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Towards Digital Energy Metering Systems by Means of Artificial Intelligence

Towards Digital Energy Metering Systems by Means of Artifici...

引用

international conference on ENERGY and ENVIRONMENT (CIEM)

作者： Carutasiu Mihail-Bogdan Lupoiu Teodor Necula Horia Power Engineering Faculty Romania

this article presents a new and innovating method to upgrade the existing energy metering system by utilizing an intelligent optical character recognition (OCR) algorithm. the proposed approach assures small costs of the entire energy system's refurbishment, and it can be implemented on both old and new energy meters. the flow is relatively simple and straightforward: an artificial neural network is used to process and recognize the digits from a photo of the meter's analog display and populate a custom database, from which the data will be later retrieved and used to give different stakeholders consumption insights and patterns, graphs, energy efficiency solutions etc. For the OCR algorithm and transfer learning processes a mature Python library, namely Tesseract, was used. the developed software solution was tested on various gas and electricity meters and the results are presented.

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Finite Element Analysis of a Magnetocell for MR Elastomers

Finite Element Analysis of a Magnetocell for MR Elastomers

引用

IEEE international Workshop on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Zuzanna Mielnik Denys Gutenko Faculty Electrical and Computer Engineering Cracow University of Technology Krakow Poland

ISBN: (纸本)9781665426060

Magnetorheological (MR) elastomers (MREs) are representatives of smart materials well known for its field-dependent stiffness. therefore, it is important to develop and analyze new methods for characterizing its mechanical properties. this paper will look into the design aspects of a magnetocell for measuring the field-dependence of the stiffness of an MR elastomer. the authors develop a finite-element model of the magnetocell, then reveal the obtained results as flux density maps and graphs of various quantities.

关键词： Magnetic flux Magnetic field measurement Magnetostatics conferences data acquisition Mechanical factors Magnetic analysis

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Designing a data Pipeline Architecture for intelligent Analysis of Streaming data 2nd

Designing a Data Pipeline Architecture for Intelligent Analy...

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2nd international conference on Science, engineering Management and Information Technology, SEMIT 2023

作者： Mysiuk, Iryna Mysiuk, Roman Shuvar, Roman Yuzevych, Volodymyr Pavlenchyk, Anatolii Dalyk, Volodymyr Ivan Franko National University of Lviv 1 Unіversytetska Str. Lviv79000 Ukraine Karpenko Physico-Mechanical Institute of the NAS of Ukraine 5 Naukova Str. Lviv79060 Ukraine Ivan Boberskyi Lviv State University of Physical Culture 11 Kostiushka Str. Lviv79000 Ukraine Lviv Polytechnic National University 12 Stepana Bandery Str. Lviv79013 Ukraine

ISBN: (纸本)9783031722837

the paper describes the process of creating a data pipeline architecture. Based on multiple data sources and processing through intermediate data warehouses and end-to-end business intelligence. this study is a continuation of the previously considered approaches in the context of the generalization of the entire chain of data interaction. the analysis used pre-collected data from social media news sites, which can be considered as one of the most popular streaming data. the information collection consists of automated work with a large amount of information from several web pages using the Selenium tool, and the data warehouse is implemented on the Elasticsearch, Kibana, and Logstash (EKL) technology stack. the analysis and classification of the results are based on working with a machine learning model and data visualization methods. the developed data pipeline architecture can be useful for developing data processing processes, highlighting vulnerabilities and advantages of methodologies of data selection, processing, and analysis. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： data streams

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Hybrid Attention Module Based on YOLOv5 for Foreign Object Debris Detection 11th

Hybrid Attention Module Based on YOLOv5 for Foreign Object...

引用

11th international conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Lu, Huan Liu, Tangyou Zhang, Jiafeng College of Information Science and Technology Donghua University Shanghai China College of Electronic and Communication Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9789819923618

Foreign object debris detection plays an important role in aircraft safety. Because the detection target is tiny and has limited feature information, foreign object debris detection is quite challenging. In this paper, we propose a new model called HAM-YOLOv5 for foreign object debris detection. Specifically, HAM-YOLOv5 is based on YOLOv5 to design hybrid attention module (HAM) for learning contextual information. For foreign object debris detection task, we add one prediction head for tiny object detection. Furthermore, we release Donghua University Foreign Object Debris Detection dataset (DHFOD) to supplement the lack of current data samples. We conduct a serial of experiments on DHFOD. the experimental results show that our method outperforms other methods, which demonstrates the effectiveness of the proposed HAM-YOLOv5. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Construction and Implementation of Artificial Intelligence Classroom Teaching Model Based on the AI Open Platform 5th

Construction and Implementation of Artificial Intelligence C...

