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6th International Conference on Mechanical Engineering Research, ICMER 2021

作者： Kananathan, J. Rajan, K. Samykano, M. Kadirgama, K. Moorthy, K. Rahman, M.M. Green Kingdom Solutions Sdn. Bhd. Taman Salak Selatan Kuala Lumpur57100 Malaysia College of Engineering Universiti Malaysia Pahang Pahang Gambang26300 Malaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Pahang Pekan26600 Malaysia College of Computing and Applied Sciences Faculty of Computing Universiti Malaysia Pahang Pahang Pekan26600 Malaysia

ISBN: (纸本)9789811915765

Fused deposition modeling (FDM) is a technique in additive manufacturing that has been used to produce various components using a variety of materials for a broad range of applications adapting layer-by-layer deposition process. Thermoplastic polymers are commonly used raw material that comes in the form of a filament. Coconut wood is highly recognized for its naturally affable ecological components, thermal resilience, and corrosion resistance. However, PLA's qualities embedded in coconut wood remain inadequate. The objective of this analysis is to investigate and analyze the tensile properties of the 3D printed specimens with varying infill percentages (25, 50, and 75%) and the infill patterns (grid, rectilinear, concentric, honeycomb, and triangle) on coconut wood reinforced PLA using the FDM technique. The specimen is printed in accordance with the ASTM standard for tensile testing, which is ASTM D638 type 1. Following that, the tensile properties of the PLA and PLA/Coconut wood were analyzed. The results demonstrate that the concentric infill pattern with a 75% infill percentage provides the strongest structure in PLA and PLA/Coconut wood composites. The PLA has a maximum tensile strength of 37.55 MPa, whereas the PLA/Coconut wood has 19.35 MPa. PLA has a maximum elastic modulus of 1.148 GPa, while the PLA/Coconut wood composite has a maximum elastic modulus of 1.121 GPa. PLA has a yield strength of 23.33 MPa, while the PLA/Coconut wood composite has a yield strength of 15.33 MPa in tensile testing. Meanwhile, the grid pattern has the weakest properties for both materials. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Composite materials

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Opposition flow control for reducing skin-friction drag of a turbulent boundary layer

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Physical Review Fluids 2024年第6期9卷 064602-064602页

作者： Giulio Dacome Robin Mörsch Marios Kotsonis Woutijn J. Baars Faculty of Aerospace Engineering Delft University of Technology 2629 HS Delft The Netherlands Institute of Applied Mathematics and Scientific Computing University of the Bundeswehr Munich 85577 Neubiberg Germany

This work explores the dynamic response of a turbulent boundary layer to large-scale reactive opposition control, at a friction Reynolds number of Reτ≈2240. A surface-mounted hot-film is employed as the input sensor, capturing large-scale fluctuations in the wall-shear stress, and actuation is performed with a single on/off wall-normal blowing jet positioned 2.4δ downstream of the input sensor, operating with an exit velocity of vj=0.4U∞. Our study builds upon the work of Abbassi et al. [Int. J. Heat Fluid Flow 67, 30 (2017)] and includes a control-calibration experiment and a performance assessment using PIV- and PTV-based flow field analyses. With the control-off calibration-experiment conducted a priori, a transfer kernel is identified so that the velocity fluctuations that are to-be-controlled can be estimated. The controller targets large-scale high-speed zones in an “opposing” mode and low-speed zones in a “reinforcing” mode. A desynchronized mode was tested for reference and consisted of a statistically similar control mode, but without synchronization to the incoming velocity fluctuations. An energy-attenuation of about 40 % is observed for the opposing control mode in the frequency band corresponding to the passage of large-scale motions. This proves the effectiveness of the control in targeting large-scale motions: an energy-intensification of roughly 45% occurs for the reinforcing control mode instead, while no change in energy, within the wall-normal range targeted, appears with the desynchronized control mode. Moreover, direct measures of the skin-friction drag are inferred from PTV data. Results indicate that the opposing control logic yields the lowest wall-shear stress (3% lower than the desynchronized control, and 10% lower than the uncontrolled flow). Finally, a FIK-decomposition of the skin-friction coefficient revealed that the off-the-wall turbulence follows a consistent trend with the PTV-based wall-shear stress measurements, although biased by

