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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jia-Ming Liang Shashank Mishra You-Jian Ma Ping-Jui Yang Ming-Ren Ou Chun-Wen Ku Jyun-You Huang Jen-Jee Chen Department of Electrical Engineering National University of Tainan Tainan Taiwan Department of Computer Science and Engineering University of Minnesota Minneapolis USA Institute of Computational Intelligence National Yang Ming Chiao Tung University Tainan Taiwan

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Electrical equipment is essential for factories, offices, and daily life. Neglecting its proper maintenance can result in fires or hazardous accidents. Currently, traditional electrical plugs can only confirm whether the electrical equipment is overloaded and cannot detect hazards or aging abnormalities in real-time. Therefore, an intelligent abnormal detection system for electrical equipment is required. Such a system needs to detect electrical information of the equipment, including voltage, current, power factor, and frequency, and combine it with AI techniques to dynamically detect abnormal conditions in the electrical equipment. In view of this, we proposed and developed an AIoT system with the following functional components: 1. Smart power sensing devices for collecting and monitoring abnormal events. 2. A real-time anomaly alerting app to receive abnormal notifications. 3. A highly efficient and fast anomaly recognition model, capable of identifying various abnormal conditions in electrical equipment. 4. A visualized electrical equipment analysis web platform for managing all electrical equipment in factories, buildings, and offices. Experimental results show that our AIoT system achieves accurate recognition results.

关键词： Visualization Reactive power Buildings Machine learning Voltage Smart systems Real-time systems Production facilities Sensors Plugs

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Self-Supervised Learning with Variational Autoencoders for Anomaly Detection in Autonomous Drone Fleets

Self-Supervised Learning with Variational Autoencoders for A...

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Smart Technologies, Communication and Robotics (STCR)

作者： Kirubhakaran M M.Sundarrajan Gowtham T D. Agila Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Master of Computer Application Excel Engineering College Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Agni College of Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

However, Unmanned Aerial Vehicles (UAVs) are increasingly being employed for different applications such as surveillance, logistics, disaster response, and smart city management, and thereby, the requirement for robust detection of anomalies and path optimization techniques has arisen. Existing traditional UAV monitoring systems typically depend on anomaly detection based on a rule or an unsupervised clustering method, i.e., DBSCAN and One-Class SVM, balancing high false positives and slow response time, correction, and suboptimal trajectory. To tackle these issues, we develop an AI-powered UAV anomaly detection and adaptive navigation framework where Isolation forests and MDPs are used to predict UAV trajectory, and DQNs are used for adaptive navigation. Experimental results in a simulated smart city arena with a multi-UAV fleet show that our proposed framework achieves 94% accuracy, 92% precision, and 91% recall, which are much superior to the existing approaches, such as One Class SVM (87% accuracy) and DBSCAN (80% accuracy). Additionally, the system compensates for false positives by 30%, improves energy efficiency by 15%, and reduces UAV collision risks by 20%, with high compatibility with real-time UAV fleet management. In the future, the scalability and the adaptability of the UAV-based smart city operation will be furthered through federated learning-based decentralised UAV coordination, reinforcement learning for autonomous swarm intelligence, and hybrid optimisation techniques for improving energy efficiency.

关键词： Support vector machines Accuracy Smart cities Navigation Scalability Autonomous aerial vehicles Energy efficiency Trajectory Anomaly detection Optimization

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Generation-Based Target Speech Extraction with Speech Discretization and Vocoder

Generation-Based Target Speech Extraction with Speech Discre...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Linfeng Yu Wangyou Zhang Chenpeng Du Leying Zhang Zheng Liang Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China

Target speech extraction (TSE) is a task aiming at isolating the speech of a specific target speaker from an audio mixture, with the help of an auxiliary recording of that target speaker. Most existing TSE methods employ discrimination-based models to estimate the target speaker’s proportion in the mixture, but they often fail to compensate for the missing or highly corrupted frequency components in the speech signal. In contrast, the generation-based methods can naturally handle such scenarios via speech resynthesis. In this paper, we propose a novel discrete token based TSE approach by combining state-of-the-art speech discretization and vocoder techniques. By predicting a sequence of discrete tokens with the auxiliary audio and employing a vocoder that takes discrete tokens as input, the target speech can be effectively re-synthesized while eliminating interference. Our experiments conducted on the WSJ0-2mix and Libri2mix datasets demonstrate that our proposed method yields high-quality target speech without interference.

