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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

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Privacy Preserving Facial Image processing Method Using Variational Autoencoder 3rd

Privacy Preserving Facial Image Processing Method Using Vari...

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

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

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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...

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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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Feature Extraction and Fusion of Multiple Convolutional Neural Networks for Firearm Detection 3rd

Feature Extraction and Fusion of Multiple Convolutional Neur...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Dhillon, Anamika Verma, Gyanendra K. Department of Computer Engineering NIT Kurukshetra Kurukshetra India

ISBN: (纸本)9789811908392

In the last few years, terror activities across the world have been raised drastically. Therefore, we need a framework that can detect these terror and illegal actions automatically. In spite of various state-of-the-art deep learning algorithms, weapon detection is still a serious challenge. This work focuses on the classification and detection of guns which integrates deep fusion of feature information to generate the most discriminant feature vector. Firstly, we utilize the fully connected layers of recent deep CNN models: Inception-ResNetv2 and MobileNetv2 for feature extraction, and then we fuse these features by using concatenation operation. To acquire a more compact presentation of features and reduce the complexity of computation, we have utilized NCA. After that, we classify the images by using an SVM classifier. Finally, to detect the guns in an image, a bounding box regression module is proposed by applying LSTM. Qualitative and quantitative outcomes indicate that our framework can detect guns with huge variations in size along with rigorous occlusion. In particular, our method achieves an accuracy rate of 98.62% with a false alarm rate of 1.38% on the gun dataset which is extracted from Kaggle. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Multi-target tracking random finite set model based on signal parameter correlation

Multi-target tracking random finite set model based on signa...

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computer science and Management Technology (ICCSMT), international conference on

作者： Tao Xu Xi Li Fucheng Guo School of Electronic Science National University of Defense Technology Changsha

The target is generally equipped with a corresponding radiation source, and the signal parameter of the radiation source can be used as an important auxiliary parameter to improve the tracking performance of the electronic reconnaissance system to the target. For the practical application scenario of multi-tracking, we proposes an improved Gaussian mixture probability hypothesis density filter (GMPHD) filtering algorithm based on the correlation of radiation source signal parameters. By using the signal parameters to expand the dimension of the target’s motion state vector, the description of the target state is improved. Based on the statistical theory of random finite sets, the multi-target state is described as a random finite set, and GMPHD is used for tracking. Simulation results show that the use of signal parameters can enhance the precision of data correlation, thereby improving the performance of the trace.

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A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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An Approach to the Presumptive Detection of Road Incidents in Cuenca, Ecuador Using the Data from the Social Media Twitter and Spanish Natural Language processing 3rd

An Approach to the Presumptive Detection of Road Incident...

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

作者： Loja-Morocho, Pablo E. Reyes-Duchitanga, Robbyn T. León-Paredes, Gabriel A. Grupo de Investigación en Cloud Computing Smart Cities & High Performance Computing Universidad Politécnica Salesiana Cuenca Ecuador

ISBN: (纸本)9783031322129

The road situation in Ecuador, specifically in the city of Cuenca and its surroundings, can vary according to various aspects related mainly to the weather, or traffic incidents caused due to reckless actions by drivers. Therefore, many times these problems can cause some drivers to be forced to delay their trip or look for alternative routes to reach their destination, others can plan their departure according to the related news they find when browsing social media, which can be somewhat time-consuming and even unlikely to find. For this reason, in this paper, a mobile application is presented whose primary function is to collect publications from social media Twitter from different Twitter accounts that are related to road incidents and to classify them using Natural Language processing (NLP) and Machine Learning (ML) models such as BERT. Then, we present an approximate location of the presumptive incident on an interactive map which allows each of these tweets to be displayed with a personalized icon that can indicate different types of road incidents and, at the same time, the details of the event that occurred. Two main experiments were carried out to find (a) the model accuracy, where a value of 80% was obtained as a result, which is considered a positive result for road incident classification, and (b) the execution of acceptance tests, where a questionnaire based on the ISO 9126 standard was presented to a group of bikers belonging to the city of Cuenca, obtaining as a result that for most users the application is new (85.8%), efficient (85.7%) and easy to use (64.3%). This research makes it possible to present news of road incidents in a centralized manner, which makes it easier for drivers to stay informed and thus avoid annoying interruptions in their circulation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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