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PIDSNeRF: pose interpolation depth supervision neural radiance fields for view synthesis from challenging input

Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Jianxin Shao, Haijian Deng, Xing Jiang, Yingtao School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States

Recently, Neural Radiance Fields(NeRF) have shown remarkable performance in the task of novel view synthesis through multi-view. The present study introduces an advanced optimization framework, termed Pose Interpolation Depth Supervision Neural Radiance Fields (PIDSNeRF), designed to address the challenges encountered by NeRF in novel view synthesis. These challenges manifest as artifacts, texture details loss, and geometric inconsistencies, particularly in scenes characterized by different lighting conditions, Textureless regions, and sparse images as inputs. The principle of PIDSNeRF involves interpolating virtual camera positions in the 3D space domain based on known cameras, Subsequently, the sparse point clouds formed during camera pose estimation is re-projected onto these virtual poses, employing an inverse angle weighting strategy, thereby generating depth-supervised rays. Furthermore, we propose a depth diffusion method that spreads depth-supervised rays along the pixel plane, forming Gaussian functions with different variances based on the diffusion distances. Finally, depth supervision during volume rendering is achieved by optimizing the Kullback-Leibler(KL) divergence between the weight distribution of sampled points along each ray and the probability density function of the corresponding Gaussian function along that ray. The above process enables a more comprehensive optimization of multi-resolution grid features that align with the sampled points along the rays. The experiments on the DTU, LLFF, and DL3DV-10K datasets demonstrate, PIDSNeRF can synthesize complete novel views within minutes, and the various metrics of the synthesized images achieve the best performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Probability density function

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Binary Program Vulnerability Mining Based on Neural Network

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computers, Materials & Continua 2024年第2期78卷 1861-1879页

作者： Zhenhui Li Shuangping Xing Lin Yu Huiping Li Fan Zhou Guangqiang Yin Xikai Tang Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Electrical and Computer Engineering University ofWaterlooWaterlooN2L 3G1Canada

Software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining ***,these tools suffer from some *** terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search ***,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information *** this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation *** leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion *** combination allows for the unified handling of binary programs across various architectures,compilers,and compilation ***,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)***,the graph embedding network is utilized to evaluate the similarity of program *** on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target *** solved content serves as the initial seed for targeted *** binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity *** approach facilitates

关键词： Vulnerability mining de-obfuscation neural network graph embedding network symbolic execution

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Green internet of things and solar energy

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Environmental Science and Pollution Research 2024年第12期31卷 18296-18312页

作者： Chinipardaz, Maryam Khoramfar, Ali Amraee, Somaieh Department of Electrical and Computer Engineering Jundi-Shapur University of Technology Dezful Iran Roux Institute Northeastern University PortlandME United States

The Internet of Things (IoT) stands out as one of the most captivating technologies of the current decade. Its ability to connect people and things anytime and anywhere has led to its rapid expansion and numerous impactful applications that enhance human life. With billions of connected devices and substantial power and infrastructure requirements, the IoT system can pose a threat to the environment. However, the IoT’s vast range of resources and capabilities can also be leveraged to assist in environmental conservation in the evolution of technologies due to massive CO2 emissions, climate change, and environmental and health issues. In this study, with the two-way integration of IoT and green practices, two distinct concepts for green IoT are presented. Among green practices, energy solutions play a vital role in greening the IoT. In this study, the energy solutions for the IoT system are divided as reducing energy consumption and using green energy sources. Solutions for reducing IoT energy consumption are studied systematically through a five-layer framework to simplify its modular design and implementation. Then, the use of green energy resources is discussed for all components of the IoT ecosystem. Leveraging IoT to make the environment and other technologies green is the other concept of green IoT. IoT technology plays a crucial role in enhancing both energy management systems and the efficient harvesting of renewable energy sources. Switching to solar energy from fossil fuel energy is one of the most fundamental green practices today. In this study, the mutual relationship between solar energy harvesting and the IoT is addressed specifically. Several promising research directions in the realm of green IoT are also highlighted. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Internet of things

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Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

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computer Modeling in engineering & Sciences 2023年第9期136卷 2291-2319页

作者： Abdullah Ahmed Al-Dulaimi Muhammet Tahir Guneser Alaa Ali Hameed Mohammad Shukri Salman Department of Electrical and Electronics Engineering Karabuk UniversityKarabuk78100Turkey Department of Computer Engineering Istinye UniversityIstanbul34303Turkey College of Engineering and Technology American University of the Middle EastEgaila54200Kuwait

