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35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022

作者： Ziedan, Nesreen I. Computer and Systems Engineering Department Faculty of Engineering Zagazig University Egypt

ISBN: (纸本)9781713871361

Multipath and non-line-of-sight (NLOS) signals are a major concern for the application of GNSS positioning in challenging environments. The positioning accuracy degradation, due to these reflected signals, hinders the dependence on GNSS systems. This has led to either integrating GNSS systems with other systems or dropping GNSS systems completely in favor of other systems. This paper proposes a novel system model for multipath delay estimation;the system is called MDE. The MDE system is integrated with a modified version of an Optimized Position Estimation (OPE) algorithm;the new algorithm is called OPE-MDE. In conventional navigation algorithms, given code delays, a position is estimated. The proposed MDE system does the opposite. Given a position, code delays are estimated. Since the actual position is not known, the MDE system can serve as a validation algorithm to generate a probability that a candidate position is correct. The idea of the proposed MDE system is to construct a model that relates the errors in the individual pseudorange measurements, of each available satellite, to the errors in the estimated receiver coordinates. The proposed OPE-MDE algorithm keeps track of the most likely paths over several steps. Each path has an associated weight. After several steps, the path with the optimal weight is taken as the estimated path, and the positions that constitute the estimated path are taken as the estimated positions. The OPE-MDE algorithm utilizes the MDE system to compute a probability that a candidate position is correct, where this probability is a part of the path weight. The proposed OPE-MDE algorithm is tested using three scenarios. One of the scenarios has high dynamics properties. The OPE-MDE algorithm shows a 34% positioning accuracy enhancement over a previous optimized position estimation algorithm. © 2022 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. All rights reserved.

关键词： Satellites

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis 10

IoT Sensor Deployment in the Wildland Urban Interface: Lever...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Purcell, Richard Naik, Kshirasagar Zaman, Marzia Lung, Chung-Horng Sampalli, Srinivas Mutakabbir, Abdul Dhindsa, Manavjit Singh Das, Ursula Dalhousie University Faculty of Computer Science HalifaxNS Canada University of Waterloo Electrical and Computer Engineering WaterlooON Canada Research and Development Cistel Technology OttawaON Canada Carleton University Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350373011

This paper investigates algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Using geospatial data analysis and a validated wildland fire growth model, we generated burn maps to guide sensor placement strategies across a defined region. We evaluated even grid distribution, random distributions, and genetic algorithm-based methods, testing each against 50,000 burn maps with sensor counts ranging from 50 to 800. Results indicate that while even grid distribution achieved the highest detection rates, its practicality in real-world applications is limited. Genetic algorithms showed promise but require further exploration to simulate field deployment accurately. Notably, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, highlighting the need for further research into the representativeness of burn maps. © 2024 IEEE.

关键词： Genetic algorithms

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Towards Utilizing Unlabeled Data for Flood Forecasting with Weakly Supervised Adaptive Learning

Towards Utilizing Unlabeled Data for Flood Forecasting with ...

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2023 Innovations in Intelligent systems and Applications Conference, ASYU 2023

作者： Abdulla, Nawaf Demirci, Mehmet Ozdemir, Suat Gazi University Graduate School of Informatics Department of Information Systems Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey Hacettepe University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350306590

The importance of accurate flood forecasting is rising as climate change makes it more difficult to predict when and where floods will occur. Flood predictions use supervised machine learning models, and flood levels are being tracked via the Internet of Things (IoT). Despite advancements in data-driven hydrological forecasting, producing reliable flood forecasts remains challenging. This research paper explores the possibility of utilizing adaptive deep learning with weak supervision to predict flooding in coastal cities that are equipped with Internet of Things technology. This approach uses weakly supervised learning for data labeling and enhancement, a bidirectional long-short-term memory (LSTM) RNN for time-series prediction and classification, and adaptive learning to handle seasonality. The results of the proposed system show that flood forecasting accuracy could be improved by 40% compared to the baseline models. © 2023 IEEE.

