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International Conference on Inventive Computation Technologies (ICICT)

作者： G. Yadagir M. Sudha Vidya Sagar Reddy G D.Shobha Rani S Sophia J Femila Roseline Collegiate Education Jayashankar Vidya Bhavan Hyderabad Telangana India Department of Electronics and Communication engineering Paavai Engineering College (Autonomous) Namakkal Tamil Nadu India School of Architecture Koneru Lakshmaiah Educational Foundation Guntur Andhra Pradesh India Department of Computer Science and Engineering Chadalawada Ramanamma Engineering college Tirupathi Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

The implementation of a robotic arm for mineral extraction in mines is a challenging task that requires advanced technology and innovation. This paper proposes a solution using artificial intelligence (AI) and a permanent magnet synchronous motor (PMSM) to control the robotic arm's movement. A robotic arm with several degrees of freedom, a PMSM to move the arm, and AI algorithms to manage its motion make up the proposed system. The PMSM gives the arm's movements excellent control and enhances the precision and effectiveness of the mineral extraction procedure. The AI algorithms are in charge of directing the robotic arm's motion by examining sensor data and making choices following the situation at hand. The system is capable of adapting to its surroundings to improve the efficiency of mineral extraction. Compared to conventional mining methods, the suggested system has several benefits, including greater safety, increased efficiency, and lower costs. The system can completely transform the mining sector and can be applied in a variety of mining applications. Using a robotic arm and AI technologies, the suggested method offers a novel approach to mineral extraction in mines. The technology has several advantages over conventional mining methods and has the potential to greatly increase the productivity and security of the mining sector.

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Improving the prediction accuracy of cyanobacterial blooms using a hybrid model integrating dimensionality reduction and deep learning

Water Biology and Security

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Water Biology and Security 2025年

作者： Chen, Jiwei Hou, Zhongbao Li, Yunxi Li, Jiexian Li, Lin School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Public Health Fu Jen Catholic University Taiwan 242062 China Khoury College of Computer Sciences Northeastern University Vancouver V6B1Z3 Canada Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China

Cyanobacteria Harmful Blooms have negatively impacted human socio-economic activities and the natural ecological environment. Predicting cyanobacterial blooms requires the application of advanced analytical models powered by artificial intelligence to accurately model bloom development. In this study, we developed a hybrid model that integrates the Temporal Convolution Network (TCN) with the Long Short-Term Memory Network (LSTM) to achieve highly accurate predictions of cyanobacterial bloom events. We used Principal Component Analysis (PCA) during the preprocessing phase to reduce data dimensionality of model input variables and enhance computational efficiency. We used a data set from the Darling River in Australia to conduct a comparative analysis with traditional deep learning models and comprehensively assess the proposed model performance. We also investigated the application of the correlation coefficient matrix method in variable selection. The results demonstrate that the proposed model outperforms conventional methods in predicting cyanobacterial blooms. When benchmarked against the Recurrent Neural Network (RNN) model, our model demonstrates a superior ability to capture the intricacies of time series data. Specifically, it achieves a 79.5 % reduction in Mean Square Error (MSE) and a 197 % increase in the Coefficient of Determination (R²). Furthermore, comparative experiments were carried out across several prediction time steps, indicating that the model achieves optimal performance in minimizing both the MSE and Mean Absolute Error (MAE) when predicting one day ahead. Notably, the error distribution is most condensed at this time step, outperforming models with prediction horizons ranging from two to seven days. We also undertook a thorough analysis of the significance of each principal component in model predictions. The results highlight that principal component 5 and principal component 2 exhibit the most substantial contribution to the p

关键词： Cyanobacteria bloom Deep learning Long short-term memory network Prediction Principal component analysis Temporal convolution network

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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Internet-of-Things technology and applications for clean energy systems

Energy Conversion and Economics

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Energy Conversion and Economics 2021年第4期2卷 183-185页

作者： Strunz, Kai Wang, Xuanyuan Guo, Qinglai Xie, Le Zhang, Song Fang, Xin Wang, Jianxiao School of Electrical Engineering and Computer Science Berlin10587 Germany State Grid Jibei Electric Power Co. LTD. Beijing China Department of Electrical Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Business Architecture and Technology ISO New England Inc. HolyokeMA United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO United States School of Electrical and Electronic Engineering North China Electric Power University Beijing China

[...]future research interests regarding IESSs are summarised. A multi-stage PMU placement strategy was developed to alleviate power system vulnerability to possible false data injection attacks, where forward dynamic programming was used to distribute the capital cost of PMUs over a certain period. Three clustering methods, including linear regression, density-based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM), were combined for bad PMU data detection.

