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arXiv 2023年

作者： Karle, Phillip Betz, Tobias Bosk, Marcin Fent, Felix Gehrke, Nils Geisslinger, Maximilian Gressenbuch, Luis Hafemann, Philipp Huber, Sebastian Hübner, Maximilian Huch, Sebastian Kaljavesi, Gemb Kerbl, Tobias Kulmer, Dominik Maierhofer, Sebastian Mascetta, Tobias Pfab, Florian Rezabek, Filip Rivera, Esteban Sagmeister, Simon Seidlitz, Leander Sauerbeck, Florian Tahiraj, Ilir Trauth, Rainer Uhlemann, Nico Würsching, Gerald Zarrouki, Baha Althoff, Matthias Betz, Johannes Bengler, Klaus Carle, Georg Diermeyer, Frank Ott, Jörg Lienkamp, Markus Germany School of Computation Information and Technology Department of Computer Engineering Germany School of Computation Information and Technology Department of Computer Engineering Professorship Cyber-Physical Systems Cyprus School of Engineering and Design Department of Mechanical Engineering Germany School of Computation Information and Technology Department of Computer Engineering Chair of Network Architectures and Services Germany Germany Technical University of Munich Garching Germany

While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the complexity of autonomous software stacks and public road traffic, the optimal validation of entire stacks is an open research problem. Our paper focuses on these two aspects. We present our autonomous research vehicle EDGAR and its digital twin, a detailed virtual duplication of the vehicle. While the vehicle’s setup is closely related to the state of the art, its virtual duplication is a valuable contribution as it is crucial for a consistent validation process from simulation to real-world tests. In addition, different development teams can work with the same model, making integration and testing of software stacks much easier, significantly accelerating the development process. The real and virtual vehicles are embedded in a comprehensive development environment, which is also introduced. All parameters of the digital twin are provided open-source at https://***/TUMFTM/edgar_digital_twin. © 2023, CC BY.

关键词： Data mining

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Haptic Dataset Augmentation with Subjective QoE Labels using Conditional Generative Adversarial Network

Haptic Dataset Augmentation with Subjective QoE Labels using...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zican Wang Xiao Xu Dong Yang Zhenyu Wang Sarah Shtaierman Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Centre for Tactile Internet with Human-in-the-Loop (CeTI) TU Dresden

This paper proposes a novel Generative Adversarial Network (GAN)-based strategy to augment subjective haptic Quality of Experience (QoE) datasets for bilateral teleoperation with haptic feedback without conducting time-consuming subjective experiments. In our previous work, we proposed a multi-assessment fusion approach to predict subjective haptic quality using a collection of objective metrics. This method requires a sufficiently large haptic dataset with QoE labels. The proposed generative approach automatically expands the existing haptic quality dataset by combining a modified conditional GAN (CGAN) and Style GAN (StyleGAN) architecture. The most important feature of our method is that it learns from the labeled training data and focuses on synthesizing signals with artifacts according to new input labels containing the QoE score, time delay, control method, and data reduction information. Extensive experiments are conducted to validate the suitability of the expanded dataset. The results show that our approach is able to generate new data, which match the label and signal distribution of the original data with categorical rank and linear correlation of over 0.85.

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Fake News Detection Using Machine Learning Models 14

Fake News Detection Using Machine Learning Models

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Aljabri, Malak Alomari, Dorieh M. Aboulnour, Menna College of Computer and Information Systems Umm Al-Qura University Department of Computer Science Makkah21955 Saudi Arabia Imam Abdulrahman Bin Faisal University Department of Computer Science College of Computer Science and Information Technology Dammam31441 Saudi Arabia College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Saudi Aramco Cybersecurity Chair Department of Computer Engineering P.O. Box 1982 Dammam31441 Saudi Arabia

ISBN: (纸本)9781665487719

Nowadays, with the widespread use of technology, fake news and rumors are spreading too. People and society are greatly impacted by fake news, which also can be used as phishing attempts and a way of stealing their information. In many areas of our lives, Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated their effectiveness. Furthermore, Natural Language Processing (NLP) has shown promising results in text classification applications. In this study, we proposed an experimental study for detecting fake news using ML models. The proposed model analyzes the main text of the news using NLP techniques and then classifies the news into fake or real news. We used a new dataset that combined multiple fake news datasets. Moreover, we studied the impact of features extraction methods on the performance of the developed models. Eight experiments were performed using Random Forest (RF) and Support Vector Machines (SVM) models, each with a different features extraction technique. The SVM model resulted in the best performance with an accuracy level of 98%. This result proves the model ability to be deployed and used in real-world with high reliability, to detect fake news. © 2022 IEEE.

