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Noise acquisition system of underwater propulsion device based on LabVIEW 3

Noise acquisition system of underwater propulsion device bas...

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3rd international conference on Intelligent Control, Measurement and signal processing and Intelligent Oil Field, ICMSP 2021

作者： Li, Zhou-Li Li, Fei-Xiang Tian, Fu-Bin Liang, Yu-Jia Xi'An Shiyou University Measurement Control Instrument Lab Xi'an China

ISBN: (纸本)9781665437158

In order to improve the performance of underwater vehicle, the noise produced by underwater vehicle is studied. In this design, PXIe-6366 data acquisition module and computer are used as the main hardware. By mastering the application technology of virtual instrument, a noise data acquisition system with the functions of configuration, acquisition and data playback is designed. At the same time, the login module and user management module are designed to ensure the security and standardization of the system. © 2021 IEEE.

关键词： data acquisition

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Medical data Enhancement and Gastric Cancer Detection Based on Improved WGAN and Multi-Channel Fusion CNNs

Medical Data Enhancement and Gastric Cancer Detection Based ...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Winston Qiu High School Affiliated to Renmin University of China Beijing China

Stomach cancer is now one of the most common malignant tumor diseases of digestive tract, and its prevalence as well as mortality rate are located in the top few tumor diseases. At the same time, China is a country with a high prevalence of gastric cancer, which is expected to turn into a more significant problem with the accelerated aging of the population, so detecting gastric cancer at an early stage can effectively improve the treatment outcome. One of the main problems faced by deep learning for processing medical images is the small size of the dataset. When the amount of data is severely insufficient, the network model is difficult to be trained stably and leads to weak generalization performance, which will directly affect the performance of the algorithm, so the study of medical data enhancement is a very important measure to alleviate the phenomenon of small-sized datasets. In this paper, we design and improve the WGAN network model by introducing the perceptual and structural loss information of medical image data on the basic structure of WGAN network. Through comparative experiments, our designed model has better performance in all evaluation indexes, which verifies the effectiveness and feasibility of the improvement. On the basis of data enhancement, we also designed a multi-channel fusion CNN model combining VGG-16 and GoogLeNet features for early gastric cancer detection. From the experimental results, it can be seen that the fusion CNN we designed can better enhance the effectiveness of early gastric cancer detection, making its accuracy reach 97.08%.

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Perspective of signal processing-based on brain-computer interfaces using machine learning methods

Perspective of signal processing-based on brain-computer int...

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3rd international conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： He, Ruyue Department of Electronics and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781643684321

The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The 'Discussion' part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms. © 2023 IOS Press. All rights reserved.

关键词： Brain computer interface

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"Fat" Rays in Three Dimensional Media to Approximate a Broadband signal Propagation 23rd

"Fat" Rays in Three Dimensional Media to Approximate a Broad...

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23rd international conference on Computational Science and Its Applications , ICCSA 2023

作者： Neklyudov, Dmitry Protasov, Maxim Institute of Petroleum Geology and Geophysics SB RAS prosp. acad. Koptuga 3 Novosibirsk630090 Russia

ISBN: (纸本)9783031368073

The paper presents a simple and robust approach for calculating frequency-dependent rays in three-dimensional media. The proposed ray tracing procedure simulates propagation of locally plane fragment of a wave front. Ray properties depend on velocity distribution in some sub-volume around the ray and on wavelength at each point. We provide a numerical comparison of "fat" rays approach with the "exact" Helmholtz solver using complex 3D SEG salt model. This comparison show promise of using the concept of "fat" rays in 3D seismic data processing, i.e. comparison show that "fat" rays approach needs up to several orders less resources than Helmholtz solver, while the error between "fat" rays wave filed and "exact" solution does not exceed several percent. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data handling

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Pretrained Sentence Embedding and Semantic Sentence Similarity Language Model for Text Classification in NLP

Pretrained Sentence Embedding and Semantic Sentence Similari...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： V.Valli Mayil T.Ratha Jeyalakshmi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Data Science & Analytics Jain University School of Sciences Banglore Karnataka

