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

Construction Robotics 2021年第2期5卷 115-130页

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

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.

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A Hybrid Intrusion Detection System Approach Using PCA and SMOTE with Advanced Ensemble Models

A Hybrid Intrusion Detection System Approach Using PCA and S...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Khaled Mahmud Sujon Nusrat Jahan Someyo Kamal Utsho Mahadi Hasan Shuvo Md Najmus Sakib Razin Department of Software Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Software Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Department of Architecture American International University-Bangladesh Dhaka Bangladesh Department of Electrical & Electronics Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A persistent issue of class imbalance in intrusion detection systems (IDS) is addressed in this research, particularly in the context of cyber-physical systems and IoT devices. Current IDS approaches often struggle to detect rare attack patterns due to the dominance of normal traffic in imbalanced datasets. To address this, we present a unique strategy that combines Principal Component Analysis (PCA) for selecting features with the Synthetic Minority Oversampling Technique (SMOTE) to deal with class imbalance, applied to four powerful classifiers: Random Forest (RF), Decision Trees (DT), XGBoost (XGB), and AdaBoost. By reducing the feature space with PCA and balancing the dataset with SMOTE, our methodology significantly enhances model performance. Evaluating on the KDDCup ’99 dataset, we observe notable improvements: for instance, Random Forest achieves 96.42% accuracy, 98.52% precision, 94.68% recall 96.55% F1-score, and 93.87% MCC. Similarly, XGBoost achieves 94.78% accuracy, 96.72% precision, 92.94% recall, 94.81% F1-score, and 92.36% MCC. These results demonstrate that the integration of SMOTE and PCA effectively addresses class imbalance and enhances the detection of minority class attacks. Our findings contribute to the advancement of machine learning techniques in IDS and provide a foundation for further research into combining oversampling methods and dimensionality reduction for robust cybersecurity solutions. Future work will focus on exploring alternative oversampling methods, incorporating deep learning models, and assessing the practical deployment of the proposed methodology in real-time network environments.

关键词： Dimensionality reduction Deep learning Accuracy Computational modeling Intrusion detection Real-time systems Decision trees Random forests Principal component analysis Testing

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Evaluating Neighbourhood Roads Through Agent-Based Modelling: A Step Towards the Optimal Pedestrian Desire Path System

SSRN

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SSRN 2024年

作者： Ma, Lei Brandt, S. Anders Seipel, Stefan Ma, Ding Department of Computer and Geospatial Sciences Faculty of Engineering and Sustainable Development University of Gävle Gävle80176 Sweden Division of Visual Information and Interaction Department of Information Technology Uppsala University Uppsala75105 Sweden Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen518060 China

As a part of road networks, sidewalks are critical in urban development and people’s lives. This study introduces the Road Evaluation by Desire Path Simulation System (RED-PaSS), an agent-based modelling (ABM) framework designed to assess the alignment of urban sidewalks with pedestrians' natural movement patterns. RED-PaSS simulates pedestrian movement to generate an optimal desire path system, representing the most direct and natural routes that pedestrians would choose in urban settings. The model compares existing sidewalks against these simulated desire paths using key metrics, including road patch distances, segment alignment with desire paths, and origin-destination (OD) flow efficiency. This approach enables urban planners to identify and address gaps in pedestrian infrastructure effectively. In this study, RED-PaSS was applied to evaluate and rank 708 neighbourhood-scale road networks across the United States using high-resolution population density data and open-source road datasets. The evaluation revealed that the sidewalks closely aligned with simulated desire paths offer more direct and efficient pedestrian routes, enhancing natural walking movement. In contrast, poorly aligned sidewalks often force pedestrians to take indirect routes, reducing walkability and efficiency. By identifying discrepancies between existing sidewalks and optimal pedestrian pathways, RED-PaSS provides actionable insights for designing more walkable urban environments and can potentially serve as a valuable tool for urban planners seeking to enhance urban road systems. This work contributes to urban planning by offering a method to improve pedestrian network efficiency, aligning infrastructure with actual pedestrian natural movement behaviours, and emphasizing the need for integrating organic pedestrian movements into formal road network designs to create more functional and walkable cities. © 2024, The Authors. All rights reserved.

