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Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana L. H. Oliveira J. Bautista Jesus Pinto-Lopera Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the Whale Optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital Signal Processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-the-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%.

关键词： Software algorithms Signal processing algorithms Software Hardware Whale optimization algorithms Steady-state Mobile robots MATLAB Farming Python

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Prototype development of VR based personal zone defense practice equipment 4

Prototype development of VR based personal zone defense prac...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Honma, Hiroki Iida, Yoshiaki Okumura, Yasuyuki Fujii, Katsuyuki Umehira, Masahiro Nanzan University Graduate Program of Mechatronics Graduate School of Science and Engineering Nagoya Japan Nanzan University Physical Education Center Nagoya Japan Nanzan University Faculty of Science and Engineering Department of Mechatronics Nagoya Japan Nanzan University Faculty of Science and Engineering Department of Electronics and Communication Technology Nagoya Japan

ISBN: (纸本)9781665419048

In a basketball game, players must act appropriately according to the position and movement of the player. This needs to be learned through team practice. Therefore, if we can build an environment that allows even one person to practice as a team, the efficiency of practice can be greatly improved. It is effective to use virtual reality (VR) technology to realize team practice alone. This paper proposes a VR-based basketball team practice learning system that provides VR environments similar to the actual team practice environments by arranging players in a virtual space. Focusing on zone defense practice, which is said to be difficult to learn even in team practice, we propose a practice system for orthodox 3-2 zone tactics. In addition, because the distance between the player and the surrounding VR player is important for this system, this paper presents the evaluation result of the 3D virtualization accuracy measured by the experiment. © 2022 IEEE.

关键词： Virtual reality

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Removal of As(V)in the presence of Cr(VI)in contaminated water from the Bajio region of Mexico using ferrihydrite-functionalized graphene oxide(GOFH):A case study

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Emerging Contaminants 2024年第3期10卷 134-144页

作者： D.R.Joya-Cardenas J.P.Rodríguez-Caicedo M.A.Corona-Rivera N.Saldana-Robles C.E.Damian-Ascencio A.Saldana-Robles Graduate Program in Biosciences University of GuanajuatoIrapuato36500Mexico Facultad de Ingenierías y Tecnologías Instituto XeriraUniversidad de SantanderBucaramanga680003Colombia Chemical Engineering Unit Academic Multidisciplinary Region Altiplano UAMRA.Autonomous University of San Luis PotosíMatehualaSan Luis Potosí78000Mexico Department of Agricultural Engineering University of GuanajuatoIrapuato36500Mexico Department of Mechanical Engineering University of GuanajuatoSalamanca36800Mexico Mechanical Engineering Department Virginia TechBlacksburgVA24061USA

The presence of heavy metals such as arsenic(As)and chromium(Cr)can be considered one of the most toxic heavy *** study aims to develop and evaluate a graphene oxide(GO)based material functionalized with ferrihydrite for the removal of As(V)in the presence of Cr(VI).The adsorbent ferrihydrite-functionalized graphene oxide(GOFH)was characterized by SEM,FTIR,XRD,XPS,surface charge,BET and *** adsorption capacities for As(V)and Cr(VI)were 160 and 66 mg g^(-1)respectively at pH 4 and 298 K,fitting the Langmuir *** binary systems,GOFH adsorbed As(V)with an adsorption capacity of 137 mg g^(-1)in the presence of 20 mg L^(-1)Cr(VI)at room *** analysis revealed an exothermic and spontaneous ***,GOFH effectively removed several coexisting anions from groundwater,including SO_(4)^(2-),CO_(3)^(2-)and Cl^(-).This work evidences the potential of GOFH for competitive adsorption of priority pollutants from complex *** GOFH material represents a technically and economically viable technology for application in groundwater decontamination,as it requires inexpensive precursors and ambient conditions during synthesis and operation.

