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Face Recognition Based on Haar like and Euclidean Distance

Face Recognition Based on Haar like and Euclidean Distance

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2020 international conference on modeling, Big Data Analytics and simulation, MBDAS 2020

作者： Wu, Hao Cao, Yu Wei, Haiping Tian, Zhuang Department of Computer and Communication Engineering Liaoning Shihua University China

In order to improve the recognition rate of multi-face image, this paper proposes a face image recognition method based on haar-like and Euclidean distance. First of all, the initial features of Haar are moved in the image and gradually enlarged, the pixel sum of the feature area is obtained quickly by using the integral graph. The Haar feature is obtained by calculating the difference between black and white pixels in the feature region, and the threshold of weak classifier is calculated. Then, the extracted facial feature data is trained and classified to form a cascade classifier for face detection. Finally, the face of the image to be detected is compared with the training samples by using Euclidean distance. The experimental results show that the time of one attendance is 5-10 seconds and the success rate of face recognition is 91.1%, which verifies the advantage of the algorithm in improving the efficiency of attendance. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Digital Twin of Piezoelectric Effect at Microscale and Macroscale PVDF Film

Digital Twin of Piezoelectric Effect at Microscale and Macro...

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IEEE/ASME international conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Yu-Hsiang Huang Tsung-Han Huang Wei-Song Hung Ching-Yuan Chang Department of Mechanical Engineering National Taipei University of Technology Taipei Taiwan Graduate Institute of Applieed Science and Technology National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665455718

This study uses computer tomography (CT) and a finite element method (FEM) criterion to establish a digital twin (DT) model for Polyvinylidene fluoride (PVDF) film. The CT provides sliced images of the PVDF film with 50 μm, and we have developed a standard procedure to rebuild the stereo and interior model of the PVDF film. We also use a d33 meter to measure the piezoelectric effect of the PVDF film and utilize the value in the FEM model. The primary material of the piezoelectric film is polyvinylidene fluoride, which has a unique dielectric effect, piezoelectric effect, and pyroelectric effect. In this study, computer tomography was used to establish the microstructure model of the film, and ANSYS software was used to establish the microstructure model. The primary purpose of simulating the piezoelectric effect of the self-made piezoelectric film is to compare the small holes contained in the film structure and the forward and reverse piezoelectric responses of different structures in the same environment. This study has established three different DT-FEM models and simulated the piezoelectric effect of the thin film. The DT-FEM model contributes to the emerging technology of material science and helps scientists to investigate the piezoelectric effect at the micro- and macro-scale.

关键词： Piezoelectric films Solid modeling Three-dimensional displays Computational modeling simulation Piezoelectric effect Software

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"Why not looking backward?" a robust two-step method to automatically terminate Bayesian optimization 23

"Why not looking backward?" a robust two-step method to auto...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Shuang Li Ke Li Wei Li Control and Simulation Center Harbin Institute of Technology China and National Key Laboratory of Modeling and Simulation for Complex Systems China Department of Computer Science University of Exeter Exeter UK

Bayesian Optimization (BO) is a powerful method for tackling expensive blackbox optimization problems. As a sequential model-based optimization strategy, BO iteratively explores promising solutions until a predetermined budget, either iterations or time, is exhausted. The decision on when to terminate BO significantly influences both the quality of solutions and its computational efficiency. In this paper, we propose a simple, yet theoretically grounded, two-step method for automatically terminating BO. Our core concept is to proactively identify if the search is within a convex region by examining previously observed samples. BO is halted once the local regret within this convex region falls below a predetermined threshold. To enhance numerical stability, we propose an approximation method for calculating the termination indicator by solving a bilevel optimization problem. We conduct extensive empirical studies on diverse benchmark problems, including synthetic functions, reinforcement learning, and hyperparameter optimization. Experimental results demonstrate that our proposed method saves up to ≈ 80% computational budget yet is with an order of magnitude smaller performance degradation, comparing against the other peer methods. In addition, our proposed termination method is robust in terms of the setting of its termination criterion.

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Application of RISE in SPECT Myocardial Perfusion Imaging, using a Cardiac Phantom

Application of RISE in SPECT Myocardial Perfusion Imaging, u...

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2021 IEEE Nuclear Science Symposium and Medical Imaging conference, NSS/MIC 2021

作者： Keliri, Aikaterini Koutsantonis, Loizos Stiliaris, Efstathios Parpottas, Yiannis Charitou, Giorgos Panagi, Sotiris Papanicolas, Costas N. The Cyprus Institute Department of Computational Sciences Nicosia Cyprus University of Luxembourg Department of Computer Science Esch-sur-Alzette Luxembourg National and Kapodistrian University of Athens Department of Physics Athens Greece Frederick University Department of Mechanical Engineering Nicosia Cyprus Frederick Research Center

ISBN: (纸本)9781665421133

The Reconstructed Image from simulations Ensemble (RISE) has been demonstrated in emission tomography as an alternative reconstruction method that uses simulation and modeling techniques to reconstruct images from projection data. In this study, we present results from the first application of RISE in Myocardial Perfusion Imaging (MPI) with Single Photon Emission Computed Tomography (SPECT). Extensive phantom experiments using a hardware cardiac phantom were performed to evaluate the efficacy of RISE in reconstructing the activity images of the myocardium with and without defect. The Ordered Subset Expectation Maximization (OSEM) method is used to provide images for comparison. The Contrast for Cold Regions was employed to quantify the detectability of the cold spots (defects) in the obtained RISE and OSEM images. Preliminary results show the capacity of RISE to provide comparable reconstructions to those of OSEM using an entirely different approach for modeling the tomographic problem. © 2021 IEEE.

