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Multi-response Optimization of Process Parameters in Stretch Blow Molding of PET Plastic Bottles

Journal of The Institution of Engineers (India): Series D 2024年 1-15页

作者： Merga, Henok Hailu Sinha, Devendra Kumar Getachew, Aklilu Workneh, Melese Rathee, Devender Singh Department of Mechanical Engineering School of Mechanical Chemical and Materials Engineering Center of Excellence Advanced Manufacturing Engineering Adama Science and Technology University Adama Ethiopia Department of Electronics Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

Plastic waste in Polyethylene Terephthalate (PET) bottles is creating new challenges, which in today’s scenario is a major concern in every industry. The defect in PET bottles mainly occurs due to improper selection of levels of process parameters during the production process. Therefore, optimizations of process parameters are very important to minimize the defects produced due to improper stretch blow molding process parameter selections. The study is carried out on 2 L bottle since they have the highest demand and the highest defect rate in the PET bottling sector. The PET bottles have been developed from the preform at various blowing temperature, blowing speed and blowing angle on commercially available SBM setup. The design and plan of experiments have been done according to L9 orthogonal array of Taguchi method. The optimization of process parameters has been done through the hybrid Taguchi grey relational analysis optimization technique on four factors namely: blowing temperature, first blow angle, second blow angle and blowing speed. Each value of parameter was taken at three levels in combination. The testing of all samples such as tensile test, wall thickness measurement and hoop stretch ratio measurement have been carried out as per ASTM standards. The morphology of the samples has been carried out through scanning electron microscope. A maximum tensile strength of 65 MPa, minimum hoop stretch ratio of 5.45 and a maximum wall thickness 0.24 mm was found at varying process parameter combinations. It has been observed that among the process parameters: blowing temperature was the most significant factor for minimizing defects while second blow angle was the least significant parameter. It was found that optimized values of the process parameters yielded 15% reduction in defects on PET bottles. The present study is expected to contribute significantly for the industries working in this field for carrying out production at optimum levels of process parameter

关键词： Plastic bottles

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Convolutional graph neural network training scalability for molecular docking 31

Convolutional graph neural network training scalability for ...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： Crampon, Kevin Giorkallos, Alexis Baud, Stephanie Steffenel, Luiz Angelo Atos Se Center for Excellence in Advanced Computing Echirolles France Université de Reims Champagne-Ardenne Cnrs MEDyC Reims France Université de Reims Champagne-Ardenne Reims France

ISBN: (纸本)9798350337631

Deep learning use is growing in many numerical simulation fields, and drug discovery does not escape this trend. Indeed, before proceeding with in vitro and then in vivo experiments, drug discovery now relies on in silico techniques such as molecular docking to narrow the number of experiments and identify the best candidates. This method explores the receptor surface and the ligand's conformational space, providing numerous ligand-receptor poses. All these poses are then scored and ranked by a scoring function allowing to predict the best poses among all, then compare different ligands regarding a given receptor or different targets regarding a given ligand. Since the 2010s, numerous deep learning methods have been used to tackle this problem. Nowadays, there are two significant trends in deep learning for molecular docking: (i) the augmentation of available structural data and (ii) the use of a new kind of neural network: the graph convolutional neural networks (GCNs). In this paper, we propose the study of training scalability of a GCN-a molecular complex scoring function-on an increasing number of GPUs and with a variety of batch sizes. After a hyperparameter analysis, we achieve an 80% reduction in the training time, but this improvement sometimes involves a performance metrics degradation that the final users must ponder. © 2023 IEEE.

