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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： De Silva, Ulindu Fernando, Leon Bandara, Kalinga Nawaratne, Rashmika Department of Electronic and Telecommunication Engineering University of Moratuwa Sri Lanka Research Centre for Data Analytics and Cognition La Trobe University VIC Australia

ISBN: (纸本)9781665464543

The proliferation of video content production has led to vast amounts of data, posing substantial challenges in terms of analysis efficiency and resource utilization. Addressing this issue calls for the development of robust video analysis tools. This paper proposes a novel approach leveraging Generative Artificial Intelligence (GenAI) to facilitate streamlined video analysis. Our tool aims to deliver tailored textual summaries of user-defined queries, offering a focused insight amidst extensive video datasets. Unlike conventional frameworks that offer generic summaries or limited action recognition, our method harnesses the power of GenAI to distil relevant information, enhancing analysis precision and efficiency. Employing YOLO-V8 for object detection and Gemini for comprehensive video and text analysis, our solution achieves heightened contextual accuracy. By combining YOLO with Gemini, our approach furnishes textual summaries extracted from extensive CCTV footage, enabling users to swiftly navigate and verify pertinent events without the need for exhaustive manual review. The quantitative evaluation revealed a similarity of 72.8%, while the qualitative assessment rated an accuracy of 85%, demonstrating the capability of the proposed method. © 2024 IEEE.

关键词： Video analysis

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Nitrogen-Doped Amorphous Carbon Homojunction from Palmyra Sugar as a Renewable Solar Cell

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Journal of Renewable Materials 2024年第1期12卷 57-69页

作者： Budhi Priyanto Imam Khambali Irma Septi Ardiani Khoirotun Nadhiyah Anna Zakiyatul Laila M.Chasrun Hasani Bima Romadhon Retno Asih Yoyok Cahyono Triwikantoro Darminto Department of Electronic Engineering Faculty of EngineeringUniversitas Muhammadiyah MalangKota MalangIndonesia Advanced Materials Research Group Department of PhysicsFaculty of Science and Data AnalyticsInstitut Teknologi Sepuluh NopemberSurabayaIndonesia Department of Electronic Engineering Faculty of Industrials EngineeringInstitut Teknologi National MalangKota MalangIndonesia

An a-C/a-C:N junction,which used palmyra sugar as the carbon source and ammonium hydroxide(NH4OH)as the dopant source,was successfully deposited on the ITO glass substrate using the nano-spraying *** current-voltage relationship of the junction was found to be a Schottky-like contact,and therefore the junction shows the characteristic *** means the a-C and a-C:N are semiconductors with different types of ***,the samples showed an increase in current and voltage value when exposed to visible light(bright state)compared to the dark condition,thereby,indicating the creation of electron-hole pairs during the *** was also discovered that the relationship between current and voltage for the a-C/a-C:N junction sample formed a curve that satisfies the rule of the photovoltaic effect when exposed to visible light from a light *** exposure of this sample to direct sunlight at AM 1.5 conditions produced a curve that meets the rules for the emergence of the photovoltaic effect with higher characteristics for the current-voltage ***,the a-C/a-C:N junction sample is a solar cell successfully fabricated using a sample method and has a maximum efficiency of 0.0013%.

关键词： Solar cell amorphous carbon palmyra sugar

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A Study in the Advancement of Wind Turbine Fault Diagnosis Using Decision Boundary Algorithm

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Transactions of the Korean Society of Mechanical Engineers, B 2024年第3期48卷 139-147页

作者： Kim, Young-Jae Kang, Hyun-Su Park, Gung-Hul Choi, In-Wook Kim, Youn-Jea Graduate School of Mechanical Engineering Sungkyunkwan Univ. Korea Republic of Data Analytics Team Doosan Enerbility Korea Republic of Wind Turbine O&M Team Doosan Enerbility Korea Republic of School of Mechanical Engineering Sungkyunkwan Univ Korea Republic of

A wind turbine power generation system is an eco-friendly system that converts kinetic energy of wind into electrical energy. Wind turbines are installed on coastlines or mountains, where it is difficult to repair in the event of a failure. Hence, it is necessary to predict and diagnose failures. In this study, fault diagnosis was performed by applying one-class support vector machine (OC-SVM) and support vector data description (SVDD), which are classification algorithms based on decision boundaries. The number of data points applied for training and validation corresponds to 30,000, and the numbers of data points for normal and failure data correspond to 20,000 and 10,000, respectively. Algorithm performance enhancement was performed via hyperparameter tuning using Bayesian optimization. Hence, for accuracy, which is an algorithm performance indicator, OC-SVM corresponded to 0.8232 and SVDD corresponded to 0.9264. © 2024 The Korean Society of Mechanical Engineers.

