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Neural Network Technology of Mine Recognition Based on data From Magnetic Field Sensors

Neural Network Technology of Mine Recognition Based on Data ...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Roman Peleshchak Vasyl Lytvyn Ivan Peleshchak Serhii Voloshyn Department of Information Systems and Networks Lviv Polytechnic National University Lviv Ukraine

Mine detection is an ongoing and growing problem that affects many people around the world due to the enormous danger that mines pose to humans. Mine detection occurs using various methods that use active sensors, including ultra-wideband (UWB) radar. Although active landmine sensors are often used for performance reasons, they can cause unintended landmine detonations because they operate on transmitted and reflected electromagnetic signals. Another way to detect mines without detonating mechanisms is the use of passive detectors. In this article, an optimal neural network structure with Adam's optimization algorithm is developed for effective mine classification based on magnetic field sensor data. This approach made it possible to increase the accuracy of mine detection to 99.23 percent.

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Innovative System for Environmental Monitoring with Connected Sensors and data Analysis for Chemical Pollutants

Innovative System for Environmental Monitoring with Connecte...

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2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Suganya, A. Suryawanshi, Mahesh A. Bagal, Manisha V. Rawat, Ruchira Kaliappan, S. Manikandan, G. Karpagam College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Bharati Vidyapeeth College of Engineering Department of Chemical Engineering CBD Navi Mumbai India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Division of Research and Development Punjab Phagwara India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331539948

The Chemical Pollutants system, which combines sophisticated data processing capabilities with networked sensors to identify and control chemical pollutants, transforms environmental monitoring. It collects real-time data on pollutants using extremely accurate sensors, which are then evaluated using cloud infrastructure and a reliable wireless connection. Predictive insights using machine learning algorithms provide notable improvements: 97% accuracy in sensor data, 3 seconds of data transmission latency, and 99.5% system uptime. There are now 1200 daily active users, and the prediction model's accuracy is 92%. Environmentally, it resulted in a 30% decrease in PM2.5 levels, a halving of high pollution alerts to 10 per month, and an 85% increase in public awareness. The number of policy changes tripled to four each year, while community engagement quadrupled to twelve. The results highlight the system's contribution to improving the timeliness and quality of environmental data, influencing public health and policy, and highlighting its efficacy and potential for wider environmental management use. © 2024 ieee.

关键词： Volatile organic compounds

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Justification of the Project Initiation Process Using the Markov Model

Justification of the Project Initiation Process Using the Ma...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Kateryna Kolesnikova Nursultan Alpysbay Tetiana Olekh Tolganay Chinibayeva International IT University Almaty Kazakhstan Odessa National Polytechnic University Odesa Ukraine

The paper presents a Markov model of the basic scheme of interaction between project participants. A Markov chain has been identified to describe a specific project, based on the determination of a set of transition probabilities unique for each project. This allows you to customize the Markov chain to reflect the properties of a particular project. Using the developed Markov model, the sensitivity of the system to a change in one of the main parameters, the level of competence of the project manager and the project team as a whole, was assessed. The mathematical description of the project model by Markov chains allows modeling the quantitative parameters of projects, namely, changes in the probabilities of system states depending on the number of project steps.

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Mutual Information based Efficient Spike Encoding on FPGA 36

Mutual Information based Efficient Spike Encoding on FPGA

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36th international conference on VLSI Design (VLSID) / 22nd international conference on Embedded systems (ES)

作者： Gundoji, Omkareshwar Banerjee, Dighanchal Dey, Sounak Pal, Arpan TCS Res Embedded Devices & Intelligent Syst Kolkata India

ISBN: (纸本)9798350346787

Spiking Neural Networks (SNN) coupled with Neuromorphic computing is considered as a bio-plausible, power and data-efficient AI/ML technique especially suitable for resource-constrained edge devices such as robots, satellites, automated cars etc. While the performance of an SNN often suffers due to lossy spike encoding of real valued data, its implementation on actual hardware is also limited due to commercial unavailability of neuromorphic chips. While on one hand, better encoding techniques are being improvised, on the other hand, FPGAs (instead of ASICs) are nowadays considered as a plausible alternative for hardware implementation. In this paper, we have shown how a Mutual Information based information maximization technique for spike encoding can be successfully implemented on a Xilinx Zynq ZedBoard. The implemented system is found to perform at par with software simulation w a minimal error of 0.014 when tested on four different univariate time series. When running at a frequency of 200MHz, the system is observed to consume 0.018 watts (dynamic power), have a latency of 10.35 ms and a throughput of 96 samples/sec. Moreover, the FPGA-based system has the advantage of being used as a plug-and-play encoder device for any neuromorphic computing platform.

