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A Self-Powered Sensing System with Embedded TinyML for Anomaly Detection 3

A Self-Powered Sensing System with Embedded TinyML for Anoma...

引用

3rd IEEE international conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Chen, Zijie Gao, Yiming Liang, Junrui School of Information Science and Technology ShanghaiTech University Shanghai201210 China

ISBN: (纸本)9798350324754

In the coming Industry 4.0 era, IoT (Internet of Things) technology plays a more and more critical role. Anomaly detection is one of the essential applications for ensuring safe manufacturing, living, etc. It equips the machine with intelligence for perceiving its situation in real-time to prevent disastrous breakdown. Such monitoring systems were usually based on commercial inertial measurement units (IMU) and artificial intelligence (AI) algorithms that run on resource-rich, power-hungry servers, causing a certain amount of energy for sensing and data transmitting. In this paper, we introduce a novel self-powered sensing system with tiny machine learning (Tiny ML) technique for anomaly detection. A lightweight piezoelectric self-powered sensor (SPS) is utilized to substitute the IMU. The system runs on a low-cost embedded system, realizing the low-power in-situ inferring. A rich dataset has been collected on a vibration platform and analyzed by six well-known machine-learning models. A compressed deep neuron network (DNN) with three hidden layers achieves an accuracy of 97.6 % given only 8-point normalized SPS data. The TinyML model is then deployed on embedded systems for on-device inferring and condition-based monitoring. Power measurement is conducted to compare the systems based on an IMU and an SPS. It has shown that the proposed self-powered sensing approach can save up to 66.74% of energy. The system provides a valuable reference for realizing pervasive sensing and ubiquitous AI. © 2023 IEEE.

关键词： Anomaly detection

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A Review of the Application and Development of Virtual Reality technology in System Development 3

A Review of the Application and Development of Virtual Reali...

引用

3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Mou, Jing Chengdu Technician College China Chengdu Industry and Trade College China

ISBN: (纸本)9781510671485

In order to address the issues present in traditional system platform applications, this study aims to expand data enhancement through the design features of system platforms. By swiftly implementing a large amount of spatial data and effectively disseminating information about services in the information space, the goal is to improve work efficiency and achieve mutual benefits. Based on the current classification of systems, which includes desktop virtual reality (VR) systems, immersive VR systems, and shared VR systems, it was found that the overall framework of virtual reality systems is generally constructed through 3D modeling, scene creation, script logic design, and functional module development, enabling practical operational functionalities within the system platform. © 2023 SPIE.

关键词： Virtual reality

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Research on Question Answering System Based on BERT Model 3

Research on Question Answering System Based on BERT Model

引用

3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Yin, Jie Jiangsu University of Science and Technology School of Computer ZhenJiang China

ISBN: (纸本)9781665459112

The core of natural language processing is the science of how computers understand and respond to the influence of human language. This is also the main research direction in the field of machine intelligence development. Question answering system is one of the development directions. How to make the answer of question answering system more intelligent is the focus of research in recent years. Based on this, this thesis proposes a question answering system based on the BERT model that combines the co-attention mechanism and the self-attention mechanism. In order to better show the representation of questions and articles at their respective semantic levels, this article will choose BERT language to encode them. Attention and fusion are then performed horizontally and vertically at different levels of granularity between questions and paragraphs. Experiments show that the model has an average EM of 46.56 and an average F1 of 58.90 on all datasets. The average F1 for the development dataset is 54.85, and the average F1 for the test dataset is 62.95. The content answered by the question answering system based on the BERT model is basically reasonable, and the intelligence of the question answering system is effectively improved. © 2022 IEEE.

关键词： Semantics

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Predictive Analytics and AI for Personalized Treatment Plans in Genetic Heart Diseases 3

Predictive Analytics and AI for Personalized Treatment Plans...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Yadav, Surjeet Mp, Sunil. Yadav, Dhananjay Kumar Vivekananda Global University Department of Mechanical Engineering Jaipur India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka562112 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

