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AI-based Image processing Technique for Dielectrophoresis (DEP) in BioMEMs Applications 16

AI-based Image Processing Technique for Dielectrophoresis (D...

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16th IEEE International conference on Semiconductor Electronics (ICSE)

作者： Tee, Clarence Augustine T. H. Majlis, Burhanuddin Yeop Li, Sheng Yu, En Hao Buyong, Muhamad Ramdzan Song, Le Yeo, W. P. Zain, Ahmad Rifqi Md Zheng, Yelong Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Zhejiang Peoples R China Univ Kebangsaan Malaysia Inst Microengn & Nanoelect IMEN Ukm Bangi 43600 Selangor Malaysia Zhejiang Inst Optoelect Jinhua 321004 Zhejiang Peoples R China Zhejiang Normal Univ Photon & Sensors Key Res Lab Jinhua 321004 Zhejiang Peoples R China Tianjin Univ State Key Lab Precis Measurement Technol & Instru Tianjin 300072 Peoples R China

ISBN: (纸本)9798350378313

The integration of artificial intelligence (AI) methodology in the flexible electronics, BioMEMs and Dielectrophoresis (DEP) system provides an accurate, comprehensive, simple and cost-effective approach especially in the real time cell sorting, target identification, monitoring, biological entities separation, analysis and postulation. Here, a novel approach integrating AI algorithms with DEP technology for BioMEMs system has been demonstrated in real-time bacteria identification via detecting, identifying simultaneously, lock-in monitoring bacterial movement trajectories, analyzing, measuring and calculating bacteria's velocities of different frequencies. The AI-based bacterial recognition system creates real-time, fast, accurate, unlimited bacteria lock-in monitoring with high accuracy. Furthermore, the bacteria can be tracked, monitored, and analyzed using big data analysis and Deep Learning (DL) in real-time via a visual control interface system, as part of the Internet of Things (IoT) and intelligent information sharing between BioMEMS sensors and IoT networks.

关键词： AI-based fractional-order differential algorithm Dielectrophoresis (DEP) Image processing BioMEMs Artificial Intelligence-Deep Learning (AI-DL)

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sensor Automation for Industrial Applications and Prediction of Energy Level for Home Appliances Using Machine Learning Algorithm 9

Sensor Automation for Industrial Applications and Prediction...

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9th International conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Selvakumari Jeya, I Jasmine Niranjani, V. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

In recent years wireless sensor networks have helped with automation in many industry domains. Wireless technology cuts cable costs, deployment time and information to transfer and process from source to destination. To make good intelligent and highly automated systems, sensors are crucial. They make it possible to detect, analyze, measure, and process a variety of alterations that occur in industrial equipment's location, length, height, appearance, displacement, etc. This paper described the industrial standardization of different WSN protocols like Zig-Bee, Wireless HART, UWB, IETF 6LowPAN, ISA100, Bluetooth and Wi-Fi. The work represented a few applications of WSNs in industry and its implementation challenges like safety, security, and availability. The work collected the energy consumption data of different home appliances. We applied the LightGBM machine learning algorithm to find a better energy consumption and prediction result. © 2023 IEEE.

关键词： Energy utilization

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Thermal Imaging Gesture Recognition Method Based on Binary Neural Network 4

Thermal Imaging Gesture Recognition Method Based on Binary N...

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4th International conference on Signal processing and Computer Science, SPCS 2023

作者： He, Jinlei Liu, Junlei Ou, Hanwen Tian, Liangliang Chongqing Three Gorges University School of Electronic & information Engineering Chongqing404100 China Chongqing University of Arts and Sciences School of Electronic Information and Electrical Engineering Chongqing402160 China

ISBN: (纸本)9781510672505

Due to the complexity of environmental factors, such as background and lighting, traditional vision-based gesture recognition algorithms tend to be less robust. Similarly, gesture recognition algorithms based on neural networks have the drawbacks of large computational requirements and high storage parameters, thereby hindering effective deployment. This paper proposes a novel gesture recognition algorithm based on infrared thermal imaging sensor, which effectively addresses the above challenges. By collecting thermal imaging gesture data and performing preprocessing, an improved binary neural network is trained and deployed on a microprocessor device. Results show that the proposed binary neural network achieves a recognition rate of 99% for three gestures, and 95% for five gestures. Compared with convolutional neural networks, which have higher computational requirements and more model storage parameters, the proposed algorithm achieves a frame rate of 44FPS on STM32F4 microprocessor and 366FPS on H7, highlighting its potential as an effective gesture recognition technology using thermal imaging sensors. © 2023 SPIE.

关键词： Gesture recognition

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The Multisource information Fusion and intelligent Diagnosis Algorithm in an Integrated Air-Ground-Space Power Transmission Line Monitoring System 3

The Multisource Information Fusion and Intelligent Diagnosis...

