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arXiv 2022年

作者： van Hulst, Jilles Poot, Maurice Kostić, Dragan Yan, Kai Wa Portegies, Jim Oomen, Tom Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology Netherlands Center of Competency ASM Pacific Technology Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands Delft Center for Systems and Control Delft University of Technology Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input. © 2022, CC BY-NC-ND.

关键词： Feedforward control

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Blockchain Anchored Federated Learning and Tokenized Traceability for Sustainable Food Supply Chains

Blockchain Anchored Federated Learning and Tokenized Traceab...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： Koushik Reddy Chaganti B. Naveen Kumar Pruthvi Krishna Gutta Sivananda Lahari Reddy Elicherla C. Nagesh K Raghavendar Dept. of Computer and Information Systems University of the Cumberlands KY USA Department of Information Technology Vignan Institute of Technology and Science Hyderabad Dept of IT Tesla Austin Texas United States Dept of CSE Dayananda Sagar University Bangalore Dept of CSE Srinivasa Ramanujan Institute of Technology Anantapur Dept of CSE Teegala Krishna Reddy Engineering College Hyderabad

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The management of food supply chains relates to just about the most imperative issues concerning sustainability, quality, and traceability, where existing methodologies usually fail to meet the demands caused by data privacy, lack of transparency, and inefficiencies concerning collaboration among stakeholders. This compromises data security, and thus there arises single points of failure. Conversely, traditional traceability frameworks fail to incite wide-scale participation and lack real-time accuracy. Having these limitations in mind, we introduce a hybrid framework consisting of three advanced methodologies: Decentralized Federated Learning with Blockchain Anchoring (DFLBA), Dynamic Tokenized Traceability Framework (DTTF), and Adaptive Supply Chain Graph Neural Networks (ASCGNN) process. DFLBA would support privacy-preserving machine learning as such that collaborative training of yield prediction and crop monitoring models by stakeholders without revealing raw data will be feasible. Blockchain anchoring ensures logs of training updates are tamper-proof, thus increasing trust and transparency. DTTF introduces tokenized incentives that enhance traceability in real-time: every product has a digital twin represented by blockchain-anchored tokens updated by stakeholders. Smart contracts enforce the compliance that will improve participation and lifecycle data accuracy. ASCGNN models the food supply chain as a dynamic graph, leveraging graph neural networks to capture stakeholder interdependencies, optimize operations, and detect fraud. Blockchain integration ensures reliability and transparency by securing edge weights. The suggested framework has demonstrated good impact, like ~95% model accuracy for yield prediction, 99% traceability coverage, and ~92% prediction accuracy for bottlenecks of the supply chain. It provides a scalable, privacy-preserving, and transparent solution that enhances the efficiency of food supply chains, and positively impacts stakeholders in

关键词： Training Accuracy Federated learning Supply chains Collaboration Predictive models Real-time systems Graph neural networks Blockchains Stakeholders

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ACT-Net: Anchor-context Action Detection in Surgery Videos

arXiv

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arXiv 2023年

作者： Hao, Luoying Hu, Yan Lin, Wenjun Wang, Qun Li, Heng Fu, Huazhu Duan, Jinming Liu, Jiang School of Computer Science University of Birmingham United Kingdom Research Institute of Trustworthy Autonomous Systems Dept. of Computer Science and Engineering Southern University of Science and Technology China Dept. of Mechanical Engineering National University of Singapore Singapore Third medical center of Chinese PLAGH China Singapore

Recognition and localization of surgical detailed actions is an essential component of developing a context-aware decision support system. However, most existing detection algorithms fail to provide high-accuracy action classes even having their locations, as they do not consider the surgery procedure’s regularity in the whole video. This limitation hinders their application. Moreover, implementing the predictions in clinical applications seriously needs to convey model confidence to earn entrustment, which is unexplored in surgical action prediction. In this paper, to accurately detect fine-grained actions that happen at every moment, we propose an anchor-context action detection network (ACTNet), including an anchor-context detection (ACD) module and a class conditional diffusion (CCD) module, to answer the following questions: 1) where the actions happen;2) what actions are;3) how confidence predictions are. Specifically, the proposed ACD module spatially and temporally highlights the regions interacting with the extracted anchor in surgery video, which outputs action location and its class distribution based on anchor-context interactions. Considering the full distribution of action classes in videos, the CCD module adopts a denoising diffusion-based generative model conditioned on our ACD estimator to further reconstruct accurately the action predictions. Moreover, we utilize the stochastic nature of the diffusion model outputs to access model confidence for each prediction. Our method reports the state-of-the-art performance, with improvements of 4.0% mAP against baseline on the surgical video dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

Dramatic Increase in Fear-Related Discussion on Twitter duri...

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Electrical Engineering and Sustainable Technologies (ICEEST), International Conference on

作者： Muhammad Waleed Karam Azhar Imran Abdulkareem Alzahrani Khattab M. Ali Alheeti Ahsan Abbas Abdulkareem A. Najem Al-Aloosy Dept. of Creative Technology Air University Islamabad Pakistan Faculty of Computer Science & IT Al Baha University Albaha Saudi Arabia Computer Networking Systems Dept. University of Anbar Ramadi Iraq

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples’ mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public; country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification.

