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arXiv 2022年

作者： Pérez, Mario Quiles Beltrán, Enrique Tomás Martínez Bernal, Sergio López Prat, Eduardo Horna Del Campo, Luis Montesano Maimó, Lorenzo Fernández Celdrán, Alberto Huertas Department of Information and Communications Engineering University of Murcia Murcia30100 Spain Bit&Brain Technologies S.L Zaragoza50006 Spain Department of Computer Architecture and Technology University of Murcia Murcia30100 Spain Communication Systems Group CSG Department of Informatics IfI University of Zurich UZH ZürichCH-8050 Switzerland

The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhance the promotion of products and generate a greater acceptance on potential buyers. Traditionally, neuromarketing studies have relied on a single biosignal to obtain feedback from presented stimuli. However, thanks to new devices and technological advances studying this area of knowledge, recent trends indicate a shift towards the fusion of diverse biosignals. An example is the usage of electroencephalography for understanding the impact of an advertisement at the neural level and visual tracking to identify the stimuli that induce such impacts. This emerging pattern determines which biosignals to employ for achieving specific neuromarketing objectives. Furthermore, the fusion of data from multiple sources demands advanced processing methodologies. Despite these complexities, there is a lack of literature that adequately collates and organizes the various data sources and the applied processing techniques for the research objectives pursued. To address these challenges, the current paper conducts a comprehensive analysis of the objectives, biosignals, and data processing techniques employed in neuromarketing research. This study provides both the technical definition and a graphical distribution of the elements under revision. Additionally, it presents a categorization based on research objectives and provides an overview of the combinatory methodologies employed. After this, the paper examines primary public datasets designed for neuromarketing research together with others whose main purpose is not neuromarketing, but can be used for this matter. Ultimately, this work provides a historical perspective on the evolution of techniques across various phases over recent years and enumerates key lessons learned. © 2022, CC BY-NC-SA.

关键词： Life cycle

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Scalable and probabilistically complete planning for robotic spatial extrusion

arXiv

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arXiv 2020年

作者： Garrett, Caelan Reed Huang, Yijiang Lozano-Pérez, Tomás Mueller, Caitlin Tobin Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Department of Architecture Massachusetts Institute of Technology

There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms. The key planning challenge is, throughout the printing process, satisfying both stiffness constraints that limit the deformation of the structure and geometric constraints that ensure the robot does not collide with the structure. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware heuristic is able to successfully balance both constraints. We empirically compare our methods on a benchmark of over 40 simulated extrusion problems. Finally, we apply our approach to 3 real-world extrusion problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrusion

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Cap-Eye-citor: A Machine Vision Inference Approach of Capacitor Detection for PCB Automatic Optical Inspection 7

Cap-Eye-citor: A Machine Vision Inference Approach of Capaci...

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7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020

作者： Susa, Julie Ann B. Mariquina, Erwin Tria, Meriam L. Adolfo, Cid Mathew Castro, Julius C. Technological Institute of the Philippines College of Engineering and Architecture Manila Philippines International Electronics and Technical Institute Department of Computer Engineering Alabang Philippines Eulogio"Amang"Rodriguez Institute of Science and Technology Department of Electronics Engineering Manila Philippines University of Gloucestershire School of Computing and Engineering United Kingdom Bohol Island State University Department of Computer Engineering Bohol Philippines

ISBN: (纸本)9780738105048

Circuit boards are one of the key elements of the electronics industry. Because of its demand for portable electronic goods, the production of circuit boards has become more significant. The most significant aspect of printed circuit board production is AOI or Automatic Optical Inspection. When generated in a single quantity, the PCB requires a small way to teach and change the AOI method for process validation. This paper proposes a mechanism of detection of capacitors trained on circuit boards using the YOLO V3 algorithm. YOLO is a form of rapid object detection based on the convolutional neural network or CNN. CNN's deep network can distinguish specific characteristics from all the image features. The study developed an AI with the same feature that the manufacturing industry uses to assist students with an effective evaluation of their circuit boards. It focuses on the capacitor in a circuit board. It will not, however, be capable of distinguishing other forms of electronic components. Nearly 60 percent of the entire test has a performance of 100 percent detection, according to the test results, while 33.33 percent is over 70 percent detection and 6.66 percent has a 0 percent detection output. Capacitor recognition has been done with an overall testing accuracy of 93.33%. © 2020 IEEE.

