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Enhancing Water Resource Management: A GUI-based Approach for Water Body Classification and Change Detection in VHRS Images 3

Enhancing Water Resource Management: A GUI-based Approach fo...

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3rd ieee international conference on intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Vasavi, S. Rajeswari, V. V. S. N Akhila Sree Kalyan, Ch. Venkata Vr Siddhartha Engineering College Computer Science & Engineering Department Andhra Pradesh India

ISBN: (纸本)9798350361186

The accurate classification of water bodies is crucial for effective water resource management, particularly when leveraging remote sensing and deep learning techniques. However, achieving precise classification in Very HighResolution Satellite (VHRS) images poses a significant challenge, necessitating accurate identification and categorization methodologies. Existing methodologies lack userfriendly interfaces and struggle with real-time integration into Geographic Information System (GIS) maps. This proposed system aims to develop a graphical user interface (GUI) that facilitates the classification of water bodies in VHRS images. The GUI employs preprocessing techniques such as Bilateral filtering and false color composites. Water body segmentation is performed using the U-Net model, followed by classification with the Random Forest Classifier. Additionally, the GUI facilitates change detection analysis, enabling to generate suitable vector data from raster images for the identification of temporal variations in water bodies. The detected changes are seamlessly integrated into GIS maps, ensuring the timely and accurate updating of spatial data. The proposed approach is evaluated using an urban dataset from Kolkata, West Bengal, India. © 2024 ieee.

关键词： Geographic information systems

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ChatAN: Interval Adaptive Normalization based on ChatGPT Knowledge Augmentation 8

ChatAN: Interval Adaptive Normalization based on ChatGPT Kno...

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8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Yang, Hanyu Zhao, Xiaoyong Wang, Lei Zhang, Jingle Li, Jianhua Beijing Information Science & Technology University School of Information & Management Beijing China Beijing Information Science & Technology University Information Systems Institute Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China City Brain Zhongguancun Science City City Brain Co. Ltd. Beijing China

ISBN: (纸本)9798350302455

Big data streams have massive and complex characteristics. Due to the lack of global static information, the normalization of large data streams is susceptible to outliers and conceptual drift, which leads to incorrect modeling results. In addition, in online anomaly detection applications, it is significant to ensure real-time data processing to prevent severe consequences from missing critical points due to data flow backlogs. In this paper, we propose a method for interval adaptive normalization based on ChatGPT knowledge augmentation. The approach combines domain knowledge obtained by ChatGPT knowledge augmentation with interval adaptive sliding windows. Experimental results on three datasets in the cybersecurity domain show that the anomaly detection algorithm incorporating our proposed normalization algorithm achieves the maximum improvement of 32.46% and 32.68% in AUCROC and AUCPR compared to the traditional normalization method. © 2023 ieee.

关键词： Anomaly detection

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A Deep Learning Framework for Prognosis Patients with COVID-19 3

A Deep Learning Framework for Prognosis Patients with COVID-...

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3rd ieee international conference on intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Shaik, Mohammad Khaja Vanaparthi, Kiranmai Swarnalatha, Gundala Reddy, Pundru Chandra Shaker Dalai, Rama Prasanna Jayaram, B. St. Ann's College of Engineering and Technology Electronics and Communication Engineering Andhra Pradesh Chirala India Telangana Hyderabad India Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India Centurion University of Technology and Management Electrical and Electronics Engineering Odisha India Rmk Engineering College Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350361186

A branch of AI known as 'deep learning' sifts through mountains of data in search of patterns and predictions using a machine learning approach called an artificial neural network. Rapid progress and widespread, successful application across many fields have been its hallmarks in recent years. A significant obstacle to healthcare transformation is the learning of useful information from complicated, high-dimensional, and diverse biological data. This research aims to give a concise synopsis of recent developments in deep learning, a survey of deep learning studies conducted on COVID-19, and some realworld applications of deep-learning(DL) strategies' for COVID19 diagnosis, prognosis, and therapy management. DL has the potential to enhance the quality and speed of drug development by analyzing medicinal imaging data, lab test results, and other pertinent data to make diagnoses, assess the course and prognosis of illnesses, and even provide healing recommendations and drug-utilize regimens. In addition, it may guide policymakers in creating effective control and preventative strategies. Additionally, we evaluate the present state of deep learning's capabilities and shortcomings in terms of COVID-19 treatment accuracy, covering issues such as the dearth of phenotypically plentiful data and the necessity for deeper learning models that are easier to understand and work with. We wrap up by talking about how to get beyond the obstacles that are standing in the way of deep learning's potential future clinical uses. © 2024 ieee.

