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23rd international conference on Human-computer Interaction, HCII 2021

23rd International Conference on Human-Computer Interaction,...

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23rd international conference on Human-computer Interaction, HCII 2021

ISBN: (纸本)9783030909628

The proceedings contain 44 papers. The special focus in this conference is on Human-computer Interaction. The topics include: Real-Time Estimation of Eye Movement Condition Using a Deep Learning Model;Development of SPIDAR-HMD for a Standalone HMD;Human Pose Estimation in UAV-Human Workspace;current Status of User Experience of the Keyboard on Smartphones: An Overall Questionnaire Analysis;Extended Reality (XR) Applications in Architectural Practice: Towards a Development Framework;a Cost-Effective Immersive Telexistence Platform for Generic Telemanipulation Tasks;Motion Primitive Segmentation Based on Cognitive Model in VR-IADL;virtual Reality for simulation and Evaluation: Technology Acceptance Models for Automotive Consumer Electronics;modeling Viewpoint of Forklift Operators Using Context-Based Clustering of Gaze Fixations;a Vibrothermal Haptic Display for Socio-emotional Communication;study on Virtual Reality Performance from the User’s Individual Characteristics;Training Tool on Structured Knowledge for Risk Management with VR Technology;An Industry-Adapted AR Training Method for Manual Assembly Operations;Interacting with FEM Simulated Tubes in AR;Improving Learnability Capabilities in Desktop VR Medical Applications;towards Improving Situation Awareness of Maritime Field Operators Using Augmented Reality;Semi-automatic Reply Avatar for VR Training System with Adapted Scenario to Trainee’s Status;A Pilot Study on Progress of Driving Skills with Immersive VR Driving Simulator;Ethical AI for Social Good;a Change in Perspective About Artificial Intelligence Interactive Systems Design: Human Centric, Yes, But Not Limited to;Haptic Finger Glove for the VR Keyboard Input;how to Explain It to Facility Managers? A Qualitative, Industrial User Research Study for Explainability;Toward AI-Based Scenario Management for Cyber Range Training.

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Bone Cancer Detection techniques using Machine Learning

Bone Cancer Detection techniques using Machine Learning

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Computational Modelling, simulation and Optimization (ICCMSO), international conference on

作者： Er. Charnpreet Kaur Urvashi Garg Department of Computer Science and Engineering Chandigarh University Mohali India

Bone cancer is one of the prevalent cancers, any fracture or abnormalities (Cancer) need to be predicted at early stage like other cancers because it spreads to the other parts of body or organs is known as metastasis. Bone cancer is also known as Osteosarcoma, which is malignant primitive bone tumor. Bone cancer is of two types primary and secondary where primary bone cancer originates from Bone and spreads to the other parts of body. On the other hand Secondary bone cancer is also called bone metastasis it occurs when cancerous cell spreads from other parts of body towards bone. This paper presents the different types of Bone cancer such as bone sarcoma and bone metastasis and their stages. As different acquisition techniques plays vital role in the detection of the bone cancer or any type of cancer so this work also discussed different image acquisition techniques. Secondly, this work discussed different classification techniques and their comparison for bone cancer detection. This paper discussed regarding the work done in the field of Bone cancer detection using different feature selection, segmentation and classification techniques on different types of images such as MRI, X-ray and CT Scan and also discussed the challenges and opportunities for Bone cancer detection.

关键词： Computational modeling Standardization Bones Cancer detection Feature extraction Metastasis X-ray imaging

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Review on Machine Learning Methods for Remaining Useful Lifetime Prediction of Lithium-ion Batteries

Review on Machine Learning Methods for Remaining Useful Life...

