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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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Experiments and modeling on thermal performance evaluation of standalone and M-cycle based desiccant air-conditioning systems

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Energy Reports 2024年 11卷 1445-1454页

作者： Bhat, Showkat Ahmad Sajjad, Uzair Hussain, Imtiyaz Yan, Wei-Mon Raza, Hafiz M.U. Ali, Hafiz Muhammad Sultan, Muhammad Omar, Hani Azam, Muhammad Waheed Bozzoli, Fabio Huang, Nen-Fu Center of Innovative Incubator National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology Taipei10608 Taiwan Research Center of Energy Conservation for New Generation of Residential Commercial and Industrial Sectors National Taipei University of Technology Taipei10608 Taiwan Department of Agricultural Engineering Bahauddin Zakariya University Bosan Road Multan60800 Pakistan Dhahran31261 Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan Department of Architecture and Engineering University of Parma Parco Area delle Science 181/A Parma43124 Italy IRC-Renewable Energy and Power Systems King Fahd University of Petroleum and Minerals Dammam Dhahran Saudi Arabia

This study aims to evaluate the performance of standalone and Maisotsenko-cycle based desiccant air-conditioning systems with silica gel through experimental and explainable artificial intelligence methods. In order to examine the desiccant systems' performance, deep neural networks have been constructed based on the optimal hyper-parameters provided by the Bayesian surrogacy. The influential factors, such as regeneration air temperature and relative humidity, primary air temperature and relative humidity, among other system features like regeneration temperature and humidity, are chosen as the inputs using feature engineering and correlation heat maps. The results indicated that with the right framework and training data, the proposed model can forecast the performance of the standalone and M-cycle-based desiccant air conditioning systems with a great accuracy (i.e. R2 = 0.99). In addition, regeneration temperature and humidity, and inlet regeneration air temperature are considered to be the primary parameters that affect the thermal performance of Maisotsenko-cycle and stand-alone desiccant systems. The regeneration conditions are shown to have a positive correlation with the primary and regeneration outlet temperatures while having a negative correlation with the primary and regeneration outlet relative humidity. The primary inlet relative humidity has the greatest impact on the primary outlet relative humidity while the regeneration inlet temperature has the highest impact on the regeneration outlet relative humidity of the system. In addition, the primary inlet relative humidity is found to have positive correlations with the outlet regeneration and primary relative humidity and outlet regeneration temperature. Also, the primary air inlet temperature is found to be directly associated with the outlet primary relative humidity and temperature for the considered systems. © 2024 The Authors

关键词： Air conditioning

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Accuracy Evaluation of Stereo Visual SLAM with Unnecessary Feature Point Elimination Using Blur Processing for AR Surgical Support System

Accuracy Evaluation of Stereo Visual SLAM with Unnecessary F...

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International Conference on Intelligent Informatics and Biomedical sciences (ICIIBMS)

作者： Masanao Koeda Yuto Kaihara Atsuro Sawada Akihiro Hamada Toshihiro Magaribuchi Osamu Ogawa Katsuhiko Onishi Hiroshi Noborio Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Japan Department of Urology Graduate School of Medicine Kyoto University Kyoto Japan Faculty of Information Science and Arts Osaka Electro-Communication University Osaka Japan

In this study, we describe an evaluation of the positional accuracy of stereo Visual SLAM with noisy feature points removed by blurring. We constructed an experimental environment that intentionally degrades the accuracy of Visual SLAM. In this environment, we placed the objects that are likely to generate noisy feature points. We evaluated the accuracy of the estimated positions of the original video and the video in which blurred regions of noise in the original video were provided to Visual SLAM. Consequently, we confirmed that the accuracy was improved compared to that of the original video.

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Image and Kernel Representations for Variational systems*

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IFAC-PapersOnLine 2023年第2期56卷 797-802页

作者： Yu Kawano Arjan J. van der Schaft Jacquelien M.A. Scherpen Graduate School of Advanced Science and Engineering Hiroshima University Higashi-hiroshima 739-8527 Japan Bernoulli Institute for Mathematics Computer Science and AI University of Groningen 9700 AK Groningen The Netherlands Faculty of Science and Engineering University of Groningen 9747 AG Groningen The Netherlands Jan C. Willems Center for Systems and Control

The Youla-Kucera parametrization, a method to parametrize all stabilizing controllers, is developed for linear and nonlinear systems based on their stable image and kernel representations. As a stepping stone of parametrizing nonlinear controllers for contraction, we investigate in this paper stable image and kernel representations for variational systems. In particular, we construct stable inner image (resp. co-inner kernel) representations having stable left (resp. right) inverses based on differential Riccati equations for contraction analysis.

