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Classification Prediction Model Based on BP Neural Network 3

Classification Prediction Model Based on BP Neural Network

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2021 3rd international conference on computer, Communications and Mechatronics Engineering, CCME 2021

作者： Jiang, Da Liu, Changhua Ou, Zuhao School of Mathematics and Computer Science Wuhan Polytechnic University China

In this paper, a method of plant image processing combined with BP neural network was proposed to predict the disease degree of brassica napus. First, the initial image preprocessing, the roots of brassica napus. were used by Otsu threshold segmentation algorithm is combined with morphological open operation, segment the rape root image with better effect, in order to reduce image size inconsistent on the effects of extracting feature parameters, can be reconstructed images after segmentation, and then obtain the root image multiple characteristic parameters, Through chart correlation analysis to select the feature parameter information, with the characteristics of the rape root disease was positively associated with the degree of disease information to refactor pixel area and the longest diameter, finally establish the BP neural network, two characteristic parameters extracted as input, root disease degree level as the output, training network and prediction and comparison. The experimental results showed that the mean square error of the classification network for brassica olerica was 0.049, and the accuracy rate of the classification results predicted by the model was about 93%. The prediction effect was good and feasible. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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Research on Bulk Acoustic Wave Resonator Based on Phononic Crystal 3

Research on Bulk Acoustic Wave Resonator Based on Phononic C...

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2022 3rd international conference on Electronic Engineering and signal processing, EESP 2022

作者： Shuang, Wang College of Science Jiamusi University Jiamusi China

With the continuous advancement of information technology and the rapid development of mobile communication technology, from smart home to automatic driving of cars, and then to the application and development of virtual reality, communication technology has evolved from the earliest communication between people to communication between things. The frequency band supported by smart phones and various small mobile terminals is increasing, and their communication frequency is also gradually increasing, which brings some challenges to the traditional RF front-end devices. The traditional RF filter can not meet the current market demand because of its own performance and size constraints. As the best option to integrate with RF circuit or microwave circuit, bulk acoustic wave filter has great development and application prospects in modern RF communication system. This paper will focus on the solid-state assembled resonators based on one-dimensional and two-dimensional phononic crystals, analyze the influence and change law of the performance of bulk acoustic wave devices based on two-dimensional phononic crystals, then explore the main loss mechanism existing in solid-state assembled devices, and propose improvement schemes, which lays a foundation for the application of solid-state assembled resonators in today's RF front-end. © Published under licence by IOP Publishing Ltd.

关键词： Automation

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Classification Techniques for Binary Motor Imagery signal for Brain-computer Interfaces 3rd

Classification Techniques for Binary Motor Imagery Signal fo...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Kant, Piyush Laskar, S.H. Hazarika, Jupitara Department of Electronics and Instrumentation Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9789811627606

Brain-computer interface (BCI) is a tool to decode human brain signals in order to enable communication with the outer world just by using electroencephalogram (EEG) signals. Motor imagery (MI) signals recorded from the scalp during the imagination of movements are used as inputs for the BCI applications. Authors did review various classification methods, used to classify MI data. Machine learning (ML) has proven to be a very effective tool in classifying MI signals. Several machine learning techniques are available to train ML models. Conventional machine learning methods can provide good classification results. However, deep learning (DL) methods are also found to provide better performance for such problems. The study compares conventional ML and deep neural networks based transfer learning as two classification approaches. Conventional machine learning was evaluated using SVM, KNN, and LDA classifiers, whereas transfer learning is implemented using alexnet, vgg16, and vgg19 networks. The study has shown the dominance of deep learning techniques over conventional classification techniques in our experiments. Transfer learning using the VGG net has resulted in a classification accuracy of 94.29%. The finding of the study suggests that although deep neural networks are computationally complex, and these networks can be very vital in the implementation of BCI technology. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Privacy Preserving Facial Image processing Method Using Variational Autoencoder 3rd

Privacy Preserving Facial Image Processing Method Using Vari...

