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Smart Servo-Operated Ball Valve with IoT Integration and PID Control for Precise Fluid Management 3

Smart Servo-Operated Ball Valve with IoT Integration and PID...

3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Bhanuse, Vijaykumar Bhosale, Shreyash Nitin Bhilare, Abhishek Mahadev Bauskar, Aneesh Rajesh Vishwakarma Institute of Technology Pune411037 India

ISBN: (纸本)9798331515911

This paper presents the design and implementation of an innovative control valve system that integrates a ball valve run by a servo motor, IoT capabilities, and PID control for precise operation. The system uses a 1/4-inch ball valve mounted in a custom 3D printed housing, driven by an MG995 servo motor. An ESP32 WROOM module microcontroller enables Wi-Fi connectivity and data transmission to the cloud via the Thingspeak platform. Ultrasonic sensors are used for tank level measurement, and the valve operation is controlled using a PID algorithm implemented in C++ on the Arduino IDE. The PID parameters are fine-tuned to achieve best performance. This solution offers a cost-effective and precise method for fluid control in various industrial applications. © 2025 ieee.

关键词： Three term control systems

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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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intelligent Inventory Model for Cigarette Automated Storage systems Based on 5G IoT Technology

Intelligent Inventory Model for Cigarette Automated Storage ...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Duan, Qi Jin, Congzhong Zhang, Shen Xing, Hao Wang, Hefei Wang, Deji Shijiazhuang Branch Logistics Center Hebei Tobacco Company Shijiazhuang China Tianjin Branch Logistics Center Cntc Tianjin China Shijiazhuang City Company Hebei Tobacco Company Shijiazhuang China Staff Development Institute Cntc Zhengzhou China

ISBN: (纸本)9798331504205

With the growing demand for refined and intelligent supply chain management in the tobacco industry, automated storage systems play a crucial role in cigarette storage and logistics. However, traditional inventory methods are time-consuming, labor-intensive, and prone to human errors. This paper proposes an intelligent inventory model based on 5G IoT technology, integrating high-precision positioning, RFID technology, edge computing, and 5G networks to achieve real-time monitoring and efficient management of cigarette inventory in automated storage systems. Empirical analysis demonstrates that the proposed model improves inventory efficiency by nearly 65% and reduces error rates to 0.1%, providing a significant advancement in inventory management for the tobacco logistics sector. © 2025 ieee.

关键词： Supply chain management

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Comparative Study of intelligent Algorithms in Fault Diagnosis of Aviation Mechanical systems 1

Comparative Study of Intelligent Algorithms in Fault Diagnos...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Shen, Jingjing Comac Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (纸本)9798331529246

The key components such as engines and turbines in aviation machinery systems operate under extreme conditions such as high temperature, high pressure, and strong vibration, which can easily lead to complex faults and pose significant challenges to accurate and timely fault diagnosis. In response to this issue, this article compares and studies the application of various intelligent algorithms in the fault diagnosis of aviation mechanical systems. Firstly, typical intelligent algorithms such as Convolutional Neural Networks (CNN) are selected to extract features and preprocess the collected fault data;Subsequently, corresponding fault diagnosis models are constructed, and each model is trained and parameter optimized;Finally, use the model for diagnostic testing. The CNN algorithm achieved a diagnostic accuracy of 0.935000 samples after 100 iterations, with a training time of 80 seconds and an inference time of 22 milliseconds. In the above data conclusions, the CNN algorithm has shown advantages in the fault diagnosis of aviation mechanical systems, providing important references for improving the efficiency and reliability of fault diagnosis. © 2025 ieee.

关键词： Convolutional neural networks

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Computer-Assisted Knowledge Points and Question Difficulty Classification Based on BERT

Computer-Assisted Knowledge Points and Question Difficulty C...