关键词： Floods

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Developing a Green AI Platform for SMEs to Analyze Open-source Information with the Approach of Society 5.0

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SN computer Science 2024年第7期5卷 923页

作者： Akarslan, Hüseyin Sağiroğlu, Şeref Management Information Systems Graduate School of Informatics Gazi University Türkiye Ankara Turkey Computer Engineering Department Engineering Faculty Gazi University Türkiye Ankara Turkey

Management and information systems are considered two major disciplines that have to be combined to benefit from knowledge for rational decision-making which means strict procedures utilizing objective knowledge and logic for many years. Effective and efficient use of already invested organizational resources is a key subject to gain a competitive advantage by making decisions with the help of AI techniques which benefit from both internal and external knowledge. This paper introduces a new model to get benefits from open-source intelligence (OSINT) in the context of external knowledge with a green computing approach for solving global organizational challenges in the scope of Society 5.0. This research has a dual background, theoretical and practical. Problem defining and hypothesis formulation was carried out by literature review. Then, the application developed according to the proposed model was put into action in some organizations for testing, and feedback from experts who participated in the proof of concept (POC) activities was evaluated and discussed empirically. Finally, the findings of our research, the advantages of our model, and both academic and business future work were summarized. It has been practically determined that the model and application we have developed are largely successful in line with our hypotheses. However, the weaknesses and advantages of our study were also revealed. Our research is the first study that combines Management Information systems and Open-Source Intelligence disciplines with the approach of Society 5.0 to support SMEs in analyzing open-source information to build better decision-making mechanisms. The developed application in this study can also be applied to solving other green distributed computing needs in different domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed computing External knowledge Green computing Management information systems (MIS) Open-source intelligence (OSINT) Society 5.0

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Next Generation Memristor Reservoir Computing 3

Next Generation Memristor Reservoir Computing

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Nikiruy, K. Ivanov, T. Ziegler, M. Rossetti, D. Corinto, F. Ascoli, A. Tetzlaff, R. Demirkol, A.S. Schmitt, N. Micro-and Nanoelectronic Systems Tu Ilmenau Department of Electrical Engineering and Information Technology Ilmenau Germany Turin Italy Institute of Circuits and Systems Tu Dresden Faculty of Electrical and Computer Engineering Dresden Germany

ISBN: (纸本)9798350378009

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators. © 2024 IEEE.

关键词： Memristors

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Extracting Motion Patterns of Greeting Gestures Using GNG with Spiking Neuron Model 13

Extracting Motion Patterns of Greeting Gestures Using GNG wi...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Obo, Takenori Wibowo, Angga Wahyu Sato-Shimokawara, Eri Shibata, Hiroki Ho, Yihsin Kobayashi, Ichiro Tokyo Metropolitan University Department of Mechanical Systems Engineering Tokyo Japan Tokyo Metropolitan University Department of Computer Science Tokyo Japan Takushoku University Department of Electronics and Computer Systems Tokyo Japan Takushoku University Faculty of Engineering Tokyo Japan

ISBN: (纸本)9798350373332

This study focuses on behavior patterns, which are characteristic actions common to groups or communities, to promote intercultural understanding. Understanding greeting gestures is crucial for comprehending social relationships, but these vary across cultures and regions. Recent advancements in sensing technology and artificial intelligence, such as RGB-D cameras and markerless motion capture systems, have made it easier to capture motion data in diverse formats. Therefore, it is important to extract relevant motion patterns and apply appropriate structuring for effective long-term archiving. We propose a method for extracting motion patterns using a Growing Neural Gas (GNG) algorithm with a Spiking Neuron Model. Additionally, this paper presents an experimental example of extracting a gesture pattern in Indonesian style. © 2024 IEEE.

关键词： Economic and social effects

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Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study

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computers in Biology and Medicine 2025年 186卷 109649-109649页

作者： Saeed, Ahmed Ismail, Mohamed A. Ghanem, Nagia M. Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt

Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly increasing due to many factors, such as unhealthy lifestyles, water and food pollution, aging, and medical diagnosis development. Detecting CRC in its early stages can help stop its growth by providing the necessary treatments, thereby saving many people's lives. There are various tests that doctors can perform to diagnose CRC;however, biopsy using histopathological images is considered the "gold standard" for CRC diagnosis. Deep learning techniques can now be leveraged to build computer-aided diagnosis (CAD) systems that can affirm if an input sample shows any symptoms of cancer and determine its stage and location with an acceptable degree of confidence. In this research, we utilize deep learning to study the CRC classification problem using weakly annotated histopathological whole slide images (WSIs). We relax the constraints of the multiple instance learning (MIL) algorithm and primarily propose WSI-label prediction functions to be integrated with MIL, which significantly enhances the performance of WSI-level classification. We also applied efficient preprocessing techniques that output a computationally power-efficient dataset representation and performed multiple experiments to compose the most efficient CAD system. Our study introduces a notable improvement over the results obtained by the baseline research where we achieved an accuracy of 93.05% compared to 84.17%. Furthermore, our results using only the weakly annotated WSIs outperformed the baseline results that are based on performing initial pre-training using a strongly annotated part of the dataset. © 2025 Elsevier Ltd

关键词： Diseases

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech Technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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