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检索条件"任意字段=8th International Conference on Extending Database Technology"

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To Develop Multi-Object Detection and Recognition Using Improved GP-FRCNN Method 8

To Develop Multi-Object Detection and Recognition Using Impr...

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8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Loganathan, Ganesh Babu Fatah, Tooraj Hassan Yasin, Elham Tahsin Hamadamen, Nawroz Ibrahim Tishk International University Faculty of Engineering Department of Mechatronics Kurdistan Region Erbil Iraq Noble Private Institute Information Technology Kurdistan Region Erbil Iraq University of Salahaddin Institute of Higher Education & Scientific Research Software Engineering Department Kurdistan Region Erbil Iraq

ISBN: (纸本)9781665497619

the Region-based Convolutional Neural Network (R-CNN), which is more rapid, has been recognized as the best detection algorithm. With our design basis for Faster RCNN (FRCNN), the designers reclassify the proposed object recognition changes with just a rough spatial estimate of the incident. the researchers solve problems such as delicate to complex environments and its principles are derived reductions each time it is -actually confronted with noisier initiatives. through which designers categorize proposed changes to object recognition using a rough geometric estimate of the scene. this simple expansion requires complex changes of scale, which the authors describe in their entirely throughout this study. Moreover, Geometric Proposals with FRCNN (GP-FRCNN) performed relatively well on small and large things, which has always been a problem for computer vision algorithms. Any use of GP-FRCNN for monitoring film analysis is simple and requires no specific geometric description. We tested the algorithm on the UA-DETRAC database, finding that GP-FRCNN beat the FRCNN by 21%. © 2022 IEEE.

关键词： Training Image recognition databases Object recognition Proposals Intelligent structures Detection algorithms

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Design of On-line Monitoring System for Foundation Pit Water Level Based on Raspberry Pi 8

Design of On-line Monitoring System for Foundation Pit Water...

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8th international conference on Hydraulic and Civil Engineering - Deep Space Intelligent Development and Utilization Forum (ICHCE)

作者： Zhao, Dianpeng Song, Yeqing Deng, Zhizhong Dai, Lei Pan, Guohua Zhejiang Traff Engn Management Ctr Hangzhou Peoples R China Hangzhou Commun investment Grp Co Ltd Hangzhou Peoples R China Res Inst CCCC second Nav Engn Bur Co Ltd Wuhan Peoples R China Chongqing Jiaotong Univ Key Lab Hydraul & Waterway Engn Minist Educ Chongqing Peoples R China

ISBN: (纸本)9781665465533

Deep foundation pits in coastal areas have the characteristics of high groundwater level, strong formation permeability, and large changes in foundation pit water level. the real-time monitoring of the water level of the foundation pit is an important basis for ensuring the safe operation of the foundation pit project. Traditional monitoring methods have disadvantages such as unstable equipment, high cost, and poor scalability. therefore, in this study, a set of online monitoring system for deep foundation pit water level was developed by combining Raspberry Pi and IoT technology. the system mainly consists of data acquisition layer, data management layer, equipment control layer and data display layer, and has good expansibility. By embedding self-compiled Python Program in Raspberry Pi, the system can be controlled remotely, and the water level of foundation pit can be collected, stored and transmitted in real time and effectively. At the same time, the foundation pit pumping is controlled according to the water level of the foundation pit, so as to realize the fine control of the foundation pit pumping and achieve the effect of energy saving and emission reduction. Based on relational database MySql and B/S architecture, it realizes centralized data management, calculation and analysis, charting and other functions. the practical application test shows that the system has good stability, reliability and expansibility, improves the water level monitoring efficiency of the deep foundation pit in the coastal area, and provides a guarantee for the safe construction of the project.

关键词： Internet of things Raspberry Pi Mysql database online monitoring excavation engineering

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Discriminative recovery for extending depth-of-field of digital fringe projection 8

Discriminative recovery for extending depth-of-field of digi...

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8th Asia Pacific conference on Optics Manufacture, APCOM 2023 and 3rd international Forum of Young Scientists on Advanced Optical Manufacturing, YSAOM 2023

作者： Song, Huixin Kong, Lingbao Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing School of Information Science and Technology Fudan University Shanghai China

ISBN: (纸本)9781510672673

Digital fringe projection (DFP) measurement technology has been widely used in research and industry due to its characteristics of fast measurement speed and easy implementation. However, when measuring objects with large surface undulations, the depth of field limitations of the camera and projector will cause part of the object to be measured to be in a state of defocus, which will reduce the measurement accuracy. this paper presents a novel method to discriminate the portion of an image that is defocused due to depth-of-field limitations and to eliminate the error caused by defocus in the phase diagram. the proposed approach first discriminates the defocused pixels in the image based on the modulation information, and then recovers the defocused regions by the information fusion method. the information fusion fixes the erroneous phase information by fusing the valid phase of the neighboring focused region with the phase of the defocused region obtained through kernel density estimation by means of the Kalman filter algorithm. Simulation and experimental results show that this method can effectively eliminate the errors caused by the depth-of-field limitation in the phase map, and can feasibly extend the depth of field of the digital fringe projection system without projecting additional images. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Information fusion

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extending Bayesian Learning to Intelligent Learning in Human Way 8

Extending Bayesian Learning to Intelligent Learning in Human...

