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KINEMATICS MODELING OF THE ABB7600 ROBOT

UPB Scientific Bulletin, Series D: Mechanical engineering 2024年第1期86卷 17-32页

作者： Nichifor, Sandra-Elena Stroe, Ion Faculty of Biotechnical Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Department of Mechanics National University of Science and Technology POLITEHNICA Bucharest Romania

The purpose of this paper is to provide a kinematics model of an ABB IRB7600 robotic arm. Regarding the direct kinematic problem, this is solved using Denavit-Hartenberg parameters. Moreover, the inverse kinematics problem is based on the known iterative methods, respectively the general problem of inverse kinematics and the geometric approach of the position to determine the joints. The developed kinematics model plays an essential role in enabling the trajectory planning for the ABB 7600 robot. By employing a fourth-order interpolation method, the motion planning process is enhanced, allowing the robot to execute precise and efficient movements along its trajectory. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Inverse kinematics

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Overview of Emerging Biological Computational Framework for Olfactory Studies Utilizing Contextual Technobiomedical engineering 2

Overview of Emerging Biological Computational Framework for ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Chaudhri, Shiv Nath Faculty of Engineering & Technology Department of Computer Science and Design Wardha India

ISBN: (纸本)9798331528713

Organoid intelligence (OI), the next paradigm of intelligence, draws inspiration from the biological learning of organs. On the other hand, artificial intelligence (AI) draws its inspiration only from the cognitive processes of the brain. While taking ethical and technological limitations into account, biological learning helps to conceptualize, accept, and apply them. Just as AI helped to make supercomputers possible, OI may help create biocomputers. Even though AI has increased the capabilities of technology to unimaginable heights, it still appears immature and is steadily moving toward cognitive computing to push the envelope even farther. In contrast to AI, OI is a new multidisciplinary computer area that attempts to be more approachable. OI has the ability to produce novel biocomputers, such as organoids, that use parts derived from live organisms to perform computational tasks. Organoids, such as human nasal organoids, are microscopic tissue cultures made from stem cells that mimic some of the functions of real organs. Although they have been used for many different purposes, including drug discovery and illness modeling, scientists are only now beginning to explore their potential for biological information processing. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Biological Information Processing Biological Learning Olfactory Organoids Organoid Intelligence (OI)

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Optimal material and geometry of 2D-FGM tapered microbeams under dynamic and static constraints

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Mechanics Based design of Structures and Machines 2024年第8期52卷 5227-5262页

作者： Abo-bakr, H.M. Abo-bakr, R.M. Shanab, R.A. Eltaher, M.A. Computers and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig Egypt Mathematical Department Faculty of Science Zagazig University Zagazig Egypt Engineering Mathematics Department Faculty of Engineering Zagazig University Zagazig Egypt Mechanical Engineering Department Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Mechanical Design and Production Department Faculty of Engineering Zagazig University Egypt

The present work applies meta-heuristic multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA)-II algorithms to predict optimal shape and material distribution of two-directional functionally graded tapered (2DFG-T) microbeams. The cross-section of the microbeam changes linearly along its length in both height and width directions. The rates of these changes are taken as the geometric design variables. Rather than the simple power law, two different trigonometric functions are introduced to provide various effective and flexible 2D- distributions of metal and ceramic phases. Using Hamilton’s principle, the mathematical model is derived based on the third order shear deformation theory (TSDT) and modified couple stress theory (MCST). Developing a computationally efficient and time saving semi-analytical approach, the governing set of variable-coefficients partial differential equations is solved for the frequency and critical buckling under different boundary conditions. Several constrained single- and multi-objective optimization problems are formulated and solved to determine optimal material and geometry parameters of 2DFG-T microbeams to achieve maximization of fundamental frequency and/or critical buckling load while minimizing the structural mass. Various optimization results demonstrate that including shape parameters in addition to material distribution parameters as design variables results in more economical and flexible profiles that is critical for the manufacture of 2DFG-T microbeams. The following model can be exploited in the optimum design of FG microscale nanobeams including their shapes and material distribution. © 2023 Taylor & Francis Group, LLC.

