrastructure Segmentation Neural Network for UAV Remote Sensing Images 5

An Infrastructure Segmentation Neural Network for UAV Remote...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Ma, Shuo Zhang, Xiaokang Fan, Haojia Li, Teng School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350357950

Infrastructure segmentation (bridges, dams, etc.) based on remote sensing images has been widely used in urban planning, land management, disaster monitoring and other monitoring fields. As a mature remote sensing monitoring platform, unmanned aerial vehicles (UA V) have become the main source of low-altitude remote sensing image acquisition of infrastructures. However, with the increasing data acquired by UAVs, the large data volume and high resolution of remote sensing images lead to challenges for the task of infrastructure segmentation. To solve the difficulties, this paper proposes a deep learning method for infrastructure segmentation based on adaptive hidden layer feature fusion, which specifically contains an encoder network, an adaptive layer selection mechanism, and a decoder network. In addition, this paper proposes a composite loss function containing two evaluation metrics, namely, boundary evaluation loss and Tversky loss, for training the proposed neural network. The validation of this paper is carried out on a real-world dataset, and the experimental results demonstrate the high accuracy and reliability of the proposed method on the infrastructure segmentation task. © 2023 IEEE.

关键词： adaptive layer selection deep learning encoder-decoder infrastructure segmentation

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Bridging the Language Gap: Enhancing English-to-Telugu Translation using NMT and Encoding Decoding Techniques 15

Bridging the Language Gap: Enhancing English-to-Telugu Trans...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Language translation, particularly in the context of natural language processing, involves converting text or speech from one language into another while preserving its meaning, context, and cultural nuances. This task is critical for enabling communication across different languages and is a significant area of research and development in artificial intelligence. Machine translation has a wide range of real-time applications that have significantly impacted various fields by bridging language barriers. Some of the real time application includes Google Translate, Microsoft Translator, Skype Translator. NMT has revolutionized language translation with models like Google's Transformer, OpenAI's GPT, Facebook's BART, and Google's T5. Development of a machine translation system to translate English sentences into Telugu. The system utilizes a neural network model based on an encoder-decoder architecture with attention mechanism. Preprocessing techniques are applied to the dataset, including lowercasing, removal of special characters, and tokenization. The sparse categorical cross-entropy loss function and Adam optimizer are used to train the model. The actual vs. projected output is used to evaluate performance. Pre-trained GloVe word embeddings are used in the implementation to improve the model's capacity to extract semantic information. © 2024 IEEE.

关键词： Attention mechanism encoder-decoder GloVe word embeddings Machine translation Neural network Pre-processing

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Transformer-Based Multivariate Time Series Forecasting

Transformer-Based Multivariate Time Series Forecasting

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Bharti, Mukesh Kumar Wadhvani, Rajesh Gyanchandani, Manasi Gupta, Muktesh Maulana Azad National Institute of Technology Department of Cse Bhopal India

ISBN: (纸本)9798350348460

This study explores multivariate time series forecasting, centering on the transformer model. It examines the shortcomings of other predictive models like Recurrent Neural Networks (RNN) and Temporal Convolutional Networks (TCN), particularly their inadequacies in handling autocorrelation. The transformer model stands out for its accuracy, thanks to its attention mechanism that focuses on essential parts of the input. The research introduces a novel approach that employs the transformer's architecture for effective feature selection in time series data. A vital aspect of this approach is the use of unsupervised pre-training, which shows superior results compared to traditional fully supervised methods. This advancement underscores the effectiveness of unsupervised learning in time series regression, offering significant benefits for diverse scientific and industrial fields. © 2024 IEEE.