关键词： Competition Ions Contamination Groundwater Adsorption capacity

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Bringing Bearing Fault Detection to Embedded Systems Using Low-Cost Machine Learning Techniques

Bringing Bearing Fault Detection to Embedded Systems Using L...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Natalia S. Teixeira M. A. Pastrana Henrique G. Moura Daniel M. Muñoz Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Automotive Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Vibration analysis is crucial for predictive maintenance of bearing systems, aiming to reduce costs and prevent failures. This paper investigates signal processing techniques such as FFTs and Hilbert Transforms for fault diagnosis, alongside decision tree models, particularly the J48 algorithm within the WEKA environment, for fault classification. The study emphasizes the integration of signal processing and machine learning for effective fault diagnosis and maintenance. Notably, a classification model with low computational cost has been chosen, facilitating implementation in embedded systems. This model also offers interpretability of outputs, as it is based on decision trees of frequency components obtained through preprocessing.

关键词： Vibrations Accuracy Computational modeling Transforms Machine learning Signal processing Robustness Mathematical models Field programmable gate arrays Predictive maintenance

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Folding angle and wing flexibility influence the flight performance of origami winged fruits

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Nonlinear Dynamics 2024年第13期112卷 10995-11011页

作者： Cai, Jing-Fang Hsu, Ya-Chun Yen, Yu-Chen Lee, Yun-Chung Peyton, Bernie Huang, Mei-Jiau Yu, Hon-Tsen Juang, Jia-Yang Department of Mechanical Engineering National Taiwan University Taipei106319 Taiwan Department of Life Science National Taiwan University Taipei106319 Taiwan Berkeley CA United States Program in Nanoengineering and Nanoscience Graduate School of Advanced Technology National Taiwan University Taipei106319 Taiwan Degree Program of Genome and Systems Biology National Taiwan University Taipei106319 Taiwan

Winged fruits possess a unique flight mechanism relying on simple geometric structures rather than neuromuscular control. This study proposes an innovative approach using origami techniques to create three-winged model fruits, serving as a proxy for understanding the flight dynamics of natural winged fruits. Paper is employed to simulate inherent wing flexibility, with the option to add plastic frames for rigidity. We comprehensively investigate the free fall motion of both rigid and flexible winged fruits through experimental and numerical analyses. Velocity–time curves reveal a three-stage descent pattern—acceleration, deceleration, and steady states. The overshoot phenomenon is attributed to rapid lift increase due to leading-edge vortices attaching stably to the wings. This insight sheds light on aerodynamics governing fruit flight. Compared with rigid wings, flexible wings exhibit two key properties—slower descent and higher self-orienting capability—that facilitate a more stable and longer-distance dispersal of seeds under crosswind conditions. Our study demonstrates the potential benefits of flexible wings in natural seed distribution. This study advances our understanding of winged fruit flight dynamics, utilizing origami as a powerful tool for biomimetic investigations. The findings have broad implications, from improving aerodynamic designs to developing efficient micro air vehicles and electronic microfliers, and understanding seed dispersal evolution. Graphical abstract: (Figure presented.). © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Fruits

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Excitations in layered materials from a non-empirical Wannier-localized optimally-tuned screened rangeseparated hybrid functional

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npj Computational Materials 2024年第1期10卷 132-140页

作者： María Camarasa-Gómez Stephen E.Gant Guy Ohad Jeffrey B.Neaton Ashwin Ramasubramaniam Leeor Kronik Department of Molecular Chemistry and Materials Science Weizmann Institute of ScienceRehovoth7610001Israel Department of Physics University of CaliforniaBerkeleyCA94720USA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Kavli Energy NanoSciences Institute at Berkeley University of CaliforniaBerkeleyCA94720USA Department of Mechanical and Industrial Engineering University of Massachusetts AmherstAmherstMA01003USA Materials Science and Engineering Graduate Program University of MassachusettsAmherstAmherstMA01003USA