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8th international conference on Information technology and Quantitative Management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhang, Lei Suo, Weilan Zhang, Ziyang Liu, Mandi Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Chinese Acad Sci Inst Sci Beijing 100190 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA2 0PF England

Bayesian learning has been successfully used in many fields to make decision or sense the outcome of causes or influences of events. However, the relations between causes and observed events are more complicated than the Bayesian inference can learn, like prediction in some psychology experiments, which should consider human experience and the interdependence of the events. this paper raises Bayesian learning to intelligent learning by putting events in a network to analysis the complicated criterions and interdependences among events with the individual probability judgments. Finally, the results of experiment used by intelligent learning illustrate more complex relations than Bayesian learning. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： Bayesian learning Intelligent learning Decision making Judgments Probabilities

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Research on short-term load forecasting based on clustering and deep learning 8

Research on short-term load forecasting based on clustering ...

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2023 8th international conference on Clean Energy and Power Generation technology, CEPGT 2023

作者： Li, Feige He, Guangshen Wang, Ye Luan, Zhongyu Xue, Yongzhong Yang, Zhaojie NARI Group Corporation State Grid Electric Power Research Institute China Realtime Database Co. Ltd Jiangsu Nanjing211000 China

In power systems, power load forecasting is essential to ensure the reliability and efficiency of power supply. Since power load is affected by many factors, including weather, seasonality, and social activities, its patterns and changes are complex and diverse, and traditional forecasting methods may make it difficult to meet demand. In this background, this study combines the K-means clustering algorithm and deep learning model. First, through K-means clustering, we grouped historical load data, dates, and temperatures to identify different load patterns. this grouping helps adapt to different load changes, thereby improving the adaptability of the forecast. then, a deep learning model is applied, combining a convolutional neural network (CNN) and a two-layer bidirectionally gated recurrent unit (BIGRU). these two models are used to deal with spatiotemporal characteristics and sequence dependence in load data respectively. CNN is used to capture the spatial features in the load data, while BIGRU processes the time series information in the data to effectively capture the complex dynamic nature of the load. K-means clustering information is entered as additional data into the CNN and BIGRU models. the experimental results of the study show that by fully considering different load patterns and data characteristics. Reducing load demand and providing more reliable short-term load forecasts improve the efficiency of the power supply, reduce energy waste, and reduce the burden on the power system. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Real-time Wearable Device for Predicting a Long Covid Patient's Condition 8

Real-time Wearable Device for Predicting a Long Covid Patien...

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8th international conference on Mechatronics Engineering, ICOM 2022

作者： AbdelGawad, AbdelRahman Tamer Toha, Siti Fauziah Diyana Nordin, Nor Hidayati Idris, Ahmad Syahrin Healthcare Engineering and Rehabilitation Research Group Department of Mechatronics Faculty of Engineering International Islamic University Malaysia Selangor Malaysia School of Electronics and Computer Science University of Southampton Malaysia Johor Malaysia

ISBN: (数字)9781839538063

ISBN: (纸本)9781839538063

this paper aims to develop a wearable device that can be able to Predict the long covid-19 patients' conditions, to notify the doctors on a real-time basis. Long covid-19 patients suffer a lot during their daily activities especially if the lasting symptom is related to the respiratory system. By developing a system, that is easy and comfortable to wear during normal daily life, we believe that we will be able to predict the long covid-19 patients' condition. the system should first detect and analyze the patient's breathing pattern using artificial intelligence then store the patient's breathing pattern along with his status in an online database, then notify the doctors in case of a critical situation. To train the model the breathing pattern of current long covid patients and normal people was captured during doing daily activities such as walking, sitting, and climbing stairs. We hope that the developed system will help in easing the suffering of long covid patients by providing better monitoring of their health. © 2022 IET conference Proceedings. All rights reserved.

关键词： Wearable technology

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Using Machine Learning to Predict Food Prices in Kazakhstan 8

Using Machine Learning to Predict Food Prices in Kazakhstan

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8th international conference on Digital Technologies in Education, Science and Industry, DTESI 2023

