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Construction of intelligent Fault Diagnosis System for Pumping Station Based on Fusion Algorithm 3

Construction of Intelligent Fault Diagnosis System for Pumpi...

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3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Chen, Taiwu Dong, Fei Wang, Haojie Xu, Zhiming Liu, Erjun Lianyungang Linhong Water Conservancy Project Management Office Jiangsu Lianyungang222006 China Tongyu River North-extended Water Supply Project Management Division of Lianyungang Lianyungang Jiangsu 222005 China Lianyungang Urban Water Engineering Management office Lianyungang Jiangsu 222005 China

ISBN: (纸本)9798331519032

The purpose of this article is to study and implement an intelligent fault diagnosis system of pumping station based on fusion algorithm, so as to improve the accuracy and efficiency of fault diagnosis of pumping station. Firstly, this article points out the limitations of traditional fault diagnosis methods, such as insufficient information from a single sensor and low diagnostic accuracy. Then this article designs and implements an intelligent fault diagnosis system for pumping stations, which includes data acquisition, preprocessing, feature extraction, fusion algorithm diagnosis and user interaction. Among them, the fusion algorithm adopts the multi-modal information fusion method based on deep learning, and realizes intelligent identification and classification of pump station faults by comprehensively analyzing the characteristic parameters from different sensors. The experimental results show that the fusion algorithm shows high accuracy in fault diagnosis of pumping stations, and the false alarm rate and false alarm rate are low. The conclusion is that the intelligent fault diagnosis system of pumping station based on fusion algorithm has high practicability and reliability, and can provide strong support for the operation and maintenance management of pumping station. © 2024 ieee.

关键词： Deep learning

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Conceptual Level Semantic Sentiment Analysis Using Twitter data

Conceptual Level Semantic Sentiment Analysis Using Twitter D...

引用

2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Ramanathan, Vallikannu Al Hajri, Hamed Ruth, Anitha Al Zahra College For Women It Department Muscat Oman Srm Institute Science And Technology Department Of Csa Chennai India

ISBN: (纸本)9798350364828

Sentiment analysis (SA) AKA opinion mining is one of the frequently used Natural Language Processing (NLP) application to identify human opinions from their social media posts or reviews. Its ultimate goal is to identify the hidden emotions from their posts. The mission of SA is the classification of the opinion from the text or message. The main task of SA is to recognize and gather human opinions on various objects, topics and events. In this paper, semantic concepts are added as extra feature to the trained data set in sentiment analysis for more accurate classification of the result. We applied conceptual semantic sentiment analysis to train the three different twitter data sets. Using this notion, we can forecast the sentiment of any tweet which does not have sentiment keywords. The main emphasis is to increase the accuracy of twitter sentiment analysis by integrating the semantic analysis of words into sentiment detection. © 2024 ieee.

关键词： Sentiment analysis

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Extrinsic Calibration of 2D Millimetre-Wavelength Radar Pairs Using Ego-Velocity Estimates

Extrinsic Calibration of 2D Millimetre-Wavelength Radar Pair...

引用

ieee/ASME international conference on advanced intelligent Mechatronics (AIM)

作者： Cheng, Qilong Wise, Emmett Kelly, Jonathan Univ Toronto Space & Terr Autonomous Robot Syst Lab Inst Aerosp Studies Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665476331

Correct radar data fusion depends on knowledge of the spatial transform between sensor pairs. Current methods for determining this transform operate by aligning identifiable features in different radar scans, or by relying on measurements from another, more accurate sensor. Feature-based alignment requires the sensors to have overlapping fields of view or necessitates the construction of an environment map. Several existing techniques require bespoke retroreflective radar targets. These requirements limit both where and how calibration can be performed. In this paper, we take a different approach: instead of attempting to track targets or features, we rely on ego-velocity estimates from each radar to perform calibration. Our method enables calibration of a subset of the transform parameters, including the yaw and the axis of translation between the radar pair, without the need for a shared field of view or for specialized targets. In general, the yaw and the axis of translation are the most important parameters for data fusion, the most likely to vary over time, and the most difficult to calibrate manually. We formulate calibration as a batch optimization problem, show that the radar-radar system is identifiable, and specify the platform excitation requirements. Through simulation studies and real-world experiments, we establish that our method is more reliable and accurate than state-of-the-art methods. Finally, we demonstrate that the full rigid body transform can be recovered if relatively coarse information about the platform rotation rate is available.

关键词： Calibration & Identification Radar Robot Sensing systems Sensor Fusion

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Holt Double Exponential Filter Algorithm Based on Spatial Limiting 2

Holt Double Exponential Filter Algorithm Based on Spatial Li...