关键词： Attention Mechanism Autocorrelation Deep Learning encoder-decoder Heteroskedasticity Quantile Regression Time Series Forecasting Transformers

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High-Resolution Satellite Imagery Analysis for Terrain and Surface Data Extraction: Techniques and Applications 7

High-Resolution Satellite Imagery Analysis for Terrain and S...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sukate, Sameeksha Wajage, Sakshi Verma, Yogesh Tawde, Parth Prasad, Jayashree MIT ADT University Computer Science and Engineering Pune India

ISBN: (纸本)9798350304268

With multiple applications and use cases of Digital Elevation Models (DEMs), focusing significantly on forming 3D models. It is used in various research areas such as flood modeling, agriculture, satellite navigation, farming, and forestry. This research paper presents a methodology for extracting Digital Elevation Models (DEMs) from satellite images using U-Net. The U-NET is a Deep Learning Model architecture that is commonly used for image segmentation tasks. It is a fully convolutional neural network that uses an encoder-decoder architecture to extract features from the input image and generate a pixel wise output. The U-Net model is ideal for DEM extraction from satellite images as it can effectively distinguish between the different features present in the images and accurately segment the ground elevation ranges. The creation of the dataset was a crucial step in the project as it ensured that the U - N et model was trained on a diverse set of images. The SWISSIMAGE dataset provided high-resolution RGB imagery, while the SRTM dataset provided the corresponding mask images with ground elevation ranges. The results obtained from the project indicate that the model can perform reasonably well even with limited data and without any significant pre-processing of the input dataset. However, there are still some challenges and limitations corresponding to the use of U-Net for DEM extraction. In conclusion, this study provides a valuable contribution to the field of remote sensing and computer vision, highlighting the potential of U-Net for DEM extraction from satellite images. In addition, the latest improvements in both software and hardware have made it possible to carry the processing with remarkable speed, even on moderately powerful hardware. We anticipate that our research will act as a catalyst for further advancements and will facilitate the integration of this research in generating, updating, and analysis of DEMs. However, further research is needed to addre

关键词： Deep learning Digital Elevation Models encoder-decoder Image segmentation Remote Sensing U-Net

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Attention-based efficient robot grasp detection network

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Frontiers of Information Technology & Electronic Engineering 2023年第10期24卷 1430-1444页

作者： Xiaofei QIN Wenkai HU Chen XIAO Changxiang HE Songwen PEI Xuedian ZHANG School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China College of Science University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System Shanghai 200093China Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education Shanghai 200093China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201210China

To balance the inference speed and detection accuracy of a grasp detection algorithm,which are both important for robot grasping tasks,we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network(AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information,and three channel attention modules are introduced in the decoder stages to extract more semantic *** lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN.A high intersection over union(IoU)value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration,but might cause a *** is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the *** design a new IoU loss calculation method based on an hourglass box matching mechanism,which will create good correspondence between high IoUs and high-quality grasp *** achieves the accuracy of 98.9%and 96.6%on the Cornell and Jacquard datasets,*** inference speed reaches 43.5 frames per second with only about 1.2×10^(6)*** proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping *** are available at https://***/robvincen/robot_gradet.

关键词： Robot grasp detection Attention mechanism encoder-decoder Neural network

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TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting 22

TransGlow: Attention-augmented Transduction model based on G...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Roudbari, Naghmeh Shafiee Poullis, Charalambos Patterson, Zachary Eicker, Ursula Gina Cody School of Engineering and Computer Science Concordia University MontrealQC Canada

ISBN: (纸本)9798350345346

The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficult due to the dynamic nature and limited data of water systems. Highly interconnected water systems can significantly affect hydrometric forecasting. Consequently, it is crucial to develop models that represent the relationships between other system components. In recent years, numerous hydrological applications have been studied, including streamflow prediction, flood forecasting, and water quality prediction. Existing methods are unable to model the influence of adjacent regions between pairs of variables. In this paper, we propose a spatiotemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder using an efficient version of the attention mechanism. The attention layer allows the decoder to access different parts of the input sequence selectively. Since water systems are interconnected and the connectivity information between the stations is implicit, the proposed model leverages a graph learning module to extract a sparse graph adjacency matrix adaptively based on the data. Spatiotemporal forecasting relies on historical data. In some regions, however, historical data may be limited or incomplete, making it difficult to accurately predict future water conditions. Further, we present a new benchmark dataset of water flow from a network of Canadian stations on rivers, streams, and lakes. Experimental results demonstrate that our proposed model TransGlow significantly outperforms baseline methods by a wide margin. © 2023 IEEE.