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Automatic facial expression recognition in an image sequence using conditional random field

Automatic facial expression recognition in an image sequence...

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International Symposium on Computational Intelligence and Informatics

作者： Mohamad Roshanzamir Mahdi Roshanzamir Abdolreza Mirzaei Mohammad Tayarani Darbandy Afshin Shoeibi Roohallah Alizadehsani Fahime Khozeimeh Abbas Khosravi Department of Computer Engineering Fasa University Fasa Iran Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran School of Architecture Islamic Azad University Taft Taft Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia

ISBN: (纸本)9798350398830

Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This will be of great help to researchers, especially in areas such as psychology. Automatic facial recognition can be derived from a static image of facial expression, but a better and more efficient way to do this is through a sequence of images. In this paper, a new method is proposed to automatically detect facial expressions from a sequence of images. Each sequence of facial images begins with a face neutral state and ends with one of the six main emotions. Motion vectors are extracted from the sequence using optical flow algorithm. These vectors are then used to train the conditional random field and finally to automatically determine the emotion. In this paper, in addition to the basic conditional random field, the hidden dynamic conditional random field is also investigated. Additionally, the effect of changing some parameters of these algorithms such as different optimization methods has been investigated. Given that a facial expression is recognized during a sequence of images, random field-based methods can be used for efficient classification of facial expressions reaching accuracy (more than 90%) competitive with the best existing methods for facial expression recognition.

关键词： Image recognition Mechatronics Face recognition Heuristic algorithms Psychology Optimization methods Feature extraction

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A Systematic Review of Generative AI for Teaching and Learning Practice

arXiv

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arXiv 2024年

作者： Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Olayinka, Olakunle Sharma, Hemlata School of Architecture Technology & Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computing & Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom School of Computer Science The University of Sheffield SheffieldS1 4DP United Kingdom Department of Computing Sheffield Hallam University SheffieldS1 2NU United Kingdom

The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage of GenAI systems in higher education (HE) and, thus, it is still unclear how to make effective use of the technology for teaching and learning practice. This paper provides an overview of the current state of research on GenAI for teaching and learning in HE. To this end, this study conducted a systematic review of relevant studies indexed by Scopus, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search criteria revealed a total of 625 research papers, of which 355 met the final inclusion criteria. The findings from the review showed the current state and the future trends in documents, citations, document sources/authors, keywords, and co-authorship. The research gaps identified suggest that while some authors have looked at understanding the detection of AI-generated text, it may be beneficial to understand how GenAI can be incorporated into supporting the educational curriculum for assessments, teaching, and learning delivery. Furthermore, there is a need for additional interdisciplinary, multidimensional studies in HE through collaboration. This will strengthen the awareness and understanding of students, tutors, and other stakeholders, which will be instrumental in formulating guidelines, frameworks, and policies for GenAI usage. © 2024, CC BY.

关键词： Artificial intelligence

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A Generalized Density Dissipation for Weakly-compressible SPH

arXiv

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arXiv 2023年

作者： Zheng, Bo Xue Cai, Zhi Wen Zhao, Pei Dong Xu, Xiao Yang Chan, Tak Shing Yu, Peng Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Department of Mathematics University of Oslo Oslo0851 Norway China Ship Scientific Research Center Wuxi214082 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Naval Architecture and Ocean Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

The weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from the pressure oscillation, which would undermine the simulation stability and accuracy. To address this issue, we propose a generalized density dissipation scheme suitable for both single-phase and multiphase flow simulations. Our approach consists of two components. Firstly, we replace the basic density dissipation with the density increment dissipation to enable numerical dissipation crossing the interfaces of different fluids in multiphase flow. Secondly, based on the dissipation volume conservation, we utilize dissipation volume correction factor (VCF) to stabilize the simulations for multiphase flows with large density ratio. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks, i.e., the sloshing under external excitations, the single and double bubbles rising, Rayleigh-Taylor instability, and Kelvin-Helmholtz instability. Additionally, our study reveals the relationship between SPH with the density dissipation and the approximate Riemann solver. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiphase flow

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Unit Test Generation in a Cluster Using Parallel Computations and Control Flow Graph Analysis 15

Unit Test Generation in a Cluster Using Parallel Computation...