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2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In recent years, with the increasing use of educational technology and online learning platforms, there has been a growing interest in developing intelligent systems that can automatically predict the knowledge points associated with educational questions. This paper presents a novel approach for knowledge point prediction in middle school mathematics questions. The dataset used in this study consists of a large collection of 591,379 middle school mathematics questions. To leverage the power of natural language processing techniques, the questions are preprocessed using a tokenizer and encoded into word embeddings using BERT (Bidirectional Encoder Representations from Transformers). Matrix dot production is then employed to calculate the similarity between each test question and the training set, and the top-n most similar vectors are selected for each test question. A voting mechanism is introduced to eliminate duplicate knowledge points and rank them based on their total scores in descending order, resulting in k candidate solutions. The performance of the proposed approach is evaluated using top-k accuracy as the evaluation metric and a lowest common ancestor (LCA) algorithm is used to calculate accuracy at different levels of the knowledge point tree. The results demonstrate the effectiveness of the proposed approach in predicting knowledge points for middle school mathematics questions and its potential for application in intelligent tutoring systems and educational assessment tools. © 2025 SPIE.

关键词： Natural language processing systems

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Reflecting intelligent Surface Performance Evaluation with Different Reflector Elements Operating at mmWave Frequency 3

Reflecting Intelligent Surface Performance Evaluation with D...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Desai, Priyadarshini K. Kulakarni, Keerti Bnm Institute of Technology Vtu Dept. of Ece Bangalore India

ISBN: (纸本)9798331515911

The 6G basically will be depended on mmWave band to cater the user demand. The major drawback of mmWave communication is the signal loss. The signal will get attenuated at a very shorter range. In order to overcome this, new technique called Reflective intelligent Surfaces (RIS) have been introduced. The basic purpose of RIS is to reflect back the incident radiation in the required direction with minimum signal loss. In this work performance of RIS structure when loaded with isotropic antenna elements and dipole antenna element is carried out. The spacing between the RIS unit elements and the number RIS unit element, for dipole and isotropic elements is kept constant. The obtained results shows that the signal strength, is better when RIS is loaded with dipole antenna as unit element, than the isotropic antenna elements. © 2025 ieee.

关键词： Dipole antennas

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Two-Stage Deep Energy Optimization in IRS-Assisted UAV-Based Edge computing systems

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ieee TRANSACTIONS ON MOBILE computing 2025年第1期24卷 449-465页

作者： Wu, Jianqiu Yu, Zhongyi Guo, Jianxiong Tang, Zhiqing Wang, Tian Jia, Weijia BNU HKBU United Int Coll Dept Comp Sci Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Peoples R China Beijing Normal Univ Adv Inst Nat Sci Zhuhai 519087 Peoples R China

Integrating wireless-powered Mobile Edge computing (MEC) with Unmanned Aerial Vehicles (UAVs) leverages computation offloading services for mobile devices, significantly enhancing the mobility and control of MEC networks. However, current research has not focused on customizing system designs for Terahertz (THz) communication networks. When dealing with THz communication, one must account for blockage vulnerability due to severe THz wave propagation attenuation and insufficient diffraction. The intelligent Reflecting Surface (IRS) can effectively address these limitations in the model, enhancing spectrum efficiency and coverage capabilities while reducing blockage vulnerability in THz networks. In this paper, we introduce an upgraded MEC system that integrates IRS and UAVs into THz communication networks, focusing on a binary offloading policy for studying the computation offloading problem. Our primary objective is to optimize the energy consumption of both UAVs and User Electronic Devices, alongside refining the phase shift of the IRS reflector. The problem is a Mixed Integer Non-Linear Programming problem known as NP-hard. To tackle this challenge, we propose a two-stage deep learning-based optimization framework named Iterative Order-Preserving Policy Optimization (IOPO). Unlike exhaustive search methods, IOPO continually updates offloading decisions through an order-preserving quantization method, thereby accelerating convergence and reducing computational complexity, especially when handling complex problems with extensive solution spaces. The numerical results demonstrate that the proposed algorithm significantly improves energy efficiency and achieves near-optimal performance compared to benchmark methods.