关键词： Ligands

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Multi-class Imbalanced Classification Problems in Network Attack Detections 1

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6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021

作者： Fern, Soon Hui Amir, Amiza Azemi, Saidatul Norlyana Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis02600 Malaysia Advanced Computing Center of Excellence Universiti Malaysia Perlis Perlis02600 Malaysia Advanced Communication Engineering Center of Excellence Universiti Malaysia Perlis Perlis01000 Malaysia

ISBN: (数字)9789811686900

ISBN: (纸本)9789811686894

Nowadays, network anomaly activities can be detected by using machine learning. The performance of the machine learning algorithms depends on the distribution of the dataset. Network anomaly detection is one of the popular topics that contain vast numbers of data and faced imbalanced multiclass classification distribution. However, most researchers have neglected the problem of multiclass imbalanced classification when undergoing their research on network anomaly detection. It cannot be denied that some researchers proposed some methods to solve this problem. Nevertheless, the methods that have been proposed still contains some drawbacks. Hence, in this research, the performance of the machine learning due to the effect of a multiclass imbalanced dataset was studied. Seven machine learning algorithms from different families will be used in this study, and their ability to classify the majority and minority class instances and achieved high classification accuracy will be identified. Besides, seven different performance metrics will also be used to evaluate the performance of the classifiers used in this study. The results show that although the classification accuracy of the Bayesian machine learning algorithms is lower than other classifiers used in this research, they perform well in detecting the minority class instances. Besides, J48 has known as the classifier that can work well in every aspect of this research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Correcting on-chip distortion of control pulses with silicon spin qubits

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Chinese Physics B 2025年第1期34卷 265-271页

作者： Ming Ni Rong-Long Ma Zhen-Zhen Kong Ning Chu Wei-Zhu Liao Sheng-Kai Zhu Chu Wang Gang Luo Di Liu Gang Cao Gui-Lei Wang Hai-Ou Li Guo-Ping Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China(USTC)Hefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Integrated Circuit Advanced Process Research and Development Center Institute of MicroelectronicsChinese Academy of Sciences(CAS)Beijing 100029China Hefei National Laboratory Hefei 230088China Beijing Superstring Academy of Memory Technology Beijing 100176China Origin Quantum Computing Company Limited Hefei 230026China

In semiconductor quantum dot systems,pulse distortion is a significant source of coherent errors,which impedes qubit characterization and ***,we demonstrate two calibration methods using a two-qubit system as the detector to correct distortion and calibrate the transfer function of the control *** methods are straightforward to implement,robust against noise,and applicable to a wide range of qubit *** two methods differ in correction accuracy and *** first,coarse predistortion(CPD)method,partially mitigates *** second,all predistortion(APD)method,measures the transfer function and significantly enhances exchange oscillation *** methods use exchange oscillation homogeneity as the metric and are suitable for any qubit driven by a diabatic *** believe these methods will enhance qubit characterization accuracy and operation quality in future applications.

关键词： quantum computation quantum dot pulse distortion

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A Novel Machine Learning Approach for Detection of Coronary Artery Disease Using Reduced Non-linear and Chaos Features

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International Journal Bioautomation 2022年第3期26卷 273-296页

作者： Singh, Ram Sewak Gelmecha, Demissie Jobir Mishra, Satyasis Dengia, Gemechu Sinha, Devendra Kumar Department School of Electrical & Computing Engineering Adama Science & Technology University Adama Ethiopia Mechanical Design & Manufacturing Engineering Department Center of Excellence in Advanced Manufacturing Engineering School of Mechanical Chemical & Materials Engineering Adama Science & Technology University Adama Ethiopia

In this research paper, authors present an automated system in this paper that integrates a ranking technique with Principal Component Analysis (PCA), Generalized Discriminant Analysis (GDA) and a 1-Norm Bidirectional Extreme Learning Machine (1-NBELM) to reliably classify normal and coronary artery disease groups. Twenty chaotic and non-linear attributes were hauling out from the Heart Rate Variability (HRV) signal to detect coronary artery disease groups. The HRV data for this study derived from a typical database of Normal Old (ELY), Young (YNG), and Coronary Artery Disease (CAD) people. Fisher, Wilcoxon and Bhattacharya were used to compute the rankings of attributes. GDA then turned the ranking features into a new feature. The Radial Basis Function (RBF) kernel was used to transfer original features to a high-dimensional feature space in GDA and PCA, and then it was deployed to 1-NBELM, which utilized the sigmoidal or multiquadric non-linear activation. Numerical experiments were performed on the combination of database sets as Young-ELY, Healthy-CAD, and Healthy ELY-CAD subjects. The numerical results show that ROC with GDA and 1-NBELM approach achieved an accuracy of 98.12±0.14, 96.21±0.12 and 99.87±0.28 for Young-CAD, Young-ELY and Healthy ELY-CAD groups with the use of sigmoidal and multiquadric activation function. The Fisher with GDA and 1-NBELM and Bhattacharya with GDA and 1-Norm Extreme Learning Machine (1-NELM) approach achieved an accuracy of 99.98±0.21 for all databases. © 2022 by the authors. Licensee Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://***/licenses/by/4.0/).