关键词： COVID-19

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An improved method for evaluating equivalent acceleration factors of repairable systems in a fleet 5

An improved method for evaluating equivalent acceleration fa...

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5th international conference on System Reliability and Safety Engineering, SRSE 2023

作者： Jiang, Renyan Li, Fengping Xue, Wei Li, Xiao Zhang, Kunpeng Wenzhou University Institute of Laser and Optoelectronic Intelligent Manufacturing Wenzhou China Wenzhou University China International Science & Technology Cooperation Base for Laser Processing Robotics Wenzhou China

ISBN: (纸本)9798350305944

One important issue in reliability modeling of repairable systems is to quantitatively evaluate the heterogeneity of systems in a fleet. Recently, a fleet heterogeneity evaluation method has been proposed in the literature. It first estimates the mean times between failure (MTBFs) of individual systems, then defines the equivalent acceleration factor (EAF) of a specific system as the ratio between the MTBFs of this system and a reference system, and finally a heterogeneity measure is defined as the ratio of the sample average and standard deviation of EAFs of all the systems. The smaller this ratio is, the larger the heterogeneity of the fleet is. It is well-known that the estimated MTBF is inaccurate when the failure number is relatively small. Thus, a main drawback of the above-mentioned method is that the MTBFs of different systems are estimated at different time points. This paper aims to address this issue by proposing an improved method for estimating the MTBFs and EAFs of systems. The proposed method uses an "average process"as the reference process and estimates the MTBFs at a common time point. Two datasets are analyzed to illustrate the proposed method and its appropriateness. © 2023 ieee.

关键词： Mean time between failures

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4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

4th International conference on Innovative Manufacturing, Me...

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4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

ISBN: (纸本)9789819988181

The proceedings contain 50 papers. The special focus in this conference is on Innovative Manufacturing, Mechatronics and Materials Forum. The topics include: Effect of Crack Length, Depth, and Location on Natural Frequencies of Railway Track;imputation Analysis of Time-Series Data Using a Random Forest Algorithm;harnessing Machine Learning, Blockchain, and Digital Twin Technology for Advanced robotics in Manufacturing: Challenges and Future Directions;humanizing Humanoids: An Extensive Review on the Potential of Prosthetic Robotic Arm with Integrated Monitoring System for Disabled People;intelligent Machining systems for Robotic End-Effectors: State-of-the-Art and Toward Future Directions;artificial Neural Network Analysis in Road Crash Data: A Review on Its Potential Application in Autonomous Vehicles;the Role and Impact of robotics Integration in Precision Machining and Manufacturing: A Comprehensive Review;nanomaterial in robotics: Bridging the Gap Between Current Applications and Future Possibilities;Optimized-ELM Based on Geometric Mean Optimizer for Bearing Fault Diagnosis;detection of Fault Features in Remanufacturing of Automotive Components Using Image processing and Computer Vision Techniques;the Significance of the Thoracic Spinal Multiple Segments During Different Pick-Object Approaches;Vibration Suppression of the Flexible Beam Structure Using PID Controller Tuned by Advanced Firefly Algorithm;Bearing Fault Diagnosis Based on Prominence Peak-Picking IMFs Selection and PSO-SSAE;investigation of Wheel Robot Grouser Width Parameter Effect on Robot Mobility in Soft Sand Terrain Using Sand Test Bed;automated Harmonic signal Removal-Based Image Feature Extraction Technique: A Comparative Study Using Online Databases;Human Mental Stage Interpretation Based on the Analysis of Electroencephalogram (EEG) signals.

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Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions 27

Exploring Domain Shift on Radar-Based 3D Object Detection Am...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Zhang, Miao Abdulatif, Sherif Loesch, Benedikt Altmann, Marco Schwarz, Marius Yang, Bin Robert Bosch GmbH Corporate Research 71272 Germany University of Stuttgart Institute of Signal Processing and System Theory 70569 Germany

ISBN: (纸本)9798331505929

The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability. © 2024 ieee.

关键词： Network security

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A QRS Complex Detection in Multi-lead ECG signals Based on Filtering and Standard Deviation Shift

A QRS Complex Detection in Multi-lead ECG signals Based on F...