Accurate prediction of electronic and optical excitations in van der Waals(vdW)materials is a long-standing challenge for density functional *** recent Wannier-localized optimally-tuned screened range-separated hybrid(WOT-SRSH)functional has proven successful in non-empirical determination of electronic band gaps and optical absorption spectra for covalent and ionic ***,for vdW materials the tuning of the material-and structure-dependent functional parameters has only been attained ***,we present a non-empirical WOT-SRSH approach applicable to vdW materials,with the optimal functional parameters transferable between monolayer and *** apply this methodology to prototypical vdW materials:black phosphorus,molybdenum disulfide,and hexagonal boron nitride(in the latter case including zero-point renormalization).We show that the WOT-SRSH approach consistently achieves accuracy levels comparable to experiments and many-body perturbation theory(MBPT)calculations for band structures and optical absorption spectra,both on its own and as an optimal starting point for MBPT calculations.

关键词： theory optimal functional

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Identification of benefits, challenges, and pathways in E-commerce industries: An integrated two-phase decision-making model

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Sustainable Operations and Computers 2023年第1期4卷 200-218页

作者： Gupta, Srikant Kushwaha, Pooja.S. Badhera, Usha Chatterjee, Prasenjit Gonzalez, Ernesto D.R. Santibanez Department of Operations and Decision Sciences Jaipuria Institute of Management Jaipur India Department of Business Analytics Jaipuria Institute of Management Indore India Department of Business Analytics Jaipuria Institute of Management Jaipur India Department of Mechanical Engineering MCKV Institute of Engineering West Bengal 711204 India Post-Doctoral Fellow Management Graduate Program Universidade Paulista Indianópolis Campus Brazil Department of Industrial Engineering and CES 4.0 Faculty of Engineering University of Talca Curico Chile

The e-commerce industry has seen significant growth over the past decade as it focuses on convenience and accessibility, leading to a surge in online shopping with more and more consumers opting for it. At the same time, the e-commerce industry faces various challenges. In order to fully harness the potential of this industry, it is important to identify its benefits and challenges and focus on pathways to mitigate the challenges and harness its growth. This study utilizes the Delphi approach and involves experts from the e-commerce domain to get their opinions to identify the top ten benefits, challenges, and pathways for the e-commerce industry. Analytic Hierarchy Process (AHP) and Criteria Importance Through Intercriteria Correlation (CRITIC) methods are subsequently, employed to prioritize the identified factors. Results of the study revealed that factors such as affordable advertising & marketing;availability and product variety;and Global reachability are the most important benefits, while technological upgradation;returns or refunds;and counterfeit products posed the greatest challenges for the industry. Government compliance check;better relationship with delivery partners;and strong data privacy and online security policies emerged as the best pathways. This study also provides valuable insights to businesses, policymakers, and researchers in the e-commerce industry on how to navigate the benefits, challenges, and pathways of this rapidly growing sector. © 2023

关键词： Electronic commerce

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Intelligent Adjustment of Temperature Control Parameters Based on Deep Reinforcement Learning for Stretch Blow Molding Machine 25

Intelligent Adjustment of Temperature Control Parameters Bas...

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25th International Conference on mechatronics Technology, ICMT 2022

作者： Hsieh, Ping-Cheng Su, Guan-Lin Lin, Yi-Te Lin, Wei-Bo National Changhua University of Education Department of Mechatronics Engineering Changhua Taiwan Feng Chia University Department of Mechanical and Computer-Aided Engineering Taichung Taiwan Feng Chia University Ph.D. Program of Mechanical and Aeronautical Engineering Taichung Taiwan