引用

3rd international conference on Big Data and Security, ICBDS 2021

作者： Qiu, Yuying Niu, Zhiyi Tian, Qikun Song, Biao Nanjing China Nanjing Institute of Technology Nanjing China University of Oulu Oulu Finland

ISBN: (纸本)9789811908514

Over the recent years, a massive number of face images are widely used in each field of Internet, which makes the privacy leakage problem due to the abuse of facial images become more serious. The existing simple face de-identification methods like occlusion or blurring seriously damage the useful information and structure of image, which leads to a poor visual effect and is of little significant in practical application. In this work, we propose a novel privacy preserving facial image processing method. The proposed method removes the identity information of face images by four different measures first, and then fuse these de-identified images through a multi-input generative model based on Variational Autoencoder (VAE) to reconstruct the useful information of image. The proposed method aims to generate images that is close to the real face but does not contain the privacy information in the original human face image, which avoids the disclosure of privacy. We demonstrate the information reconstruction capabilities of our method with contrast experiments on the CelebA datasets. Experimental results emphasize the superiority of our method in terms of maintaining useful information and visual effects over traditional face de-identification methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Damage detection

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings 3

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Xu, Zhen Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings. © 2022 IEEE.

关键词： adaptive weights cross-swap generalization inter-domain intra-domain normalization RUL

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Application of Nonstationary Time Series Prediction to Shanghai Stock Index Based on SVM 22

Application of Nonstationary Time Series Prediction to Shang...

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3rd Asia-Pacific conference on Image processing, Electronics and computers, IPEC 2022

作者： Yang, Chun Ou, Kaiman Hong, Shaoyong School of Accounting Guangzhou Huashang College Guangzhou China School of Data Science Guangzhou Huashang College Guangzhou China

ISBN: (纸本)9781450395786

With the development of computer software and hardware system, machine learning methods are more and more used in various industries of social development. In the aspect of stock index prediction, the current prediction method has gradually changed from the traditional statistical analysis method to the artificial intelligence analysis method. Based on the original sample data, this paper uses support vector machine regression (SVR) model to predict the opening price of Shanghai stock index. The parameters of SVR model are optimized and debugged by grid search method (grid), particle swarm optimization (PSO) and genetic algorithm (GA). The analysis results show that the three types of support vector machine prediction models based on the original sample data can fully reflect the time-varying law of stock index and have high prediction accuracy. Among them, genetic algorithm support vector machine regression (GA-SVR) model shows that the minimum root mean square error (RMSE) is 14.730 and the minimum average absolute percentage error (MAPE) is 0.375%. GA-SVR model has good prediction effect and has certain significance for the prediction of stock price. © 2022 ACM.

关键词： Genetic algorithms

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Protection of Substation Grounding Grid by Gadolinium Magnesium Sacrificial Anode 3

Protection of Substation Grounding Grid by Gadolinium Magnes...

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2022 3rd international conference on Electronic Engineering and signal processing, EESP 2022

作者： Peng, Dingda Liu, Ruilong Huang, Wenxun Wang, Xiaobo Xia, Fubing Zhai, Zhe China Railway First Survey and Design Institute Group CO LTD. Shaanxi Xian710043 China Xian Rail Transit Group Company Limited Shaanxi Xian710021 China School of Materials Science and Engineering Xi'An University of Technology Shaanxi Xian710048 China

Since lower galvanic efficiency and potential reversal might occur in magnesium (Mg) sacrificial anode applied in anti-corrosion project for grounding grid in substation, rare earth gadolinium (Gd) was incorporated into Mg sacrificial anode material for activating and improving the performance of the anode, and the influence of Gd concentration on properties of Mg sacrificial anode was evaluated by electrochemical method. The results revealed that the incorporation of Gd could effectively improve the electrochemical properties of magnesium sacrificial anode. The present of 0.1%(wt) Gd in the anode caused the best electrochemical properties as follows: the OCP decreased to as low as -1.78V (SEC) while the galvanic efficiency increased to as high as 62%, and the minimal anodic corrosion current, 0.0186mA, was realized, also the reverse potential and current density in DC leakage process increased from 500mV to 900mV and 1mA/dm2 to 2.27mA/dm2, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Gadolinium

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Accuracy Analysis and Verification of Ground-Based Radar Differential Interferometry 3rd

Accuracy Analysis and Verification of Ground-Based Radar Dif...