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2nd international conference on Signal Processing and intelligent computing, SPIC 2024

作者： Wei, Yingzi Wang, Zhihao Gu, Kanfeng Shenyang Ligong University College of Information Science and Engineering Shenyang China Shenyang Institute of Automation of the Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368888

In practice, for the Kinect camera is susceptible to the influence of light, distance and acquisition objects, resulting in the skeletal node data acquired by the camera will inevitably introduce some noise, making the node data deviate from the real value of the problem, this paper proposes a spatial limitation based on holt double-exponential filter algorithm for filtering data anomalies. The method first obtains human 3D skeletal data through Kinect, and then carries out spatial limiting processing for each data, when the skeletal node data exceeds the limitation of the amplitude, take the intersection coordinates of the spatial straight line and the sphere with the above momentary smoothing value as the sphere center, and the amplitude as the radius as the valid value of the data;when the skeletal data is within the limitation of the amplitude, it is believed that the data itself is a valid value, and finally the data after the limitation of amplitude is fed into the holt filter to filter the data. Finally, the amplitude-limited data is fed into the holt filter to smooth the data for prediction. The experimental results show that the algorithm in this paper can effectively remove the influence of data noise and provide a good smoothing prediction process for the data. © 2024 ieee.

关键词： holt double exponential filter skeletal data spatial limiting

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Renewable Energy data acquisition and Analysis Software Tool Based on IoT Sensors Network for Solar Irradiance or Wind Power Density Forecasting 1

Renewable Energy Data Acquisition and Analysis Software Tool...

引用

1st international conference on Smart Energy systems and Artificial Intelligence, SESAI 2024

作者： Michel, Kam Olle Camel, Tanougast Harry, Ramenah Universite de Toulouse 2 Laboratoire de Recherche Specialise dans l'Analyse et l'Architecture des Systemes Toulouse France Metz France

ISBN: (纸本)9798350349689

This paper presents a renewable data acquisition and analysis software tool based on Internet of Things (IoT) sensors network for global solar irradiance and wind power density forecasting. This developed tool uses an adapted Weibull method to characterize global solar irradiation or wind speed variabilities to forecast of harvesting photovoltaic (PV) or wind turbine energies. The software implementation of the proposed forecasting model interfaced to weather sensors allows a real-Time and intelligent management of deployed renewable energy systems in the France mainland territory. © 2024 ieee.

关键词： Wind power

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Real Time data Fusion and intelligent Decision-Making Algorithm for Low-Voltage Distribution Network Based on Cloud Edge Collaboration

Real Time Data Fusion and Intelligent Decision-Making Algori...

引用

2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Zhang, Yu Deng, Renjie Yu, Shanshan Guangdong Power Grid Co. Ltd. Guangzhou Power Supply Bureau Guangdong Guangzhou510000 China

ISBN: (纸本)9798350369212

As the distribution network scale expands and intelligence levels improve, network edge devices are generating an ever-increasing amount of data and computing, causing significant stress on data storage, cloud computing, and transmission bandwidth. Traditional cloud computing models struggle with real-time data analysis, processing, and response due to transmission delays and resource constraints, leading to non-real-time information flow handling that undermines the stability of energy flow and control reliability. Addressing these challenges, this article introduces a real-time data fusion and intelligent decision-making algorithm tailored for low-voltage distribution networks, leveraging cloud-edge collaboration. By harnessing the strengths of both cloud and edge computing, this algorithm ensures real-time data capture, processing, analysis, and decision-making. Experimental results demonstrate that our proposed algorithm not only resolves the real-time limitations of traditional cloud computing models but also enhances the intelligence and operational efficiency of distribution networks, thus bolstering the stable operation and sustainable growth of power systems. © 2024 ieee.

关键词： Cloud platforms

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Grounded Language acquisition from Object and Action Imagery 20

Grounded Language Acquisition from Object and Action Imagery

引用

20th ieee international conference on advanced Video and Signal-Based Surveillance (AVSS)

作者： Kubricht, James Yang, Zhaoyuan Qiu, Jianwei Tu, Peter GE Aerosp Res Cincinnati OH 45215 USA GE Vernova Res Cambridge MA USA GE HealthCare Res Chicago IL USA

ISBN: (纸本)9798350374292;9798350374285

Deep learning approaches to natural language processing have made great strides in recent years. While transformer models have demonstrated impressive knowledge and reasoning capabilities, it is unclear how produced symbols are grounded in data from the world. In this paper, we explore the development of a private language for visual data representation by training emergent language (EL) encoders/decoders in both i) a traditional referential game environment and ii) a contrastive learning environment utilizing a within-class matching training paradigm. An additional classification layer-utilizing neural machine translation and random forest classification-was used to transform symbolic representations (sequences of integer symbols) to class labels. These methods were applied in two experiments focusing on object recognition and action recognition. For object recognition, a set of sketches produced by human participants from real imagery was used and for action recognition, 2D trajectory images were generated from 3D motion capture systems. In order to interpret the symbols produced for data in each experiment, a Gradient-weighted Class Activation Mapping (GradCAM) method was used to identify pixel regions indicating semantic features which contribute evidence towards symbols in learned languages. Results indicate that: i) symbols used to represent images appear to shift focus between different semantic components in an image and ii) this shift occurs gradually over the course of a sentence.

