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Learning Deposition Policies for Fused Multi-Material 3D Printing

Learning Deposition Policies for Fused Multi-Material 3D Pri...

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IEEE international conference on robotics and automation (ICRA)

作者： Kang Liao Thibault Tricard Michal Piovarči Hans-Peter Seidel Vahid Babaei Max Planck Institute for Informatics Beijing Jiaotong University Institute of Science and Technology Austria

3D printing based on continuous deposition of materials, such as filament-based 3D printing, has seen widespread adoption thanks to its versatility in working with a wide range of materials. An important shortcoming of this type of technology is its limited multi-material capabilities. While there are simple hardware designs that enable multi-material printing in principle, the required software is heavily underdeveloped. A typical hardware design fuses together individual materials fed into a single chamber from multiple inlets before they are deposited. This design, however, introduces a time delay between the intended material mixture and its actual deposition. In this work, inspired by diverse path planning research in robotics, we show that this mechanical challenge can be addressed via improved printer control. We propose to formulate the search for optimal multi-material printing policies in a reinforcement learning setup. We put forward a simple numerical deposition model that takes into account the non-linear material mixing and delayed material deposition. To validate our system we focus on color fabrication, a problem known for its strict requirements for varying material mixtures at a high spatial frequency. We demonstrate that our learned control policy outperforms state-of-the-art hand-crafted algorithms.

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An Innovative Approach to Glass Classification Using Deep Neural Network

An Innovative Approach to Glass Classification Using Deep Ne...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Md Shohanur Rahman Tania Sultana Wahid Tausif Islam Md. Rifat Ahmed Khan Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE American International University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

A novel method for classifying glasses using deep neural networks is presented. The study uses data from the USA Forensic Science Service to classify six different varieties of glass according to their oxide level. The dataset is carefully cleaned up using preprocessing methods like Min-Max Scaling and Label Encoding to improve data quality and make it more neural network architecture compatible. The deep neural network model performs admirably in this difficult task, correctly categorizing glass samples with an accuracy of 81.40%. The analysis of the results shows that different varieties of glass behave differently, pointing out both areas of strength and improvement. In the future, the work may entail investigating more sophisticated deep learning techniques including transfer learning and attention processes, as well as broadening the dataset to include a greater range of glass samples. Moreover, validation tests conducted in the actual world and the model's implementation in industrial settings may offer important new perspectives on its usefulness. In general, this study advances automated glass categorization methods, which may have consequences for activities related to environmental sustainability, product creation, and quality assurance in a variety of sectors.

关键词： Accuracy Quality assurance Service robots Transfer learning Refining Glass Artificial neural networks Recycling Manufacturing Sustainable development

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SLRNet: Shallow Convolutional Neural Network for Efficient Sign Language Recognition

SLRNet: Shallow Convolutional Neural Network for Efficient S...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Lamia Mostafa Faria Jerin Brishti Eishita Sultana Akther MD. Khaliluzzaman MD Jiabul Hoque Department of Computer Science and Engineering International Islamic University Chittagong (IIUC) Chattogram Bangladesh Department of Computer and Communication Engineering International Islamic University Chittagong (IIUC) Chattogram Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

In recent times, recognizing hand gestures from a sign image has become a vital research field in the areas of computer vision and human-computer interaction. Many researchers have taken advantage of various features and deep learning architectures (DLA) to update the performance of existing sign recognition systems. In small datasets such as ASL, shallow DLAs such as convolutional neural networks (CNN) work well. However, the deeper deep learning architectures are overfitted on the small datasets. To overcome this problem, in this work, two shallow CNN models, SLRNet1 and SLRNet2, are proposed. The proposed models use four convolutional layers to extract the features from the sign image and two fully connected layers to recognize the sign. The first and second models gain 98.94% and 99.14% accuracy, respectively. Both models achieve significant improvements with respect to the present state of the ***— Two regularized hand gesture recognition models, SLRNet1 and SLRNet2, were developed to work efficiently on small datasets without overfitting and perform fine-tuning to achieve better accuracy.

