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Characterization of Fluorinated Polyimide Films

MRS Online Proceedings Library 2020年第1期264卷 263-268页

作者： Pradnya V. Nagarkar Jiong Ping Lu David Volfson Klavs F. Jensen Stephen D. Senturia Electronics Packaging Program and Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge Electronics Packaging Program and Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Electronics Packaging Program and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge

X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) have been used to characterize the polyimide film based on 4,4′ hexafluoro-isopropylidene -bis pthalic anhydride (HFDA) and 4,4′ -bis (4-aminophenoxy) biphenyl (APBP). Films of varying thicknesses made from diluted precursors were studied by IR and XPS. An elemental analysis and a tentative peak assignment for C 1s in XPS is presented. The HFDA-APBP thick films are stoichiometric in composition and binding energies are in good agreement with data on hexafluorodianhydride-oxydianiline (HFDA-ODA). For thinner films, certain chemical modifications were observed at high cure temperatures.

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The Effect of Tax Assessment and Tax Collection On Tax Revenues 2022

The Effect of Tax Assessment and Tax Collection On Tax Reven...

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13th International Conference on E-Business, Management and Economics, ICEME 2022

作者： Meiryani, Meiryani Andini, Vidhiya Warganegara, Dezie Leonarda Budiarto, Adelia Yulma Maisaroh, Fida Kerta, Johan Muliadi Accounting Department School of Accounting Bina Nusantara University Jakarta11480 Indonesia Doctoral Program Binus Business School Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Bandung Campus Jakarta11480 Indonesia

ISBN: (纸本)9781450396394

The purpose of this study was to determine the effect of tax audit and tax collection on tax revenue at Tax office (KPP) Pratama West Jakarta, using 35 research samples. The research sample was taken by purposive sampling. The method used in this research is multiple regression analysis with the F test and t test. Before using multiple regression analysis, the classical assumption test was carried out first which included the normality test, multicollinearity test, heteroscedasticity test and autocorrelation test. The results of simultaneous hypothesis testing (F test) shows that tax audit and tax collection have an influence on tax revenue. Meanwhile, based on the results of the partial hypothesis test (t test), it shows that the tax audit has an influence on tax revenue and for the tax collection variable has a positive effect on tax revenue. tax audits are carried out as a means of law enforcement for taxpayers who are negligent in fulfilling their tax obligations so that inhibiting factors in the implementation of tax audits can be overcome and efforts to increase state revenue from the tax sector can be achieved, so that the impact of the better tax audits conducted by tax officials will be can increase tax revenue. collection efforts are carried out by taking into account the optimization of the number of taxpayers being collected, the higher the level of tax collection, the higher the level of tax revenue. © 2022 ACM.

关键词： Taxation

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Convolutional Neural Network and Industrial Robot Arm applied to an automatic coffee bean selection system 25

Convolutional Neural Network and Industrial Robot Arm applie...

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2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2022

作者： Calderon, C.A. Robles, John Morocho, Sulay Sarango, Roger Universidad Técnica Particular de Loja Department of Computer Science and Electronics Ecuador Universidad Internacional de la Rioja UNIR Industry 4.0 Master Program Ecuador

ISBN: (纸本)9781665494083

The objective of this paper is to design and implement an automatic coffee bean selection system, based on the integration of a Scara Epson robot arm with a Convolutional Neural Network - based classifier. The implemented system extracts the elements that were identified as coffee beans with shape and color alterations. The hardware architecture consists of: Epson Scara LS10 robot arm, RC-90B controller, 4-megapixel webcam, an extraction end effector, and white illumination. The software architecture consists of: image acquisition, segmentation and preprocessing algorithms, training and classification algorithms with Convolutional Neural Networks (224/2 input/output layers), and robot arm motion control algorithms. For the performance evaluation of the automatic classification algorithms, 18 tests were performed considering 3 different cases of separation between grains, greater than 5 mm, 3 to 4 mm, and less than 2 mm. As a result, an effectiveness percentage of 100% was obtained for the first and second range of separation, and a percentage of 61.5% for the third range, due to the overlapping between coffee beans. © 2022 IEEE.

关键词： Convolution

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Research and development on locomotive HMI display interface

Research and development on locomotive HMI display interface

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4th International Conference on Intelligent Systems Design and Engineering Applications, ISDEA 2013

作者： Cheng, Zhuo Zheng, Xiang Department of Computer Science and Technology Dalian Neusoft University of Information Dalian China Post-doctoral Mobile Research Station of Control Science and Engineering Dalian University of Technology Dalian China School of Electronics and Information Engineering Dalian Jiaotong University Dalian China

ISBN: (纸本)9781479927913

Locomotive development is one key way to enhance the railway transporting efficiency. Human machine Interface (HMI) of driver cab is to watch the locomotive running status and to operate some key apparatus such as pantograph up and down, isolation, inhibition and so on. In addition, driver can download locomotive event information includes fault type, apparatus working status, system data. HMI needs to communicate and transfer data with Main Process Unit (MPU) by WorldFIP bus. This paper introduces basic HMI structure, WorldFIP communication theory, HMI display and control function. Through the test in the laboratory and locomotive, data can communicate with MPU and graph show and logic control is similar or better than foreign device. © 2013 IEEE.

关键词： Locomotives

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Location management for PCS networks with consideration of mobility patterns

Location management for PCS networks with consideration of m...