关键词： Support vector machines

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Strategic Applications of Deep Belief Networks in Anomaly Detection and Generative Modeling for Business Intelligence 5th

Strategic Applications of Deep Belief Networks in Anomaly De...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Bansal, Abhay Singh, Jagendra Mathur, Anubhav Alghamdi, Mohammed. I. Anant, B.C. Agnihotri, Kuldeep Kaliyar, Rohit Kumar School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Media Bennett University Greater Noida India Computer Science Department Faculty of Computing and Information Al-Baha University Al-Baha65779 Saudi Arabia School of Management Studies Sandip University Madhubani India Director ISBA Group of Institutes Indore Madhya Pradesh Indore India

ISBN: (纸本)9789819791118

This study examines the strategic deployment of Deep Belief Networks in detecting anomalies and performing generative modeling in business intelligence efforts. Three kinds of DBN models, such as Temporal-DBN, Variational-DBN, and Sparse-DBN, were reviewed and assessed for their performance, especially in conducting anomaly detection in a diverse range of data acquired from the online domain and real-time banking transactions. Evaluation measures, including accuracy, precision, recall, and F1 scoring, indicate that the Temporal-DBN model is significantly superior to others, showing an accuracy percentage of 97.67% relative to Variational-DBN and Sparse-DBN, which were rated at 93.45% and 90.23%, respectively. Examining the receiver operating curves further reinforced the efficacy of the Temporal-DBN model at a higher percentage rate of 0.978 against Variational-DBN and Sparse-DBN, which were rated at 0.953 and 0.922, respectively, indicating higher discrimination ability between normal and anomalous instances. Theoretical data analysis, particularly through confusion matrices, demonstrated that Temporal-DBN provides a balanced performance of the minimized number of misclassified cases, with higher true positive rates and true negative rates compared to Variational-DBN and Sparse-DBN. Overall, these measures and outcomes imply that Deep Belief Networks, especially the Temporal-DBN model, have the potential to maximize anomaly detections and the generative modeling efforts of business intelligence solutions. They also suggest increased theoretical and methodological innovation for wider applications in conducting analysis and data-driven decision-making in a broad range of business fields using deep learning methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A survey on computationally efficient neural architecture search

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Journal of Automation and Intelligence 2022年第1期1卷 8-22页

作者： Shiqing Liu Haoyu Zhang Yaochu Jin Chair of Nature Inspired Computing and Engineering Faculty of TechnologyBielefeld University33619 BielefeldGermany Department of Computer Science University of SurreyGuildfordGU27XHUnited Kingdom Engineering Research Center of Digitized Textile&Apparel Technology Ministry of EducationCollege of Information Science and TechnologyDonghua UniversityShanghai 201620China

Neural architecture search(NAS)has become increasingly popular in the deep learning community recently,mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks(DNNs).However,NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS,and training DNNs is computationally *** solve this major limitation of NAS,improving the computational efficiency is essential in the design of ***,a systematic overview of computationally efficient NAS(CE-NAS)methods still *** fill this gap,we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods,together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational *** remaining challenges and open research questions are also discussed,and promising research topics in this emerging field are suggested.

关键词： Neural architecture search(NAS) One-shot NAS Surrogate model Bayesian optimization Performance predictor

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Time-series satellite remote sensing reveals gradually increasing war damage in the Gaza Strip

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National Science Review 2024年第9期11卷 181-191页

作者： Shimaa Holail Tamer Saleh Xiongwu Xiao Jing Xiao Gui-Song Xia Zhenfeng Shao Mi Wang Jianya Gong Deren Li State Key Laboratoryof Information Engineering in Surveying Mapping and Remote SensingWuhan University Geomatics Engineering Department Faculty of Engineering at Shoubra Benha University School of Computer Science Wuhan University Collaborative Innovation Center of Geospatial Technology National Engineering Research Center for Multi-media Software Institute of Artificial IntelligenceWuhan University

War-related urban destruction is a significant global concern, impacting national security, social stability,people's survival and economic development. The effects of urban geomorphology and complex geological contexts during conflicts, characterized by different levels of structural damage, are not yet fully understood globally. Here we report how integrating deep learning with data from the independently developed Luo Jia3-01 satellite enables near real-time detection of explosions and assessment of different building damage levels in the Israel–Palestine conflict. We found that the damage continually increased from 17 October 2023 to 2 March 2024. We found 3747 missile craters with precision positions and sizes, and timing on vital infrastructure across five governorates in the Gaza Strip on 2 March 2024, providing accurate estimates of potential unexploded ordnance locations and assisting in demining and chemical decontamination. Our findings reveal a significant increase in damage to residential and educational structures, accounting for 58.4% of the total—15.4% destroyed, 18.7% severely damaged, 11.8% moderately damaged and 12.5% slightly damaged—which exacerbates the housing crisis and potential population displacement. Additionally, there is a 34.1% decline in the cultivated area of agricultural land, posing a risk to food security. The Luo Jia3-01 satellite data are crucial for impartial conflict monitoring, and our innovative methodology offers a cost-effective, scalable approach to assess future conflicts in various global *** first-time findings highlight the urgent need for an immediate ceasefire to prevent further damage and support the release of hostages and subsequent reconstruction efforts.