关键词： nonlinear systems variational systems image representations kernel representations contraction

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Multi-view Graph Convolutional Networks with Differentiable Node Selection

arXiv

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arXiv 2022年

作者： Chen, Zhaoliang Fu, Lele Xiao, Shunxin Wang, Shiping Plant, Claudia Guo, Wenzhong College of Computer and Data Science Fuzhou University China School of Systems Science and Engineering Sun Yat-sen University China Faculty of Computer Science University of Vienna Austria

Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top-k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Priority-Driven Task Scheduling and Resource Allocation in Cloud Environment

Priority-Driven Task Scheduling and Resource Allocation in C...

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Ubiquitous Networking (UNet), International Conference on

作者： Hicham Ben Alla Said Ben Alla Abdellah Ezzati Mathematics and Computer Science Department LAVETE laboratory Science and Technical Faculty Hassan 1 University Settat Morocco Industrial Systems Engineering Department LAVETE laboratory National School of Applied Sciences - Berrechid Hassan 1 University Settat Morocco

ISBN: (数字)9798350356144

ISBN: (纸本)9798350356151

Currently, efficient task scheduling and resource allocation processes are crucial for optimizing performance in cloud computing environments. In this paper, we introduce a novel approach that prioritizes tasks based on their specific requirements and allocates resources accordingly. Task priority is determined through a scoring threshold that considers different requirements such as deadline, temporal requirements, and task length. To ensure efficient matching, virtual machines (VMs) are categorized into three distinct groups using K-means clustering. Simulations conducted within the CloudSim simulator demonstrate the effectiveness of this method, leading to significant reductions in makespan and energy consumption, while simultaneously improving resource utilization compared to existing algorithms.

关键词： Cloud computing Energy consumption Processor scheduling Computational modeling Clustering algorithms Dynamic scheduling Resource management

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An Ensemble Approach for Classification of Tympanic Membrane Conditions Using Soft Voting Classifier

SSRN

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SSRN 2022年

作者： Akyol, Kemal Uçar, Emine AtiLa, Ümit Uçar, Murat Department of Computer Engineering Faculty of Engineering Kastamonu University Kastamonu Turkey Department of Management Information Systems Faculty of Business and Management Science Iskenderun Technical University Hatay Turkey Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey Department of Management Information Systems Faculty of Business and Management Science Iskenderun Technical University Hatay Turkey

Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices used by field experts for diagnosis of the disease relevant to otitis media are the most important drawback in the widespread use of these devices. Therefore, there is a need for computer-aided middle ear disease diagnosis systems in order to eliminate the subjectivity and high-cost problems in the diagnosis of the disease. In order to increase accuracy and stability in automatic diagnosis of different eardrum conditions such as normal, earwax plug, myringosclerosis and chronic otitis media, this study proposes a voting ensemble framework including 5 different pre-trained convolutional neural network models. All pre-trained models used in the proposed framework were trained using Public Ear Imagery dataset with 880 otoscopy images. In this framework, performances of both soft and hard voting ensemble frameworks were discussed. The proposed soft voting ensemble framework achieved high performance for different cases of tympanic membrane by 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. The results obtained indicated the highest classification performance ever achieved on the current dataset. As a result, the voting ensemble framework benefiting from the prediction results of the pre-trained deep learning models increased the accuracy in classification of tympanic membrane conditions. © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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Code Converters Based on a Dynamic Optical RAM for Information System Facilities

Code Converters Based on a Dynamic Optical RAM for Informati...

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IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Andriy Semenov Serhii Tsyrulnyk Olena Semenova Anton Khloba Andrii Rudyk Serhii Baraban Faculty of Information Electronic Systems Vinnytsia National Technical University Vinnytsia Ukraine Department of Computer Science and Economic Cybernetics Vinnytsia National Agrarian University Vinnytsia Ukraine Department of Automation Electrical Engineering and Computer-Integrated Technologies National University of Water and Environmental Engineering Rivne Ukraine Faculty of Computing and Telecommunications Poznan University of Technology Poznan Poland

ISBN: (数字)9798350368178

ISBN: (纸本)9798350368185

In this paper, the authors propose a new dynamic optical RAM based on a fiber-optic line. This device significantly increases the speed of information flows by reducing the time required to operate the RAM by 10. The proposed optical RAM increases the productivity and efficiency of information exchange between slow and fast devices of information and communication systems without software costs, which reduces the probability of failure by two times with the same buffer memory capacity. The authors proposed variants of the practical application of the new dynamic optical RAM. On its basis, a shift register, a serial-to-parallel code converter, a parallel-to-serial code converter, and a TDM switch for information system facilities were created. The time characteristics of signals in code converters based on a dynamic optical RAM are presented.

关键词： Optical fibers Codes Optical switches Optical buffering Random access memory Time division multiplexing Shift registers Optical fiber devices High-speed optical techniques Information systems

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Transformative Applications and Key Challenges of Generative AI

Transformative Applications and Key Challenges of Generative...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Attila Kovari Jozsef Katona Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Department of Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI.

关键词： Power engineering Accuracy Generative AI Education Chatbots Market research Problem-solving Reliability

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