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MACHINE LEARNING FOR ELLIPTIC PDES: FAST RATE GENERALIZATION BOUND, NEURAL SCALING LAW AND MINIMAX OPTIMALITY 10

MACHINE LEARNING FOR ELLIPTIC PDES: FAST RATE GENERALIZATION...

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10th International Conference on Learning Representations, ICLR 2022

作者： Lu, Yiping Chen, Haoxuan Lu, Jianfeng Ying, Lexing Blanchet, Jose Institute for Computational & Mathematical Engineering Stanford University StanfordCA94305 United States Department of Computing and Mathematical Sciences Caltech PasadenaCA91125 United States Mathematics Department Duke University DurhamNC27708-0320 United States Department of Mathematics Stanford University StanfordCA94305 United States Department of Management Science & Engineering Stanford University StanfordCA94305 United States

In this paper, we study the statistical limits of deep learning techniques for solving elliptic partial differential equations (PDEs) from random samples using the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs). To simplify the problem, we focus on a prototype elliptic PDE: the Schrödinger equation on a hypercube with zero Dirichlet boundary condition, which is applied in quantum-mechanical systems. We establish upper and lower bounds for both methods, which improve upon concurrently developed upper bounds for this problem via a fast rate generalization bound. We discover that the current Deep Ritz Method is sub-optimal and propose a modified version of it. We also prove that PINN and the modified version of DRM can achieve minimax optimal bounds over Sobolev spaces. Empirically, following recent work which has shown that the deep model accuracy will improve with growing training sets according to a power law, we supply computational experiments to show similar-behavior of dimension dependent power law for deep PDE solvers. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Boundary conditions

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DQ-Whisper: Joint Distillation and Quantization for Efficient Multilingual Speech Recognition

DQ-Whisper: Joint Distillation and Quantization for Efficien...

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IEEE Spoken Language Technology Workshop

作者： Hang Shao Bei Liu Wei Wang Xun Gong Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

As a popular multilingual and multitask pre-trained speech model, Whisper has the problem of curse of multilinguality. To enhance multilingual capabilities in small Whisper models, we propose DQ-Whisper, a novel joint distillation and quantization framework to compress Whisper for efficient inference. Firstly, we propose a novel dynamic matching distillation strategy. Then, a quantization-aware distillation framework is introduced to integrate quantization with distillation. Experimental results on various multilingual datasets show that our suggested distillation approach can effectively enhance the multilingual capabilities of small Whisper models without increasing computational costs. Up to 5.18x reduction in model size is achieved with marginal performance degradation. In addition, quantization is compatible with distillation, which can result in a higher compression rate.

关键词： Degradation Quantization (signal) computational modeling Conferences Merging Speech recognition Multilingual computational efficiency

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QAUST:Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 998-1011页

作者： Fatima Zohra Smaili Shuye Tian Ambrish Roy Meshari Alazmi Stefan T.Arold Srayanta Mukherjee P.Scott Hefty Wei Chen Xin Gao Computational Bioscience Research Center(CBRC) ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Biology Southern University of Science and Technology of China(SUSTC)Shenzhen 518055China Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109USA College of Computer Science and Engineering University of Ha’ilHa'il 55476Saudi Arabia Biological and Environmental Sciences and Engineering(BESE)Division King Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Molecular Bioscience University of KansasLawrenceKS 66047USA

The number of available protein sequences in public databases is increasing ***,a sig-nificant percentage of these sequences lack functional annotation,which is essential for the understanding of how bio-logical systems ***,we propose a novel method,Quantitative Annotation of Unknown STructure(QAUST),to infer protein functions,specifically Gene Ontology(GO)terms and Enzyme Commission(EC)*** uses three sources of information:structure information encoded by global and local structure similarity search,biological network information inferred by protein–protein interaction data,and sequence information extracted from functionally discriminative sequence *** three pieces of information are combined by consensus averaging to make the final *** approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation(CAFA)benchmark *** results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or *** further demonstrate that a previously unknown function of human tripartite motif-containing 22(TRIM22)protein predicted by QAUST can be experimentally validated.