关键词： Printed circuit boards

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Robotic additive construction of bar structures: Unified sequence and motion planning

arXiv

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arXiv 2021年

作者： Huang, Yijiang Garrett, Caelan R. Ting, Ian Parascho, Stefana Mueller, Caitlin T. Building Technology Program Department of Architecture Massachusetts Institute of Technology CambridgeMA02139 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Princeton University CREATE Laboratory School of Architecture PrincetonNJ08544 United States

Additive robotic construction of building-scale discrete bar structures, such as trusses and space frames, is increasingly attractive due to the potential improvements in efficiency, safety, and design possibilities. However, programming complex robots, such as manipulators with seven degrees of freedom, to successfully complete construction tasks can be tedious, challenging, or impossible for a human to do manually. Namely, the structure must be constructed in a sequence that preserves structural properties, such as stiffness, at each step. At the same time, this sequence must allow for the robot to precisely manipulate elements within the in-progress structure while respecting geometric constraints that, for example, ensure the robot does not collide with what it has built. In this work, we present an automated and newly generalized planning approach for jointly finding a construction sequence and robot motion plan for additive construction that satisfies these requirements. Our approach can be applied in a variety of additive construction processes, and we demonstrate it specifically on spatial extrusion and discrete bar assembly in this paper. We demonstrate the effectiveness of our approach on several simulated and real-world extrusion and assembly tasks, including a human-scale physical prototype, for which our algorithm is deployed for the first time to plan the assembly of a complicated double tangent bar system design. © 2021, CC BY.

关键词： Additives

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Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

arXiv

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arXiv 2024年

作者： Hou, Jinbo Qiu, Kehai Zhang, Zitian Yu, Yong Wang, Kezhi Capolongo, Stefano Zhang, Jiliang Li, Zeyang Zhang, Jie The Department of Electronic and Electrical Engineering the University of Sheffield Sheffield United Kingdom The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Brunel University London United Kingdom The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China The Design & Health Lab Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Italy The Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang China The Centre for Wireless Innovation Queen’s University of Belfast BelfastBT3 9DT United Kingdom

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building’s wireless performance can be significantly enhanced while ensuring indoor daylight performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Daylighting

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A Generalized Density Dissipation for Weakly-compressible SPH

arXiv

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arXiv 2023年

作者： Zheng, Bo Xue Cai, Zhi Wen Zhao, Pei Dong Xu, Xiao Yang Chan, Tak Shing Yu, Peng Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Department of Mathematics University of Oslo Oslo0851 Norway China Ship Scientific Research Center Wuxi214082 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Naval Architecture and Ocean Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

The weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from the pressure oscillation, which would undermine the simulation stability and accuracy. To address this issue, we propose a generalized density dissipation scheme suitable for both single-phase and multiphase flow simulations. Our approach consists of two components. Firstly, we replace the basic density dissipation with the density increment dissipation to enable numerical dissipation crossing the interfaces of different fluids in multiphase flow. Secondly, based on the dissipation volume conservation, we utilize dissipation volume correction factor (VCF) to stabilize the simulations for multiphase flows with large density ratio. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks, i.e., the sloshing under external excitations, the single and double bubbles rising, Rayleigh-Taylor instability, and Kelvin-Helmholtz instability. Additionally, our study reveals the relationship between SPH with the density dissipation and the approximate Riemann solver. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiphase flow

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A Generalized Density Dissipation for Weakly-Compressible Sph

