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Service-Centric Architectures in Cloud Security: An In-Depth Analysis

Service-Centric Architectures in Cloud Security: An In-Depth...

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2023 international conference on advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Natya, S. Vivek, V. Yadav, Dhananjay Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Due to its affordable and high-quality services, cloud computing has lately attracted a lot of interest. Due to the on- demand, pay-per-use characteristics that cloud services provide, which encourage companies to outsource parts of their operations with better service delivery and more value, they have become an essential component of both businesses et people's everyday lives during the last ten years. In the next years, a continued migration towards cloud-based services is predicted by the current market trend, which started in 2019. Despite the many benefits that the cloud computing paradigm offers to both enterprises and consumers, security issues are expected to be the biggest cloud computing challenges in 2020. Security problems are a result of a number of variables, including the streaming nature of these solutions and the technology that support cloud computing, which includes hybridization and multitenancy. This research examines security concerns in service-driven cloud computing in order to shed light on the situation of the industry at the moment. This paper's main contribution is an analysis of cloud security over the last ten years and the presentation of a thorough taxonomy of security concerns throughout the three-layer architecture, which includes Infrastructure-as-a-Service (IaaS), Platform-as- (PaaS), and Software-as-a-Service (SaaS). © 2023 IEEE.

关键词： Cloud computing Hypervisor Infrastructure-as-a-Service (IaaS) Multitenancy Platform-as-a-Service (PaaS) Security issues Service-driven cloud computing Software-as-a-Service (SaaS) and Denial of Service (DoS) Virtualization

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Future application and prospect of space electron beam processing technology 3

Future application and prospect of space electron beam proce...

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2023 3rd international conference on Mechatronics technology and Aerospace engineering, ICMTAE 2023

作者： Ma, Yanlong Zhao, Jipeng Yang, Wenbo Ma, Tianju Gu, Sendong He, Jun Yu, Bin Tang, Xiaoyan Science and Technology on Vacuum Technology and Physics Laboratory Lanzhou Institute of Physics Lanzhou730000 China

Technological advances in space electron beam processing could fundamentally alter how spacecraft and space stations are built. The primary topics covered in this essay are the application circumstances for electron beams in space, the research situations for electron beam welding, additive manufacturing, and surface modification, and an analysis of the potential future applications for this technology. It demonstrates the viability of space applications of electron beam processing technologies. © Published under licence by IOP Publishing Ltd.

关键词： Electron beams

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PCA-MD Planetary Gear Damage Identification Method Based on Acoustic Emission Signals 7

PCA-MD Planetary Gear Damage Identification Method Based on ...

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7th international conference on robotics, Control and Automation engineering, RCAE 2024

作者： Qiao, Shuai Dong, Shishang Zhang, Hao You, Jun Li, Ming Guo, Li Cheng, Zian Yuan, Xuechao Anhui Polytechnic University School of Electrical Engineering Wuhu China Anhui Polytechnic University Anhui Institute of Future Engineering Wuhu China Anhui Polytechnic University Anhui Institute of Future Research Wuhu China Wuhu Coogao Automation Technology Co. Ltd Wuhu China

ISBN: (纸本)9798350355642

In order to study the damage identification of planetary gears in a reducer, a PCA-MD (Principal Component Analysis-Mahalanobis Distance) method for damage identification of planetary gears based on Acoustic Emission (AE) signals was proposed. In this paper, AE signals were collected using a DS5-16C Acoustic Emission instrument and RS-2A piezoelectric ceramic sensors, with a sampling frequency of 3MHz and a preamplifier gain of 40dB. First, a set of AE signals from healthy planetary gears was collected as a reference signal, and damage to the planetary gears was simulated on the experimental platform, collecting AE signals in the damaged state. Then, noise interference in the signals was removed through Empirical Mode Decomposition (EMD), and 13 feature parameters of the denoised AE signals were calculated. Through PCA, the 13-dimensional feature parameters were reduced to 2 dimensions, simplifying the complexity of data processing without losing the original valid information. Finally, the parameters obtained after dimensionality reduction were combined with Mahalanobis Distance (MD) to further establish a method for identifying damage in planetary gears of reducers. The damage condition of the planetary gears was judged by calculating the Mahalanobis distance between healthy and damaged signals. The experimental results showed that the PCA-MD method could identify early damage in planetary gears. © 2024 IEEE.

