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2023 International Conference on Applied Mathematics and Digital Simulation, AMDS 2023

作者： Chen, Ao Chen, Tianding He, Hong Wang, Hui Fan, Yimei Yu, Xin School of Physics and Information Engineering Minnan Normal University Fujian Province China School of Computing and Information Science Fuzhou Institute of Technology Fujian Province China School of Data and Computer Science Xiamen Institute of Technology Fujian Province China

In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV's equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations. © Published under licence by IOP Publishing Ltd.

关键词： DC-DC converters

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A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning Information System Using IOT Based Traffic Prediction Model

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Ear...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Murali, N. David, D. Beulah Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350334357

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design. © 2023 IEEE.

关键词： Machine learning

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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AI based Analysis of Hyperspectral Images with Spectacular Efficacy in Video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Jadhav, Samarth Yogesh More, Rutuja Rajatam Tingare, Bhagyashree Ashok Lakshmi, G. Prasanna Kolse, Shivam Darwante, N.K. Sanjivani College of Engineering Department of Information Technology MH Kopargaon India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Sanjivani College of Engineering Department of Electronics and Computer Engineering MH Kopargaon India

ISBN: (纸本)9798350390049

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control. © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced VANET Technology for Emergency Vehicle Safety in Time-Critical Scenario 8

Advanced VANET Technology for Emergency Vehicle Safety in Ti...

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8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions. © 2023 IEEE.

关键词： Object detection

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AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

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A Hybrid Deep Learning Framework for Stock Price Forecasting with Sentimental Analysis 13

A Hybrid Deep Learning Framework for Stock Price Forecasting...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Banoth, Shobhan Yadala, Sucharitha Vice President-Data/Information Management Lead Engineer Citi Bank United States School of Computer Science and Engineering Vnr Vignana Jyothi Institute of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9798350380583

Social media electronic word-of-mouth data affects stock market confidence and trading. As a result, stock market sentiment research of comments is essential for stock market forecasting. Unfortunately, English is becoming the de facto language for sentiment analysis. This study translates writings from places where English is not the local language into English so that multilingual sentiment analysis can be performed. In order to predict stock market movements, this research combined social media's unstructured data with structured data, such as transaction records and technical indicators. Parameter determination has a significant impact on the effectiveness of forecasting models;in recent years, deep-learning(DL) and machine-learning(ML) models have developed as effective means of dealing with anticipating issues. In order to anticipate stock market indices and business stock prices using hybrid data in places where English is not the native language, this research utilized Long-Short-Term Memory(LSTM) models that utilized the geneticalgorithm(GA) to choose parameters. The created LSTMGA model outperformed the other ML-strategies with different types of data in terms of accuracy when it came to generating forecasts using hybrid multilingual sentiment data. As a result, the stock market forecasting method that combines the suggested LSTMGA model with hybrid sentiment analysis is both practical and encouraging. © 2024 IEEE.

关键词： Marketplaces

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data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年第4期16卷 2816-2829页

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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INFOCON: CONCEPT DISCOVERY WITH GENERATIVE AND DISCRIMINATIVE INFORMATIVENESS 12

INFOCON: CONCEPT DISCOVERY WITH GENERATIVE AND DISCRIMINATIV...

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12th International Conference on Learning Representations, ICLR 2024

作者： Liu, Ruizhe Luo, Qian Yang, Yanchao HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong School of Electronics Engineering and Computer Science Peking University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks. We propose that the decision to conceptualize a physical procedure should not depend on how we name it (semantics) but rather on the significance of the informativeness in its representation regarding the low-level physical state and state changes. We model manipulation concepts - discrete symbols - as generative and discriminative goals and derive metrics that can autonomously link them to meaningful sub-trajectories from noisy, unlabeled demonstrations. Specifically, we employ a trainable codebook containing encodings (concepts) capable of synthesizing the end-state of a sub-trajectory given the current state - generative informativeness. Moreover, the encoding corresponding to a particular sub-trajectory should differentiate the state within and outside it and confidently predict the subsequent action based on the gradient of its discriminative score - discriminative informativeness. These metrics, which do not rely on human annotation, can be seamlessly integrated into a VQ-VAE framework, enabling the partitioning of demonstrations into semantically consistent sub-trajectories, fulfilling the purpose of discovering manipulation concepts and the corresponding sub-goal (key) states. We evaluate the effectiveness of the learned concepts by training policies that utilize them as guidance, demonstrating superior performance compared to other baselines. Additionally, our discovered manipulation concepts compare favorably to human-annotated ones while saving much manual effort. Our code is available at: https://***/InfoCon/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Trajectories

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