关键词： Population statistics

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Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection

arXiv

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arXiv 2022年

作者： Madan, Neelu Ristea, Nicolae-Cǎtǎlin Ionescu, Radu Tudor Nasrollahi, Kamal Khan, Fahad Shahbaz Moeslund, Thomas B. Shah, Mubarak Department of Computer Science University of Central Florida OrlandoFL32816 United States The Department of Architecture Design and Media Technology Aalborg University Denmark The Department of Telecommunications University Politehnica of Bucharest Romania The Department of Computer Science University of Bucharest Romania SecurifAI The Romanian Young Academy The Department of Computer Science University of Bucharest Romania Milestone Systems Denmark United Arab Emirates Linköping University Sweden

Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on industrial production lines and impending event detection in video surveillance to finding lesions in medical scans. Regardless of the domain, anomaly detection is typically framed as a one-class classification task, where the learning is conducted on normal examples only. An entire family of successful anomaly detection methods is based on learning to reconstruct masked normal inputs (e.g. patches, future frames, etc.) and exerting the magnitude of the reconstruction error as an indicator for the abnormality level. Unlike other reconstruction-based methods, we present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. The proposed self-supervised block is extremely flexible, enabling information masking at any layer of a neural network and being compatible with a wide range of neural architectures. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss. Furthermore, we show that our block is applicable to a wider variety of tasks, adding anomaly detection in medical images and thermal videos to the previously considered tasks based on RGB images and surveillance videos. We exhibit the generality and flexibility of SSMCTB by integrating it into multiple state-of-the-art neural models for anomaly detection, bringing forth empirical results that confirm considerable performance improvements on five benchmarks: MVTec AD, BRATS, Avenue, ShanghaiTech, and Thermal Rare Event. We release our code and data as open source at: https://***/ristea/ssmctb. Copyright © 2022, The Authors. All rights reserved

关键词： Convolution

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Landslide Disaster Assessment with Integration of Seismic, Social Media and Remote Sensing Data

Landslide Disaster Assessment with Integration of Seismic, S...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Deepak Rawat M. L. Sharma Roshan Kumar Nishant Saxena Atul Kumar Abdul Kadir Centre of Excellence in Disaster Mitigation and Management Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Earthquake Engineering Roorkee Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Electronics and Information Technology Miami College Henan University Henan Kaifeng China Department of Architecture and Planning Indian Institute of Technology Roorkee Roorkee Uttarakhand India Computer Science and Engineering Department Indian Institute of Technology Roorkee Roorkee Uttarakhand India

On February 7, 2021, a rockfall in the Rishiganga Valley killed about 200 people. For the integration of seismic, social media, and remote sensing, a case study on this tragedy has been conducted. This study explores the role of social media in managing Himalayan landslides in conjunction with remote sensing data analytics. We look at how these technologies can enhance damage management, quick mobilization, and early warning systems. A literature review and case studies of the successful use of these technologies in other disaster-prone areas will support these suggestions. The intention is to equip decision-makers and experts in disaster management with the practical knowledge they need to effectively prepare for and respond to Himalayan landslide events, saving lives and minimizing the impact of natural hazards.

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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

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Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

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Journal of computer Science & technology 2020年第1期35卷 209-220页

作者： Sa Wang Yan-Hai Zhu Shan-Pei Chen Tian-Ze Wu Wen-Jie Li Xu-Sheng Zhan Hai-Yang Ding Wei-Song Shi Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518055China Alibaba Inc. Hangzhou 311121China Department of Computer Science Wayne State UniversityMichiganMI 48202U.S.A

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be *** resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable *** of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource *** this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC *** is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural *** experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.

关键词： resource management neural network resource efficiency tail latency

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A Novel Voronoi-based Convolutional Neural Network Framework for Pushing Person Detection in Crowd Videos

arXiv

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arXiv 2023年

作者： Alia, Ahmed Maree, Mohammed Chraibi, Mohcine Seyfried, Armin Institute for Advanced Simulation Forschungszentrum Jülich Jülich52425 Germany Department of Computer Simulation for Fire Protection and Pedestrian Traffic Faculty of Architecture and Civil Engineering University of Wuppertal Wuppertal42285 Germany Department of Information Technology An-Najah National University Nablus Palestine Department of Information Technology Arab American University Jenin Palestine

