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Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network 3

Exploring Turkish Speech Recognition via Hybrid CTC/Attentio...

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3rd international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Yolwas, Nurmement Ren, Zeyu Wang, Huiru Slamu, Wushour Xinjiang University Xinjiang Key Laboratory of Signal Detection College of Information Science and Engineering Urumqi China Xinjiang University Xinjiang Multilingual Info. Technology Laboratory College of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350302356

In recent years, there has been rapid development in deep learning-based End-To-End speech recognition technology. However, the performance of Turkish speech recognition systems has been hindered by the lack of Turkish speech data. To address this, this paper focuses on several speech recognition tuning techniques. Experimental results demonstrate that the best performance is achieved when utilizing a combination of speed perturbation and noise addition for data augmentation, along with the beam search width set to 16. Furthermore, to maximize the utilization of effective feature information and enhance feature extraction accuracy, a novel feature extractor called LSPC is proposed. By combining LSPC with the LiGRU network, a shared encoder structure is formed, and model compression is achieved. The results indicate that when using only Fbank features, the performance of LSPC surpasses that of MSPC and VGGnet, resulting in a respective improvement of 1.01% and 2.53% in the word error rate (WER). Finally, building upon the aforementioned advancements, a new multifeature fusion network is proposed as the primary encoder structure. The results demonstrate that the WER of the proposed feature fusion network, based on LSPC, further improves by 0.82% and 1.94% when compared to single-feature extraction using LSPC, specifically utilizing Fbank features and spectrogram features. As a result, our model achieves comparable performance to advanced End-To-End models. © 2023 IEEE.

关键词： Speech recognition

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The improved quantum genetic algorithm with its application in fault diagnosis 3

The improved quantum genetic algorithm with its application ...

引用

2023 3rd international conference on Computer, Remote Sensing and Aerospace, CRSA 2023

作者： Xiang, Hao Guangxi Colleges and Universities Key Lab. of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China

In this paper, by analyzing the characteristics of the simulated annealing algorithm (SA) and the real double-chain coding target gradient quantum genetic algorithm (DCQGA), the real double-chain coding target gradient quantum genetic simulated annealing algorithm (DCQGSAA) is proposed. Because the performance of LSSVR is extraordinarily sensitive to its key parameters, the proposed algorithm is used to optimize these parameters, then a hybrid non-parametric prediction model is put forward. This model is used in fault prediction of liquid rocket engine thrust. The simulation results show that the proposed model is effective for small samples and multi-dimensional fault prediction. © Published under licence by IOP Publishing Ltd.

关键词： Simulated annealing

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The deblending processing of multi-well 3D DAS-VSP data in the Middle East 3

The deblending processing of multi-well 3D DAS-VSP data in t...

引用

3rd international Meeting for Applied Geoscience and Energy, IMAGE 2023

作者： Li, Fei Yu, Gang Liu, Haibo Song, Jiawen Zhang, Yanbin Chen, Yuanzhong Yin, Tiankui Han, Jianxin Li, Qingfeng BGP Inc. Hebei Zhuozhou China Ltd. Sichuan Chengdu China

The high productivity blended acquisition technique greatly improves the acquisition efficiency. However, this also introduces serious interference noise from adjacent sources. An deblending method based on sparse inversion is used to separate the effective major source signal from the massive blended offshore 3D DAS-VSP data, which was the world largest multi-well (6 wells in 1st location and 7 wells in 2nd location) 3D DAS-VSP survey data through the joint OBN and 3D DAS-VSP data acquisition project offshore Abu Dhabi in the Middle East. The deblending results of offshore 3D DAS-VSP data show that the blending interference noise has been greatly reduced, while the major source signal has been effectively recovered. © 2023 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

关键词： Blending

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LabVIEW Implementation of Bio-signal Zero-Watermarking Using Tchebichef Moments 1

引用

3rd international conference on Digital Technologies and Applications

作者： Daoui, Achraf Yamni, Mohamed Karmouni, Hicham Sayyoun, Mhamed Qjidaa, Hassan Jamil, Mohammed Ouazzani Univ Sidi Mohamed Ben Abdellah Fez Dhar El Mahrez Fac Sci Elect Signals & Informat Syst Lab Fes Morocco Sidi Mohamed Ben Abdeilah Univ Natl Sch Appl Sci Lab Engn Syst & Applicat Fes Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco Univ Fez UPF Fac Engn Sci FSI Private SLab Sustainable Syst & Environm SED Fes Morocco

ISBN: (数字)9783031298608

ISBN: (纸本)9783031298592;9783031298608

In this paper, we propose an efficient and robust method for the protection of patient data (ID) when transmitting biomedical signals (bio-signals) from one storage device to another one. The proposed zero-watermarking method is naturally imperceptible because it doesn't involve modifying the original signal. The features of the latter are extracted based on Tchebichef moments, and then these features are used in combination with the ID image (watermark) to produce the zero-watermark binary image that hides the patient ID. Then, the zero-watermark binary image is transmitted with the patient bio-signal to a different storage device. To recover the patient ID image, the transmitted bio-signal and the zero-watermark are used. As a platform, we use LabVIEW to implement the proposed application. The results of the performed simulations show the high efficiency of the proposed method in terms of simplicity of implementation, high security, and good noise robustness.