关键词： Attention encoder-decoder Graph Neural Net-works Spatiotemporal forecasting

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Time Series Forecasting using Sequence-to-Sequence Deep Learning Framework

Time Series Forecasting using Sequence-to-Sequence Deep Lear...

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9th International Conference on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Du, Shengdong Li, Tianrui Horng, Shi-Jinn Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781538694039

Time series forecasting has been regarded as a key research problem in various fields. such as financial forecasting, traffic flow forecasting, medical monitoring, intrusion detection, anomaly detection, and air quality forecasting etc. In this paper, we propose a sequence-to-sequence deep learning framework for multivariate time series forecasting, which addresses the dynamic, spatial-temporal and nonlinear characteristics of multivariate time series data by LSTM based encoder-decoder architecture. Through the air quality multivariate time series forecasting experiments, we show that the proposed model has better forecasting performance than classic shallow learning and baseline deep learning models. And the predicted PM2.5 value can be well matched with the ground truth value under single timestep and multi-timestep forward forecasting conditions. The experiment results show that our model is capable of dealing with multivariate time series forecasting with satisfied accuracy.

关键词： Time series forecasting LSTM encoder-decoder PM2.5 Sequence-to-sequence deep learning

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Plant Leaf Disease Detection and Classification Using Segmentation encoder Techniques

Open Agriculture Journal

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Open Agriculture Journal 2024年 18卷

作者： Trivedi, Payal Narayan, Yogendra Ravi, Vinayakumar Kumar, Prashant Kaur, Prabhjot Tabianan, Kayalvily Singh, Prabhishek Diwakar, Manoj Department of Biotechnology Chandigarh University Punjab Mohali India School of Technology Woxsen University Telangana Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun India Graphic Era Hill University Uttarakhand Dehradun India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Faculty of Information Technology Inti International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia

Aims: Agriculture is one of the fundamental elements of human civilization. Crops and plant leaves are susceptible to many illnesses when grown for agricultural purposes. There may be less possibility of further harm to the plants if the illnesses are identified and classified accurately and early on. Background: Plant leaf diseases are typically predicted and classified by farmers tediously and inaccurately. Manual identification of diseases may take more time and may not accurately detect the disease. There could be a major drop in production if crop plants are destroyed due to slow detection and classification of plant illnesses. Radiologists used to segment leaf lesions manually, which takes a lot of time and work. Objective: It is established that deep learning models are superior to human specialists in the diagnosis of lesions on plant leaves. Here, the “Deep Convolutional Neural Network (DCNN)” based encoder-decoder architecture is suggested for the semantic segmentation of leaf lesions. Methods: A proposed semantic segmentation model is based on the Dense-Net encoder. The LinkNet-34 segmentation model performance is compared with two other models, SegNet and PSPNet. Additionally, the two encoders, ResNeXt and InceptionV3, have been compared to the performance of DenseNet-121, the encoder used in the LinkNet-34 model. After that, two different optimizers, such as Adam and Adamax, are used to optimize the proposed model. Results: The DenseNet-121 encoder utilizing Adam optimizer has been outperformed by the LinkNet-34 model, with a dice coefficient of 95% and a Jaccard Index of 93.2% with a validation accuracy of 97.57%. Conclusion: The detection and classification of leaf disease with deep learning models gives better results in comparison with other models. © 2024 The Author(s).

关键词： Classification Convolutional neural network (CNN) Detection encoder-decoder Plant leaf disease Semantic segmentation

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