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Green Energy, Computing and Sustainable Technology (GECOST), international conference on

作者： Nicholas Kwong Howe Su Filbert H. Juwono W. K. Wong I. M. Chew Dept. of Electrical and Computer Eng Curtin University Miri Malaysia Computer Science Program University of Southampton Malaysia Johor Malaysia

ISBN: (纸本)9781665486644

Electric cars are considered as the most ecologically friendly and low-cost means of transportation in the future. As a result, battery technology advancement is of interest for many researchers. Lithium-ion batteries are mostly used for electric vehicles. However, if the Remaining Useful Lifetime (RUL) drops below capacity degradation, devastating device failure will occur. Hence, it is important to predict the RUL to prevent such problems. Data-driven methods are demonstrated to be superior to model-based methods for this reason. This paper provides a review on Machine Learning (ML), one of the data-driven methods, and summarizes various approaches that have been used in lithium-ion (Li-ion) batteries RUL prediction. In addition, comparison of model-based and ML methods are discussed. In particular, the comparison of three ML methods,i.e., Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning(DL) are also presented. simulation results show that SVM is able to provide higher RUL accuracy than LSTM and ANN.

关键词： Support vector machines Lithium-ion batteries Training Machine learning algorithms Biological system modeling simulation Transportation

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Prediction of CO Emission Index for Aviation Gas Turbine Combustor Using Flamelet Generated Manifold Combustion Model

Prediction of CO Emission Index for Aviation Gas Turbine Com...

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ASME Turbomachinery Technical conference and Exposition (Turbo Expo) - Sustainable Energy - Accelerating the Transition by Advancing Turbine Technology

作者： Patwardhan, Saurabh Nakod, Pravin Orsino, Stefano Yadav, Rakesh Xu, Fang Verma, Vishwas Ansys India Pvt Ltd Pune Maharashtra India Ansys Inc Lebanon NH USA Ansys Inc San Diego CA USA Honeywell Aerosp Phoenix AZ USA Honeywell Technol Solut Bangalore Karnataka India

ISBN: (纸本)9780791884959

Carbon monoxide (CO) has been identified as one of the regulated pollutants and gas turbine manufacturers target to reduce the CO emission from their gas turbine engines. CO forms primarily when carbonous fuels are not burnt completely, or products of combustion are quenched before completing the combustion. Numerical simulations are effective tools that allow a better understanding of the mechanisms of CO formation in gas turbine engines and are useful in evaluating the effect of different parameters like swirl, fuel atomization, mixing etc. on the overall CO emission for different engine conditions like idle, cruise, approach and take off. In this paper, a thorough assessment of flamelet generated manifold (FGM) combustion model is carried out to predict the qualitative variation and magnitude of CO emission index with the different configurations of a Honeywell test combustor operating with liquid fuel under idle condition, which is the more critical engine condition for CO emission. The different designs of the test combustor are configured in such a way that they yield different levels of CO and hence are ideal to test the accuracy of the combustion model. Large eddy simulation (LES) method is used for capturing the turbulence accurately along with the FGM combustion model that is computationally economical compared to the detailed/reduced chemistry modeling using finite rate combustion model. Liquid fuel spray breakup is modeled using stochastic secondary droplet (SSD) model. Four different configurations of the aviation gas turbine combustor are studied in this work referring to earlier work by Xu et al. [1]. It is shown that the FGM model can predict CO trends accurately. The other global parameters like exit temperature, NOx emissions, pattern factor also show reasonable agreement with the test data. The sensitivity of the CO prediction to the liquid fuel droplet breakup model parameters is also studied in this work. Although the trend of CO variation is ca

关键词： Combustion Combustion chambers Gas turbines Aviation Emissions Flamelet generated manifold Fuels Engines Drops Sprays Chemistry Carbon monoxide Pollution Nitrogen oxides Large eddy simulation modeling Turbulence computer simulation Temperature

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Dynamically Selected Mixup Machine Unlearning

Dynamically Selected Mixup Machine Unlearning

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhiwen Zhou Ximeng Liu Jiayin Li Junxi Ruan Mingyuan Fan College of Computer and Data Science Fuzhou University Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China