关键词： Flow control Turbulence control Turbulent boundary layers

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Location Anonymous Query Algorithm based on Road Networks

Location Anonymous Query Algorithm based on Road Networks

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Qing Haohua Roliana Ibrahim Hui Wen Nies Department of Applied Computing and Artificial Intelligence Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia Guangzhou College of Technology and Business Guangzhou Guangdong China

ISBN: (纸本)9781665489638

In this paper, we study the location anonymous query algorithm based on the Voronoi graph division of the road networks. The Voronoi diagram spatial analysis model has unique functions and application potential in the description of the location problems, the spatial partitioning of the landmarks based on the Voronoi diagram, on the one hand, it achieves the division of the spatial extent covered by the POI under the principle of shortest distance, and on the other hand, it also deals with the problem of the spatial partitioning. Hence, we propose the novel ideas of improving the division performance and also the query algorithm enhancement. Through the simulation, we select the Foshan city data as the target to evaluate the proposed model.

关键词： Analytical models Roads Urban areas Data models Partitioning algorithms

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Marie's ChemLab: A Mobile Augmented Reality Game to Teach Basic Chemistry to Children 16

Marie's ChemLab: A Mobile Augmented Reality Game to Teach Ba...

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16th European Conference on Games Based Learning, ECGBL 2022

作者： Arztmann, Michaela Alfaro, Jessica Lizeth Domínguez Blattgerste, Jonas Jeuring, Johan Van Puyvelde, Peter Department of Education Faculty of Social and Behavioral Sciences Utrecht University Utrecht Netherlands Department of Chemical Engineering Faculty of Engineering KU Leuven Leuven Belgium Mixed Reality Lab University of Applied Sciences Emden/Leer Electrical Engineering and Information Technology Emden Germany Department of Information and Computing Sciences Faculty of Science Utrecht University Utrecht Netherlands

ISBN: (纸本)9781914587511

Digital games are widely used in education to motivate students for science. In parallel, the role of augmented reality (AR) in education is increasing. This paper introduces Marie's ChemLab: a marker-less handheld AR application in which K-12 students learn about science. In Marie's ChemLab, users perform interactions in their physical environment using virtual AR objects displayed on a handheld device. The utilized interaction concept is based on the TrainAR framework (Blattgerste et al, 2021), which originally was envisioned for procedural AR training and layered feedback mechanisms. The original framework has been enhanced to support gamified interactions with virtual objects, specifically for K-12 students. In total, 239 early secondary school students played the final application as part of a larger study. The usability score indicated marginally acceptable usability, and the application was successfully implemented in a classroom setting. © 2022 Dechema e.V.. All rights reserved.

关键词： Augmented reality

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An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques 12

An evaluation of measures to dissociate language and communi...

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2nd ACM SIGHIT International Health Informatics Symposium, IHI'12

作者： Gaspers, Judith Thiele, Kristina Cimiano, Philipp Foltz, Anouschka Stenneken, Prisca Tscherepanow, Marko Semantic Computing CRC 673 Bielefeld University Bielefeld Germany Clinical Linguistics CITEC Bielefeld University Bielefeld Germany Clinical Linguistics CRC 673 Bielefeld University Bielefeld Germany Applied Informatics Faculty of Technology Bielefeld University Bielefeld Germany

ISBN: (纸本)9781450307819

Reliably distinguishing patients with verbal impairment due to brain damage, e.g. aphasia, cognitive communication disorder (CCD), from healthy subjects is an important challenge in clinical practice. A widely-used method is the application of word generation tasks, using the number of correct responses as a performance measure. Though clinically well-established, its analytical and explanatory power is limited. In this paper, we explore whether additional features extracted from task performance can be used to distinguish healthy subjects from aphasics or CCD patients. We considered temporal, lexical, and sublexical features and used machine learning techniques to obtain a model that minimizes the empirical risk of classifying participants incorrectly. Depending on the type of word generation task considered, the exploitation of features with state-of-the-art machine learning techniques outperformed the predictive accuracy of the clinical standard method (number of correct responses). Our analyses confirmed that number of correct responses is an adequate measure for distinguishing aphasics from healthy subjects. However, our additional features outperformed the traditional clinical measure in distinguishing patients with CCD from healthy subjects: The best classification performance was achieved by excluding number of correct responses. Overall, our work contributes to the challenging goal of distinguishing patients with verbal impairments from healthy subjects. Copyright © 2012 ACM.