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IEEE INFOCOM 2005

作者： Li, Jie Kubota, Atsushi Kameda, Hisao Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Sci. City Ibaraki 305-8573 Japan Doctoral Program in Science and Information Engineering University of Tsukuba Tsukuba Sci. City Ibaraki 305-8573 Japan

ISBN: (纸本)0780389689

This paper addresses the dynamic location management for personal communication service (PCS) networks with consideration of mobility patterns. The popular hexagonal cellular architecture is considered. In this paper, we first introduce a coordinate system for the hexagonal cellular architectures. Then, we develop an all-direction mobility model based on the coordinate system for the hexagonal cellular architecture to cope with the mobility patterns of mobile terminals (MTs). The shape and size of the local area (LA) in the dynamic location management scheme is determined by minimizing the total location management cost with bounding the paging cost The optimization problem is transferred to maximize expected number of cells traversed by the MT in the LA with a given size of the LA. The analytic model of calculating the probabilities of any MT's moving is established. An algorithm is proposed to solve the optimization problem efficiently. © 2005 IEEE.

关键词： Data transfer

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Optimal Location to Use Solar Energy in an Urban Situation

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computers, Materials & Continua 2023年第4期75卷 815-829页

作者： Ngakan Ketut Acwin Dwijendra Indrajit Patra N.Bharath Kumar Iskandar Muda Elsayed M.Tag El Din Faculty of Engineering Udayana UniversityBali80361Indonesia An Independent Researcher NIT DurgapurWest Bengal700027India Department of Electrical and Electronics Engineering Vignan’s Foundation for Science Technology and ResearchGuntur522001India Department of Doctoral Program Faculty Economic and BusinessUniversitas Sumatera UtaraMedan20222Indonesia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

This study conducted in Lima, Peru, a combination of spatial decisionmaking system and machine learning was utilized to identify potentialsolar power plant construction sites within the city. Sundial measurementsof solar radiation, precipitation, temperature, and altitude were collectedfor the study. Gene Expression programming (GEP), which is based on theevolution of intelligent models, and Artificial Neural Networks (ANN) wereboth utilized in this investigation, and the results obtained from each werecompared. Eighty percent of the data was utilized during the training phase,while the remaining twenty percent was utilized during the testing phase. Onthe basis of the findings, it was determined that the GEP is the most suitablenetwork for predicting the location. The test state’s Nash-Sutcliffe efficiency(NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Followingthe generation of the final map based on the results of the GEP model, itwas determined that 9.2% of the province’s study area is suitable for theconstruction of photovoltaic solar power plants, while 53.5% is acceptable and37.3% is unsuitable. The ANN model reveals that only 1.7% of the study areais suitable for the construction of photovoltaic solar power plants, while 66.8%is acceptable and 31.5% is unsuitable.

关键词： Solar energy renewable energy machine learning

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Protein Secondary Structure Prediction using N-Grams and 1-Dimensional Convolutional Neural Network

Protein Secondary Structure Prediction using N-Grams and 1-D...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Annisa Rizqiana Afiahayati Department of Computer Science and Electronics Master Program in Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In this study, we initiated an approach to predict Protein Secondary Structure Prediction (PSSP) by using n-grams modeling, namely $n$ adjacent amino acid sequences, to represent the amino acid sequence and 1-Dimensional Convolutional Neural Network (CNN). We compare n-grams with a one-hot encoding approach and find that n-grams provide better performance. The analysis results show that bigrams are more effective than trigrams in describing protein amino acid sequence patterns. We also combine it with the PSSM profile feature to improve model performance. The results of this study reveal that the n-grams approach, especially bigrams, in combination with PSSM, can produce more accurate protein secondary structure predictions than previous models, with superior accuracy evaluations of Q8 68.1 % and Q3 82.75 % on the CB513 dataset.

关键词： Proteins Accuracy Convolution Neural networks Predictive models Amino acids Encoding Convolutional neural networks Bioinformatics Kernel

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Prospects and limitations of Organic Thin Film Transistors (OTFTs)

Prospects and limitations of Organic Thin Film Transistors (...

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2nd International Conference on computer science, Engineering and Applications, ICCSEA 2012

作者： Kaushik, B.K. Kumar, Brijesh Negi, Y.S. Mittal, Poornima Department of Electronics and Computer Engineering Indian Institute of Technology Roorkee 247667 India Polymer Science and Technology Program DPT Indian Institute of Technology Roorkee 247667 India Department of Electronics and Communication Engineering Graphic Era University Dehradun 248001 India

ISBN: (纸本)9783642301100

Organic Transistor (OT) modeling, fabrication and applicability has undergone remarkable progress during last ten years. Organic Thin Film Transistors (OTFTs) have received significant attention recently because of their considerable utility. They can be fabricated at lower temperature and significantly reduced cost as compared to Hydrogenated Amorphous Silicon Thin Film Transistors (a-Si: H TFTs). Fabrication of OTFTs at low temperature allows utilization of wide range of substrates, thereby permitting usage of organic transistors as future candidate for many low-cost electronics applications that require flexible polymeric substrates such as RFID tags, smart cards, electronic paper, and active matrix flat panel displays. This paper provides detailed insight of OTFTs, their operating principles, device materials and various structures such as Top Gate Top Contact (TGTC), Top Gate Bottom Contact (TGBC), Bottom Gate Top Contact (BGTC) and Bottom Gate Bottom Contact (BGBC). Although OTFTs find tremendous and widespread applications, but is marred by few limitations related to factors such as speed, compatibility, stability, degradability and variability. This paper comprehensively discusses the performance and limitations of OTFTs. © 2012 Springer-Verlag GmbH.

关键词： Electronic paper

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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