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Conv-TasNet Adaptive Noise Cancellation Model Enhanced by WaveNet

Conv-TasNet Adaptive Noise Cancellation Model Enhanced by Wa...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Kun Zhao Yaojie Hou College of Computer Science and Technology (College of Big Data) Taiyuan University of Technology Jinzhong China College of Electrical and Automation Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Traditional speech denoising methods often struggle with low signal-to-noise ratios (SNR) and poor clarity in complex noise environments. To address these limitations, this study introduces an adaptive denoising model that integrates Conv-TasNet with WaveNet enhancements. The proposed model incorporates causal convolution within the Conv-TasNet separator to better capture long-term dependencies and employs WaveNet's gating activation mechanism to effectively handle nonlinear speech features. Additionally, a mask weighting strategy is implemented to emphasize target speech signals while suppressing background noise. Experimental results demonstrate that the enhanced model significantly outperforms the original Conv-TasNet, achieving an SNR improvement from –5 dB to 33.74 dB and reducing mean squared error (MSE) to nearly zero. These findings confirm the model's superior performance and robustness in complex noisy environments, highlighting its potential for advanced speech enhancement applications.

关键词： Adaptation models Convolution Computational modeling Particle separators Noise reduction Speech enhancement Feature extraction Computational efficiency Noise measurement Signal to noise ratio

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Single to Diverse Video Generation Using Conditional Diffusion Probabilistic Model

Single to Diverse Video Generation Using Conditional Diffusi...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), international conference on

作者： Ayushi Verma Tapas Badal Siddharth Gupta Saatvik Sinha Indrajeet Gupta School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and AI S R University Warangal India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

In the interdisciplinary field of computer vision and deep learning, the generation of video sequences stands as a critical task, with wide-ranging applications from entertainment to surveillance systems. We introduce a novel framework based on the Conditional Diffusion Probabilistic Model (CDPM), featuring significant modifications to the convolution block for handling complex patterns and temporal dynamics. Our model integrates Global Response Normalization (GRN) and Sigmoid Linear Unit (SiLU) activation functions, enhancing its ability to interpret high-dimensional video data. This integration reflects our commitment to advancing video generation technology. The model's effectiveness is validated by its performance on various datasets: achieving an NNFDIV score of 0.57 in the SinGAN-GIF dataset and 0.44 in the HP-VAE-GAN dataset, outperforming existing methods. It also excels in NIQE, RECO, and PSNR metrics, indicative of superior image quality and coherence. These results collectively highlight the model's robustness in diverse video generation, marking a notable advancement in computer vision and deep learning.

关键词： measurement Deep learning Analytical models computer vision Boolean functions Computational modeling Video sequences

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A Bus Load Classification Method Based on Temporal Convolutional Attention Based Deep Embedded Clustering Model

A Bus Load Classification Method Based on Temporal Convoluti...

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international conference on Information systems and computer Aided Education (ICISCAE)

作者： Qian Chen Mengcong Tang Haiyun Wang Haixing Cheng Liping Yang Xiaolei Tian Yuxuan Zhang Zhihong Huang State Grid Beijing Electric Power Research Institute Beijing China State Grid Beijing Electric Power Company Beijing China Beijing Tsingsoft Technology Co. Ltd Beijing China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

With the rapid development of China's new power system, the proportion of distributed photovoltaic/wind power integration continues to increase, making the bus load characteristics more complex. Accurately identifying and revealing typical bus load fluctuation characteristics can provide crucial data support for power system operation and planning. Therefore, this paper proposes a bus load classification method based on the Temporal Convolutional Attention based Deep Embedded Clustering Model (TCA-DEC). First, a bus load feature classification framework based on deep embedded clustering is established, deeply mining the intrinsic characteristics of load fluctuations through unsupervised deep learning. Then, a feature encoding-reconstruction network based on temporal convolutional attention mechanism is proposed to extract significant features of different types of loads and encode them through a convolutional network. Finally, an example analysis based on actual measurement data from a city in southern China shows that the proposed method can accurately classify bus load characteristics with wind power and photovoltaic integration, improving the silhouette coefficient by 16.12%-20.51% and reducing the DB index by 23.81%-33.330/0 compared to conventional clustering methods.

关键词： Photovoltaic systems Fluctuations Power measurement Urban areas Wind power generation Feature extraction Planning Power system reliability Indexes Load modeling

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ResNet-CNN Model for Plant Disease Classification for E-Agriculture Applications

ResNet-CNN Model for Plant Disease Classification for E-Agri...