关键词： Learning algorithms

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Effectiveness of Machine Learning Algorithms on Battling Counterfeit Items in E-commerce Marketplaces

Effectiveness of Machine Learning Algorithms on Battling Cou...

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International Research Conference on Smart computing and Systems Engineering (SCSE)

作者： Hmkt Gunawardhana Btgs Kumara Kapila Rathnayake Prasad M. Jayaweera Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Department of Computer Science Faculty of Applied Sciences University of Sri Jayawardanapura Sri Lanka

For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.

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EEG Source Localization Using Spatio-Temporal Neural Network

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China Communications 2019年第7期16卷 131-143页

作者： Song Cui Lijuan Duan Bei Gong Yuanhua Qiao Fan Xu Juncheng Chen Changming Wang Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China College of Applied Sciences Beijing University of Technology Beijing 100124 China The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing 100088 China Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing 100069 China

Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.

关键词： electroencephalogram LSTM source localization spatio-temporal modeling

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Classification of epilepsy period based on combination feature extraction methods and spiking swarm intelligent optimization algorithm

Classification of epilepsy period based on combination featu...

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作者： Duan, Lijuan Lian, Zhaoyang Chen, Juncheng Qiao, Yuanhua Miao, Jun Li, Mingai Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing University of Technology Beijing China Applied Sciences Beijing University of Technology Beijing China School of Computer Science Beijing Information Science and Technology University Beijing China

Epilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time-frequency features and principal component features of epilepsy. The time-frequency features are obtained by WPT or STFT-PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance. © 2020 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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Enhanced chaos-based image compression-encryption algorithm utilizing 2D compressive sensing and genetic algorithm optimization

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Multimedia Tools and Applications 2024年 1-38页

作者： Nanfak, Arnaud de Dieu Nkapkop, Jean Mvogo Ngono, Joseph Tabekoueng Njitacke, Zeric Lessouga Etoundi, Christophe Magloire Effa, Joseph Yves Laboratory of Electronic Electrical Engineering Automation and Telecommunication National Higher Polytechnic School of Douala University of Douala PO Box 2701 Douala Cameroon Technology and Applied Sciences Laboratory University Institute of Technology University of Douala P.O. Box 8698 Douala Cameroon Applied Computing Laboratory Faculty of Science University of Douala P.O. Box 2701 Douala Cameroon University of Buea P.O. Box 63 Buea Cameroon Department of Physics Faculty of Science University of Ngaoundere P.O. Box 454 Ngaoundere Cameroon

This paper introduces an innovative image compression and encryption algorithm that leverages the genetic algorithm (GA), compressive sensing (CS), and a newly developed hyperchaotic map. In this algorithm, the GA is employed to optimize the compression parameters, including those related to generating the measurement matrix and performing thresholding operation. The optimization objective is centred around on the Peak-Signal-to-Noise-Ratio (PSNR) of the reconstructed image. The encryption part involves employing the Arnold transform and byte-wise XOR operator for confusion and diffusion, respectively. The generation of the measurement matrix and chaotic sequences for encryption is facilitated by a novel two-dimensional fractional-Sine–Cosine (2DFSC) hyperchaotic map introduced in this research. Simulation outcomes demonstrate dynamic parameter values adjustments through optimization, resulting in improved decompression efficiency across different compression rates and image types. Assessment of security and decompression performance confirms the effectiveness of the proposed algorithm, showing a good compression effect, a good reconstruction efficacity even for low compression ratio, good speed and high security compared to some recent similar systems. This work may therefore be suitable to compress and secure data in wireless body area network applications, including those related to the internet of medical things. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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Computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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