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15th IEEE International Scientific and Technical Conference on computer Sciences and Information Technologies, CSIT 2020

作者： Bazylevych, Roman Czarnul, Pawel Franko, Andrii Ukraine University of Information Technology and Management Department of Software Lviv Polytechnic National University Lviv Rzeszow Poland University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk Department of Computer Architecture Gdansk Poland Lviv Polytechnic National University Department of Software Lviv Ukraine

ISBN: (纸本)9781728174433

A parallelization approach implemented in a cluster with a shared file system has been considered in the paper. The study presents metrics of parallelization for running tasks of C language nested loops analysis in a cluster and subsequent unit test code generation for the given data. The parallelization method used the shared file system and ran in a decentralized mode without a master node. The results are useful for testing the integration of third-party components and negative testing of safety-critical systems. © 2020 IEEE.

关键词： C (programming language)

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A Generalized Density Dissipation for Weakly-Compressible Sph

SSRN

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SSRN 2023年

作者： Zheng, Bo Xue Cai, Zhi Wen Zhao, Pei Dong Xu, Xiao Yang Chan, Tak Shing Yu, Peng Department of Mathematics University of Oslo Oslo0851 Norway Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China China Ship Scientific Research Center Wuxi214082 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Naval Architecture and Ocean Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Technology Xi’an University of Science and Technology Xian710054 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from numerical pressure noise, which can significantly undermine simulation stability and accuracy. In response, this study introduces a novel density dissipation scheme aimed at suppressing such unphysical noise. The proposed scheme overcomes the limitations of conventional density dissipation schemes, which are ineffective at fluid interfaces in multiphase flow simulations. As a result, the novel scheme is generally applicable to the entire computational domain, encompassing both single-phase flow and multiphase flow. This advancement is achieved through two key components. Firstly, the traditional density dissipation is replaced with a density increment dissipation approach that enables dissipation to cross interfaces separating different fluids. Secondly, based on dissipation volume conservation, we employ a dissipation volume correction factor (VCF) to stabilize simulations characterized by large density ratios. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks: sloshing under external excitations, the rising of single and double bubbles, Rayleigh-Taylor instability, and Kelvin-Helmholtz instability. Additionally, this study reveals the relationship between SPH utilizing density dissipation and the approximate Riemann solver. © 2023, The Authors. All rights reserved.

关键词： Multiphase flow

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YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction

arXiv

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arXiv 2025年

作者： Lin, Ziyu Wu, Yunfan Ma, Yuhang Chen, Junzhou Zhang, Ronghui Wu, Jiaming Yin, Guodong Lin, Liang Guangdong Key Laboratory of Intelligent Transportation System School of intelligent systems engineering Sun Yat-sen University Guangzhou510275 China Department of Architecture and Civil Engineering Chalmers University of Technology Sven Hultins gata 6 GothenburgSE-412 96 Sweden School of Mechanical Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

Detecting traffic signs effectively under low-light conditions remains a significant challenge. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. Firstly, we introduce the High-Resolution Feature Map for Small Object Detection (HRFM-TOD) module to address indistinct small-object features in low-light scenarios. By leveraging high-resolution feature maps, HRFM-TOD effectively mitigates the feature dilution problem encountered in conventional PANet frameworks, thereby enhancing both detection accuracy and inference speed. Secondly, we develop the Multi-branch Feature Interaction Attention (MFIA) module, which facilitates deep feature interaction across multiple receptive fields in both channel and spatial dimensions, significantly improving the model’s information extraction capabilities. Finally, we propose the Prior-Guided Enhancement Module (PGFE) to tackle common image quality challenges in low-light environments, such as noise, low contrast, and blurriness. This module employs prior knowledge to enrich image details and enhance visibility, substantially boosting detection performance. To support this research, we construct a novel dataset, the Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios, including urban, highway, and rural environments under varying weather conditions. Experimental evaluations demonstrate that YOLO-LLTS achieves state-of-the-art performance, outperforming the previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, and achieving superior results on the CCTSDB2021 dataset. Moreover, deployment experiments on edge devices confirm the real-time applicability and effectiveness of our proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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