关键词： Spiking Neural Network Neuromorphic computing Spike Encoding FPGA Rate Encoder Mutual Information Information Theory

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Adaptive Federated Learning for Efficient Network Traffic Management in Edge computing 4

Adaptive Federated Learning for Efficient Network Traffic Ma...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Babu, Kuricheti Suresh Venkatasubramanian, S. Kishore, S. Sree, K. Kavya Ramya Susanna, Ch.Lavanya Rao, Goda Srinivasa Lakkshmanan, Ajanthaa Pace Institute of Technology and Sciences Department of Computer Science and Engineering AndhraPradesh Valluru India Saranathan college of engineering Department of Computer Science and Business Systems Trichy India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Aditya University Department of Ai&ml Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Kl University Department of Cse Vaddeswaram Andhra Pradesh Guntur India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603 203 India

ISBN: (纸本)9798331529635

With the increasing prevalence of edge computing environment, there is a need to establish effective network traffic management that are able to manage vast data while also protecting the privacy of users. The proposed method enhances efficiency of bandwidth, reduce the network latency and manages computational demands without managing data privacy. This is achieved by utilizing the design of edge devices. The adaptable nature of the method enables it to react in real time to different traffic situations. It optimizes the distribution of resources and improves the overall performance of the network. Additionally, it can reduce the possibility of centralized data exposure, providing robust privacy preservation through the allowance of local model training on edge devices. Finally, this method is superior. The employment of federated learning to network traffic management provide efficiency to optimize the traffic in edge computing which is both scalable and safe. This makes it suitable for a wide variety of applications that are utilized in next generation networks © 2024 ieee.

关键词： Federated learning

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A Defect Detection Algorithm of Round Tablets Based on Image Information 9

A Defect Detection Algorithm of Round Tablets Based on Image...

引用

9th international conference on intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Lianqi Zhou, Rui Sun, Yuan Chen, Xiaojin Yang, Zhiwang College of Electrical Engineering and Automation Xiamen University of Technology Fujian Xiamen China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Fujian Xiamen China

ISBN: (纸本)9798350376548

This paper presents a novel circular tablet defect detection algorithm based on machine learning techniques to address the challenge of insufficient data for training samples. Utilizing cameras, lenses, and light sources, we establish an image acquisition hardware system for capturing tablet images, and on the basis of image preprocessing, the designed defect detection algorithm is used for detection, forming a complete and efficient round tablet defect detection process. The experimental results have been demonstrated that the performance of the sample missed detection rate was 0.2%, the false detection rate is 0.6%, and the total error rate is 0.8%, all lower than 1%. The proposed detection algorithm does not require sample training and can be well applied to the identification of defects such as abnormal tablet circular areas and foreign matter on the surface of tablets. © 2024 ieee.

关键词： Detection algorithm Image information Tablet defects

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Planetary Defense Using Machine Learning Early Warning systems for Hazardous Asteroids

Planetary Defense Using Machine Learning Early Warning Syste...

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

作者： Sandeep, Avala Reddy, Panta Jeevan Perkin, T. Madhu Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

Within the dynamic field of planetary defence, machine learning has emerged as a key component that is essential to early warning systems that are tasked with forecasting the orbits and trajectories of potentially dangerous asteroids. Through the application of a broad range of sophisticated algorithms, such as logistic regression, XGBoost, decision trees, k-nearest neighbours (KNN), and multi-layer perceptron (MLP), a group of committed scientists are hard at work developing reliable models that will be crucial in classifying asteroids into different orbit classes. These classifications, particularly the potentially dangerous asteroids and Near-Earth Objects (NEOs), provide us with important information that helps us determine risk levels and develop practical mitigation plans. Thus, the combination of astronomy and artificial intelligence has enabled a significant advancement in our ability to examine the data and features of these celestial planets, providing insight into their mysterious motions and any possible risks. With this innovative method, we may both advance our knowledge of the dynamic universe and improve our capacity to protect Earth from the impending threat of asteroidal collisions. In a time when space travel and the secrets of the cosmos entice us with both wonder and danger, this marriage of science and technology serves as a symbol of human ingenuity and our commitment to safeguarding our home planet. © 2024 ieee.

关键词： Decision trees

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Design and research of Red Boat Park intelligent energy management system based on source-grid-load-storage coordination 2

Design and research of Red Boat Park intelligent energy mana...