关键词： Failure analysis

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Self-supervised probabilistic models for exploring shape memory alloys

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npj Computational Materials 2024年第1期10卷 1338-1347页

作者： Yiding Wang Tianqing Li Hongxiang Zong Xiangdong Ding Songhua Xu Jun Sun Turab Lookman State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an710049China National Engineering Laboratory for Big Data Analytics School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’an710049China AiMaterials Research LLC Santa FeNM87501USA

Recent advancements in machine learning(ML)have revolutionized the field of high-performance materials ***,developing robust ML models to decipher intricate structure-property relationships in materials remains challenging,primarily due to the limited availability of labeled datasets with well-characterized crystal *** is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic *** a selfsupervised probabilistic model(SSPM)that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures,utilizing solely the existing crystal structure data from materials *** significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal *** showcase SSPM’s capability by discovering shapememory alloys(SMAs).Amongst the top 50 predictions,23 have been confirmed as SMAs either experimentally or theoretically,and a previously unknown SMA candidate,MgAu,has been identified.

关键词： alloys probabilistic shape

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Customer Profiling Based on Mobile Apps GPS data : A Case Study on Westfield Shopping Malls 30

Customer Profiling Based on Mobile Apps GPS Data : A Case St...

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30th International Conference on Geoinformatics, Geoinformatics 2023

作者： Zhou, Fangzhou Zhang, Xianghui Wang, Xinglei Cheng, Tao University College London SpaceTimeLab for Big Data Analytics Department of Civil Environmental and Geomatic Engineering London United Kingdom

ISBN: (纸本)9798350344240

In order to provide a personalised experience to customers, it's essential for shopping centers to understand its customer base and their shopping behaviors. Building a well-developed customer profile is critical for improving marketing efficiency, expanding market share, and building long-term, stable business ties with trading partners. Currently most shopping malls or retail business use footfall or customer surveys to grasp the customer behaviors, which are insufficient to obtain accurate and representative information about the customers. This study aims to provide a detailed customer profile for shopping centers using GPS datasets. We choose the two Westfield shopping malls in London as the case study area. In order to uncover additional customer information, this study focuses four research questions:(1) Origin places of customers;(2) Their transportation mode to the mall;(3) The average dwell time of customers;(4) The pattern of return visitors. According to the results, malls can develop a range of marketing initiatives to provide a better shopping experience for customers and attract more of them. © 2023 IEEE.

关键词： Shopping centers

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MOMENTUM BENEFITS NON-IID FEDERATED LEARNING SIMPLY AND PROVABLY 12

MOMENTUM BENEFITS NON-IID FEDERATED LEARNING SIMPLY AND PROV...

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12th International Conference on Learning Representations, ICLR 2024

作者： Cheng, Ziheng Huang, Xinmeng Wu, Pengfei Yuan, Kun Peking University China University of Pennsylvania United States National Engineering Labratory for Big Data Analytics and Applications AI for Science Institute Beijing China

Federated learning is a powerful paradigm for large-scale machine learning, but it faces significant challenges due to unreliable network connections, slow communication, and substantial data heterogeneity across clients. FEDAVG and SCAFFOLD are two prominent algorithms to address these challenges. In particular, FEDAVG employs multiple local updates before communicating with a central server, while SCAFFOLD maintains a control variable on each client to compensate for "client drift" in its local updates. Various methods have been proposed to enhance the convergence of these two algorithms, but they either make impractical adjustments to the algorithmic structure or rely on the assumption of bounded data heterogeneity. This paper explores the utilization of momentum to enhance the performance of FEDAVG and SCAFFOLD. When all clients participate in the training process, we demonstrate that incorporating momentum allows FEDAVG to converge without relying on the assumption of bounded data heterogeneity even using a constant local learning rate. This is novel and fairly surprising as existing analyses for FEDAVG require bounded data heterogeneity even with diminishing local learning rates. In partial client participation, we show that momentum enables SCAFFOLD to converge provably faster without imposing any additional assumptions. Furthermore, we use momentum to develop new variance-reduced extensions of FEDAVG and SCAFFOLD, which exhibit state-of-the-art convergence rates. Our experimental results support all theoretical findings. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Momentum