关键词： signal zero-watermarking Medical data hiding Tchebichef Moments LabVIEW implementation signal copyrights protection

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Elastic Digital Twin Network Modeling fulfilling Twining Dynamic in Network Life Cycle 3

Elastic Digital Twin Network Modeling fulfilling Twining Dyn...

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3rd IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Wang, Shoufeng Chen, Hua-Min Ouyang, Ye Li, Fan Chen, Xuan Guo, Jianchao Li, Yun Bian, Sen Wang, Xidong Li, Zhanwu INC. Beijing China Beijing University of Technology Faculty of Information Technology Beijing China China Unicom Beijing China China Unicom Beijing Branch Beijing China

ISBN: (纸本)9798350318470

Digital Twin (DT) is expected to boost efficiency and save cost, for manuscription, system optimization, maintenance, etc. DT realizes this via experimenting candidate solutions in a replica of physical equipment and environment. In the context of telecommunication, Digital Twin Network (DTN) depicts the replica of physical telecommunication network. However, there are three challenges being debated most for DTN. The first is the differentiated requirements during stages in Network Life Cycle (NLC), and these requirements drive that a single DTN cannot fit in the stages in NLC. The second is the decision delay from DTN. This delay makes current DTN hard to be utilized. The third is the cost on full functional copying physical telecommunication infrastructure and management system. Against all the three challenges above, the Elastic Digital Twin Network Modeling (EDiTNetMdl) is proposed in this paper. EdiTNetMdl treats DTN modeling in hierarchy and levels of abstraction. Class and inheritance are designed for representing telecommunication Network Elements (NE) in hierarchy. NE functions and connections are modeled as methods in Classes, with inheritance hierarchy. Full data acquisition is fulfilled in bottom NE instances, well abstract metadata and function description is provided to high level NE and connections. The reduced processing time of EdiTNetMdl supports more flexibility in network decision making with prediction. © 2023 IEEE.

关键词： data acquisition

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3D Reference-Based Skeletal Movement Evaluation

3D Reference-Based Skeletal Movement Evaluation

引用

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

作者： Lehmann, Lars Seidel, Roman Hirtz, Gangolf Digital Signal Processing and Circuit Technology Chemnitz University Reichenhainer Str. 70 Chemnitz Germany

ISBN: (纸本)9789897586422

In a medical therapy, the exact execution of the training exercises developed by the therapist is a crucial task for the success of the therapy. Currently, a therapist has to treat up to 15 patients at the same time on an outpatient basis. To compensate this deficit, an automated assistance system needs to be created. Previous approaches have focused on a parameterised segment angle-based assessment for training exercise feedback. This work focuses on a reference-based approach. This reference is created by the therapist and thus corresponds to the ideal movement model and can be individually adapted to the patient. It is necessary to compare this reference with the patients’ real movement in real time, to detect deviations and to output them as errors. For this purpose, the reference can be adapted to the body size of the patient and the patients’ current position and orientation can be taken into account, or it can be described by reference segments, i.e. an angle-based comparison of the reference. Our work highlights the the segment and reference-based assessment approach and compares them to each other. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Feedback control

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Deblending seismic data using multi-stage iterative source separation with priors - a case study using streamer 3D data 3

Deblending seismic data using multi-stage iterative source s...

引用

3rd international Meeting for Applied Geoscience and Energy, IMAGE 2023

作者： Ibanez-Jacome, Wilson Hostetler, Jarred Kumar, Rajiv Brouwer, Wouter Gerrit Hydal, Saeeda Amin, Yousif Izzeldin Kamil

Conventional seismic data acquisition surveys usually require a firing time delay from each source to prevent energy overlaps between the numerous shot records. Recent advancements in continuous seismic recording systems and data separation workflows have enabled us to acquire seismic data from multiple sources simultaneously, thereby significantly reducing operational costs and enhancing data acquisition efficiency. To separate (deblend) the signal from each source, however, the simultaneous source approach introduces new challenges to our data processing workflows. A wide range of source separation technologies exist to deblend the data, but many of these technologies rely on coherent noise attenuation techniques that produce results with a substantial amount of seismic interference residual noise. In addition to the quality limitations, coherent noise attenuation techniques require a considerable amount of additional time to develop the optimal processing workflow for a given dataset. In this abstract, we present an alternative method to deblend the High Point 3D broadband long-offset simultaneous-source towed-streamer data using an innovative algorithm that progressively models the source-separated signal while safely removing interference from the data, yielding significantly improved source-separation results compared to those obtained with the previous algorithm. © 2023 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