关键词： Multiobjective optimization

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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Computer Modeling in engineering & sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Methodology for microclimatic urban canyon design case study surrounding MUST university campus in Cairo

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HBRC Journal 2024年第1期20卷 589-603页

作者： Abd El-Razik, Mahmoud M. Hafez, Yasmine K. Hendy, Alaa H. Fawzy, Mariam M. Cairo Egypt Faculty of Engineering & Technology Architecture Engineering & Environmental Design Department Arab Academy for Science Technology & Maritime Transport Giza Egypt Nile University Giza Egypt

Climate change is a major issue all over the world which has a great impact on global warming especially in countries with hot arid climates like Egypt. With rapid urbanization, streets in Greater Cairo and new cities are mostly car-oriented, which increases the Urban Heat Island(UHI) and, hence, affects the overall outdoor thermal comfort. Looking at the urban development in recent years, almost 10 new private universities are in operation in Cairo’s new cities together with a number of large recreational projects where they lack the appropriate vegetation surrounding their borders which in turn increases the UHI effect, decreasing the walkability’s thermal comfort and in turn contributes poorly to the surrounding climate. This paper focuses on the urban canyons in streets surrounding universities as they usually have high traffic rates in terms of both vehicular and pedestrian flows. This large flow of pedestrians necessitates taking into consideration the thermal comfort of the users. MUST University in the Al-Motamayez district on the 6th of October city was chosen as a case study. This case study is considered the basis for achieving an understanding and developing a methodology approach for such studies by using Envi-Met software for the simulation of different scenarios focusing on choosing trees appropriate to the climate zone of the city. Results for the simulation of suggested scenarios of intervention show a 30% improvement toward thermal comfort, which could be considered a first step for future guidelines development toward achieving thermal comfort and decreasing UHI. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Vegetation

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A Comprehensive Review on Data Visualization Techniques for Real-Time Information 7

A Comprehensive Review on Data Visualization Techniques for ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Choudhary, Vishal Kumar Kumar, Gulshan Suman, Saurabh Kumar, Abhishek Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India

ISBN: (纸本)9798350359299

Research on real-time data visualization methods is necessary to achieve the most accurate and clear representation of information. Creating specific boards and modifying current platforms are two key tasks in performing an appropriate information visualization. An overview of the studies on dashboard platforms, data visualization methods, and accessible strategies is given in this article. To evaluate the effects of the various data visualization approaches on the finished dashboard, a generic and dynamic dashboard based on real-time data was created. Consequently, a preliminary collection of cues, charts, tables, and reports will allow users to interact with the data. produced by the Dashboard itself. Making informed decisions across a variety of businesses requires the capacity to acquire and comprehend real-time information in today's data-driven environment. Real-time data visualization techniques are now essential for turning complex data into insights that can be put to use. This study examines the value of real-time data visualization and offers a summary of strategies and equipment that enable businesses to efficiently use real-time data. © 2024 IEEE.

关键词： Data visualization

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A Review on Role of IoT and Machine Learning in Agriculture 7

A Review on Role of IoT and Machine Learning in Agriculture

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kumar, Gulshan Choudhary, Vishal Kumar Kumar, Shravan Kumar, Rajan Kumar, Mukesh Gourshettiwar, Palash M. Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798350359299

The agricultural area has undergone a significant transformation owing to the progress made in IoT. It is imperative to have a dependable remote monitoring solution right now. This study aims to accomplish two goals. First, a solution based on an app is offered, which effectively assists in remotely administering the field by displaying the current sensor values. In an Internet of Things based prototype surveillance system, a multi-class categorization method utilizing machine learning and deep learning is incorporated to classify labels such as clear farm, cow, horse, wild boar, and wild elephant. The analysis involved Support Vector Machines and Convolutional Neural Networks, with the accuracy measure being employed to determine the optimal model. The advancements in the Internet of Things (IoT) have significantly transformed the daily lives of individuals. A comprehensive understanding of soil properties gives a variety of soil-related information that can be used to improve crop yields. Machine learning is a cutting-edge technology that aids with agricultural precision and provides answers to crop yield issues. Significant informative advances and Machine Learning (ML) open up new opportunities for deciphering, quantifying, and comprehending data-intensive prediction in agricultural settings. This study thoroughly examines a number of machine learning topics for Internet of Things-based smart agriculture systems. © 2024 IEEE.

