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Comprehensive Research on Speaker Recognition and its Challenges

Comprehensive Research on Speaker Recognition and its Challe...

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Smart data Intelligence (ICSMDI), international conference on

作者： Venkata Syama Sowmya Sri Hari Arun Kumar Annavarapu Vamsi Shesamsetti Sathwik Nalla Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

The study of speech is a subfield of the measurement of uniquely identifying and measurable patterns in human activity. It communicates details about a person's characteristics. As an illustration, consider gender, age, emotion, and health status. Voice Identification is the process of pointing person purely with the help of vocal pulses. Using voice assistants and smart gadgets as a base, several academics are exploring in this field. This article offers detailed information on speaker recognition. Additionally, a different adversarial technique is described in advance of the well-known machine learning methods. This study provides an overview of the speaker recognition topic by covering its system, modelling approaches, applications, and some underlying theories. The strengths of speaker recognition technologies will be discussed.

关键词： Surveys Deep learning Biometrics (access control) Virtual assistants Biological system modeling Humanoid robots signal processing

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Handling data Heterogeneity in Federated Learning with Global data Distribution

Handling Data Heterogeneity in Federated Learning with Globa...

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3rd international conference on Image processing and Vision Engineering, IMPROVE 2023

作者： Nagaraju, C. Sen, Mrinmay Mohan, C. Krishna Deparment of Computer Science IIT Hyderabad India Department of Artificial Intelligence IIT Hyderabad India

ISBN: (纸本)9789897586422

Federated learning, a different direction of distributed optimization, is very much important when there are restrictions of data sharing due to privacy and communication overhead. In federated learning, instead of sharing raw data, information from different sources are gathered in terms of model parameters or gradients of local loss functions and these information is fused in such way that we can find the optima of average of all the local loss functions (global objective). Exiting analyses on federated learning show that federated optimization gets slow convergence when data distribution across all the clients or sources are not homogeneous. Heterogeneous data distribution in federated learning causes objective inconsistency which means global model converges to a another stationary point which is not same as the optima of the global objective which results in poor performance of the global model. In this paper, we propose a federated Learning(FL) algorithm in heterogeneous data distribution. To handle data heterogeneity during collaborative training, we generate data in local clients with the help of a globally trained Gaussian Mixture Models(GMM). We update each local model with the help of both original and generated local data and then perform the similar operations of the most popular algorithm called FedAvg. We compare our proposed method with exiting FedAvg and FedProx algorithms with CIFAR10 and FashionMNIST Non-IID data. Our experimental results show that our proposed method performs better than the exiting FedAvg and FedProx algorithm in terms of training loss, test loss and test accuracy in heterogeneous system. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Federated learning

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signal processing Grand Challenge 2023 – E-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients

Signal Processing Grand Challenge 2023 – E-Prevention: Slee...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kleanthis Avramidis Kranti Adsul Digbalay Bose Shrikanth Narayanan Signal Analysis and Interpretation Lab University of Southern California Los Angeles CA

This paper presents the approach and results of USC SAIL’s submission to the signal processing Grand Challenge 2023 – e-Prevention (Task 2), on detecting relapses in psychotic patients. Relapse prediction has proven to be challenging, primarily due to the heterogeneity of symptoms and responses to treatment between individuals. We address these challenges by investigating the use of sleep behavior features to estimate relapse days as outliers in an unsupervised machine learning setting. We extract informative features from human activity and heart rate data collected in the wild, and evaluate various combinations of feature types and time resolutions. We found that short-time sleep behavior features outperformed their awake counterparts and larger time intervals. Our submission was ranked 3rd in the Task’s official leaderboard, demonstrating the potential of such features as an objective and non-invasive predictor of psychotic relapses.

关键词： Heart rate Machine learning Feature extraction Acoustics Behavioral sciences data mining Task analysis

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Exploration and Application of Transformer Substation Automation data Channel Based on UHF Wireless Technology

Exploration and Application of Transformer Substation Automa...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Mengkun Hua Tianjin University of Technology Tianjin China

According to the investigation of automation test task in the construction of new energy transformer substation, it is found that the automatic data channel of OPGW fibre-optical high-voltage line is delayed due to various reasons, which seriously affects the project grid-connected power generation. Therefore, the automation test before the project is put into operation must look for a reliable wireless data channel to replace the formal fiber-optic communication channel, in order to complete the relevant electrical automation test tasks. According to the characteristics and advantages of UHF wireless technology, this paper studies deeply the realization method of communication link based on UHF wireless technology, and fully demonstrates the flexibility of UHF wireless technology as an advance debugging channel in transformer substation construction, it has a positive development prospect.