关键词： Principal component analysis

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Controllable Majorana vortex states in iron-based superconducting nanowires

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National Science Review 2022年第9期9卷 195-203页

作者： Chuang Li Xun-Jiang Luo Li Chen Dong E.Liu Fu-Chun Zhang Xin Liu School of Physics and Institute for Quantum Science and Engineering Huazhong University of Science and Technology Wuhan National High Magnetic Field Center and Hubei Key Laboratory of Gravitation and Quantum Physics State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Kavli Institute for Theoretical Sciences and CAS Center for Excellence in Topological Quantum Computing University of Chinese Academy of Sciences Collaborative Innovation Center of Advanced Microstructures Nanjing University

To reve al the non-Abelian braiding statistics of Major ana zero modes(MZMs),it is crucial to design a Majorana platform,in which MZMs can be easily manipulated in a broad topological nontrivial parameter *** is also an essential step to confirm their *** this study,we propose an iron-based superconducting nanowire system with Majorana vortex states to satisfy desirable *** system has a radius-induced topological phase transition,giving a lower bound for the nanowire *** the topological phase,the iron-based superconducting nanowires have only one pair of MZMs over a wide range of radii,chemical potential and external magnetic *** wave function of MZMs has a sizable distribution at the side edge of the *** property enables the control of the interaction of MZMs in neighboring vortex nanowires and paves the way for Majorana fusion and braiding.

关键词： iron-based superconductor topological phase transition superconducting vortex Majorana zero modes nanowire

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Polarization-dependent photoinduced metal–insulator transitions in manganites

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Science Bulletin 2024年第2期69卷 183-189页

作者： Lina Deng Weiye Zhang Hanxuan Lin Lifen Xiang Ying Xu Yadi Wang Qiang Li Yinyan Zhu Xiaodong Zhou Wenbin Wang Lifeng Yin Hangwen Guo Chuanshan Tian Jian Shen State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computingand Department of PhysicsFudan UniversityShanghai 200433China Shanghai Research Center for Quantum Sciences Shanghai 201315China Zhangjiang Fudan International Innovation Center Fudan UniversityShanghai 201210China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093China Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum PhysicsShanghai 201315China

In correlated oxides,collaborative manipulation on light intensity,wavelength,pulse duration and polarization has yielded many exotic discoveries,such as phase transitions and novel quantum *** view of potential optoelectronic applications,tailoring long-lived static properties by light-induced effects is highly *** far,the polarization state of light has rarely been reported as a control parameter for this ***,we report polarization-dependent metal-to-insulator transition(MIT)in phaseseparated manganite thin films,introducing a new degree of freedom to control static ***,we observed giant photoinduced resistance jumps with striking features:(1)a single resistance jump occurs upon a linearly polarized light incident with a chosen polarization angle,and a second resistance jump occurs when the polarization angle changes;(2)the amplitude of the second resistance jump depends sensitively on the actual change of the polarization *** transmittance measurements reveal that the origin of the above phenomena is closely related to the coexistence of anisotropic *** results represent a first step to utilize light polarization as an active knob to manipulate static phase transitions,pointing towards new pathways for nonvolatile optoelectronic devices and sensors.