A key task in the field of Natural Language processing (NLP) is determining semantic similarity between text sentences. Sentence pair modeling, Textual similarity and language modeling are few important tasks in NLP. Traditional machine learning algorithms require an enormous quantity of training data, but it is a timeconsuming process. Pre-trained models can be modified for a variety of downstream applications since they use methods for generically learning the characteristics of neural network topologies and language representations. Bidirectional Encoder Representations from Transformers- BERT & GPT are the popular architectures in NLP which enable to use minimal fine-tuning effort to produce effective results. In this work a fine-tuned BERT model that is suitable for semantic sentence similarity which predicts the entailment, neutral and contradictory categories of sentence pairs is presented. The fine-tuning feature promotes the training phase of model whichis widely effective across different types of semantic similarity models. The performance analysis of our system shows that the fine-tuned model reduces the number of neurons in the neural network there by reducing storage and time spent in expensive training task to create deep learning model.

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A Comprehensive Study: Evaluating Machine Learning Algorithms with Credit Card Transaction data 3rd

A Comprehensive Study: Evaluating Machine Learning Algorithm...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Basak, Sarnali Shanto, Shaikh Qutub Department of Computer Science and Engineering Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9789819739363

The financial sector places significant emphasis on the detection of credit card fraud, and machine learning techniques have emerged as a promising solution. Nevertheless, questions regarding the interpretability of machine learning models and their resilience against adversarial attacks within the realm of credit card fraud detection persist. This study aims to bridge these gaps and underscore their pivotal role in advancing the field. It involves a comprehensive analysis of a credit card transaction dataset and the development of multiple machine learning models for fraud detection. Python scripts leverage well-known libraries such as Pandas, NumPy, Seaborn, Scikit-Learn, TensorFlow, and Matplotlib for data processing, visualization, and machine learning tasks. For fraud detection, the dataset is partitioned into training and testing sets, and three machine learning models, namely XGBoost, Random Forest, and Support Vector Machine (SVM), are applied. Performance metrics, encompassing accuracy, sensitivity (recall), F1-score, Matthews Correlation Coefficient (MCC), Balanced Classification Rate (BCR), and confusion matrices, are computed and presented. Additionally, a Long Short-Term Memory (LSTM) neural network is deployed for sequence-based fraud detection. This research offers a comprehensive roadmap for analyzing credit card transaction data and constructing machine learning models to detect fraudulent activities, underscoring the importance of data exploration and model evaluation in fraud detection systems. It emphasizes the significance of feature engineering in enhancing the effectiveness of fraud detection models and equips decision-makers with the ability to make informed choices based on model predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Human shooting pose accuracy recognition algorithm based on optimized YOLOv5 3

Human shooting pose accuracy recognition algorithm based on ...

引用

3rd international conference on Computer Vision and data Mining, ICCVDM 2022

作者： Chang, ZhanJun Yang, DeGuo School of software College Shenyang University of Technology China

ISBN: (纸本)9781510661363

Aiming at the practical problem that it is difficult to accurately and accurately recognize the shooting pose, this paper proposes a human shooting pose recognition algorithm based on optimized YOLOv5. Firstly, YOLOv5 detection algorithm is adopted in the target detection module of Alphapose. Based on YOLOv5, a four-scale feature fusion structure is proposed to enhance the detection ability of smaller scale objects by adding a small target detection layer. Secondly, the C3 module in the original small-scale detection layer is improved, and the original bottleneck module is stacked by three Transformer encoders, so as to improve the ability of YOLOv5 to learn the feature information of small targets. In addition, this paper proposes a new algorithm based on alignment and matching of key points to evaluate the accuracy of shot pose. The analysis of test results shows that the improved YOLOv5 model can achieve 90.2% recognition rate and 89.5% average accuracy on the human detection algorithm, and the recognition efficiency of key points can be improved from the original 28 frames per second to 105 frames per second. Combined with the human key point shooting evaluation algorithm, The scheme proposed in this paper can meet the requirement of accuracy and real-time of recognizing human shooting posture. © COPYRIGHT SPIE.