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OntoSmartDCU: A Multi-Agent Microservice Based Automated, Dynamic Ontology and Knowledge Graph Creation Framework From Real-Time Sensor data for the Smart DCU Digital Twin 10

OntoSmartDCU: A Multi-Agent Microservice Based Automated, Dy...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Mandal, Sukanya O'connor, Noel E. Dublin City University School of Electronic Engineering Dublin Ireland Dublin City University Insight SFI Research Centre for Data Analytics Dublin Ireland

ISBN: (纸本)9798350373011

This paper presents OntoSmartDCU, a novel Multi-Agent Microservice (MAMS) based framework for automated and dynamic Ontology and Knowledge Graph (KG) creation from real-time sensor data. Designed specifically for the Smart Dublin City University (DCU) Digital Twin (DT), OntoSmartDCU facilitates the continuous acquisition, interpretation, and integration of sensor data streams into an evolving knowledge representation. The architecture consists of interconnected microservices and autonomous agents, each responsible for specific tasks such as data gathering and processing, ontology creation, and KG population. These agents act as intelligent microservices, operating in parallel and communicating to achieve a common goal. This modular design facilitates scalability, flexibility, and adaptability to heterogeneous sensor environments. The dynamic nature of OntoSmartDCU will enable the DT to accurately reflect the real-time operational state of the university campus, serving as the foundation for various applied use cases. OntoSmartDCU offers a significant advancement in DT technology, providing a robust and scalable solution for real-time knowledge representation and data integration in complex cyber-physical systems. © 2024 IEEE.

关键词： Knowledge graph

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1695-1712页

作者： Xiaodong Yang Runze Diao Tao Liu Haoqi Wen Caifen Wang College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China Institute of China TelecomWanwei China TelecomWanwei Information Technology Co.Ltd.Lanzhou730030China College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Key Laboratory of Cryptography and Data Analytics Northwest Normal UniversityLanzhou730070China

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading *** signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity ***,most certificateless signatures still suffer fromvarious security *** present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature *** ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and *** scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota *** addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing *** results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

关键词： Electricity carbon trading certificateless signature blockchain forgery attack carbon quota

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Effect of Boron Substitution on Magnetic and Electronic Properties of Single-Layered Graphene Studied by Density Functional Theory Method 3

Effect of Boron Substitution on Magnetic and Electronic Prop...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Agustin, A.D. Asih, R. Arifin, R. Darminto Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Mechanical Engineering Faculty of Engineering Universitas Muhammadiyah Ponorogo 63471 Indonesia

Graphene is a two-dimensional material that has special characteristics. The electronic properties of graphene show zero band gap conditions. The magnetic properties of graphene can be created by modifying the electronic properties through atomic substitution. In this research, we study the magnetic and electronic properties of single-layer graphene substituted with boron (B) atoms, because it has almost the same atomic radius as carbon (C) atoms, resulting in only small lattice deformation. The spin-polarized density functional theory (DFT) method implemented in the Quantum Espresso package was selected to perform the calculations. The simulated models are a 4×4×1 supercell of pristine graphene structure consisting of 32 C atoms and boron-substituted graphene with a variety number of atoms (B = 1 and 2 atoms). The results of band gap energy obtained after the structure was optimized are 0.19 and 0.21 eV (spin-down and spin-up) for G-B and 0.36 and 0.37 eV (spin-down and spin-up) for G-2B. Boron substitution in graphene opens the bandgap and shifts the Fermi energy level. It also influences the magnetic moment of the graphene layer, estimated at 0.22 and 0.06 μB/cell for G-B and G-2B, respectively. This research shows that modifying graphene by substituting boron makes the graphene material semiconductive and weakly magnetic. © Published under licence by IOP Publishing Ltd.

关键词： Graphene

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