Machine unlearning is a significant part of machine learning security because machine learning models are not immune to attacks such as poisoning attacks. In addition, various studies have proved that it is feasible to obtain training set information from the model, which can lead to leakage of user data privacy. Therefore, there is an urgent need for a method to remove specific data from the model training set. Naive retraining will bring huge time and resource costs, while SISA can achieve a balance between unlearning cost and model performance to a certain extent by dividing the dataset into multiple independent shards. As forgotten data grows, the performance of SISA is still unable to meet the actual needs. In this paper, we propose a method, which dynamically selects the shards that need to be retrained. First, we divide the dataset into multiple independent shards, when the forgotten data makes multiple shards need to be retrained at the same time, our approach is to mix these affected shards together, and then train the mixed shards individually to achieve global forgetting. The simulation results show that compared with SISA, we can get more than 3× speedup on different datasets, and the accuracy of the aggregation model is also improved by 0.5% and 2.1% on MNIST and CIFAR10, respectively.

关键词： Training Privacy Data privacy Costs simulation Computational modeling Machine learning

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Design and Implementation of HDB3 Clock Recovery Module with Large Frequency Range

Design and Implementation of HDB3 Clock Recovery Module with...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Haoxiang Li Shengbo Lu Zixiang Zhao College of Computer Science and Electronic Engineering Hunan University Changsha China School of Electrical Engineering and Automation Hefei University of Technology Hefei China College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China

With the development of the communication and information industry, the demand for high-speed data transmission is growing. One of the essential tasks of the receiver is to extract synchronization information from the received data polluted by noise and recover the data and clock accurately. Therefore, clock data recovery (CDR) is crucial in optical transmission systems and other digital transmission fields. However, the traditional bit synchronous clock recovery can only recover signals of a specific frequency. If a front-end frequency meter is added at the beginning of signal input to measure the input signal frequency, and the real-time control bit synchronization extraction circuit uses the input signal frequency as its working frequency, the operating range of the bit synchronization clock recovery circuit can be significantly improved. This will be the perfect combination of clock recovery performance and time cost. This paper studies the basic principles of bit synchronous clock recovery circuit and dynamic frequency meter. The HDB3 dynamic clock in a wide frequency range is extracted in FPGA using an all-digital design method. Through simulation and verification, the bit synchronous CDR with a pre-dynamic frequency meter has a more comprehensive recovery range, reaching 64Kb/s to 32.768Mb/s.

关键词： Meters Frequency measurement System-on-chip Synchronization Frequency synchronization Integrated circuit modeling Task analysis

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computer simulation of Bent Reinforced Concrete Elements with External Composite Reinforcement 2nd

Computer Simulation of Bent Reinforced Concrete Elements wit...

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2nd international Scientific conference on Innovations and Technologies in Construction, BUILDINTECH BIT 2021

作者： Merkulov, S.I. Esipov, S.M. Esipova, D.V. Kursk State University Kursk Russia Belgorod State Technological University V. G. Shukhov Belgorod Russia

ISBN: (纸本)9783030729097

The article discusses the issues and features of modeling the external reinforcement of reinforced concrete structures during bending using the ANSYS software. A method for modeling flexible reinforced concrete elements reinforced in a stretched zone with external reinforcement made of composite materials based on carbon fibers, tested during field experiments, is presented. This was used as clarifying factors to develop a methodology with different approaches to the reinforcement of reconstructed reinforced concrete. Qualitative and quantitative relationships between experimental and numerical studies are established, confirming the prerequisites of the study. The limit values of deformations of concrete, steel reinforcement, composite reinforcement during destruction are determined. A comparative characteristic of variational approaches to modeling the joint work of external reinforcement and damaged concrete is given. Comparative analyses of the dependence of the increase in load-bearing capacity on the degree of damage and load of the element were also performed. Theoretical and actual values of the opening width and height of cracks in the zone of pure bending, including before and after reinforcement, are determined. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Carbon fibers

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simulation and Analysis of Initial Stages of Negative Discharges in Air for Needle-Plane Electrode Configuration

Simulation and Analysis of Initial Stages of Negative Discha...