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2024 international conference on Computer Engineering, Network and intelligent Multimedia, CENIM 2024

作者： Thahab, Ahmed Toman Jassim Mohammed, Samir Abdulaah Murdas, Ibrahim University of Babylon Dept. of Electrical Engineering Babil Iraq

ISBN: (纸本)9798350368802

One of the most vital parts of the electrocardiogram (ECG) is the QRS complex. It is considered as an essential basis for determination of heart condition. Since the nature of an ECG signal is non stationary, the accuracy of detecting R -peaks is decreased which may result in substantial increase in false negatives or positives. In this paper, we propose a multi-lead QRS detection algorithm. A certain random number of leads are input to the algorithm which is based on filtering. However, each lead is applied to a stationary wavelet transform and two median filters to reduce the effect of other parts in the ECG signal. A smoothing filter is applied reducing the amplitudes of unwanted high frequency content. In order to further reduce the amplitudes of the P and T waves, the ECG signal is shifted towards the negative part of the Y-axis proportional to its average standard deviation. The QRS candidates of each lead are arranged in vectors and input to a combining algorithm which is based on voting. The algorithm proposed in this paper assures acceptable detection rate and serves as an active basis for various ECG processing. The performance of the proposed algorithm is evaluated on the St. Petersburg INCART database which is a 12-lead database. Experimental results show that a sensitivity of 99.828% and detection error rate of 0.636% is achieved. © 2024 ieee.

关键词： Database systems

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Data processing model for equipment monitoring and diagnostics systems 5

Data processing model for equipment monitoring and diagnosti...

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5th international Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2023

作者： Sultanov, Mahsud M. Boldyrev, Ilya A. Menshikov, Pavel D. National Research University MPEI Digital Technology Research Laboratory Volzhsky Russia

ISBN: (纸本)9798350399523

In this paper, considers the application of equipment monitoring and diagnostics system on the example of turbine condenser. The analysis of the absolute error of the measuring channel after the whole sequence of signal conversion is performed. The measuring channel was simulated using SimInTech, the results were processed using the Office software package. To analyze the obtained data on the system states it is necessary to apply the methods of signal processing. It should be taken into account that the data are not complete and have a number of errors associated with the method of their acquisition, conversion, averaging, and recording in a database and, if necessary, recovery from this averaged sample to analyze the original signal. © 2023 ieee.

关键词： signal processing

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Native Multi-Band Audio Coding Within Hyper-Autoencoded Reconstruction Propagation Networks 48

Native Multi-Band Audio Coding Within Hyper-Autoencoded Reco...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Petermann, Darius Jang, Inseon Kim, Minje Indiana University Department of Intelligent Systems Engineering BloomingtonIN47408 United States Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of

ISBN: (纸本)9781728163277

Spectral sub-bands do not portray the same perceptual relevance. In audio coding, it is therefore desirable to have independent control over each of the constituent bands so that bitrate assignment and signal reconstruction can be achieved efficiently. In this work, we present a novel neural audio coding network that natively supports a multi-band coding paradigm. Our model extends the idea of compressed skip connections in the U-Net-based codec, allowing for independent control over both core and high band-specific reconstructions and bit allocation. Our system reconstructs the full-band signal mainly from the condensed core-band code, therefore exploiting and showcasing its bandwidth extension capabilities to its fullest. Meanwhile, the low-bitrate high-band code helps the high-band reconstruction similarly to MPEG audio codecs' spectral bandwidth replication. MUSHRA tests show that the proposed model not only improves the quality of the core band by explicitly assigning more bits to it but retains a good quality in the high-band as well. © 2023 ieee.

关键词： audio coding autoencoders deep learning multi-band spectral bandwidth replication U-Net

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intelligent Model Management: Using Reinforcement Learning to Detect Data Drift and Retrain Industrial Machine Learning systems 26

Intelligent Model Management: Using Reinforcement Learning t...

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26th international conference on Digital signal processing and its Applications, DSPA 2024

作者： Loufakis, Michail Manis, Othonas Kioroglou, Christos Kolokas, Nikolaos Ioannidis, Dimosthenis Tzovaras, Dimitrios Stankovski, Mile Center for Research & Technology Hellas Information Technologies Institute Thessaloniki57001 Greece Titan Cement Group Greece Cyril and Methodius University Faculty of Electrical Engineering and Information Technologies Skopje Macedonia

ISBN: (纸本)9798350361728

In the evolving landscape of industrial processes, maintaining the accuracy of machine learning (ML) models is a formidable challenge due to the phenomenon of data drift. This paper introduces an innovative reinforcement learning (RL) framework designed to monitor and adaptively retrain ML models in response to data drift, ensuring sustained model performance in industrial settings. We apply our methodology to a real-world scenario within the cement milling industry, demonstrating that our RL agent can effectively identify optimal retraining intervals and maintain high model accuracy over time. Our results indicate that ML models managed by the RL agent surpass the performance of static models, highlighting the potential of RL in continuous model management. The task-agnostic nature of our approach also suggests its applicability across various domains, paving the way for future research in adaptive systems and realtime data processing. © 2024 ieee.

关键词： Reinforcement learning

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