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3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Li, Zhiyu Liu, Jinhu Zeng, Xiao Liu, Junhong Wang, Qiang Ruan, Bei Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Zhuhai519000 China

ISBN: (纸本)9798331519032

Aiming at the shortcomings of traditional monitoring methods in monitoring scope, real-time and accuracy, this paper proposes an Air-Ground-Space monitoring system which integrates satellite remote sensing, unmanned aerial vehicle inspection and ground sensors. The system realizes all-round and multi-level monitoring of transmission lines through Multisource information Fusion (MIF) technology, which significantly improves the comprehensiveness and accuracy of monitoring data. In this paper, a multi-modal fusion algorithm based on deep learning is designed, which can integrate data from different dimensions and modes, and extract more comprehensive and accurate transmission line state information through feature extraction and fusion module. The algorithm designs feature extraction networks on satellite remote sensing data, UAV inspection images and ground sensor data respectively, and introduces attention mechanism for adaptive fusion of cross-modal information. The experimental results show that the multimodal fusion algorithm is significantly superior to a single data source in monitoring accuracy, with an average monitoring accuracy of 94.74%. Support vector machine (SVM) is used as the classification algorithm to classify the fused multi-source information, and the accurate identification of transmission line faults is realized. The experimental data show that the algorithm has high accuracy and recall in identifying broken wires, damaged insulators and icing faults, which is significantly superior to the traditional machine learning algorithm. In addition, the hardware and software platform of the Air-Ground-Space transmission line monitoring system is built, and the system integration and performance evaluation are carried out. The experimental results show that the system can receive and process information from different data sources in real time, and give diagnosis results quickly, which meets the requirements of real-time monitoring. The system has obviou

关键词： Support vector machines

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7th International Russian Automation conference, RusAutoCon 2024

7th International Russian Automation Conference, RusAutoCon ...

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7th International Russian Automation conference, RusAutoCon 2024

ISBN: (纸本)9783031824937

The proceedings contain 37 papers. The special focus in this conference is on Russian Automation. The topics include: Mapping and Path Planning Methods for Highly Automated Vehicles in Agriculture;morphological Analysis and Synthesis Features of Technological Processes;a Modern Method of Wireless Control of Unguarded Railway Crossings;duties and Obligations of the Railway Staff Concerned When the Microprocessorized Contactless Controlling Gauge Device Signal Is Triggered;integration of Automated Management Systems for Enterprises’ Transport and Technical Services;development of an Automation Module for Planning Trajectories for Painting Aircraft Fuselage Elements;identification of Metal Sheets in the Flow, Based on the Marking Imprint, Using Neural networks;study of the Performance of Adaptive sensor networks for Collecting and processing Thermoelectric Data;development and Research of a Cartographic Model for Municipal Planning as the Basis of an intelligent Geoinformation System;BIM Visual Programming of Bionic Architecture Construction Using Dynamo and Revit;development of a Model and Algorithms for Trigger Control of Technological Processes of Resource Provision;system for Statistical Assessment of Means of Controlling Engineers’ Qualification for information Support to Manage the Recruiting Process in Industry;smart Enumeration Technology;comparative Analysis of C-Band Conical Horn Antenna Sparse Structures Characteristics at Different Frequencies;software Implementation of Heuristic Methods of Optimization and Integration into a Cloud Service;computer System for Modeling Fluid Flow Around Bodies and Its Potential in Industry;the Effect of Regular and Irregular Segmentations on Characteristics and Charge Distribution Densities of Microstrip Lines;reinforcement Learning with External Teacher for Building Energy Management.

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Vehicle information Monitoring and Identification on Highway Bridges Using an Array of Fiber Bragg Grating sensors and Deep Learning Algorithms 4

Vehicle Information Monitoring and Identification on Highway...

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4th International Computational Imaging conference, CITA 2024

作者： Duan, Zhenhui Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Liu, Zhaoying Shen, Yongxu Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510688834

Traditional traffic management methods are increasingly inadequate in coping with the growing pressure on traffic systems. Consequently, there is a need for a more intelligent and efficient approach to monitoring and identifying vehicle information. Current vehicle monitoring methods often suffer from environmental interference and limited coverage.. To address these challenges, we propose a novel method for monitoring and identifying vehicle information on bridges using an array of Fiber Bragg Grating (FBG) strain sensors, combined with a hybrid deep learning algorithm based on Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM). Strain data were collected from a continuous hollow slab beam on a section of a highway bridge. Vehicles were classified and labeled using on-site video data to create a dataset, which was subsequently used to train and test the proposed CNN-LSTM model. The results demonstrate that the proposed method can accurately identify and classify the lane position, vehicle type, and vehicle speed category, achieving detection accuracies of 94.0%, 93.11%, and 90.67%, respectively. The DWT-CNN-LSTM method, utilizing the FBG strain sensor array, leverages existing strain monitoring platforms, supplements current traffic monitoring systems, and offers high efficiency and cost-effectiveness. This approach provides valuable data references for management and statistical analysis. © 2025 SPIE.