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2nd international conference on advanced Electronics, Electrical and Green Energy, AEEGE 2023

作者： Zhao, Jiazhen Chen, Jieguang Wang, Fa Xu, Weiming Jiaxing HengChuang Electric Power Design and Research Institute Co. Ltd. Jiaxing China

ISBN: (纸本)9798350322866

To accelerate the diversification, cleaning, and decarbonization of the energy supply while increasing efficiency, reducing consumption, and promoting electrification, it is urgent to carry out 'zero-carbon' demonstration work in the energy sector. This paper investigates the data integration status and energy demand of the Energy Internet at both the supply and consumption links, using the Jiaxing Red Boat Base Zero-Carbon Park as a case study. A system architecture is designed to integrate massive data from the power side, grid side, load side, and energy storage side, utilizing IoT data acquisition and big data analysis technologies. The functional modules of the system are developed and tested, combining the functional requirements of 'low carbon, safety, and economy' dispatching business. Various demand response strategies are implemented, promoting flexible resource allocation and creating a 'source-grid-load-storage coordination' zero-carbon demonstration area. © 2023 ieee.

关键词： Parks

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Design and implementation of FC-AE-ASM data acquisition and forwarding system 7

Design and implementation of FC-AE-ASM data acquisition and ...

引用

7th international conference on intelligent computing and Signal Processing, ICSP 2022

作者： Wang, KuiSheng Zhang, Yao Wang, Li Ning, YueJie School of Computer Science Xi'an Shiyou University Xi'an China Airborne Network Bus Xi'an Xiangteng Microelectronics Technology Co. Ltd Xi'an China

ISBN: (纸本)9781665478571

The FC-AE-ASM (Fibre Channel-Avionics Environment-Anonymous Subscriber Message) data acquisition and forwarding system collects and forwards FC link data. As it's core and foundation, the software provides a strong basis for real-time monitoring of FC communication status, which will directly affect the performance of the whole system. In this paper, based on the analysis of FC-AE-ASM fiber channel protocol, the software function and architecture design are proposed, and the design idea of data acquisition and forwarding is introduced emphatically. The software and system hardware can meet the requirements of high bandwidth fiber channel data acquisition and forwarding. © 2022 ieee.

关键词： data acquisition

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ImageFed: Practical Privacy Preserving Intrusion Detection System for In-Vehicle CAN Bus Protocol 9

ImageFed: Practical Privacy Preserving Intrusion Detection S...

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9th ieee international conference on Big data Security on Cloud (BigdataSecurity) / 9th ieee international conference on High Performance and Smart computing (ieee HPSC) / 9th ieee international conference on intelligent data and Security (ieee IDS)

作者： Taslimasa, Hamideh Dadkhah, Sajjad Pinto Neto, Euclides Carlos Xiong, Pulei Iqbal, Shahrear Ray, Suprio Ghorbani, Ali A. UNB Fac Comp Sci Fredericton NB Canada Natl Res Council Canada Cybersecur Ottawa ON Canada Natl Res Council Fredericton New Brunswick NJ USA

ISBN: (纸本)9798350312935

In vehiclenetworks handle the communication among Electronic Control Units (ECUs) and sensors with a serial protocol called Controller Area Network (CAN), which takes advantage of a broadcast mechanism liar transferring data. However, the lack of authentication and encryption in CAN bus might lead to the vehicle being compromised by an untrusted agent. Meanwhile, AI -based Network Intrusion Detection systems (NIDS) introduced in the literature require a centralized cloud server to undertake the resource -consuming task of processing network data, which also occupies the bandwidth while transferring data from vehicles to the remote server. Moreover, many car manufacturers avoid sharing information with the remote server to prevent further exposure of vehicles' private data. We utilize Federated Learning (FL) as a suitable solution to previous challenges, where data is trained locally on the vehicle side, and partially trained models are sent to the server. Nevertheless, private training inside the vehicle requires low memory utilization while achieving low latency and high performance compared to Centralized Learning (CL) approaches. In this study, we propose a practical privacy-preserving IDS (ImageFed) approach by employing federated Convolutional Neural Network (CNN). To evaluate the robustness of ImageFed in real -world applications, we investigate two scenarios that may cause performance degradation in FL, such as non-independent and identically distributed (non-iid) clients and scarcity of training data during the process. The resulting analysis proves the robustness of ImageFed while delivering an average 99.54% fl -score, 99.87% accuracy, and low detection latency.

关键词： Terms Internet of Vehicles (IoV) Security Intrusion Detection systems (IDS) Federated Learning (FL) I)eep Learning (DL) Convolutional Neural Networks (CNN)

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