关键词： Source separation

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Exploiting Deep Learning Architectures for Effective Real-Time data Analysis in Machine Learning 3

Exploiting Deep Learning Architectures for Effective Real-Ti...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shreenidhi, H.S. Rajarajeswari, S. Bhagwat, Suvarna R. Sharma, Khushboo Seth, Kanika Yadav, Rakesh Kumar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper seeks to discover how deep state-of-the-art architectures can be leveraged for robust actual-time information evaluation in device studying packages. The paper begins by supplying a comprehensive assessment of contemporary the modern-day in deep brand new-based totally facts evaluation technology. It then examines case research state-of-the-art real-time records analysis programs to illustrate the viability and blessings of modern-day existing deep latest architectures. Programs encompass self-driving vehicle generation, laptop vision, natural language processing, and time-collection evaluation. This targeted evaluation is supplemented by assessing ultra-modern capability shortcomings and demanding situations associated with using these architectures in actual-time scenarios. Subsequently, the paper provides a set of modern-day pointers and first-rate practices to help practitioners efficiently leverage deep trendy architectures for actual-time information evaluation. With the aid of combining a comprehensive review of current deep-gaining knowledge state modern-based records evaluation technology, a detailed dialogue present day its utility guiding ideas, and a summary of the latest potential shortcomings and demanding situations, this paper offers a practical manual to the effective of deep learning architectures for real-time data analysis. © 2023 IEEE.

关键词： architectures challenges combining recommendations shortcomings

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Performance Evaluation of Various Machine Learning Algorithms for Lung Cancer Prediction Using Demographic data 3rd

Performance Evaluation of Various Machine Learning Algorithm...

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3rd international conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Sharmila, Mulagada Surya Kumar, K. Shiridi Ganie, Shahid Mohammad Hemachandran, K. Rege, Manjeet Woxsen Univ Sch Business Hyderabad 502345 Telangana India Woxsen Univ AI Res Ctr Dept Analyt Hyderabad 502345 India Univ St Thomas Dept Software Engn & Data Sci St Paul MN USA

ISBN: (纸本)9783031686160;9783031686177

Lung cancer is among the top deadly diseases, affecting human beings globally. Therefore, it is crucial to predict and detect this disease as early as possible, allowing the doctors and the patients to take the appropriate and essential actions. Techniques like machine learning can be applied to the same. In this study, we used machine learning to predict cancer in the lungs. We explored five machine learning algorithms, viz., Decision Tree (DT), Support vector machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF). A publicly available dataset that contains demographic information of 284 patients with 16 parameters is used to conduct this study. An extensive explorative analysis is performed to improve the quality assessment of the dataset. Among five algorithms, Logistic Regression (LR) exhibited best findings in terms of accuracy, precision, recall, specificity, f1-score, and negative predicted values (NPV). Compared to similar research works, the proposed model achieved better results based on various performance evaluation metrics. The proposed model can be used for other illnesses that have similar symptoms by using transfer learning approach.

关键词： Lung cancer prediction Machine learning algorithms Predictive modeling Disease prediction Lifestyle data

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RJB-Net: Residual Deep Learning with Joint Bilateral Denoising Network for Remote Sensing Image Fusion 3

RJB-Net: Residual Deep Learning with Joint Bilateral Denoisi...

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3rd IEEE Asian conference on Innovation in Technology, ASIANCON 2023

作者： Patel, Krupa Misra, Indranil Patel, Narendra Moorthi, S. Manthira Dhar, Debajyoti Birla Vishvakarma Mahavidyalaya Engineering College Dept. of Computer Science and Engineering Anand India Signal and Image Processing Group Ahmedabad India

ISBN: (纸本)9798350302288

Remote sensing image fusion amalgamate information from panchromatic and multispectral remote sensing images to generate an optimum representative image. In this paper, we have proposed a novel method for image fusion known as Residual Deep Learning with Joint Bilateral Denoising Network (RJB-Net). The RJB-Net procedure is initiated with panchromatic image denoising using a joint bilateral filter to suppress noise and retain edge information. RJB-Net model is trained at image patches covering all major spectral classes. The denoised panchromatic image along with multispectral data is input to the residual deep learning network for inference and generates a fused image. The remote sensing datasets used for RJB-Net training and evaluation are Indian Cartosat-1 panchromatic and Resourcesat-2 multispectral imagery. The experiment section compares the RJB-Net denoising output with prominent spatial domain image filtering methods both qualitatively and quantitatively. It is found that RJB-Net fused images inject the spatial information from panchromatic images in a controlled manner while retaining the spectra characteristics of multispectral data. The merging performance of RJB-Net is compared with state-of-the-art remote sensing image fusion techniques that range from classical to recent deep learning model. The proposed method RJB-Net generates superior results among all fusion methods at different feature targets and compared the performance both visually as well as with image quality metrics. © 2023 IEEE.

关键词： Remote sensing

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