关键词： Epicyclic gears

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RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar 38

RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Rad...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ding, Fangqiang Wen, Xiangyu Zhu, Yunzhou Li, Yiming Lu, Chris Xiaoxuan University of Edinburgh United Kingdom Georgia Institute of Technology United States New York University United States AI Centre Department of Computer Science UCL United Kingdom

3D occupancy-based perception pipeline has significantly advanced autonomous driving by capturing detailed scene descriptions and demonstrating strong generalizability across various object categories and shapes. Current methods predominantly rely on LiDAR or camera inputs for 3D occupancy prediction. These methods are susceptible to adverse weather conditions, limiting the all-weather deployment of self-driving cars. To improve perception robustness, we leverage the recent advances in automotive radars and introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction. Our method, RadarOcc, circumvents the limitations of sparse radar point clouds by directly processing the 4D radar tensor, thus preserving essential scene details. RadarOcc innovatively addresses the challenges associated with the voluminous and noisy 4D radar data by employing Doppler bins descriptors, sidelobe-aware spatial sparsification, and range-wise self-attention mechanisms. To minimize the interpolation errors associated with direct coordinate transformations, we also devise a spherical-based feature encoding followed by spherical-to-Cartesian feature aggregation. We benchmark various baseline methods based on distinct modalities on the public K-Radar dataset. The results demonstrate RadarOcc's state-of-the-art performance in radar-based 3D occupancy prediction and promising results even when compared with LiDAR- or camera-based methods. Additionally, we present qualitative evidence of the superior performance of 4D radar in adverse weather conditions and explore the impact of key pipeline components through ablation studies. © 2024 Neural information processing systems foundation. All rights reserved.

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Adapting a Transdisciplinary Approach to Regional Development in the Case of Facilitating Planning of Energy Systems 31

Adapting a Transdisciplinary Approach to Regional Developmen...

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31st ISTE international conference on Transdisciplinary engineering, TE 2024

作者： Fredricson, Magnus Linnéusson, Gary Aslam, Tehseen School of Engineering Science University of Skövde Skövde Sweden Skaraborg Municipal Association Skövde Sweden

ISBN: (纸本)9781643685502

In this paper, we aim to facilitate regional development through collaborative meta-governance processes, involving municipalities, energy companies and more actors. The case in point involves increased electrification through the development of local energy systems in a sub-region comprising 15 municipalities and 21 grid owners and is an exemplary case where a transdisciplinary engineering approach is not only suitable but the only path forward. The complex problem landscape comprises interdependencies across different roles, such as politicians, civil servants, and engineers at energy companies, where autonomous entities act independently. We employ a design science research approach to create artefacts to support the meta-governance mechanisms needed to accelerate social change processes. One artefact is a system dynamics simulation model to analyze scenarios considering the electrification of vehicles and implementing large wind and solar energy units to enable the establishment of new industries. We provide brief overviews of how three artefacts assist in visualizing 1) roles, 2) structures, and 3) scenarios to the decision-makers, to facilitate various transdisciplinary decision-making processes in regional development. In the discussion, we synthesize our learnings into a model to support mitigating powerlessness in this complex multi-stakeholder context. Finally, we lay out future research to further contribute to the social change and regional development we believe is necessary. © 2024 The Authors.

关键词： Electrification

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Lightweight Facial Super-Resolution Reconstruction of Blueprint Separable Residual Networks 24

Lightweight Facial Super-Resolution Reconstruction of Bluepr...

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3rd international Symposium on Control engineering and robotics, ISCER 2024

作者： Pan, Jiahao Ling, Zhihao College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9798400709951

Current deep learning-driven facial super-resolution models tend to adopt more complex designs, leading to escalating computational complexity and memory requirements. Such designs, focused solely on singular reconstruction performance, not only consume substantial computational resources but also struggle to adapt to practical application scenarios. Addressing these challenges, this paper presents a lightweight dual-branch facial super-resolution reconstruction network, founded on a blueprint separable residual structure. The proposed model leverages reparameterizable blueprint separable convolutions to enhance feature aggregation and improve model performance without incurring additional computational costs. By integrating anti-aliasing and Gaussian blur, the model effectively supervises facial boundary prior information with low operational complexity, while fully utilizing the semantic information of facial images to enhance reconstruction results. Additionally, an ultra-low computational cost attention mechanism module captures positional information and channel relationships, leading to exceptional reconstruction performance. Experimental results demonstrate that the model exhibits significant superiority in terms of reconstruction quality, complexity, and robustness on the CelebA-HQ and Helen test datasets. © 2024 ACM.

关键词： Anti-aliasing

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High-Precision Localization of Mobile Robot Based on Particle Filters Combined with Triangle Matching 7th

High-Precision Localization of Mobile Robot Based on Parti...

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7th international conference on Cognitive Systems and Information Processing, ICCSIP 2022

作者： Zhou, Huaidong Tuo, Wanchen Chou, Wusheng Robotics Institute School of Mechanical Engineering and Automation Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819906161

Robot location is a fundamental technology in the field of autonomous robotics and has been widely studied. However, high-precision localization of the mobile robot is still a serious challenge. To investigate this point further, this paper proposes a method to improve the autonomous robot localization accuracy, which adjusts the number of particles in the AMCL algorithm by fusing the information of LRL. LRL data is used to improve the localization accuracy of mobile platforms and prevent kidnapped robot problems. The algorithm is conducted and verified in a real robot system. The experiment results show that the proposed method can achieve high-precision locations in real-world scenarios, and verify the effectiveness of the improved localization system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Efficient Thermal Management and Environmental Adaptability: Design of an Autonomous Temperature-Controlled Sealed Cooling System for High-Power Edge Servers 10

Efficient Thermal Management and Environmental Adaptability:...