关键词： Discrete wavelet transforms

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Dynamic Domain ID Based on DDS for AIoT

Dynamic Domain ID Based on DDS for AIoT

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2023 International conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Su, Weibin Machica, Ivy Kim D. Dumdumaya, Cristina E. Gamboa, Randy S. Xu, Gang He, Zhengfang College of Information and Computing University of Southeastern Philippines Davao City8000 Philippines Edge Computing Network Engineering Research Center Yunnan Technology and Business University Kunming651701 China

ISBN: (纸本)9798400708268

A recent development in the field is the emergence of a novel Internet of Things (IoT) framework known as the Artificial Intelligence of Things (AIoT). Based on this framework, it can enable smart autonomous vehicles, unmanned aerial vehicles, and intelligent industrial production lines to effectively reduce costs and expand the scope of sensor perception by utilizing collaborative sensor networks and shared computing resources provided by edge computing. The sensor network formed by traditional internet has successfully achieved global unified wide area connectivity. However, for a certain control object, sensors that are far away from it do not need to participate in the control process, while local existing sensors fail due to the movement of the control object in a specific direction. So this requires a new type of network that can dynamically update the domain. To enhance the real-time responsiveness of regional communication involving controllers and controlled devices, it is essential for the network to establish dynamic local connections that adapt to the spatial location changes of cars, unmanned aerial vehicles, or production line logistics. As a response to this requirement, this paper introduces a novel edge computing distributed communication network and a dynamically updated domain identity architecture. Every endpoint node has the capability to autonomously form a local cluster based on its spatial location for signal transmission. By dynamically polling and accessing this cluster, it achieves close sharing of sensors and AI computing power, leading to a cost reduction in building a comprehensive intelligent control system compared to traditional single-machine AI systems. This holds significant potential for advancing the implementation of comprehensive AI projects. © 2023 Copyright held by the owner/author(s).

关键词： Edge computing

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OIDM: An Observability-based intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical Systems 63

OIDM: An Observability-based Intelligent Distributed Edge Se...

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63rd IEEE conference on Decision and Control, CDC 2024

作者： Wang, Shigeng Jin, Tiankai Ma, Yehan Chen, Cailian Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350316339

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness. © 2024 IEEE.

关键词： Stochastic systems

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DRLIC: Deep Reinforcement Learning for Irrigation Control 21

DRLIC: Deep Reinforcement Learning for Irrigation Control

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21st ACM/IEEE International conference on information processing in sensor networks (IPSN)

作者： Ding, Xianzhong Du, Wan Univ Calif Merced Merced CA 95343 USA

ISBN: (纸本)9781665496247

Agricultural irrigation is a major consumer of freshwater. Current irrigation systems used in the field are not efficient, since they are mainly based on soil moisture sensors' measurement and growers' experience, but not future soil moisture loss. It is hard to predict soil moisture loss, as it depends on a variety of factors, such as soil texture, weather and plants' characteristics. To improve irrigation efficiency, this paper presents DRLIC, a deep reinforcement learning (DRL)-based irrigation system. DRLIC uses a neural network (DRL control agent) to learn an optimal control policy that takes both current soil moisture measurement and future soil moisture loss into account. We define an irrigation reward function that facilitates the control agent to learn from past experience. Sometimes, our DRL control agent may output an unsafe action (e.g., irrigating too much water or too little). To prevent any possible damage to plants' health, we adopt a safe mechanism that leverages a soil moisture predictor to estimate each action's performance. If it is unsafe, we will perform a relatively-conservative action instead. Finally, we develop a real-world irrigation system that is composed of sprinklers, sensing and control nodes, and a wireless network. We deploy DRLIC in our testbed composed of six almond trees. Through a 15-day in-field experiment, we find that DRLIC can save up to 9.52% of water over a widely-used irrigation scheme.

关键词： Irrigation Soil measurements Moisture measurement Current measurement Wireless networks Soil moisture Weather forecasting

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Short-Text Semantic Similarity Model of BERT-Based Siamese Network 23

Short-Text Semantic Similarity Model of BERT-Based Siamese N...

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10th International conference on Wireless Communication and sensor networks, icWCSN 2023

作者： Jiang, Haoyu College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781450398466

People convey their emotions and thoughts through words, the medium of human thoughts. Up against the vigorous development of streaming media, the calculation of text similarity is imperative in the field of natural language processing. Any text-related field is inseparable from text semantic similarity. The calculation of text semantic similarity plays a key role in document management, document classification, and document relevance. Besides, popular natural language processing tasks in some trendy fields, such as artificial intelligence, human-machine translation, problem system, intelligent chat system, and nomenclature recognition, are intertwined with text semantic similarity calculation. In recent years, many excellent researchers have studied the algorithms and models of text semantic similarity from different dimensions. In this paper, a new short-text cosine similarity calculation model of the BERT-based Siamese network is proposed. © 2023 ACM.

关键词： Semantics

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