引用

3rd international conference on 3D Imaging Technologies - Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2021

作者： Han, Chunming Meng, Yunqi Li, Guangfu Ding, Yixing Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789811924514

Ground-based interferometry radar system (GBRI) has progressively become an important means of deformation monitoring in recent years due to its all-weather suitability and high accuracy. Studies on deformation monitoring accuracy have rarely based on signal analysis in the past;hence, detailed radar parameter setting rules need further discussion. This work establishes a theoretical link between the parameters of the front-end radar system and the accuracy index of the topographic surveyor's measurements in practical application. The interferometric phase error plays a decisive role in the final deformation measurement accuracy of the system. From the perspective of signal modeling, a general theoretical equation for performance analysis is given. The equation shows the quantitative relationship between part of the design parameters of radar system and deformation monitoring accuracy. The displacement estimation variance Cramer Rao lower bound (CRLB) is derived and tested by simulation. The applicability of this derivation was demonstrated in a displacement measured approach carried out with echo signals of the corner reflector obtained by a ground-based real aperture radar sensor during a fast continuous observation. The results show that the ground-based radar system can achieve sub-millimeter deformation measurement accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interferometry

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A Preliminary Case Study: Predicting Postoperative Pain Through Electrocardiogram 22nd

A Preliminary Case Study: Predicting Postoperative Pain Thro...

引用

22nd international conference on Computational science and its Applications (ICCSA)

作者： Sebastiao, Raquel Univ Aveiro Inst Elect & Informat Engn Aveiro IEETA Dept Elect Telecommun & Informat DETI P-3810193 Aveiro Portugal

ISBN: (纸本)9783031104503;9783031104497

Currently pain is mainly evaluated by resorting to self-reporting instruments, turning the objective evaluation of pain barely impossible. Besides the inherent subjectivity due to these reports, the perception of pain is influenced by several factors. Moreover, cognitive impairments and difficulties in expressing pose a burden difficulty in pain evaluation. Beyond less efficient pain management, the consequences of an incorrect pain assessment may result in over or under dosage of analgesics, with potentially harmful consequences due to the undesirable side-effects of wrong doses. Therefore, a quantitative and accurate assessment of pain is critical for the adaptation of healthcare strategies, providing a step further in personalized medicine. Thus, the analysis of Autonomic Nervous System (ANS) reactions, which can be assessed continuously with minimally invasive equipment, offers an excellent opportunity to monitor physiological indicators when in the experience of pain. The goal of the proposed work is to classify the presence of pain in post-operative records. The results show accuracy and precision of around 85%, and recall and F1-score of 92%, indicating that the experience of postoperative pain can be classified by relying on physiological data.

关键词： Postoperative pain ECG signal processing Prediction problems Machine learning Decision support

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Analysis of a SLAM-Based Laser Scanner for the 3D Digitalization of Underground Heritage Structures. A Case Study in the Wineries of Baltanas (Palencia, Spain) 3rd

Analysis of a SLAM-Based Laser Scanner for the 3D Digitaliza...

引用

3rd Florence Heri-Tech international conference, Florence Heri-Tech 2022

作者： Camiña, Javier Sánchez-Aparicio, Luis Javier Corrochano, Cristina Mayo Sanz-Arauz, David González-Aguilera, Diego Universidad Politécnica de Madrid Madrid Spain Department of Cartographic and Land Engineering Higher Polytechnic School of Ávila University of Salamanca Ávila Spain

ISBN: (纸本)9783031203015

This paper aims at evaluating the performance of a Wearable Mobile Mapping Solution (SLAM-based) for the 3D digitalization of underground heritage sites. This evaluation was carried out by using as study case one of the largest wineries of the underground complex of Baltanás. For its digitalization we have tested not only the standard protocol (based on the good practices) but also a proposed protocol on which the laser head is turned 45° with respect to the direction of advance. As the ground truth we have used a high accuracy 3D point cloud which was obtained with a static laser scanner. The results, after an in-depth statistical analysis, demonstrate that the proposed protocol outperforms the standard one, showing better accuracies with similar acquisition and processing times. Additionally, we have tested the influence of different SLAM parameters in the final 3D point cloud. The results of this work have revealed that the Wearable Mobile Mapping Solutions are suitable tools for the 3D digitalization of underground heritage sites, obtaining centimetric accuracy and reducing the time required for its data acquisition and processing, requiring just only the 8% of the time with respect to a TLS point cloud. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data acquisition

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