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10th international conference on advances in Machinery, Material science and engineering Application, MMSE 2024

作者： Gao, Mingliang Ding, Zhifu Gao, Shan Tang, Yu Sun, Hongliang Sun, Yi Edge Computing Business Unit of Inspur Electronic Information Industry Co. Ltd. Jinan Shandong250013 China EVYD Technology Sdn Bhd China CRRC Changchun Railway Vehicles Co. Ltd. China

ISBN: (纸本)9781643685465

This paper addresses the thermal management challenges encountered by edge servers operating in harsh environments by proposing an innovative thermal management strategy aimed at enhancing the performance and reliability of edge computing platforms. Given the significance of edge computing as the cornerstone of 5G, IoT, and AI technologies, servers must maintain stability under conditions of high power density and complex environmental factors. This study integrates finned heat pipe coolers with die-cast aluminum enclosures, complemented by custom air ducts and intelligent variable-speed fans, significantly boosting the conduction and dissipation of heat. This ensures optimized operating temperatures for critical components such as CPUs and GPUs. A specifically designed active heating mechanism safeguards smooth startup and operation under extreme cold conditions. Advanced sealing techniques significantly enhance system protection and durability. This adaptive temperature-controlled cooling architecture effectively mitigates the thermal load of high-compute chips, ensuring high performance and stability of edge servers across diverse environmental settings, laying a solid foundation for the broad application of edge computing technologies and contributing to the advancement and development of next-generation information technologies. © 2024 The Authors.

关键词： cooling Edge server high power sealed

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Leveraging DeepLabV3+ for Lung Region Delineation in Chest Radiographs using Convolutional Neural Network Approach 2

Leveraging DeepLabV3+ for Lung Region Delineation in Chest R...

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2nd international conference on Data science and Information System, ICDSIS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Divekar, Rajiv Vachharajani, Hardik Pune India Srv Media Pvt. Ltd Pune India Management It & Business Analytics Pune India Parul Institute Of Technology Parul University Computer Science And Engineering Engineering And Technology Vadodara India Pune India Quality Management And Benchmarking Pune India

ISBN: (纸本)9798350381665

The advent of deep learning technologies has revolutionized the field of medical imaging, particularly through the application of sophisticated convolutional neural network (CNN) frameworks like DeepLabV3+ for lung region delineation in chest X-ray images. Given the critical importance of accurate lung segmentation for diagnosing pulmonary diseases, this paper introduces custom modifications to the DeepLabV3+ architecture to address the unique challenges of lung radiographs, such as variability in lung shapes and obscured boundaries due to pathological conditions. Our modified approach enhances segmentation accuracy and computational efficiency, crucial for effective diagnosis and treatment planning. Evaluations on a diverse dataset of chest radiographs demonstrate that our customized DeepLabV3+ model substantially outperforms the baseline and other state-of-the-art methods, achieving a Dice Coefficient (DSC) of 0.93 and an Intersection over Union (IoU) of 0.87. These results not only show significant improvements in capturing detailed lung features but also highlight the model's potential in clinical settings for more precise patient diagnoses and care strategies. This study not only advances the application of deep learning in medical imaging but also sets a benchmark for future research in domain-specific challenges, illustrating the impact of tailored CNN modifications on medical diagnostic accuracy and performance. © 2024 IEEE.

关键词： Medical imaging

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Deep Learning Prediction of Exonic Sequence 8

Deep Learning Prediction of Exonic Sequence

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8th IEEE international conference on engineering Technologies and Applied sciences, ICETAS 2023

作者： Abass, Yusuf Aleshinloye Arbaaeen, Ammar Nile University of Nigeria Department of Computer Science Abuja Nigeria UmmAl-Qura University Department of Computer Science Mecca Saudi Arabia

ISBN: (纸本)9798350327090

One of the fundamental pillars of medicine is that decision-making is based on the integration of multidimensional data and complex decision-making. Cancer presents a unique environment for medical decisions because of its diverse forms and progression. Despite advances in technology, reliable identification, characterization, and monitoring of malignancies remain a challenge. The pieces of a gene sequence can reveal details about the types of gene mutations. The exons of the prostrate genes were obtained. The DNA sequence is encoded using k-mer in a deep neural network (DNN) and bidirectional long-short-term memory (Bi-LSTM) model, and the class label is encoded using one-hot encoding. Various categorization criteria were used to evaluate the models. Our experimental findings show that the DNN model prediction achieves 96 percent testing accuracy and 99 percent training accuracy. Training accuracy for the Bi-LSTM model is 95%, and testing accuracy is 91%. Biotechnologists and doctors now have more options thanks to the quick developments in genomic technology. The development of life science technology presents new difficulties. The use of various deep learning models in bioinformatics and medicine presents a number of challenges. Furthermore, the multidisciplinary approach has resulted in the creation of improved models for prediction tasks. © 2023 IEEE.

关键词： Forecasting

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