Recently,the demand for renewable energy has increased due to its environmental and economic *** panels are the mainstay for dealing with solar energy and converting it into another form of usable *** panels work under suitable climatic conditions that allow the light photons to access the solar cells,as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these ***,the panels are unable to work under these conditions.A layer of snow forms on the solar panels due to snowfall in areas with low ***,it causes an insulating layer on solar panels and the inability to produce electrical *** detection of snow-covered solar panels is crucial,as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper *** paper presents five deep learning models,■-16,■-19,ESNET-18,ESNET-50,and ESNET-101,which are used for the recognition and classification of solar panel *** this paper,two different cases were applied;the first case is performed on the original dataset without trying any kind of preprocessing,and the second case is extreme climate conditions and simulated by generating motion ***,the dataset was replicated using the upsampling technique in order to handle the unbalancing *** conducted dataset is divided into three different categories,namely;all_snow,no_snow,and partial *** fivemodels are trained,validated,and tested on this dataset under the same conditions 60%training,20%validation,and testing 20%for both *** accuracy of the models has been compared and verified to distinguish and classify the processed *** accuracy results in the first case showthat the comparedmodels■-16,■-19,ESNET-18,and ESNET-50 give 0.9592,while ESNET-101 gives *** the second case,the models outperformed their counterparts in the first case by evaluating performan

关键词： Deep learning CNN models image classification solar panels solar panel defect detection

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A Survey on Wind Speed Forecasting in Smart Grids Using Deep Learning Algorithm Applications 28

A Survey on Wind Speed Forecasting in Smart Grids Using Deep...

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28th International electrical Power Distribution Conference, EPDC 2024

作者： Majidi-Gharehnaz, Hassan Hayati, Mohammad Mohsen Khalilzadeh, Reza Shahinzadeh, Hossein Abapour, Mehdi Gharehpetian, Gevork B. University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Tarbiat Modares University Department of Electrical Engineering Tehran Iran Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350385502

The crisis resulting from the use of fossil fuels has heightened the push towards increasing the share of renewable energy production, particularly from sources like wind and solar power. While wind energy holds special significance due to its potential availability round the clock, relying on wind power comes with various challenges. One such challenge is the uncertainty surrounding the production levels of wind farms. In response to this challenge, researchers have consistently sought ways to predict wind speed, with one of the most crucial methods being the application of machine learning techniques. These data-driven methods exhibit a commendable ability to forecast wind speed by leveraging historical data and other relevant inputs. Recent years have witnessed substantial advancements in deep learning methods, further enhancing the appeal of these approaches. To achieve this objective, this article endeavors to explore the role of applying deep learning methods in wind speed forecasting. A comprehensive survey of the application of these algorithms is conducted, and ultimately, a comparative analysis of these methods is performed, taking into account the specific types of wind speed forecasting problems. To achieve this, the article delves into the examination of nine machine learning methods, namely convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), deep belief network (DBN), physics-informed neural networks (PINN), Autoencoder, generative adversarial network (GAN), deep reinforcement learning (DRL), and hybrid models. The goal is to provide insights into the effectiveness of these approaches and to facilitate a comprehensive understanding of their application in addressing wind speed forecasting challenges. © 2024 IEEE.

关键词： Wind power

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Diversified and compatible web APIs recommendation based on game theory in IoT

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Digital Communications and Networks 2024年第4期10卷 1198-1209页

作者： Wenwen Gong Huiping Wu Xiaokang Wang Xuyun Zhang Yawei Wang Yifei Chen Mohammad R.Khosravi College of Information and Electrical Engineering China Agricultural UniversityBeijing100000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computer Science St.Francis Xavier UniversityAntigonishNSCanada Department of Computing Macquarie UniversitySydneyNSW2122Australia Department of Computer Engineering Persian Gulf UniversityBushehr***Iran Department of Electrical and Electronic Engineering Shiraz University of TechnologyShiraz71557-13876Iran

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

关键词： Internet of things Web APIs recommendation Game theory Diversity and compatibility

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Barrier Function-Based Backstepping Fractional-Order Sliding Mode Control for Quad-Rotor Unmanned Aerial Vehicle under External Disturbances

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IEEE Transactions on Aerospace and Electronic Systems 2024年第1期60卷 716-728页

作者： Liu, Bowen Wang, Yingxun Mofid, Omid Mobayen, Saleh Khooban, Mohammad Hassan Beihang University School of Automation Science and Electrical Engineering Beijing100191 China University of Zanjan Department of Electrical Engineering Zanjan45371-38791 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliou64002 Taiwan Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark

The main control objective for the quad-rotor system is the attitude and position tracking control which is accomplished in this article using the backstepping fractional-order sliding mode control approach combined with the adaptive tuning based barrier function theory. The quad-rotor's dynamical model is obtained at the appearance of the disturbance that is entered to the quad-rotor system by an exterior force. Hence, the attitude and position tracking errors between the quad-rotor's actual states and their desired trajectory are defined. Afterward, the fractional-order sliding surfaces are defined to guarantee finite-time reachability of the defined tracking error. The designed fractional-order surface eases the control process due to the removal of the high-derivative of virtual controllers. Then, the extended adaptive barrier function laws named fractional-order-based adaptive barrier function laws are employed to eliminate the knowledge of the upper bounds of external disturbances. The Lyapunov theory concept mixed with the backstepping control strategy is used to demonstrate that the fractional-order sliding surface reaches the neighborhood of origin in the finite time. Finally, simulation results in various scenarios on the MATLAB/ Simulink environment are provided to show that the designed control procedure in this article is an efficient control technique to assure that attitude and position's trajectories of the quad-rotor system track desired trajectories properly in the existence of the external disturbances. Moreover, the designed method is compared to the existing method to validate the efficiency of the proposed method. Lastly, experimental results using the Speedgoat real-time target machine is implemented to assure validity of the recommended method. © 1965-2011 IEEE.