SSRN

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SSRN 2023年

作者： Zheng, Bo Xue Cai, Zhi Wen Zhao, Pei Dong Xu, Xiao Yang Chan, Tak Shing Yu, Peng Department of Mathematics University of Oslo Oslo0851 Norway Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China China Ship Scientific Research Center Wuxi214082 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Naval Architecture and Ocean Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Technology Xi’an University of Science and Technology Xian710054 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from numerical pressure noise, which can significantly undermine simulation stability and accuracy. In response, this study introduces a novel density dissipation scheme aimed at suppressing such unphysical noise. The proposed scheme overcomes the limitations of conventional density dissipation schemes, which are ineffective at fluid interfaces in multiphase flow simulations. As a result, the novel scheme is generally applicable to the entire computational domain, encompassing both single-phase flow and multiphase flow. This advancement is achieved through two key components. Firstly, the traditional density dissipation is replaced with a density increment dissipation approach that enables dissipation to cross interfaces separating different fluids. Secondly, based on dissipation volume conservation, we employ a dissipation volume correction factor (VCF) to stabilize simulations characterized by large density ratios. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks: sloshing under external excitations, the rising of single and double bubbles, Rayleigh-Taylor instability, and Kelvin-Helmholtz instability. Additionally, this study reveals the relationship between SPH utilizing density dissipation and the approximate Riemann solver. © 2023, The Authors. All rights reserved.

关键词： Multiphase flow

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Handwritten Digit Recognition using Deep Learning Networks

Handwritten Digit Recognition using Deep Learning Networks

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Telecommunications, Optics and computer Science (TOCS), IEEE Conference on

作者： Shiying Yu Jinmei Liu Hang Shu Zhiwei Cheng School of Geodesy and Geomatics Wuhan University Wuhan China School of Arts and Sciences Rutgers University New Jersey NJ United States School of Computer and Information Science South West University Chongqing China Department of Architecture and Civil Engineering Xi’an University of Science and Technology Xi’an China

ISBN: (纸本)9781665470544

Handwritten digit recognition has been widely researched. Numerous classification methods have been proposed. This article improves the recognition of handwritten numbers method based on CNN neural network. Recognition of handwritten digits has made great progress, but due to insufficient recognition accuracy, it will have a certain impact on work efficiency. In this paper, a two-layer CNN network with two fully connected layers can achieve a good prediction performance. In our framework, the activation function used is the ReLU function, to some extent, this avoids the difficulties of gradient disappearance and gradient saturation. At the same time, dropout is added to the full connection layer to discard the neurons in the network with a certain probability, which can avoid certain accidents, and the final trained network will be more robust and the generalization performance will be stronger. This method effectively improves the accuracy of recognition in the final experiment, which achieves 98.75% accuracy on theMNIST dataset and 99.25% on the CIFAR-100 dataset.

关键词： Industries Deep learning Handwriting recognition Text recognition Neurons Predictive models Optics

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A Smart Capacity Enhancement and Estimation Model for Hybrid Buildings by using Light Weight Deep Learning Model

A Smart Capacity Enhancement and Estimation Model for Hybrid...

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Disruptive Technologies (ICDT), International Conference on

作者： Senthil Kumar Nageswara Rao Atyam Khadar Nawas K Shashank Awasthi M. Kotteeswaran K. S. Thirunavukkarasu KL School of Architecture Koneru Lakshmanan Education Foundation Guntur Andhra Pradesh India Electrical and Electronics Department CMR Institute of Technology Bengaluru Karnataka India School of Computing Science and Engineering Vellore Institute of Technolog Chennai Tamil Nadu India Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India School of Management and Commerce Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India School of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards.

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Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling

Cerebral micro-bleeding identification based on a nine-layer...

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作者： Wang, Shuihua Sun, Junding Mehmood, Irfan Pan, Chichun Chen, Yi Zhang, Yu-Dong School of Computer Science and Technology Henan Polytechnic University Jiaozuo China School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom Department of Informatics University of Leicester Leicester United Kingdom Department of Computer Science and Engineering College of Electronics and Information Engineering Sejong University Seoul Korea Republic of School of Computer Science and Technology Nanjing Normal University Nanjing China School of Business Nanjing Normal University Nanjing China Jiangsu Laboratory of Lake Environment Remote Sensing Technologies Huaiyin Institute of Technology Huaian China

Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. © 2019 John Wiley & Sons, Ltd.

关键词： Convolution

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