Hereditary cardiac conditions, which are the focus of this abstract, pose a serious problem for medical science. The article 'Predictive Analytics and AI for Personalized Treatment Plans in Genetic Heart Diseases' explores novel techniques that utilize the potential of predictive analytics and artificial intelligence (AI) to revolutionize the diagnostic and therapeutic landscape of cardiac illnesses. In this paper, we discuss how predictive analytics and AI may be used to design individualized therapies for hereditary heart problems. We take a look at the influence that genetic variants have on illness manifestation and how typical one-size-fits-all treatments fall short. Data-driven insights, risk classification, and prognostic powers are just a few of the ways in which predictive analytics is ready to transform the industry. Together with artificial intelligence (AI), which can mimic human intellect, they hold the possibility of customizing treatment strategies with unparalleled accuracy. In this article, we'll go over the value of genetic profiling and how it's being used in conjunction with machine learning algorithms to forecast things like illness susceptibility and response to therapy. The moral and legal implications of using AI in medicine are also discussed. The confidence and privacy of our patients are protected by our emphasis on ethical procedures and data protection. Predictive analytics and artificial intelligence have already started to improve the treatment of hereditary heart disorders, as evidenced by case studies and success stories. The applications are as varied as the genetic differences themselves, ranging from early illness prediction to individualized pharmaceutical regimes and surgical procedures. Predictive analytics and artificial intelligence are merging to change how we think about inherited heart problems. Readers will walk away from this piece with a solid grasp of how predictive analytics and AI might influence the future of indivi

关键词： Machine learning

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Vibration Analysis and Test of Working Arm of the Anchor Drilling robot 17th

Vibration Analysis and Test of Working Arm of the Anchor Dri...

引用

17th international conference on Intelligent robotics and applications

作者： Zhou, Xu You, Qihang Wang, Xin Zhang, Licai An, Siyuan Zhu, Aibin Northwestern Polytech Univ Sch Mech Engn Xian Shaanxi Peoples R China Shanxi Tiandi Coal Min Machinery Co Ltd Taiyuan Shanxi Peoples R China Changan Univ Sch Construct Machinery Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9789819607709;9789819607716

Aiming at the problems of complex and harsh working environments and low reliability of the working arm of the anchor drilling robot, the dynamic characteristics of the working arm are studied based on the dynamic simulation analysis and vibration test analysis. In view of the influence of working load and deformation on the rigidity of the working arm, the static finite element analysis is implemented by simulating the actual working conditions. The stress and strain distribution of the working arm under the roof anchor drilling condition is derived, and on this basis, the modal analysis of the working arm with pre-stress is carried out. The six orders of vibration mode and the corresponding inherent frequencies are obtained. A vibration test system of the working arm is set up to achieve the three-dimensional vibration accelerations of the working arm under the roof anchor drilling condition. The fast Fourier transform of the vibration acceleration signals is conducted to obtain the vibration spectrum. The dynamic characteristics of the working arm are analyzed through the vibration frequency and the inherent frequency, which is of great reference value for improving the reliability of the working arm.

关键词： Working arm Anchor drilling robot Vibration analysis

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Research on Encrypted Network Traffic Recognition Based on Neural Architecture Search Network 24

Research on Encrypted Network Traffic Recognition Based on N...

引用

3rd international Symposium on robotics, Artificial intelligence and Information Engineering, RAIIE 2024

作者： Wang, Yongsheng Wei, Xiaoxu Chen, Zhenwei Dong, Bin School of Information Engineering Wuhan University of Technology Wuhan430070 China School of Automotive Engineering Wuhan University of Technology Wuhan430070 China Wuhan Greenet Information Service Co. Ltd. Wuhan430070 China

ISBN: (纸本)9798400718311

Encrypted network traffic not only protects user privacy and information security, but also brings hidden dangers to network security. It is of great significance for network maintenance and operation to identify and classify the characteristics of encrypted network traffic effectively. This paper proposes a mobile application identification and classification engine based on neural architecture search network, which adopts NASNet network structure and can realize the identification of mobile application encrypted traffic from the perspective of OSI network layer. The engine can further perceive the service quality of users' Internet experience, and then evaluate the overall quality of the network quantitatively. The experimental results show that the NASNET-based neural network can identify encrypted traffic with an average accuracy of 99%, and has the characteristics of high accuracy rate, high recall rate and high F1-score, which can effectively deal with the security of encrypted traffic in mobile applications. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Network layers

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Design and Implementation of Speech Signal Feature Extraction Algorithm Based on MATLAB 3

Design and Implementation of Speech Signal Feature Extractio...

引用

3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Yang, Jianli Yang, Long Chen, Yin Chongqing University of Technology Chong Qing400056 China

ISBN: (纸本)9781643684444

Speech signal is a widely-used type of information, with the development of artificial intelligence, the processing algorithms of speech signals is also rapidly evolving. The key to speech signal processing is to extract the feature information of speech signals. This paper extracts the fundamental period and LPC parameters of speech, and detects the resonance peak information of speech signals by focusing on real-time collected speech signals and related preprocessing. Based on understanding the principles of typical algorithms, the programming implementation and algorithmic results are provided, which can be applied to many places where speech signals are used, such as speech compression, speech recognition, and speech synthesis. © 2023 The Authors.