关键词： signal encoding

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Research on the Collaborative Interaction between Electricity and Computing Power for Green Development-Taking data Centers as an Example

Research on the Collaborative Interaction between Electricit...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Wei Tang Chenglong Zhang Jinhui Duan Zhenda Hu State Grid Energy Research Institute Co. Ltd. Beijing China Center of Energy and Research Power Economic Research Institute of State Grid Fujian Electric Power Company Fuzhou China

The allocation of electricity and computing resources is an important factor affecting economic development and social stability. Through research on the coordinated operation of computing power and electricity, resource allocation can be optimized, resource utilization efficiency can be improved, investment in power grid construction can be reduced, fossil energy consumption and carbon emissions can be reduced, and low-carbon and green development of electricity can be promoted. This article takes data centers as an example to study and analyze the planning and operation related issues of computing power collaboration. The focus is on the perspective of power supply and demand balance, and proposes a theoretical method for data centers to participate in demand response and achieve collaborative interaction with the power grid. Firstly, the composition of the adjustable load potential of the data center and the mechanism of participating in grid interaction were analyzed. Then, a data center energy supply and load energy consumption model is established. Finally, considering delay sensitive loads and delay tolerant loads, based on the time scheduling mechanism, spatial scheduling mechanism, and server optimization management mechanism of the data center, a potential calculation model for the participation of the data center in power demand response is proposed. This study provides theoretical support for the inclusion of the data center in power grid scheduling.

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Lead-free (Na0.5Bi0.5)0.45Ba0.40Sr0.15TiO3ceramic: Synthesis, structure and polarization response

Lead-free (Na0.5Bi0.5)0.45Ba0.40Sr0.15TiO3ceramic: Synthesis...

引用

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

作者： Md. Aliful Islam Md. Abdullah Zubair Dept. of Nanomaterials and Ceramic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

In the present study, environmental-friendly lead-free $($ (Na 0.5 Bi 0.5 ) 0.45 Ba 0.40 Sr 0.15 TiO 3 (NB-BST) ceramics were prepared by conventional solid-state reaction route. The structural and electrical properties of the ceramics were investigated for 950°C, 1000°C, and 1050°C sintering temperatures. Grain size was found to be ~0.267-0.447 µm. The structural analysis revealed cubic symmetry with a pm-3m space group. P-E loop shape of NB-BST ceramic was found to be strongly dependent on sintering temperature

关键词： Temperature dependence Temperature Shape Sintering Lead Perovskites Ceramics

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data Analytics for Pandemic Management using MapReduce and Apriori Algorithm 3

Data Analytics for Pandemic Management using MapReduce and A...

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3rd international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Kumar, Shashwat Chuli, Anannya Jain, Aditi Prasanth, Narayanan Vellore Institute of Technology Vellore632014 India

Amidst the profound impact of the COVID-19 pandemic on global economies and healthcare systems, eIIective data analysis has become paramount. Our research paper, titled"data Analytics for Pandemic Management Using MapReduce and Apriori Algorithm," presents a comprehensive framework to analyze pandemic data. We harness the power of the MapReduce and Apriori algorithms, with a parallel processing model achieving an average speedup of 50%. This approach involves data collection, preprocessing, and algorithmic application to extract valuable insights from pandemic-related data. Notably, our findings reveal a substantial 22.29% support rate for"n95 masks," indicating high demand. Additionally, we identify strong co-occurrence patterns, exemplified by the perfect support rate of 1.00 between"n95 masks" and"chloroquine," highlighting their interconnectedness. Our framework goes beyond data analysis, enhancing personalized marketing, and optimizing inventory management for eIIscient resource allocation during crises. We also uncover robust associations, such as the 0.220 confidence in joint purchases of"butyl rubber gloves" and"surgical masks".Hence, the research aligns seamlessly with the pressing need for eIIective pandemic data management and response strategies. By validating our approach through numerical insights, we aim to contribute to mitigating pandemic-related challenges and support global eIIorts to better prepare for and manage future health emergencies. © 2023 Elsevier B.V.. All rights reserved.

关键词： MapReduce

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