关键词： Hands Human computer interaction Sign language Computer vision Image recognition Accuracy Computational modeling Assistive technologies Feature extraction Convolutional neural networks

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Enhancing Cybersecurity with an Investigation into Network Intrusion Detection System Using Machine Learning

Enhancing Cybersecurity with an Investigation into Network I...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Md Shohanur Rahman Wahid Tausif Islam Md. Rifat Ahmed Khan Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE American International University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

The growing dependence on networked systems underscores the importance of robust security measures to fend off potential intrusions. This research undertakes the task of assessing different machine-learning models concerning network intrusion detection. Following an in-depth examination and comparison, models such as Decision Tree, Logistic Regression, XGBoost, and Random Forest exhibit promising aptitude in precisely identifying potential intrusions. Notably from our proposed model, Random Forest emerged as the top performer among these models, attaining the highest accuracy scores (99%). Its proficiency lies in delivering precise predictions and effectively capturing the intricacies of network intrusion detection. However, the Support Vector Machine (SVM) displayed limitations in its performance throughout this research. These discoveries illuminate the positive and negative aspects of these patterns, providing valuable knowledge about their efficiency in detecting intrusions. The study underscores the necessity for further fine-tuning of successful models and the exploration of advanced techniques to fortify network security. This research, as a whole, signifies an initial and crucial stride in the direction of formulating network intrusion detection systems that are more effective and reliable within the realm of network security.

关键词： Support vector machines Logistic regression Refining Network intrusion detection Network security Decision trees Reliability Computer security Random forests Robots

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3rd EAI international conference on Sustainable Energy for Smart Cities, SESC 2021

3rd EAI International Conference on Sustainable Energy for S...

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3rd EAI international conference on Sustainable Energy for Smart Cities, SESC 2021

ISBN: (纸本)9783030970260

The proceedings contain 13 papers. The special focus in this conference is on Sustainable Energy for Smart Cities. The topics include: Electric Vehicle Battery Charger with Vehicle-to-Vehicle (V2V) Operation Mode;a Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models;maximization of Solar Power Extraction from Photovoltaic Modules Using Energy Harvesting Solutions for Smart Cities;A Comprehensive Comparison of Voltage and Current control Techniques for Three-Phase VSI Converters;Development of a Modular Multilevel Cascade Converter Based on Full-Bridge Submodules with a Common DC Bus;standard Energy Renovation at the Urban Scale in the Moroccan Context;technical-Economic Analysis of a Power Supply System for Electric Vehicle Charging Stations Using Photovoltaic Energy and Electrical Energy Storage System;assessing Household Electricity Consumers’ Willingness to Load Shift;a Feature and Classifier Study for Appliance Event Classification;computer Studies of the Operation of a Three-Phase Four Wire Shunt Active Power Filter Applied to the Industry;A Three-Phase Multilevel AC-DC Converter Operating as a Shunt Active Power Filter: Validation Considering an Industrial Environment.

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A new SPICE modeling approach of annular MOSFET in 350 nm commercial CMOS process

A new SPICE modeling approach of annular MOSFET in 350 nm co...

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Electronic Information Engineering and Computer Technology (EIECT), international conference on

作者： He Cheng Zhijia Yang Chao Zhang Zhipeng Zhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Annular gate MOSFETs have emerged as potential solutions for radiation-hardened electronics, demonstrating exceptional total ionizing dose tolerance. However, accurate modeling of annular MOSFETs is crucial when designing analog integrated circuits for use in radiation environments, especially when implementing commercial CMOS processes. To enable circuit simulation using tools like SPECTRE/SPICE, foundry-provided PDKs must incorporate a rigorously verified compact model of annular MOSFET behavior. TCAD simulation can then be used to numerically evaluate the current characteristics of the annular MOSFET against the refined compact model constructed from the foundry-provided PDK. In this paper, by aligning the current characteristics of a standard NMOS between TCAD and SPICE simulations, we can verify the material process conditions. Then, based on the material process parameters, the model of the annular gate MOSFET is established using the TCAD simulator. Finally, reasonable agreements are obtained between the TCAD and SPICE simulation results for the I DS -V GS and I DS -V DS characteristics.

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Development of a Web Application to Support Validation of Indoor Robots Using AR

Development of a Web Application to Support Validation of In...