关键词： Metal-insulator phase transitions Polarization of light Electronic phase separation Selective domain controllability

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Classification of COVID-19 Symptoms Using Multilayer Perceptron

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer Science and Mathematics 2023年第4期4卷 99-110页

作者： Azam, Nurulain Nusrah Mohd Ismail, Mohd Arfian Mohamad, Mohd Saberi Ibrahim, Ashraf Osman Jeba, Shermina Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan 26600 Malaysia Center of Excellence for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Gambang26300 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medicine and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah 88400 Malaysia Department of Computing Muscat College Muscat Oman

The COVID-19 virus had easily affected people worldwide through direct contact. Individuals diagnosed with positive COVID-19 virus may be affected with many symptoms, such as fever, tiredness, dry cough, difficulty in breathing, sore throat, chest pain, nasal congestion, runny nose, and diarrhea. An individual can also be diagnosed with COVID-19 even when he does not have any symptoms or be in contact with an infected person. Data classification was required due to the size of COVID-19 data that will be analyzed for future countermeasures determination. Some problems in data classification occurred due to unorganized data, such as time consumption, human error in complexity of symptom features and the diagnosis process data needed expert knowledge. This study aimed to use the artificial neural network (ANN) approach, which was multilayer perceptron (MLP) to classify the COVID-19 data by using patient symptom data. The MLP process involved data collection, data normalization, MLP design, MLP training, testing, and MLP verification. From the experiments, the MLP method was able to obtain an accuracy rate of 77.10%. In conclusion, the MLP method could classify the COVID-19 data and achieve a high accuracy rate. © 2023 College of Education, Al-Iraqia University. All rights reserved.

关键词： Multilayer neural networks

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Comparative Analysis of Trilateration and Fingerprinting for Wi-Fi-Based Human Localization

Comparative Analysis of Trilateration and Fingerprinting for...

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Communication Engineering and Emerging Technologies (ICoCET), IEEE International Conference on

作者： Shelvine Tang Siti Zuraidah Ibrahim Aini Syuhada Md Zain Sugchai Tantiviwat Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering & Technology Center of Excellence for Advanced Communication Engineering Universiti Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering & Technology Center of Excellence for Advanced Computing (AdvComp) Universiti Malaysia Perlis Perlis Malaysia Department of Electrical Education Faculty of Industrial Education and Technology Rajamangala University of Technology Srivijaya Songkhla Thailand

ISBN: (数字)9798331504144

ISBN: (纸本)9798331504151

This study compares two Wi-Fi-based human localization techniques—trilateration and Wi-Fi fingerprinting—within a controlled indoor environment using three strategically placed Wi-Fi access points (APs). Trilateration estimates user positions by calculating distances from the APs based on Received Signal Strength Indicator (RSSI) values, while Wi-Fi fingerprinting matches real-time RSSI readings to a pre-constructed database using the K-Nearest Neighbors (KNN) algorithm. Conducted in a university hostel bedroom, the experiments evaluated the algorithms across five positions. Trilateration faced limitations from signal attenuation and multipath effects, achieving correct localization in only 3 out of 5 positions. In contrast, Wi-Fi fingerprinting matches real-time RSSI readings to a pre-constructed database using the K-Nearest Neighbors (KNN) algorithm, yielding superior results by correctly identifying 4 out of 5 positions. Optimized with k = 3, Wi-Fi fingerprinting achieved 76.50% overall accuracy and 75.30% validation accuracy. These findings demonstrate Wi-Fi fingerprinting as a more reliable method for indoor localization in complex environments.

关键词： Location awareness Accuracy Databases Fingerprint recognition Nearest neighbor methods Real-time systems Sensors Received signal strength indicator Reliability Wireless fidelity

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Convolutional graph neural network training scalability for molecular docking

Convolutional graph neural network training scalability for ...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Kevin Crampon Alexis Giorkallos Stephanie Baud Luiz Angelo Steffenel Center for Excellence in Advanced Computing Atos SE Echirolles France CNRS MEDyC Université de Reims Champagne-Ardenne Reims France Université de Reims Champagne-Ardenne Reims France

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