关键词： Single photon emission computed tomography

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Role of Artificial Intelligence in the Management of Food Waste 2

Role of Artificial Intelligence in the Management of Food Wa...

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2nd international conference on Artificial Intelligence and Data Sciences, AiDAS 2021

作者： Anggraeni, Maria Cecilia Silaban, Chrysanti Anastasya Anggreainy, Maria Susan Cahyadi, Ervan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665417266

Food waste is caused by a complex collection of interconnected behavior at both the supplier and customer levels. Computational and mathematical models provide various methods for simulating, diagnosing, and predicting various aspects of the dynamic food waste generation and prevention system. This paper describes three modeling methods that have been used to analyze food waste in the past. Bayesian networks and machine learning algorithms are applied to help determine how much food is discarded at the household level. Agent-Based simulation was used to gain insight into how innovation and adoption of a particular technology can help minimize retail food waste. The first BN-ABM integrated model assesses consumer food waste levels affected by particular features and aspects, resulting in the model reaching equilibrium. Proofing that there is a need for policy interventions, including training, economic incentives, and campaigns, to obtain the resulting model transformation. These interventions are ready to be assessed by the model, but further study is needed to understand the effects of enforcing these structures on the accuracy of the BN-ABM predictive model. The second ABM model aims to determine the factors that establish the adoption of food waste reduction technology at the retail level, which is influenced by particular but not limited to economic factors, including a strong network between retailers and consumer's awareness regarding food waste reduction technology. These findings can study the effects of policy intervention regarding food waste reduction at the retail and consumer level. The third ABM model employed a general food chain network model consisting of consumers, traders, and producers to simulate the dynamic change of product flow between agents and to be able to assess the effect of agent's behavioral contrast. Resilience is measured by the ability to deal with shocks, and efficiency is the share of total food manages to be dispatched to consum

关键词： Bayesian networks

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Performance Analysis of FISSER Model in Rural-Urban Cognitive Radio Networks 3

Performance Analysis of FISSER Model in Rural-Urban Cognitiv...

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2021 3rd international conference on computer modeling, simulation and Algorithm, CMSA 2021

作者： Oki, Olukayode Olwal, Thomas Adigun, Matthew Department of Information Technology Walter Sisulu University South Africa Department of Electrical Engineering Tshwane University of Technology South Africa Department of Computer Science University of Zululand South Africa

In the past few decades, Cognitive Radio network (CRN) has been regarded in the literature as the most promising technology for performing dynamic spectrum management. One of the major aspects of spectrum management is referred to as the spectrum-reconfiguration decision-making ability of CR users. Dynamic spectrum reconfiguration has previously been reported to improve the spectrum utilisation in CRN. In exploiting spectrum reconfiguration to improve spectrum utilisation, various approaches have been adopted to develop models. However, none of these research works has been evaluated in the urban and rural settlement context. In a CRN environment, the frequency availability is not static like that of traditional networks. There are resource-rich regions such as in rural areas, where many frequencies are available for the Secondary Users (SUs) and resource-poor regions such as urban areas, where there are only a few frequencies available for SUs. Hence, this paper proposed a novel Foraging Inspired Spectrum Selection and Reconfiguration (FISSER) model. The performance of the FISSER model has been analysed through computer simulations both in the rural and urban CRN, using high and low perceptual radii. A high perceptual radius has been shown in the literature to improve energy efficiency and data communication performance in ad hoc networks. However, the simulation results reported in this paper show that even though the high perceptual radius improves the nodes' energy efficiency it does not achieve optimal data communication performance compared to when the nodes use a low perceptual radius. Also, the efficacy of FISSER model was tested by comparing it against the reinforcement learning model. Based on the obtained results, it was shown that the FISSER model achieved better results in both the network throughput and the channel switching time. © Published under licence by IOP Publishing Ltd.

关键词： Decision making

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14th international conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2020

14th International Conference on Innovative Mobile and Inter...