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5th international conference on Computer Science and Educational Informatization, CSEI 2023

作者： Wang, Yi Zhang, Congpin Gong, Xin Ye, Siqi Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Xinxiang China

ISBN: (纸本)9789819994915

With the development of the intelligent era, artificial intelligence education is gradually carried out in primary and secondary schools in order to cultivate artificial intelligence talents. To address the current problem of insufficient artificial intelligence classroom experience, the article constructs an artificial intelligence classroom teaching model based on David Cooper's experiential learning theory, combined with the AI open platform, and carries out practical classroom teaching activities in primary school F based on the teaching model. After the implementation of the course, the students made an evaluation and analysed the data to find out the effect of the course implementation, thus verifying the feasibility and effectiveness of the teaching model, with a view to providing teachers with new ideas to solve the problem of artificial intelligence classroom teaching. the practical experience shows that the teaching is effective. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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Federated learning for Getting the IoT Arrangement of Smart City Against Digital threats

Federated Learning for Getting the IoT Arrangement of Smart ...

引用

intelligent Systems and Embedded Design (ISED), international conference on

作者： Vikash Bhardwaj Shekhar Rakesh Kumar Saini Department of Electrical Engineering Dewan VS Institute of Engineering & Technology Meerut (U.P.) INDIA Department of Electronics & Communication Engineering Dewan VS Institute of Engineering & Technology Meerut (U.P.) INDIA School of Computing DIT University Dehradun Uttarakhand INDIA

IoT gadgets have the potential to attach real components with instruments, computer software, hardware, as well as the Web network aimed at information exchange. IoT uses remote management to shrewdly increase efficiency and profitability, but the risk to security and protection grows. Digital risks are expanding gradually, resulting in insufficient levels of safety and categorization. A few IoT flaws are revealed when programmers use the Web, necessitating additional security measures for the smart city's IoT devices. Reduced IoT strings are necessary for effective interruption identification frameworks (IIFs). AI computations then use a large and complex dataset to provide accurate findings. the output of AI might apply in identifying irregularities among IoT system architectures. In this paper seven information parameters from the TON-IoT telemetry dataset together with a few AI-classifiers as well as a Federated-learning prototype in detection of interruption. By means of the Indoor Regulator, GPS Tracker, Carport Entryway, and Modbus datasets, the suggested IDS was able to achieve an exactness of 99.99%.

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learning to Prompt for Vision-Language Emotion Recognition

Learning to Prompt for Vision-Language Emotion Recognition

引用

international conference on Affective Computing and intelligent Interaction Workshops and Demos (ACIIW)

作者： Hongxia Xie Hua Chung Hong-Han Shuai Wen-Huang Cheng the Institute of Electronics National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Computer Science and Information Engineering National Taiwan University (NTU)

In the realm of vision-language tasks, the selection of prompts plays a crucial role in determining model performance, particularly in complex tasks such as emotion recognition. Despite the promise shown by models like CLIP and CoOp, their performance can exhibit significant variability, contingent upon the selection and adaptability of prompts. Addressing this challenge, this paper introduces an innovative method that exploits the philosophy of learning to learn. this novel approach facilitates the design of an emotion recognition model capable of dynamically optimizing prompt selection according to the specific demands of a given task. We empirically demonstrate that our approach outperforms established models such as CLIP and CoOp in both few-shot and zero-shot settings across three datasets, indicating its potential to enhance the generalization and adaptation capabilities of vision-language emotion recognition models.

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Spatio-Temporal RANSAC for Robust Estimation of Ground Plane in Video Range Images for Automotive Applications

Spatio-Temporal RANSAC for Robust Estimation of Ground Plane...