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IEEE Symposium Power Modulator

作者： Mohammad Hamidieh Mona Ghassemi The Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA

This paper presents a model for the initial stages of negative discharge in the atmospheric air under an extremely nonuniform field condition caused by a needle-plane electrode configuration. To model the dynamics of charged species, the drift-diffusion model is employed while taking into account the secondary electron emissions and addressing the mathematical challenges to solve the problem. simulations are carried out in the COMSOL Multiphysics software. Analyses are performed mainly on the variations of the generation and loss rates of the charged species, their concentration evolution, and corresponding electric field distribution alterations.

关键词： Electrodes Analytical models Spontaneous emission Atmospheric modeling Voltage Mathematical models Discharges (electric)

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Information system of ecological monitoring "small mammals as bioindicator" 15th

Information system of ecological monitoring "small mammals a...

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15th international Scientific-Practical conference on Mathematical modeling and simulation of Systems, MODS 2020

作者： Talakh, Mariia Holub, Serhii Department of Computer Science Yuriy Fedkovych Chernivtsi National University Chernivtsi Ukraine Department of Automated Systems Software Cherkasy State Technological University Cherkasy Ukraine

ISBN: (纸本)9783030581237

The article presents the results of research on population dynamics Martes martes L. and Lepus europaeus Pall. depending on weather factors in the national park "Vyzhnytskyi" during the period 2002–2019 years. The effectiveness of the proposed method of multilevel monitoring for the studied bioindicator’s species was shown and the factors having a reliable influence on their population size were highlighted. For assessing the complex state of ecosystems often several bioindicator species are used. In this case, the trophic criterion was used as an approach for the formation of a complex bioindicator - the selected species belong to small mammals, while at the same time they are at different trophic levels and can be connected by food bonds. In this case, a complex bioindicator was obtained by the synthesis of models created for each of the bioindicators species. modeling was carried out using a multi-row algorithm for group accounting of arguments, and a polynomial was used as a reference model. The proposed method allows to prioritize the use of individual bioindicators, depending on the modeling results. And proposed in this work set of indicators can be expanded by both bioindicators and climate characteristics. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Population statistics

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1st international Artificial Intelligence conference, IAIC 2023

1st International Artificial Intelligence Conference, IAIC 2...

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1st international Artificial Intelligence conference, IAIC 2023

ISBN: (纸本)9789819712762

The proceedings contain 86 papers. The special focus in this conference is on Artificial Intelligence. The topics include: A Paper Citation Link Prediction Method Using Graph Attention Network;research on Emotional Analysis of Tibetan Short Text Based on Fusion Sentiment Lexicon;A Review of Solving Non-IID Data in Federated Learning: Current Status and Future Directions;a Review of Relationship Extraction Based on Deep Learning;BERT and LLM-Based Multivariate Hate Speech Detection on Twitter: Comparative Analysis and Superior Performance;Deep Learning for Protein-Protein Contact Prediction Using Evolutionary Scale modeling (ESM) Feature;fedTag: Towards Automated Attack Investigation Using Federated Learning;Deep Learning Based SQL Injection Attack Detection;RSCC: Robust Semi-supervised Learning with Contrastive Learning and Augmentation Consistency Regularization;enhanced Prototypical Network for Few-Shot Named Entity Recognition;Regularized DNN Based Adaptive Compensation Algorithm for Gateway Power Meter in Ultra-High Voltage Substations;Dynamic Occlusion Expression Recognition Based on Improved GAN;Contrastive Learning Based on AMR Graph for Logic Reasoning;automated Detection and Recognition of Wild Dolphin Behaviors Using Deep Learning;M-GFCC: Audio Copy-Move Forgery Detection Algorithm Based on Fused Features of MFCC and GFCC;a Survey of Homogeneous and Heterogeneous Multi-source Information Fusion Based on Rough Set Theory;formula Maintenance of Single Material Tobacco Compatibility Based on Co-formulation Analysis Method;multi-physical Field Collaborative simulation Optimization Technology and Reliability Analysis of Power Amplifiers;research and Design of Hydrological Data Visualization Based on Digital Twin;Design of Constraint FH-LFM DFRC Waveform;a Construction of IoT Malicious Traffic Dataset and Its Applications;meta-learning Based on Multi-objective Optimization;research and Implementation of Cooperative Utilization of Network Security Devices for Typ

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