关键词： Wireless communication Partial discharges Partial discharge measurement Wireless sensor networks Automation Array signal processing Fault location

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Surface Damage Detection of Line Insulators Using Deep Learning Algorithms to Avoid Insulation Failure

Surface Damage Detection of Line Insulators Using Deep Learn...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： K.M. Rayhan Shuvo Dip Roy Md. Fahimul Haque Sadid Kazi Firoz Ahmed Abu Hena Shatil Faculty Of Engineering American International University - Bangladesh (AIUB) Dhaka Bangladesh

The power system's reliability dramatically depends on the high voltage line insulators. However, the surface of these insulators is frequently damaged because of the outdoor environment, which includes complicated landforms and unpredictable weather. Damage to the insulator's surface can lead to short circuits, permanent damage to the transmission line, and even blackouts. To deliver quality service, it is essential to keep track of the condition of these insulators. As traditional fault-detection systems have become more time- and labor-intensive, a YOLOv4-based detection approach is proposed here to achieve fast and precise damage detection and classification of line insulators. YOLOv4 is a Deep Learning (DL) algorithm model that operates on the darknet framework. The research findings show that 97.711% is the maximum average, depending on detecting YOLOv4 for insulators. Insulator damage has a maximum AP value of 98.17%, and discolored Insulator has a maximum AP value of 97.07%. When the system is trained on the insulator data set, the overall m-AP (mean Average Precision) value is 97.65%. The detecting speed in virtual environments for YOLOv4s is 43 FPS, and it has a greater detection rate.

关键词： Deep learning signal processing algorithms Virtual environments High-voltage techniques Production Inspection signal processing

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Classification of EEG signal Based on Pre-Trained 2D CNN Model for Epilepsy Detection 2nd

Classification of EEG Signal Based on Pre-Trained 2D CNN Mod...

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3rd international conference on Digital Technologies and Applications

作者： Edderbali, Fatima Harmouchi, Mohammed Essoukaki, Elmaati Ilassan First Univ Settat Lab Radiance Mater & Instrumentat FST Settat Morocco Hssan First Univ Settat Lab Hlth Sci & Technol Settat Morocco

ISBN: (纸本)9783031298561;9783031298578

Epilepsy is a chronic neurological disease due to extreme electric discharge in the brain. Epileptic seizures gravely affect the social life and psychology of patients, knowing the lack of neurologists and the fact that analyzing EEG signals is time-consuming and must be done by an expert;therefore, it is important to correctly diagnose patients with epilepsy automatically. In this work, we start with preprocessing raw EEG data, and we extract the spectrogram of every channel. In the next step, we propose an image category classification method that combines the extractor of the feature pattern, which is AlexNet, which is a pretrained convolutional neural network (CNN), with a trainable classifier support vector machine (SVM). Alex-Net passed the feature vectors to the SVM. The spectrogram images that we extracted are used as the input layer. The number of classes is two: one for normal cases and the second for epileptic cases. We split the training and test samples. Finally, spectrogram images are trained by many classifiers, and we find that the SVM in this study had the best mean accuracy, reaching 97.52%, which means that this model is effective for epilepsy classification.

关键词： Epilepsy EEG Deep learning CNN AlexNet SVM

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Research on data Quality Improvement Program Based on Big data Application

Research on Data Quality Improvement Program Based on Big Da...

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Information Technology,Big data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Xuerong Zuo School of Marxism Hohai University Nanjing Jiangsu China

In the rapidly developing information age, the explosive growth of data increases the difficulty of big data application. Through analyzing the challenges faced by big data applications such as data collection, data timeliness, data security, data quality and data expected results, based on the key characteristics of big data and the measurement indicators of data quality, the improvement procedures of data quality are proposed, that is, following the pre-processing stage, the quality measurement stage, the quality defect identification stage and the pre-processing work tracing stage, in addition, the effect of the data quality improvement procedure is evaluated, to improve the data quality and reduce the potential risks in the application process of big data.