关键词： Contrastive Learning

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Trends and Challenges in AI-Driven Microservices for Cloud-based Airline Reservation systems: A Review 3

Trends and Challenges in AI-Driven Microservices for Cloud-b...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Barua, Biman Barua, Imon Shamim Kaiser, M. Mozumder, Mohammad Jashim Uddin Institute of Information Technology Jahangirnagar University Dhaka Bangladesh Department of CSE BGMEA University of Fashion & Technology Nishatnagar Turag Dhaka Bangladesh SY Systems Co. Ltd. Nagoya Japan Bangabandhu Sheikh Mujib Medical University Dhaka Bangladesh

ISBN: (纸本)9798331527549

The rapid advances in Artificial Intelligence (AI), microservices architecture, and cloud computing highly impact airlines in optimizing reservation systems. This review paper attempts to analyze the ongoing trend, challenges, and future opportunities of AI-enabled microservices toward cloud-based airline reservation systems. AI with machine learning and deep learning supports dynamic pricing, personalized services, and better demand forecasting, transforming booking, pricing, and customer experience. While microservices architecture offers scalability and flexibility, in conjunction with cloud computing, it optimizes airline demands and system performance. At the same time, challenges such as data privacy and security, scalability, interoperability, and costs are still present with adopting cloud-based microservices. Case studies are provided in this paper, as well as a thorough exposition of future directions of AI in airline reservation systems, such as the application of emerging AI techniques like reinforcement learning, evolution toward microservice architecture, and the influence of new cloud computing models such as edge and hybrid clouds. In addition, the work points out research gaps in areas such as real-time predictive analytics and security measures. Findings indicate a transforming potential of service on airline reservation systems for AI-facilitated microservices and cloud computing and provide possible suggestions for overcoming the current challenges to harness great efficiency from them. © 2025 ieee.

关键词： Interoperability

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Hybrid Laplace-Gaussian Differential Privacy to Secure data Aggregation in Edge-IoT systems 1

Hybrid Laplace-Gaussian Differential Privacy to Secure Data ...

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1st ieee international conference on Advances in Signal Processing, Power, Communication, and computing, ASPCC 2024

作者： Yadav, Paritosh Kumar Pandey, Sudhakar Singh, Prerna Pandey, Parth National Institute of Technology Information Technology Raipur India Rajeev Gandhi Institute of Petroleum Technology Information Technology Amethi India

ISBN: (纸本)9798350355093

The conventional cloud-enabled Internet of Things (IoTs) framework has seen several new sensing applications and techniques introduced by the blooming Internet of Things (IoTs) devices. As a result, the new network architecture gradually turns bidirectional, allowing IoT devices to do light processing in addition to serving as data harvesters. By moving some processing back to the 'local,' edge computing, a novel architecture that is on the horizon, dramatically reduces latency and reliance associated with standard cloud-enabled networks. But when edge computing returns data and models to IoT devices, new security threats appear. Big data is being produced by rapidly expanding smart city applications like smart delivery, smart communities, and smart health, and it's being shared freely online. systems for the Internet of Things (IoT) are central to smart city applications, as conventional cloud computing is unable to provide the essential needs of intelligent Internet of Things systems. Owing to the sensitive nature of smart city applications, several privacy-preserving techniques, including homomorphic encryption, differential learning, and anonymity, have been used over the years. In addition, the resource consumption for maintaining data privacy in edge computing environments - which have fewer resources than cloud data centers - has received little attention. Differential privacy (DP) in particular has proven to be a successful privacy-preserving technique in the context of edge computing. This paper focuses on implementing differential privacy in edge-enabled IoT systems to address these privacy concerns. Differential privacy (DP) in particular has proven to be a successful privacy-preserving technique in the context of edge computing. © 2024 ieee.

关键词： Anonymity

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Design and Development of Big data System for Blast Furnace Ironmaking 6

Design and Development of Big Data System for Blast Furnace ...

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ieee 6th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Chen, Qifu She, Jinhua Wu, Min Zhang, Zhuofu Liao, Zhenhong China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Tokyo 1920982 Japan Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Hunan Valin Lianyuan Iron & Steel Co Ltd Loudi 417000 Peoples R China

ISBN: (纸本)9798350311259

The process of blast furnace ironmaking is composed of several sub-processes. The process of blast furnace smelting is complicated and the data of each sub-process are scattered and not concentrated. The traditional working mode of subjective judgment based on manual experience is not conducive to the stable production of the blast furnace. Therefore, this paper develops a big data system for the production process of blast furnace, which is of great significance to realize the digital production and intelligent production of blast furnace. According to the system architecture of blast furnace ironmaking, this paper mainly designs and develops the data acquisition layer, data storage layer, data analysis layer and data application layer of the big data system.

关键词： blast furnace silicon content SVR GWO VMD

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