关键词： Protein function prediction GO term EC number Protein structure similarity Functionally discriminative motif

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On Convergence of a Truncation Scheme for Approximating Stationary Distributions of Continuous State Space Markov Chains and Processes

arXiv

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arXiv 2022年

作者： Infanger, Alex Glynn, Peter W. Institute for Computational & Mathematical Engineering Stanford University United States Department of Management Science & Engineering Stanford University United States

In the analysis of Markov chains and processes, it is sometimes convenient to replace an unbounded state space with a "truncated" bounded state space. When such a replacement is made, one often wants to know whether the equilibrium behavior of the truncated chain or process is close to that of the untruncated system. For example, such questions arise naturally when considering numerical methods for computing stationary distributions on unbounded state space. In this paper, we use the principle of "regeneration" to show that the stationary distributions of "fixed state" truncations converge in great generality (in total variation norm) to the stationary distribution of the untruncated limit, when the untruncated chain is positive Harris recurrent. Even in countable state space, our theory extends known results by showing that the augmentation can correspond to an r-regular measure. In addition, we extend our theory to cover an important subclass of Harris recurrent Markov processes that include non-explosive Markov jump processes on countable state space. © 2022, CC BY.

关键词： Markov processes

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Analysis of Panoramic Images using Deep Learning For Dental Disease Identification 3

Analysis of Panoramic Images using Deep Learning For Dental ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mallick, Mohamed Midhun Kumar, A. Govindaraju, Sakthi Kumar, A. Suresh Kandasamy, Manivel Anitha, P. Rathinam Group of Institutions Coimbaotore India Indian Biological Data Center Center for Biotechnology Database Engineer Faridabab India Galogotias University School of Computing Science and Engineering Delhi NCR India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Oasys Institute of Technology Trichy India

ISBN: (纸本)9781665462167

Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and long- lasting dental illnesses, affecting people at all stages of their lives. There are a few different sorts of dental x-rays, each of which records a different perspective on dental living systems for caries detection and has the highest symptomatic accuracy, whereas panoramic radiography has the lowest Despite this, Bitewing has a few downsides, such as patient unease. Because of the lack of knowledge among administrators, Bitewing imaging frequently results in increased patient radiation exposure as a result of the need for image retakes. On the other hand, panoramic x-ray scans, also known as Orthopantomogram (OPG), are commonly utilized to capture the whole mouth with a small amount of radiation. OPG has a low radiation content, is simple to use, takes less time, and provides exceptional patient comfort. In this way, when compared to intraoral frameworks, pediatric, impaired, and senior people would profit greatly from panoramic imaging frameworks. This research work explores panoramic photos to detect accurate dental disease using deep learning. As a result, most patients can benefit from panoramic imaging, which covers a large portion of the maxillofacial region, including all of the teeth. © 2023 IEEE.

关键词： Image enhancement

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Video Object Forgery Detection using Image Processing Techniques

Video Object Forgery Detection using Image Processing Techni...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Lavanya Kumari M. Siva N. Muthukumaran D. Dharani K. Kavitha Sahithi V. Ashwin Kumar Department of Computer Science and Engineering (Data Science) Vignan Institute of Technology and Science Hyderabad India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Video surveillance has been crucial for security in recent years, thus it’s critical to guarantee the accuracy of these recordings. Unfortunately, it is easy to fake surveillance films by erasing an object from a scene and leaving no visual evidence. In this study, an approach for video intra-frame forgery forensics based on the SSIM (Structural Similarity Index Measure) is proposed. This system can recognize forged video frames automatically. In order to extract the steganography features, the method first decompresses the input video into a sequence of frames. It next calculates each frame’s motion residual map. The ability to tell whether or not an object is deleted from a video is a key problem in video security.

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Revolutionizing Skin Cancer Detection with Raspberry Pi-Embedded ANN Technology in an Automated Screening Booth

Revolutionizing Skin Cancer Detection with Raspberry Pi-Embe...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Srinivasan G. Indra T R Saravanan S. Murugan C. Srinivasan M. Muthulekshmi Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In this ground-breaking endeavor, we describe an automated skin cancer screening booth that can transform current approaches to early detection. The merging of Raspberry Pi-controlled technology with cutting-edge Artificial Neural Network (ANN) models forms the basis of our groundbreaking breakthrough. The booth uses high-resolution imaging sensors to acquire detailed photographs of the user's skin, which are then analyzed in real-time by an ANN. The ability to identify potentially malignant anomalies in a timely and precise manner is made possible by this combination. In public places, user contact is facilitated by the use of an intuitive touchscreen interface, which ensures both accessibility and convenience of use. The ANN model, trained on a wide variety of data, is very good at differentiating between normal and malignant skin diseases, with the findings being instantly shown on the interface. Beyond its technological capabilities, our technology bears the prospect of broad application, which will turn screening for skin cancer into a preventative healthcare tool available to everyone. This all-encompassing strategy highlights the significance of early detection and argues for a solution that is both scalable and effective at the crossroads of technology and public health.

关键词： Technological innovation Accuracy Artificial neural networks Transforms User interfaces Touch sensitive screens Skin

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