关键词： Terahertz communications Autonomous aerial vehicles Optimization Resource management Servers Computational modeling Costs Mobile edge computing deep learning unmanned aerial vehicles intelligent reflective surface terahertz communications

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Real-Time Object Detection for an intelligent Retail Checkout System 3

Real-Time Object Detection for an Intelligent Retail Checkou...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Mugada, Swathi Patil, Prachi Bhausaheb Preethi, V.J. Deeksha, K. M.S.Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India

ISBN: (纸本)9798331515911

Customers are made to wait for extended hours in busy shopping settings as traditional limits of barcode and RFID-based billing systems have a predisposition towards errors and inefficiencies in scanning untagged or weighted items. The methods that are in use today can't keep up with the demand and lead to queues at check-out counters causing customer discontentment. This paper presents an idea of a novel Smart Billing Counter, based on Object Detection using advanced deep learning, YOLOv8, and OCR technologies for smooth billing processes. It captures the image of items placed at the counter, identifies what it is with minimal error, extracts necessary information from labels concerning weighted items, and generates a bill automatically. The unique capability of this solution is to recognize identical items in one frame with total cost by quantity to optimize checkout times and minimize the possibility of errors or fraud. At an inference speed of 37.2ms per image with 93.6% accuracy, this significantly enhances operational efficiency and ensures that grocery retailers provide their customers with smooth, error-free, and swift checkout processes. This innovation is likely, therefore, to bring about a transformation in retail billing systems. It will enhance customer satisfaction and operations efficiency quite highly. © 2025 ieee.

关键词： Sales

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Research on the Application of intelligent systems in the Collection of Digital Graphics of Intangible Cultural Heritage with AI Technology

Research on the Application of Intelligent Systems in the Co...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Huang, Zhaoyang School of Design Art Xiamen University of Technology Xiamen China

ISBN: (纸本)9798331504205

This study focuses on the field of artificial intelligence (AI) and explores the application of intelligent systems in the compilation of non-material cultural heritage (NMC) pattern intelligence. Aiming to improve the efficiency and accuracy of drawing data aggregation, the paper has innovated an intelligent architecture based on generative adversarial network (GAN) to achieve automatic and accurate extraction of NMC graphic features. The first step is to tailor the deep learning program for NMC patterns and confirm its feasibility through simulation models. The system is optimized with the help of massive NMC image data, and the fluctuation of algorithm configuration on the collection effect is fully considered. Empirical data show that this intelligent platform has an accuracy of up to 97.5% in the NMC pattern capture operation, and greatly reduces the frequency of human intervention. More strikingly, in the face of the challenge of the diversity of NMC items, the system shows excellent adaptability and is suitable for the collection of a wide range of NMC graphics. Through detailed statistical analysis, the paper confirms the practical value and robust performance of the system. © 2025 ieee.

关键词： Generative adversarial networks

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Research on intelligent Fault Diagnosis and Predictive Maintenance Model in Ship Power System

Research on Intelligent Fault Diagnosis and Predictive Maint...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Guan, Yewei Hubei Communications Technical College Wuhan430202 China

ISBN: (纸本)9798331504205

This paper proposes an intelligent fault diagnosis and predictive maintenance model based on decision tree combined with random forest algorithm. Through multi-dimensional analysis of ship power system operation data, the model can effectively identify potential faults in the system and achieve accurate fault prediction. In this paper, an intelligent algorithm framework is proposed, which combines the high efficiency of decision tree with the advantage of diversity and robustness, to enhance the precision of diagnosis and forecast. The simulation experiment is tested based on the actual operation data of a certain type of ship power system. The results show that the fault diagnosis accuracy of the model reaches 98.6%, and the recall rate of fault prediction is 96.2%. Compared with traditional methods, the model reduces the diagnosis time and false alarm rate by 27% and 15% respectively. The research results verify the practicality and efficiency of the proposed model in complex power systems. The research in this paper provides a feasible technical path for the intelligent operation and maintenance of ship power systems, which has high application value and promotion potential. © 2025 ieee.

关键词： Predictive maintenance

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