Analyzing the microscopic dynamics of pushing behavior within crowds can offer valuable insights into crowd patterns and interactions. By identifying instances of pushing in crowd videos, a deeper understanding of when, where, and why such behavior occurs can be achieved. This knowledge is crucial to creating more effective crowd management strategies, optimizing crowd flow, and enhancing overall crowd experiences. However, manually identifying pushing behavior at the microscopic level is challenging, and the existing automatic approaches cannot detect such microscopic behavior. Thus, this article introduces a novel automatic framework for identifying pushing in videos of crowds on a microscopic level. The framework comprises two main components: i) Feature extraction and ii) Video labeling. In the feature extraction component, a new Voronoi-based method is developed for determining the local regions associated with each person in the input video. Subsequently, these regions are fed into EfficientNetV1B0 Convolutional Neural Network to extract the deep features of each person over time. In the second component, a combination of a fully connected layer with a Sigmoid activation function is employed to analyze these deep features and annotate the individuals involved in pushing within the video. The framework is trained and evaluated on a new dataset created using six real-world experiments, including their corresponding ground truths. The experimental findings indicate that the suggested framework outperforms seven baseline methods that are employed for comparative analysis purposes. © 2023, CC BY.

关键词： Feature extraction

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Smart Prediction Models for Energy Efficiency in Smart Residential Buildings: A Deep Learning Approach

Smart Prediction Models for Energy Efficiency in Smart Resid...

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2023 International Conference for Technological Engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Vasantharaj, A. Ahamed, M. Irshad Prema, K. Mudigonda, Aditya Sapaev, J.B. Thandapani, Kavitha Kalaignar Karunanidhi Institute of Technology Information Electronics and Communication Engineering India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineerin Tamilnadu Nagapattinam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Vgy Technology Avadi Department of Electronics and Communication Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350336474

Using power in houses has given rise to several ecological issues, including air pollution. Predicting building energy use is essential for better regulating or reducing energy usage. Machine Learning (ML) techniques have been used to estimate the power use of operating buildings. However, there has been little research on using ML algorithms to anticipate the prospective energy usage of buildings during the initial design phase. When lowering energy-inefficient buildings, it is crucial to tackle the underlying issue: accurately anticipating energy consumption before construction. This is necessary to prevent the future development of environmentally detrimental buildings. This research introduces a new and innovative Hybrid Deep Learning Model for Predicting Energy Efficiency in Smart Residential Buildings (HDL-PEE-SRB). The suggested framework has two steps: cleaning the information and constructing the model. The information cleansing step utilizes pre-processing methods to manipulate the original information and incorporates lag values as supplementary characteristics. During model-building, the hybrid model undergoes training using the processed data. The HDL model combines fully linked layers, Unidirectional Long Short-Term Memory (Uni-LSTM), and bi-directional LSTMs (Bi-LSTM), which are stacked together. The suggested model is assessed using standard energy usage databases, demonstrating higher accuracy than commonly used HDL models. The proposed model surpasses other HDL models, demonstrating the most favorable performance with a MAPE of 1.94% and an accuracy of 98.06%. © 2023 IEEE.

关键词： Forecasting

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An anonymous and privacy-preserving lightweight authentication protocol for secure communication in UAV-assisted IoAV networks

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computer Communications 2025年 239卷

作者： Shariq, Mohd Jamil, Norziana Rawat, Gopal Singh Chaudhry, Shehzad Ashraf Masud, Mehedi Cangelosi, Angelo Department of Information Systems and Security College of IT UAE University P.O.Box 15551 Al Ain Abu Dhabi United Arab Emirates Bennett University Greater Noida201310 India Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul34398 Turkey Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Manchester Centre for Robotics and AI University of Manchester United Kingdom

With the rapid proliferation of the Internet of Things (IoT), autonomous vehicles (AVs), or self-driving cars, rely heavily on real-time data sharing and message exchanges over wireless networks. AVs use sensors, artificial intelligence, machine learning, and advanced algorithms to perform various functions, enabling users to operate without human intervention. Owing to the flexibility and high mobility of drones, they could aid in the operations of AVs. However, the security and privacy are the main concerns;specifically, the threat of physical capture and violation of anonymity are the main hurdles for realization of secure communication among the AVs and drones. To address these challenges, we propose an anonymous and provably Secure Lightweight Authentication Protocol for unmanned-aerial-vehicle-assisted Internet of Autonomous Vehicles (SLAP-IoAV). The proposed protocol uses cryptographic primitives such as exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. An informal security analysis found that SLAP-IoAV is secure against several known attacks, and a performance analysis established that the protocol has less computational and communication overhead than existing competitive protocols. Additionally, Scyther simulation results confirm that no security vulnerabilities are present. Overall, our protocol delivers superior security and performance, making it well-suited to real-world applications in the AV industry. © 2025 Elsevier B.V.

关键词： Authentication Protocol

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