ISBN: (数字)9781665461955

ISBN: (纸本)9781665461955

Stretch blow molding is the main technology used in the production of PET bottles. The stretch blow molding machine is usually composed of preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Among them, the temperature control of the heating system is one of the key factors affecting the quality of PET bottle, especially in the face of the environmental temperature changes greatly between morning and evening in some seasons. The on-site operators of the stretch blow molding machine often need to adjust the infrared heating lamps in the heating system several times. The adjustment process is highly dependent on experience of the personnel. It has become a production challenge for the bottle manufacturers. Therefore, this paper takes the heating system of the stretch blow molding machine as the object and uses the deep reinforcement learning method to develop an intelligent adjustment technique of temperature control parameters. The proposed method can improve the problems such as the interference of environmental temperature changes and the aging variation of infrared heating lamps. The experimental results show that the proposed method can adjust the temperature control parameters automatically in the heating process to eliminate the effect of the environmental temperature change and to control the surface temperature of the preforms stably within ±3 °C of the target temperature (the requirement is within±5 °C). © 2022 IEEE.

关键词： Deep learning

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Evaluation of the Influence of Eggshell (ES) Concentration on the Degradation Behavior of Mg-2.5Zn Biodegradable Alloy in Simulated Body Fluid

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ACS Biomaterials Science and engineering 2023年第5期9卷 2376-2391页

作者： Radwan, A. Bahgat Okonkwo, Paul C. Murugan, Srinivasan Parande, Gururaj Taryba, Maryna Montemor, M. Fatima Al-Mansoori, Layla Elrayess, Mohamed A. Al-Qahtani, Noora Gupta, Manoj Youssef, Khaled M. Case, Raymundo Shakoor, R.A. Abdullah, Aboubakr M. Center for Advanced Materials Qatar University Doha2713 Qatar Department of Mechanical and Mechatronics Engineering Dhofar University Salalah211 Oman Mahendra Engineering College Tamil Nadu Namakkal637503 India Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 Singapore117576 Singapore Centro de Quimica Estrutural Instituto Superior Técnico Universidade de Lisboa Avenida Rovisco Pais Lisboa1049-001 Portugal Qatar University Doha2713 Qatar Materials Science and Technology Graduate Program College of Arts and Sciences Qatar University Doha2713 Qatar Department of Materials Science and Engineering Texas A&M University College StationTX77843 United States

Currently, permanent vascular stents are fabricated using titanium and stainless steel implants that are nondegradable and offer high stability, but they have certain disadvantages. For example, the prolonged exposition of aggressive ions in the physiological media and the existence of defects in the oxide film create conditions for corrosion to occur, thus triggering unwanted biological events and compromising the mechanical integrity of the implants. Moreover, when the implant does not need to be permanent, there is the need to submit the patient for a second surgery for implant removal. As a solution for nonpermanent implants, biodegradable magnesium alloys have been deemed a promising substitute, for example, for cardiovascular-related applications and the construction of orthopedic devices. A biodegradable magnesium alloy (Mg-2.5Zn) reinforced by zinc and eggshell was employed in this study as an environment-conscious magnesium (eco) composite (Mg-2.5Zn-xES). Disintegrated melt deposition (DMD) was used to fabricate the composite. Experimental studies were conducted to investigate the biodegradation performance of Mg-Zn alloys containing 3 and 7 wt % eggshell (ES) in simulated body fluid (SBF) at 37 °C. Different corrosion techniques were used to study the corrosion behavior of the Mg-2.5Zn-xES composites, including weight loss measurements, hydrogen evolution, potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and scanning vibrating electrode technique (SVET). Scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) were employed to scrutinize the corroded surfaces’ morphology and composition. The outcomes indicated that Mg-2.5Zn-3ES possesses the lowest degradation activity. © 2023 American Chemical Society.

关键词： Electrochemical impedance spectroscopy

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A motion-level-aware denoising framework for x-ray fluoroscopic images

A motion-level-aware denoising framework for x-ray fluorosco...

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Medical Imaging 2023: Physics of Medical Imaging

作者： Jo, Geonhui Jang, Minhee Jeon, Sun-Young Kim, Wonjin Choi, Jang-Hwan Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of

ISBN: (纸本)9781510660311

Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. F

关键词： Deep learning

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