关键词： Backstepping

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Performance Comparison of Different Methods of Torque Ripple Reduction for Permanent Magnet-assisted Synchronous Reluctance Motor 3

Performance Comparison of Different Methods of Torque Ripple...

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3rd International Conference on electrical Machines and Drives, ICEMD 2023

作者： Amiri, Amir Hosein Abdollahi, Seyed Ehsan Hasanzadeh, Saeed Babol Noshirvani University of Technology Electrical and Computer Engineering Department Babol Iran Qom University of Technology Electrical and Computer Engineering Department Qom Iran

ISBN: (纸本)9798350393743

In recent years, synchronous reluctance machines (SynRM) have been widely used in many different industrial applications because of their high efficiency, fast dynamic response, robustness, and lower production costs, making them suitable for traction applications. However, their usage is limited due to their low power factor and high torque ripple. To improve the power factor Ferrite magnets are commonly used in their rotor (PM-assisted SynRM). Adding these Ferrite magnets increases the torque ripple as well. To enhance the torque ripple many different methods are proposed such as skewing, employing different winding configurations, and rotor topology modifications. Machine designers should select a suitable technique as each method has limitations. This paper is aiming at analyzing the effects of these three different methods on the performance of the machine. A four-pole 1.5 kw SynRM is designed and the magnets are placed in the barriers of the rotor while keeping the insulation ratio constant then the performance of different methods is evaluated and compared. First, the rotor is skewed, and then a short-pitch winding is used for the stator. Finally, a new design for barrier ends is proposed, and their effect on the torque profile is independently evaluated. The simulations are done in 2D FEM and the results are evaluated and discussed. © 2023 IEEE.

关键词： Topology

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Enhancing Mobility with Next-Generation Partial Hand Prosthesis Using Arduino Uno and Flex Sensors 2

Enhancing Mobility with Next-Generation Partial Hand Prosthe...

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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Matury, Sai Sathwik Sam Stewart, X. Ra, Nawin Kumba, Krishna School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350388664

The proposed work delves into the comprehensive design, development, and evaluation process of a 3D printed prosthetic arm, employing servo motors controlled by an Arduino Uno microcontroller to enable gesture mimicry through flex sensors integrated into a wearable glove. The primary objective is to provide an affordable and accessible solution for individuals with upper limb disabilities. The paper outlines the intricacies of the design process, system architecture, programming nuances, and rigorous testing procedures, showcasing promising results in terms of gesture accuracy, adaptability, and user-friendliness. The prosthetic arm demonstrates high precision in replicating various hand gestures. Future work will focus on enhancing the durability and responsiveness of the system to further improve its practical application. User feedback has been overwhelmingly positive, indicating significant potential for real-world adoption. © 2024 IEEE.

关键词： Servomotors

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Implementation of 5G Network Slicing Using Open Source Software 12

Implementation of 5G Network Slicing Using Open Source Softw...

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12th International electrical engineering Congress, iEECON 2024

作者： Vanichchanunt, Pisit Ritruechai, Onpimon Wuttiananchai, Navy Thossaporn, Peeraporn Wuttisittikulkij, Lunchakorn Paripurana, Sukritta King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand Chulalongkorn University Faculty Of Engineering Department Of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

In modern communications, network slicing is essen-tial network operation for creating and managing an independent logical network which meets the requirement of a service. Net-work slicing enables available network resources to be managed and allocated efficiently and dynamically according to varying demands of users. This paper aims to design and implement 5G network slicing using open source software in order to serve very low latency services. The implementation requires the integration of orchestration, network function virtualization, and software defined network technologies to deploy necessary network functions and to manage the life cycles of network slices. Open source software tools such as Open5GS, Open Source MANO, OpenStack, OpenDaylight, and srsRAN are selected for implementing 5G core, orchestration and management functions, cloud computing platform, software defined network, and radio access network respectively since they offer compatibility in the software integration as well as allow the prototype to be created in a timely and economical manner. To obtain very low latency, User Plane Function and edge computing are implemented based on the latency of internal connections. In the experiment, different scenarios of network slices are deployed and tested to measure the key performance indices such as a delay, download speed and upload speed. The results show that the implemented network slices can offer very low latency at the expense of available bandwidth reduction due to the communication for orchestration and management in the hardware. However, this can be improved by using hardware with higher performance. © 2024 IEEE.

关键词： Network function virtualization

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