作者： Sapakova, Saya Sapakov, Askar Madinesh, Nurgul Almisreb, Ali Dauletbek, Meruyert International Information Technology University Manas St. 34/1 Almaty050000 Kazakhstan Kazakh National Agrarian Research University Valikhanov St. 137 Almaty050000 Kazakhstan Information System Department Al-Farabi Kazakh National University Almaty Kazakhstan Faculty of Natural Sciences and Engineering International University of Sarajevo Bosnia and Herzegovina

the relevance of using machine learning to predict consumer prices for food in Kazakhstan is due to the need to effectively manage prices and ensure the availability of food products for the population. Machine learning can analyze multiple factors, such as inflation, seasonal fluctuations, and changes in supply and demand, and create accurate forecasts, which is critical in times of economic uncertainty and food price volatility. In this work, we used a machine learning method to predict food prices in Kazakhstan. the main focus was on the application of machine learning methods in this context. We used monthly rural, urban, and mixed-price data from 2005 to 2020 to develop and test the models. the study examines the use of machine learning models such as Decision Tree, Random Forest and Gradient Boosting using a dataset obtained from the World Food Program price database. this helps accurately predict future food prices. We also assessed the accuracy of these models using various metrics such root mean square error (RMSE), root mean square error (MSE), as a mean absolute error (MAE) and coefficient of determination (R2). In our work, we came to the conclusion that the Random Forest model performed best in all measurement metrics. Experimental results confirm that the high accuracy produced by the Random Forest model has (0.99) in predicting future food price values. © 2023 Copyright for this paper by its authors.

关键词： Decision trees

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Transaction database Encryption technology based on Blockchain technology

Transaction Database Encryption Technology based on Blockcha...

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international conference on Information Systems Engineering (ICISE)

作者： TongNan Huang Wuhan Donghu University Wuhan China

In order to improve the security and reliability of digital asset transaction management, this paper summarizes the current data transaction mode and believes that the ability of data transaction platform to retain transaction data has become a potential threat to both sides of the transaction. A transaction database encryption technology based on blockchain technology and open source and open mechanism is proposed. the whole technology system includes public area, member area and node equipment, and a transaction platform service system is built in the third party. Take the user credit information sharing data standard as an example. It realizes such functions as membership authentication, submission of data index, blockchain generation of shared data index, information query and access, and information supervision. through the verification of the transaction database encryption technology in this paper, it is found that it can effectively resist malicious tampering and network anomalies, and ensure the security of transaction information data. Combining the test results, it is found that the leader node can trigger an automatic election at each outage, with a success rate of 100% and the average time of about 14.5s.

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Secure database Authentication from Vulnerability Detection using Encryption Mode of Standardization 8

Secure Database Authentication from Vulnerability Detection ...

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8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Pasupuleti, Mohan Diraviam, A.Justin Maniunath, K. Nageshwaran, A. Selvi, S. Arun, S. Sree Venkateswara College of Engineering Department of Cse Ap Nellore India Francis Xavier Engineering College Department of Eee Rajapalayam India Mother Theresa Institute of Engineering and Technology Department of Cse AP Chittoor India Psg College of Arts&Science Department of Cse Coimbatore India Prathyusha Engineering College Department of Ece Chennai India

ISBN: (纸本)9781665497619

As the digital era is drastically increasing in the upcoming years, the security breach is also getting increased. the vulnerability or flaw occurs due to the malicious attack over the data. the security breach of the data has to be checked as it occurs due to the vulnerability state. Vulnerability or theft cause the data losses the integrity over the data. Data confidentiality loss occurs due to the security implications in the website. the website of the organization such as e-commerce sector has to be monitored as the intruder tries to attack the confidential information from the webpage by phishing technique. the illegitimate website which is trapped by the intruder which compromises the confidential information of the user and the credential information such as credit card, account details etc. In this paper, the novelty research is to risk the minimal factor by encrypting the credential information using the encryption mode of standardization. Encryption algorithm which is highly efficient in nature which can be used to minimal the risk factor of the intruder attack. the intruder tries to maliciously attack on the credentials of the data. Some of the malicious attack such as attacking the code, SQL server, cross-site scripting attacks, phishing, spoofing attack occurs on the website. these malware attacks maliciously attack the network mechanism. To secure the authentication, the encryption standard is used to secure the credentialed of the data by securing the data in an efficient way. If the data security is preserved then the minimal range of risk factor procures. By using the AES 256 standard scheme, the data security SQL server and cloud infrastructure can be secured. For the vulnerability prediction, the data mining is used for the classifying the exploitation based on the analysis. © 2022 IEEE.

关键词： databases Phishing Standards organizations Authentication Organizations Credit cards Encryption

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Research on the database Model Supporting the Setting and Sharing of Data Security Levels in Ubiquitous Smart Learning Spaces

Research on the Database Model Supporting the Setting and Sh...

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international conference on Image, Vision and Computing (ICIVC)

作者： Song Lei Ren Shuaitao Zhou Hua Southwest Forestry University Information and Technology Center Kunming China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

In response to the need for learners in ubiquitous smart learning spaces to communicate with others while maintaining personal privacy, we propose a new model to address these issues. *** to ensure the security and reliability of data during transmission. *** to ensure the safe and classified storage of learners' data in space. this paper first analyzes the importance of data protection in the ubiquitous smart learning network space. then, a four-level database model is constructed based on different permissions and encryption technologies for storing data, with each level of the database physically isolated. Learners can freely choose to store their personal data in the corresponding level of the database. Finally, the efficiency of the model was verified under simulated experimental conditions, and the security of this storage model was analyzed.

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