关键词： COVID-19 Support vector machines Social networking (online) Blogs Text categorization Support vector machine classification Media

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Broadband Corrugated Modified Vivaldi Antenna for Microwave based Imaging Applications 24

Broadband Corrugated Modified Vivaldi Antenna for Microwave ...

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24th International Conference on computer and Information technology, ICCIT 2021

作者： Talukder, Md Siam Samsuzzaman, Md Paul, Liton Chandra Masud, Md Abdul Azim, Rezaul Moniruzzaman, Md Patuakhali Science and Technology University Faculty of Computer Science and Engineering Patuakhali Bangladesh Pabna University of Science and Technology Dept. of ElectricalElectronic and Communication Engineering Pabna Bangladesh University of Chittagong Dept. of Physics Chittagong Bangladesh Universiti Kebangsaan Malaysia Dept. of ElectricalElectronic and Systems Engineering Bangi Malaysia

ISBN: (纸本)9781665494359

This paper explored a Broadband Corrugated Modified Vivaldi Antenna for head imaging applications using microwaves. The antenna is made up of a tapered feeding circular slotted patch and a rectangular modified wing shape ground with an electrical dimension of $0.49\lambda\times 0.41\lambda\times 0.009\lambda$ at lower frequency of 1.67 GHz. Front and back radiator are placed on a substrate of FR-4 material having a permittivity of 4.3 and a thickness of 1.5 mm, which are less expensive and more widely accessible. The designed antenna works between 1.67 and 6.37 GHz, allowing the imaging system to function over a large fractional bandwidth of about 116.92 %. With an appropriate impedance matching and a directionally stable radiation characteristics, the antenna has a decent gain of 9 dBi. A 50$\Omega$ microstrip line feeds the antenna. The simulation tools CST was used to evolve and optimize the design. © 2021 IEEE.

关键词： Microstrip antennas

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Ontology-Based Method for Semantic Association Rules

Ontology-Based Method for Semantic Association Rules

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IEEE India Conference (INDICON)

作者： Abhilash C B Kavi Mahesh Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad Dharwad India Dept. of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad India

ISBN: (纸本)9781665452724

Finding interesting association rules is a popular and current topic in data mining. The Apriori family of algorithms is built around two rule extraction measures: support and confidence. Even though these two measures are easy to compute, they yield many rules, the majority of which are redundant and may not be of interest to the user. Also, by themselves, support and confidence do not generate strong rules. Additional measures are required to mine interesting facts from data. Ontologies have become the fundamental building blocks for structuring and formalizing data. With the semantic structuring of information, the implicit relationship between data elements makes the analyst get important facts from the data. Our study proposes a novel framework for interestingness in data by combining domain ontology with semantic interestingness measures. The ontology-based method infers rules that are semantically enriched and strong. We analyze the quality of the rule considering the factors defined by the domain experts. It is observed that our methodology generates semantically enriched rules that are more acceptable to domain experts.

关键词： COVID-19 Current measurement Semantics Knowledge based systems Influenza Ontologies Resource description framework

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Scalability in Autoencoder-based OFDM Communication System

Scalability in Autoencoder-based OFDM Communication System

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Seizan Tsugawa Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 ㏈ at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 ㏈ at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 ㏈, but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0 . As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication.

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Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method

Evaluating the Solutions to Predict the Impact of Lung Cance...

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International Conference on Smart systems and Inventive technology (ICSSIT)

作者： R. Sundar Sudhir Ramadass D. Meeha Balambigai Subramanian S Siva Shankar Gayatri Parasa Dept. of Computer Science and Engineering Madanapalle Institute of Technology & Science Sterck Systems Chennai Dept. of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engg CollegeAvadi Chennai Dept. of ECE Kongu Engineering College Perundurai Dept. of CSE -AI&ML KG Reddy college of Engineering and Technology Chilukuru Village Telangana Dept. of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh

Using symptoms as a basis for diagnosing lung cancer, Lung cancer detection was accomplished using several different machine-learning regression strategies. By comparing the efficacy of several regression algorithms for predicting lung cancer, considering factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure. Lung cancer predictions and evaluations are made using regression methods such as the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. With a predictive accuracy of 96%, multiple regression is superior to other regression techniques when identifying future lung cancer cases. The r-squared value, which can be calculated using several regression machine learning approaches, may also be used to evaluate the association between the various symptoms and lung cancer. Lung cancer is diagnosed using the r-squared value, which is calculated using several algorithms and takes into symptoms, including chronic illness.

关键词： Machine learning algorithms Correlation Linear regression Anxiety disorders Lung cancer Machine learning Prediction algorithms

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Intelligent Constellation Generation based on Autoencoder Communication System

Intelligent Constellation Generation based on Autoencoder Co...

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kaisei Matsumoto Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

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