关键词： Support vector machines

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STUDY ON A SIMPLIFIED MODEL OF A HIGH-RISE SHEAR-TYPE BUILDING FOR EVALUATION OF Q–Δ RESONANCE

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2023年第803期88卷 59-70页

作者： Anamizu, Ryosuke Kohiyama, Masayuki School of Science for Open and Environmental Systems Graduate School of Science and Technology Keio University Japan Dept. of System Design Engineering Faculty of Science and Technology Keio University Japan

To evaluate Q–Δ resonance, which is a torsional resonance phenomenon caused by geometric non-linearity, we proposed a simplified model for an n-story non-eccentric building model with a shear-type structure. We designed and fabricated a four-story small-scale specimen assuming a non-eccentric steel structure with a height of 324 m, and conducted shaking table tests. As a result, the occurrence of Q–Δ resonance was confirmed as predicted by the simplified model. In addition, the simplified model reproduced the resonance amplitude in a finite element model of the specimen with the out-of-plane rotation of slabs constrained and its validity was confirmed. © 2023 Authors. All rights reserved.

关键词： Tall buildings

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EJUSTCO:Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation

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Nuclear science and Techniques 2023年第9期34卷 155-176页

作者： Joseph Konadu Boahen Ahmed S.G.Khalil Mohsen A.Hassan Samir A.Elsagheer Mohamed Materials Science and Engineering Department School of Innovative Design EngineeringEgypt-Japan University of Science and Technology(E-JUST)179New Borg El-Arab CityEgypt Physics Department Environmental and Smart Technology GroupFaculty of ScienceFayoum UniversityFayoum 63514Egypt Faculty of Engineering Aswan UniversityAswanEgypt

Gamma ray shielding is essential to ensure the safety of personnel and equipment in facilities and environments where radiation *** Monte Carlo technique is vital for analyzing the gamma-ray shielding capabilities of *** this study,a simple Monte Carlo code,EJUSTCO,is developed to cd simulate gamma radiation transport in shielding materials for academic *** code considers the photoelectric effect,Compton(incoherent)scattering,pair production,and photon annihilation as the dominant interaction mechanisms in the gamma radiation shielding *** reduction techniques,such as the Russian roulette,survival weighting,and exponential transformation,are incorporated into the code to improve computational *** the exponential transformation parameter typically requires trial and error as well as ***,a deep learning neural network is proposed as a viable method for predicting this parameter for the first *** model achieves an MSE of 0.00076752 and an R-value of *** exposure buildup factors and radiation dose rates due to the passage of gamma radiation with different source energies and varying thicknesses of lead,water,iron,concrete,and aluminum in single-,double-,and triple-layer material systems are validated by comparing the results with those of MCNP,ESG,ANS-6.4.3,MCBLD,MONTEREY MARK(M),PENELOPE,and *** errors of 5.6%,2.75%,and 10%are achieved for the exposure buildup factor in single-,double-,and triple-layer materials,respectively.A significant parameter that is not considered in similar studies is the gamma ray *** the EJUSTCO code,the total number and energy albedos have been *** results are compared with those of MCNP,FOTELP,and *** general,the EJUSTCO-developed code can be employed to assess the performance of radiation shielding materials because the validation results are consistent with theoretical,experimental,and literary results.

关键词： Monte Carlo Gamma rays Shielding Artificial neural network Simulation

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Bitcoin Price Prediction Using Deep Learning Models 4

Bitcoin Price Prediction Using Deep Learning Models

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Hajare, Rasika Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares the use of three deep learning models, namely recurrent neural networks (RNN), gated recurrent unit (GRU), and a convolutional neural network-long short-term memory (CNN-LSTM) for Bitcoin price prediction. Historical Bitcoin price data is used in the dataset, and every one of the models is trained and tested based on its performance to predict future prices based on past data. The proposed models include the RNN and GRU, which incorporate sequential dependency mappings to learn complex sequences, and the CNN-LSTM structures combine convolution layers for feature extraction and LSTM layers for sequencing. Different models' competency is discussed using the averaged Mean Squared Error (MSE) and graphs of the model's prediction against actual prices. The experimental results reveal that the proposed CNN-LSTM model has better prediction accuracy than RNN and GRU in time-series forecasting tasks, indicating that more complex architecture hybrid models might provide better forecasting performance. In analyzing and analyzing cryptocurrency price dynamics, this investigation gives information about the opportunities and challenges of these models to enhance future research. © 2025 IEEE.

关键词： Prediction models

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