关键词： war damage monitoring satellite remote sensing missile crater detection type of damaged building building damage level detection Gaza Strip

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Deep Neural Networks Based Approach for Battery Life Prediction

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computers, Materials & Continua 2021年第11期69卷 2599-2615页

作者： Sweta Bhattacharya Praveen Kumar Reddy Maddikunta Iyapparaja Meenakshisundaram Thippa Reddy Gadekallu Sparsh Sharma Mohammed Alkahtani Mustufa Haider Abidi School of Information Technology and Engineering Vellore Institute of TechnologyVelloreIndia Department of Computer Science and Engineering Baba Ghulam Shah Badshah UniversityRajouriIndia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadhSaudi Arabia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadhSaudi Arabia

The Internet of Things(IoT)and related applications have witnessed enormous growth since its *** diversity of connecting devices and relevant applications have enabled the use of IoT devices in every *** the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely *** this work,an optimized deep neural networks based model is used to predict the battery life of the IoT *** present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed *** dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical *** processed data is normalized using the Standard Scaler *** Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the *** optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.

关键词： Battery life prediction moth flame optimization one-hot encoding standard scaler

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Life Test Plan Optimization for Electrical Insulation During Temperature Load: A Semi-Optimal Approach

Life Test Plan Optimization for Electrical Insulation During...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vishnoi, Rahul Verma, Sanjeev Kumar Sharma, Vishal Yuvaraj, K. Gopinath, S. Dongre, Deepika Tmu Department of Ece India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India School of Media Film & Television Studies Iimt University Meerut India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641105 India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

Considering that the Arrhenius equation between stress condition and lifetime is valid and that the proportional lifespan at an excessive pressure reflects some reliable statistical properties. It is essential to demonstrate the highest diagnostic strategy from the perspective of effectiveness under such living scenarios. Knowing the semi-optimal test plan, where effectiveness is similar to that of the maximum one and test conditions are straightforward, is additionally helpful. The goal of minimization is to determine the ideal amount of test subjects with each test stressors, and we assume that there are three sample stress levels. The root-average-squared error for the duration in use circumstance serves as the benchmark for optimization problems. We were using the hyperparameters in an actual trial situation to adjust for realism. If predictability of the Characteristic curve is necessary, we have ended up finding that there is just a subtle distinction between both the optimal results obtained and the mainstream test result when making comparisons it to the data achieved that use the mainstream tester, in which test samples are equitably represented to each experiment anxiety levels. The traditional testing phase is one of the partially ideal test plans, in our opinion. The agreement between the theory and computer findings has been examined. © 2023 IEEE.

关键词： Aging paradigm Diagnostic strategy Electrical insulation Electrostatic shielding Hyperparameters Life test plan optimization Lifetime Linear regression technique Mainstream test result Minimization Optimization problems Root-average-squared error Statistical properties Test subjects Thermal degradation

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Safe-By-Design Digital Twins for Human-Robot Interaction: A Use Case for Humanoid Service Robots

Safe-By-Design Digital Twins for Human-Robot Interaction: A ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jon Škerlj Mazin Hamad Jean Elsner Abdeldjallil Naceri Sami Haddadin Department of Computer Engineering School of Computation Information and Technology Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich (TUM)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Integrating humanoid service mobile robots into human environments presents numerous challenges, primarily concerning the safety of interactions between robots and humans. To address these safety concerns, we propose a novel approach that leverages the capabilities of digital twin technology by tailoring it to incorporate comprehensive and robust safety concepts. This paper introduces a "safe-by-design" digital twin that operates alongside the real twin robot in the loop, engaging real-time safety framework during physical interactions with the surrounding environment, including *** validate the effectiveness of our proposed safe-by-design digital twin framework, we conducted experiments using a humanoid service mobile robot alongside simulated human counterparts. Our results demonstrate the capability of the integrated impact safety module within the proposed digital twin approach to limit the velocities of both the robot’s base and arms, adhering to injury biomechanics-based safety thresholds. These findings emphasize the promise of our proposed approach for ensuring the physical safety of humanoid service mobile robots operating in dynamic human environments. It enables the digital twin to preemptively identify potential safety hazards and formulate safe intervention actions to ensure the robot’s compliance with safety regulations, paving the way for safer and more widespread adoption of robotic systems in various service domains.

关键词： Service robots Tracking Humanoid robots Human-robot interaction Robot sensing systems Safety Digital twins

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YOLOv2PD:An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

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computers, Materials & Continua 2021年第12期69卷 3015-3031页

作者： Chintakindi Balaram Murthy Mohammad Farukh Hashmi Ghulam Muhammad Salman A.AlQahtani Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Real-time pedestrian detection is an important task for unmanned driving systems and video *** existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such ***,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road *** proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction *** addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection *** proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor *** the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller *** results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian ***,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.

关键词： computer vision K-means clustering multi-layer feature fusion strategy pedestrian detection YOLOv2PD

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