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14th international conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2020

ISBN: (纸本)9783030503987

The proceedings contain 62 papers. The special focus in this conference is on Innovative Mobile and Internet Services in Ubiquitous Computing. The topics include: A convolutional neural network model for object detection based on receptive field;the study and realization of a binary-based address sanitizer based on code injection;modeling the risk of data breach incidents at the firm level;iot cryptosecurity: overview and potential solutions;"guess who?" large-scale data-centric study of the adequacy of browser fingerprints for web authentication;adaptive architecture and principles for securing the iot systems;a visual particle system based on mechanism model data in digital twin;an eeg emotion classification system based on one-dimension convolutional neural networks and virtual reality;iot node elimination and selection for completing tasks in opportunistic networks: a fuzzy logic approach;implementation of a moving omnidirectional access point robot and a position detecting system;uplink resource allocation based on short block-length regime in heterogeneous cellular networks for smart grid;multiuser channel access with rts-cts algorithm for li-fi communications;design and implementation of a novel testbed for automotive security analysis;reinforcement learning-based fuzzing technology;research on iot device vulnerability mining technology based on static preprocessing and coloring analysis;computer-aided english education in china: an online automatic essay scoring system;survey of cyberspace resources scanning and analyzing;modeling and analysis of cyberspace threat sources based on vulnerabilities;a dynamic instrumentation technology for iot devices;a fuzzy-based simulation system for driving risk management in vanets considering weather condition as a new parameter.

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ELMVDP: extreme learning based virtual data position exploration and incorporation method for escalation of time series forecasting accuracy

ELMVDP: extreme learning based virtual data position explora...

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Information Technology (OCIT), OITS international conference on

作者： Sarat Chandra Nayak Satchidananda Dehuri Sung-Bae Cho Department of Computer Science Yonsei University Seoul South Korea Department of Computer Science Fakir Mohan University Balasore Odisha India

Time series data are correlated in a nonlinear fashion which makes the future data prediction challenging. Particularly, the correlation among data at the fluctuation points is insignificant and it is hard to capture the underlaying nonlinearity at those points by conventional prediction systems. The accuracy of time series forecasting (TSF) is vastly influenced by the current and immediate past data rather by far away data points. This article proposes an extreme learning-based method for exploration of virtual data positions (ELMVDP) from the training data and incorporates them to the original time series to intensify the TSF accuracy of a single hidden layer neural network. Specifically, this method is useful for the time series having less volume of data which may not suffice to train a TSF model. The effectiveness of ELMVDP method is evaluated on time series available in the literature, compared with few similar deterministic and stochastic approaches, and observations from simulation studies show that ELMVDP method yields better predictions than others.

关键词： Learning systems Solid modeling Time series analysis Government Neural networks Training data Stochastic processes

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DrivAerNet++: a large-scale multimodal car dataset with computational fluid dynamics simulations and deep learning benchmarks 24

DrivAerNet++: a large-scale multimodal car dataset with comp...

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Proceedings of the 38th international conference on Neural Information Processing Systems

作者： Mohamed Elrefaie Florin Morar Angela Dai Faez Ahmed Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA Morphing and Optimization Solutions BETA CAE SYSTEMS USA Inc Farmington Hills MI Department of Computer Science Technical University of Munich Garching Germany

ISBN: (纸本)9798331314385

We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and estateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. Each entry in the dataset features detailed 3D meshes, parametric models, aerodynamic coefficients, and extensive flow and surface field data, along with segmented parts for car classification and point cloud data. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification. With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes. Along with rigorous dataset validation, we also provide ML benchmarking results on the task of aerodynamic drag prediction, showcasing the breadth of applications supported by our dataset. This dataset is set to significantly impact automotive design and broader engineering disciplines by fostering innovation and improving the fidelity of aerodynamic evaluations. Dataset and code available at: https://***/Mohamedelrefaie/DrivAerNet

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4 Bit and 8 Bit Convolution Using Vedic Multiplier

4 Bit and 8 Bit Convolution Using Vedic Multiplier

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), international conference on

作者： Rahul Kamdi Prasheel Thakre Aniket Pathade Sandeep Kumar Tiwari Kamlesh Kalbande Electronics & Telecommunication Engineering YCCE Nagpur India Electronics & Communication Engineering RCOEM Nagpur India Department of Directorate of Research Jawaharlal Nehru Medical College DMIMS Sawangi (M) Wardha India Electronics & Telecommunication Department Govt. Polytechnic Yavatmal India Computer Science and Engineering (IoT) G H Raisoni College of Engineering Nagpur India

One of the fundamental processes in digital signal processing is convolution. The mathematical process of mixing two signals to create a third signal is called convolution. Utilizing a Vedic multiplier, Urdhava Triyagbhyam Sutra or UT sutra, based on one of the sixteen Vedic mathematics sutras, linear and circular convolution processing is accelerated. The Urdhava Triyagbhyam multiplier compared to other traditional multipliers offers faster results. Multiplier having pipelining architecture is also used to compute the convolution (circular and linear) of two sequences because it speeds up the multiplication process. Xilinx ISE Design suite 14.7 software is used to carry out the simulation and synthesis using VHDL. The area and delay for 4 bit and 8 bit circular and linear convolution is computed.

关键词： VHDL Convolution Computational modeling Digital signal processing computer architecture Market research Software

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