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international conference on control, automation and Systems ( ICCAS)

作者： Kento Hirose Masanobu Koga Creative Informatics Kyushu Institute of Technology Iizuka Fukuoka Japan

In this paper we report on the web application which was developed to help users considering the purchase of an indoor robot to verify the effectiveness of the robot in their environment of use. The application uses a robot model in rdTF format that works in a web browser. For the simulation calculations, it use a physical calculation engine compiled with WebAssembly or other tools to run at high speed, so that the robot can run at high speed even in a web browser. Similarly, for animation, it use a library compatible with WebGL, which enables high-speed display on a Web browser. In order to enable users to check the results of the indicated task and confirm the validity of the robot's purchase by checking the 3D animation in the operating environment, the trajectory of the task is generated in conjunction with ROS.

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Automated Requirements Engineering in Agile Development: A Practitioners Survey

Automated Requirements Engineering in Agile Development: A P...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Muhammad Aminu Umar Kevin Lano Department of Informatics King’s College London United Kingdom Department of Computer Science Ahmadu Bello University Zaria Nigeria

Requirements Engineering (RE) in the context of agile development is the process of discovering, analysing, validating, and managing software requirements through engaging with the stakeholders. RE activities are central to software development and its success. Advances in automation have been explored in requirements engineering activities and processes in the industry. However, the application of automated support is still limited in practice. In this study, we examine automated RE support in agile development through a qualitative analysis using industry experts. We have found that the application of automated RE support tools is primarily in the experimental stage, largely due to the limited practical implementation in industry settings. The current focus has been on research experiments, with relatively fewer instances of real-world industry application. Although experts are optimistic about its huge potential to reduce development efforts and costs if automation is harnessed. Therefore, we anticipate that these findings will be beneficial for practitioners responsible for developing RE tools for agile development, as well as for the researchers.

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A 3D/2D Coronary Artery Registration Method Based on Deep Reinforcement Learning

A 3D/2D Coronary Artery Registration Method Based on Deep Re...

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Computer, control and robotics (ICCCR), international conference on

作者： Jun Yan Zhuang Fu Zeyu Fu Yisheng Guan School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China School of Electromechanical Engineering Guangdong University of Technology Guangzhou China

At present, vascular interventional surgery mainly depends on the conventional coronary angiography (CCA) and coronary computed tomography angiography (CCTA). However, due to various reasons such as different imaging principles and different operation methods, both CCA and CCTA imaging technologies can only be used separately. In order to integrate the two kinds of medical image information, medical image registration technologies based on artificial intelligence have emerged. In this paper, we propose a 3D/2D coronary artery registration method based on the combination of feature and deep reinforcement learning. Based on the centerline feature, we perform a projection transformation to unify the dimensions between two modes, then design a deep reinforcement learning algorithm, complete the mapping of the state space by convolutional neural network (CNN), discretize the actions of the agent, and use the Euclidean distance to define the reward function for fine coronary artery registration. The method is used for experiments with clinical CCA and CCTA data sets, and compared with other registration algorithms. The results show that the registration error rate of the proposed method is lower than that of other algorithms, it has strong accuracy and robustness, and can handle the registration problems of complex vascular segments.

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Application of Udwadia-Kalaba Approach to Tracking control of Surgical Robot

Application of Udwadia-Kalaba Approach to Tracking Control o...

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Service robotics (ICoSR), international conference on

作者： Feifei Qin Zexuan Liu Junwei Fang Binrui Wang College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

There is a significant anticipated shift from operating theater (OT) surgery to digital OT, with Robotic Assisted Surgery (RAS) emerging as the most representative aspect of this transformation. RAS which uses robot to assist the surgeon while operating a patient offers several technical advantages such as greater precision, and more accurate compared with conventional surgery. The paper introduces an explicit dynamic control equation for a surgical slave robot, leveraging the UK theory, which provides a succinct and precise equation of motion for constrained mechanical systems with holonomic or nonholonomic constraints. The performance specifications, including the required trajectory, are regarded as servo constraints in a second-order matrix format. These secondorder constraints are integrated into the dynamics and constraint-following control. The UK theory is utilized to derive a closed-form expression for the control force necessary to accurately guide the surgical robot along the predefined trajectory. Notably, no approximations, linearizations, or auxiliary variables like Lagrangian multipliers or pseudogeneralized speeds are employed in the dynamic modeling or analysis. Simulation results show the simplicity, efficacy and accuracy of the proposed approach when applied to the surgical robot.

关键词： Medical robotics Accuracy Closed-form solutions Trajectory tracking Simulation Dynamics Surgery Mathematical models Trajectory Mechanical systems

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