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Machine Learning Trends in Design for Additive Manufacturing 3rd

Machine Learning Trends in Design for Additive Manufacturing

引用

3rd international conference on Design Tools and Methods in Industrial Engineering, ADM 2023

作者： Trovato, Michele Belluomo, Luca Bici, Michele Campana, Francesca Cicconi, Paolo Dipartimento di Ingegneria Industriale Elettronica e Meccanica Università degli Studi ROMA TRE Rome00146 Italy Dipartimento di Ingegneria Meccanica e Aerospaziale Sapienza Università di Roma Rome00184 Italy

ISBN: (纸本)9783031520747

Additive Manufacturing is becoming a widespread manufacturing system in several industrial fields such as automotive, aerospace, biomedical, etc. Design for Additive Manufacturing represents the branch of research that considers the technological constraints from the early stages of design, arriving at a geometrical model to be exported in G-code. The limitations of additive manufacturing are related to the complexity of the process, the high costs, the processing time, and the difficulties of ensuring adequate geometric and dimensional tolerances. A data-driven approach can be a solution to improve the Design for Additive Manufacturing. Artificial Intelligence and Machine Learning methods are employed in the literature to shorten the time for assessing the optimal combination of parameters and supporting decision-making. The current state of the art shows three macro-areas to apply Machine Learning methods in Design for Additive Manufacturing. These applications concern Geometrical Design Level, Process Configuration Level, and Process Monitoring Level. This paper aims to identify and classify the Machine Learning methods and algorithms most used in Design for Additive Manufacturing practices, analyzing parameters, results, processes, and materials involved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 3D printing

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Performance Analysis of the AVR Using An Artificial Neural Network and Genetic Algorithm Optimization Technique

Performance Analysis of the AVR Using An Artificial Neural N...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Niloy Goswami Md. Redowan Habib Abu Hena Shatil Kazi Firoz Ahmed Electrical and Electronics Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

The Automatic Voltage Regulator (AVR) is required to maintain a steady output voltage from the generator, and it relies heavily on the Proportional Integral Derivative (PID) controller. For the function of controlling industrial loops, a controller known as the PID controller is frequently used on account of its straightforward architecture, uncomplicated implementation, and excellent dependability. Traditional approaches to tuning the PID controller have their limits, but those limits may be overcome by incorporating more sophisticated tuning approaches. The main aim of this study is to provide the ideal design for tuning a PID controller using a Genetic Algorithm (GA) and an Artificial Neural Network (ANN) in order to further improve the PID-based AVR system. The performance of the suggested approach is afterward compared with one another. The results of a simulation carried out in MATLAB show that GA tuning techniques give better performance.

关键词： Regulators signal processing algorithms Artificial neural networks signal processing Control systems Automatic voltage control Time-domain analysis

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Design and Analysis of IoT-Based Adaptive Microgrid System including Renewable Energy Sources for Decentralized Zones

Design and Analysis of IoT-Based Adaptive Microgrid System i...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Ether Deowan Ahsan Kabir Nuhel Mir Mohibullah Sazid Rehenuma Tabassum Meghla Iftekhar Haider Md. Rifat Hazari Department of Electrical and Electronics Engineering American International University-Bangladesh Dhaka Bangladesh

To enhance living conditions and alleviate poverty, emerging nations require energy services that are dependable, accessible, safe, and efficient. This research study proposes an IoT-based smart microgrid system for rural areas with an enhanced control system for an efficient microgrid operation which may, in turn, solves multiple issues in the rural area. The proposed system is a combination of solar and wind power generators, diesel power stations, and backup storage, having the functionality of detecting the instantaneous fault of a branch with the help of the Bewlice lattice diagram and support vector machine algorithm and could be controlled remotely at any moment over the internet. Likewise, a power monitoring system would provide the authorities with technical attributes related to energy that would be utilized to give power to the rural area in an emergency through the Android Application, which can be retrieved and displayed using a cloud platform named ThingSpeak.

关键词： Support vector machines Renewable energy sources Operating systems signal processing algorithms Microgrids Wind power generation signal processing

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