关键词： Feature extraction

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Integrating Sustainable Energy Systems and Artificial intelligence: Transforming the Future of Energy 3rd

Integrating Sustainable Energy Systems and Artificial Intell...

引用

3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Kumar, Raman Kaur, Rupinder Gitanjali Channi, Harpreet Kaur Kaur, Karneet Department of Mechanical and Production Engineering Guru Nanak Dev Engineering College Punjab Ludhiana141006 India Department of Information Technology Guru Nanak Dev Engineering College Punjab Ludhiana141006 India Department of Electrical Engineering Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9789819783540

Integrating sustainable energy systems (SESs) and artificial intelligence (AI) has emerged as a promising approach to address the energy sector's challenges and accelerate the transition to a sustainable future. This review presents the key concepts, implications, and potential applications of integrating SESs and AI. It explores various renewable energy sources (RESs) such as solar, wind, hydro, geothermal, and biomass, highlighting their benefits and advancements in AI integration. It examines the emerging trends and opportunities in integrating AI and SESs. Policy, research, and development implications are discussed, emphasizing the need for supportive regulatory frameworks, research advancements, and stakeholder collaboration. In the end, it highlights the transformative potential of this integration. It calls for concerted efforts to harness its benefits and drive the global energy transition toward a more sustainable and intelligent future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy transition

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Research on ground information identification method for far field remote sensing images based on transfer learning 3

Research on ground information identification method for far...

引用

3rd international conference on Geographic Information and Remote Sensing technology, GIRST 2024

作者： Yang, Ximiao Wang, Dong Liu, Mingming Shen, Boyang Zhao, Hui Wang, Liangshun School of Management Beijing Institute of Technology Beijing China College of Instrumentation and Science Southeast University Nanjing China Beijing Institute of Technology Beijing China Forestry College Shanxi Agricultural University Shanxi Taiyuan China China CITIC Bank Innovation Laboratory Beijing China Hainan University Hainan Haikou China

ISBN: (纸本)9781510689077

In recent years, with the vigorous development and widespread application of artificial intelligence (AI) technology, AI has empowered remote sensing technology, significantly shortening the cycle of remote sensing image interpretation, improving accuracy, and promoting the transformation of remote sensing technology applications. However, the interpretability and generalization transfer ability of some existing models are average, and the results have obvious limitations. This article focuses on the problem of ground information interpretation based on satellite remote sensing images, and conducts research on hyperspectral image classification methods based on EfficientNet deep learning. Furthermore, the EfficientNet transfer learning model is optimized based on an improved GJO. While extracting shallow features such as edges and textures, the problem of high intra class differences and low inter class differences in hyperspectral remote sensing images is effectively addressed, which can effectively improve the efficiency of satellite remote sensing image land recognition. Finally, the comparative classification experiment of deep transfer learning also proves the progressiveness of the method proposed in this paper. © 2025 SPIE.

关键词： Contrastive Learning

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An IOT-based Language Recognition System for Indigenous Languages using Integrated CNN and RNN 3

An IOT-based Language Recognition System for Indigenous Lang...

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3rd international conference on Smart Data intelligence, ICSMDI 2023

作者： Cerna, Patrick D. Ututalum, Charisma S. Evangelista, Ryan S. Darkis, Aldaruhz T. Asiri, Masnona Sabdani Muallam-Darkis, Jehana A. Northern Negros State College of Science and Technology Sagay City Philippines Sulu State College Suc President Ii Sulu Philippines Graduate School / Institute of Technology PolyTechnic University of the Philippines Philippines School of Computer Studies and Engineering Sulu State College Sulu Philippines School of Graduate Studies Sulu State College Sulu Philippines

ISBN: (纸本)9781665464871

Automatic Speech Recognition (ASR) aims to establish communication between humans and computers in a more natural way. The main aim of this study is to build hardware-based automatic speech recognition for Indigenous People (IP)'s ancestral dialects, in particular for Manobo, Mandaya, and B'laan using Raspberry Pi. Jasper is an open source toolkit used for creating voice-activated, always-on applications. The researcher recording audio data from research participants, the study's participants will be located in Davao Occidental and Sarangani for B'laan, Agusan Del Sur for Manobo, and Davao Oriental for Mandaya. A functional microphone and raspberry pi boards serve as the experiment's hardware where audi o input is being fine-tuned from a raspberry pi-powered device that records audio in waveform format, which includes Mandaya, Manobo, and Malita words and phrases. The Tensorflow STFT technique will be used to analyze, generate, transform, and characterize audio signals. JiWER plugins for Similarity measures will also be used The WER output is 98.53%, an acceptable percentage for the number of datasets used © 2023 IEEE.

关键词： Speech recognition

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