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Intelligent Algorithms for Computational Intelligence systems (IACIS), international conference on

作者： Arava Vamsi Kumar S. V. N. Sreenivasu Kandi Venkata Narasimha Reddy Department of Computer Science & Engineering Narasaraopeta Engineering College Narasaraopet India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The advancement of an economy's innovation capacity is closely tied to the progress in agriculture. Effective plant disease management is crucial for optimizing crop yields and ensuring agricultural sustainability. This project focuses on developing a Plant Disease Detection and Classification Network (PDDC-Net) by integrating deep learning models. The preprocessing stage standardized the dataset images by eliminating various types of interference. The PDDC-Net employs a Residual Network (ResNet)-Convolution Neural Network (CNN), featuring residual networks, to enhance feature extraction and classification accuracy. The PDDC-Net model demonstrated exceptional performance in detecting and classifying plant leaf diseases. Specifically, it achieved an accuracy rate of 99.84% across various crop diseased leaves, including those from potato, tomato, and pepper plants. Further, the suitable pesticide also suggested for the recognised disease. The proposed PDDC-Net effectively addresses the challenge of plant disease identification with high accuracy, underscoring its potential as a robust tool for agricultural disease management and innovation.

关键词： Support vector machines Smart agriculture measurement Plant diseases Technological innovation Accuracy Biological system modeling Computational modeling Sustainable development Residual neural networks

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Optimizing Urban Traffic Management with Machine Learning Techniques: A Systematic Review

Optimizing Urban Traffic Management with Machine Learning Te...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Bohdan Vihurskyi Business Data Technologies *** Services LLC Sunnyvale USA

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

Traffic congestion is a serious contemporary issue, especially in large cities. The use of machine learning (ML) in addressing this and other problems related to optimizing traffic on public roads is very promising. Techniques such as neural networks and reinforcement learning can yield good results in solving traffic prediction problems, anomaly detection, finding optimal routes and available parking slots, and optimizing traffic at intersections. Real-world examples of using machine learning technologies in cities like Los Angeles already demonstrate their effectiveness in practice, significantly improving the basic metrics of the city’s road infrastructure efficiency. However, the practical application of machine learning approaches has its disadvantages and challenges, the addressing of which is crucial for further research. Overall, traffic optimization is a comprehensive topic and requires further research in this area to study the effectiveness of using certain models, and the feasibility of their practical application, which ultimately leads to the ability to build efficient and optimized transport systems that can improve our mobility and general quality of life.

关键词： measurement Systematics Atmospheric modeling Roads Transportation Machine learning Traffic control

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joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach

Joint Mobile Edge Caching and Pricing: A Mean-Field Game App...

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international conference on Data Engineering

作者： Yin Xu Xichong Zhang Mingjun Xiao Jie Wu An Liu Sheng Zhang School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Computer and Information Sciences Temple University Philadelphia USA School of Computer Science and Technology Soochow University Suzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines.

关键词： Economics Computational modeling Stochastic processes Pricing Games Differential games Data engineering

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Formal modeling and Verification of Industrial Robotic Arm - A Case Study

Formal Modeling and Verification of Industrial Robotic Arm -...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Ilmaan Zia Muhammad Abdul Basit Ur Rahim Tairan Liu Zhangying He Department of Computer Science and Engineering California State University Long Beach CA USA Department of Mechanical and Aerospace Engineering California State University Long Beach CA USA

Robotic arm systems are revolutionizing industries by automating operations and extending capabilities beyond human limitations, prioritizing accuracy and precision. These systems redefine boundaries across various sectors, including healthcare, mobile automation, and space exploration. Ensuring the safety of the robotic arm and its surrounding environment is paramount during its operations. This paper presents a detailed investigation of formal modeling and verification in the context of an industrial robotic arm, specifically a multi-joint mechanism equipped with advanced capabilities such as object manipulation, obstacle detection, and avoidance. The study uses the model-checking tool to capture the complex behavior of the system precisely. In-depth simulation and verification techniques are used for the formal model, focusing on its crucial properties, including safety, liveness, fairness, and deadlock-freeness.