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international conference on intelligent Transportation

作者： Faisal Mufti Robert Mahony Jochen Heinzmann Faculty of Engineering and Information Technology Australian National University Canberra Australia Australian National University Canberra ACT Australia

this paper considers the problem of ground plane estimation in range image data obtained from Time-of-Flight camera. We extend the 3D spatial RANSAC for ground plane estimation to 4D spatio-temporal RANSAC by incorporating a time axis. Ground plane models are derived from spatio-temporal random data points, thereby robustifying the algorithm against short term temporal effects such as passing cars, pedestrians, etc. the computationally fast and robust estimation of ground plane leads to reliable identification of obstacles and pedestrians using statistically derived spatial thresholds. Experimental results with real video data from range sensor mounted on a vehicle moving in a car park are presented.

关键词： Robustness Automotive applications Cameras Automotive engineering Spatiotemporal phenomena Detection algorithms Laser modes intelligent transportation systems Driver circuits Control systems

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Rough Sets, Fuzzy Sets, data Mining and Granular Computing 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Aijun An Jerzy Stefanowski Sheela Ramanna Cory J. Butz Witold Pedrycz Guoyin Wang

ISBN: (数字)9783540725305

ISBN: (纸本)9783540725299

this volume contains the papers selected for presentation at the 11th Int- national conference on Rough Sets, Fuzzy Sets, data Mining, and Granular Computing (RSFDGrC 2007), a part of the Joint Rough Set Symposium (JRS 2007) organized by Infobright Inc. and York University. JRS 2007 was held for the ?rst time during May 14–16, 2007 in MaRS Discovery District, Toronto, Canada. It consisted of two conferences: RSFDGrC 2007 and the Second Int- national conference on Rough Sets and Knowledge Technology (RSKT 2007). the two conferences that constituted JRS 2007 investigated rough sets as an emerging methodology established more than 25 years ago by Zdzis law Pawlak. Roughsettheoryhasbecomeanintegralpartofdiversehybridresearchstreams. In keeping with this trend, JRS 2007 encompassed rough and fuzzy sets, kno- edgetechnologyanddiscovery,softandgranularcomputing,dataprocessingand mining, while maintaining an emphasis on foundations and applications. RSFDGrC 2007 followed in the footsteps of well-established international initiatives devoted to the dissemination of rough sets research, held so far in Canada, China, Japan, Poland, Sweden, and the USA. RSFDGrC was ?rst - ganized as the 7th international Workshop on Rough Sets, data Mining and Granular Computing held in Yamaguchi, Japan in 1999. Its key feature was to stress the role of integrating intelligent information methods to solve real-world, large, complex problems concerned with uncertainty and fuzziness. RSFDGrC achieved the status of a bi-annual international conference, starting from 2003 in Chongqing, China.

关键词： theory of Computation Artificial Intelligence data Mining and Knowledge Discovery database Management Mathematical Logic and Formal Languages Computation by Abstract Devices

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Potato Leaf Disease Classification using Convolutional Neural Networks 11

Potato Leaf Disease Classification using Convolutional Neura...

引用

11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Wasalwar, Yash Prashant Bagga, Kishan Singh Joshi, Vikash Kailash Joshi, Anuradha G H Raisoni College of Engineering Dept. of Computer Science and Engineerng Nagpur India G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798350348422

the vegetable Potato is quite familiar to all of us. After crops like rice and wheat, one of the most widely grown crops in India is the potato. But like other crops, Potato is also vulnerable to diseases. Two diseases namely Early blight and Late blight severely hinder the growth of potatoes. these diseases, if not discovered in its early stages, damage the crops, affects the production and apparently results in significant losses. the aim is to fasten the process of illness detection so as to increase the potato production by detecting the mentioned diseases at an early stage of Cultivation by using CNN, a Deep learning based approach. the potato crop needs to be diagnosed by detecting and classifying leaves, whether they are healthy or infected and CNN algorithm is to be implemented for the same. By collecting the visual features of the leaves of distinct potato species, this study offers a deep learning-based approach for recognising the particular early and late blight infections in potatoes. Image processing was found the best way to detect and analyze the particular diseases as it helped us to extract the very essential information from the images of the leaves. Using a dataset obtained from subsidiary of plant village dataset, consisting of 3000 samples having 1000 samples each of healthy, early blight and late blight infected leaves, a custom model was trained with some advancements from our end to detect and categorize the leaves into healthy and diseased. the CNN based model gave an accuracy of 99% when prepared over 80% train data. the output shows that CNN outperforms all in the field of Potato disease classification. © 2023 IEEE.

关键词： Crops

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