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Integrated Local and Global Information for Health Risk Prediction Model

Integrated Local and Global Information for Health Risk Pred...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： You, Tao Dang, Qiaodong Li, Qing Northwestern Polytechnical University School of Computer Science Xi'an710072 China

ISBN: (纸本)9798350337488

Electronic health record (EHR) data has been widely used in health risk prediction models, and it has an important preventive and intervention role in healthcare. Existing approaches typically regard EHR data in a monolayer observational model, and they assume that visits are monotonically decreasing in importance over time. However, in healthcare practice, clinical experts usually focus on diseases and visits that are closely related to the target disease. In addition, the duration of different categories of diseases has a fixed model, as chronic diseases are usually consistently diagnosed during patient visits. To make full use of this disease category information, a hierarchical self-attentive model is proposed that can model patient representations at both the local and global levels. Specifically, a disease duration matrix with multiple times is constructed for disease clustering. We combine the category information to compute dependencies between diseases and disease embeddings. We further explore the pattern of patient health development from a spatio-temporal perspective. Visit embeddings are updated by learning the effects between different visits via a self-attentive mechanism. In addition, the time interval, a special kind of medical event, is introduced to enhance visit sequence temporal modeling. Extensive experiments on two real-world datasets demonstrate the sota performance of the model. At the same time, we demonstrate the plausibility and interpretability of the model through case studies. © 2023 IEEE.

关键词： data mining disease classification electronic health records health risk prediction interpretability

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Advanced Deep Learning Framework for Early Breast Cancer Detection with Graph Convolutional Networks

Advanced Deep Learning Framework for Early Breast Cancer Det...

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Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： A. Sadeeshkumar Seema Sharma Amshavalli M Asma Anjum Ashwani Gupta N Aparna Department of Electrical and Electronics Engineering St. Joseph's College of Engineering OMR Chennai India Department of Computer Science and Engineering JECRC University Jaipur India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thudupathi India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Computer Science and Information Technology MJP Rohilkhand University Bareilly India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Valley Campus Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Women with breast cancer are very common. Early signs are rare; hence the condition is often identified late. Later-stage treatment outcomes are less predictable and harder. Complementary screening programs and preventative checkups have considerably improved early breast cancer identification. Reducing the number of unscreened women and increasing early-stage breast cancer detection demands better screening methods. Iterative model training included preprocessing, segmentation, feature extraction, and detection. Preprocessing was necessary for well-read results. Corners were identified for segmentation using Shi-Tomasi. Feature extraction uses SIFT. Without modifying photo size, this approach discovers similar photos. The research models were all GCN-trained. CNNs and SVMs were employed to evaluate the GCN-based model. GCN's mean accuracy of 92.34 percent beat the competition. This shows that the GCN-based technique improves breast cancer diagnosis. Due to early discovery and treatment, this strategy may improve patient outcomes.

关键词： Training Temperature measurement Accuracy Graph convolutional networks Infrared imaging Transforms Feature extraction Breast cancer Integrated circuit modeling Tumors

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Demand Side Management and Voltage Control through Smart Inverter Enabled DER with Networked Micro-Grid System

Demand Side Management and Voltage Control through Smart Inv...

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computer, Electronics & Electrical Engineering & their Applications (IC2E3), international conference on

作者： Shailendra Singh Rajesh Kumar Bikram Das S. P. Singh Electrical Engineering Department NIT Agartala Tripura India Electrical Engineering Department IIT (BHU) Varanasi India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

The widespread installation of distributed energy resources (DER) across the electric power network infrastructure, especially in the distribution grid has emerged as opportunities and challenges for network operators to manage the operation and control of electric power systems. These DERs are leveraged with inverter-based resources that can unlock the potential benefits for distribution network management such as demand side management, voltage management, etc. In this context, this paper introduced smart inverter-based Volt/VArcontrol to deploy the conservation voltage regulation (CVR) in order to manage the load demand. A predictive model-driven scenario-based voltNAR control scheme to enable the CVR operation has been used. To check the effectiveness of CVR deployment, and communication-free, adjustive droop-based voltage control scheme based on local measurement has been introduced in coordination with a networked microgrid controller. Quick voltage support has been achieved by superimposing the responses of multiple inverter-based CMG controllers to mitigate the voltage fluctuations under transient events. The results of the simulations verify the suggested control scheme and show that CVR with CMG operation can significantly lower peak power demand. Additionally, the CMG controller system manages voltage regulation more intelligently than other controls.

关键词： Voltage measurement Demand side management Voltage fluctuations Fluctuations Predictive models Distributed power generation Power systems Voltage control Transient analysis Load modeling

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