Searched for: subject%3A%22Deep%255C%2Bneural%255C%2Bnetworks%22
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Sharma, Bhawana (author), Sharma, Lokesh (author), Lal, C. (author), Roy, Satyabrata (author)
The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data. Research in the field of IoT security is an emerging topic. IoT networks are becoming more vulnerable to new assaults as a result of the growth in devices and the production of massive data. In order to recognize the attacks, an intrusion...
journal article 2024
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Xu, Y. (author)
Micro air vehicles (MAVs) have shown significant potential in modern society. The development in robotics and automation is changing the roles of MAVs from remotely controlled machines requiring human pilots to autonomous and intelligent robots. There is an increasing number of autonomous MAVs involved in outdoor operations. In contrast, the...
doctoral thesis 2023
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Nembhani, Prithvish Vijaykumar (author)
Artificial intelligence, machine learning, and deep learning have been the buzzwords in almost every industry (medical, automotive, defense, security, finance, etc.) for the last decade. As the market moves towards AI-based solutions, so does the computation need for these solutions increase and change with time. With the rise of smart cities...
master thesis 2023
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Thorpe, Matthew (author), van Gennip, Y. (author)
Neural networks have been very successful in many applications; we often, however, lack a theoretical understanding of what the neural networks are actually learning. This problem emerges when trying to generalise to new data sets. The contribution of this paper is to show that, for the residual neural network model, the deep layer limit...
journal article 2023
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Wan, Z. (author), Chang, Z. (author), Xu, Y. (author), Šavija, B. (author)
In this paper, optimization of vascular structure of self-healing concrete is performed with deep neural network (DNN). An input representation method is proposed to effectively represent the concrete beams with 6 round pores in the middle span as well as benefit the optimization process. To investigate the feasibility of using DNN for...
journal article 2023
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Moradi, M. (author), Broer, Agnes A.R. (author), Chiachío, Juan (author), Benedictus, R. (author), Loutas, Theodoros H. (author), Zarouchas, D. (author)
A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic...
journal article 2023
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Li, Wanda (author), Xu, Zhiwei (author), Sun, Yi (author), Gong, Qingyuan (author), Chen, Y. (author), Ding, Aaron Yi (author), Wang, Xin (author), Hui, Pan (author)
Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on...
journal article 2023
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Tavakoli, Ali (author), Hashemi, Javad (author), Najafian, Mahyar (author), Ebrahimi, Amin (author)
Solid-liquid phase transformation of a phase change material in a rectangular enclosure with corrugated fins is studied. Employing a physics-based model, the influence of fin length, thickness, and wave amplitude on the thermal and fluid flow fields is explored. Incorporating fins into thermal energy storage systems enhances the heat transfer...
journal article 2023
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Yang, Y. (author), Jia, Bin (author), Yan, Xiao Yong (author), Chen, Yan (author), Song, Dongdong (author), Zhi, Danyue (author), Wang, Y. (author), Gao, Ziyou (author)
Accurate estimation of intercity heavy truck mobility flows is of vital importance to urban planning, transportation management and logistics operations. The inaccessibility of big data related to intercity transport systems and the heterogeneity of trucking activities pose challenges for the reliable estimation. Recently, the advance of...
journal article 2023
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Luopan, Yaxin (author), Han, Rui (author), Zhang, Qinglong (author), Liu, Chi Harold (author), Wang, Guoren (author), Chen, Lydia Y. (author)
Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge devices....
conference paper 2023
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Perez Dattari, R.J. (author), Kober, J. (author)
Learning from humans allows nonexperts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on...
journal article 2023
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Moradi, M. (author), Broer, Agnes A.R. (author), Chiachío, Juan (author), Benedictus, R. (author), Zarouchas, D. (author)
Health indicators are indices that act as intermediary links between raw SHM data and prognostic models. An efficient HI should satisfy prognostic requirements such as monotonicity, trendability, and prognosability in such a way that it can be effectively used as an input in a prognostic model for remaining useful life estimation. However,...
conference paper 2023
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Zeng, Cheng (author), Huang, Jinsong (author), Wang, H. (author), Xie, Jiawei (author), Zhang, Yuting (author)
Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases...
journal article 2023
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Li, Ruohan (author), Dong, Y. (author)
Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane...
journal article 2023
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de Wagter, C. (author)
Highly automated Unmanned Aerial Vehicles (UAVs) or "flying robots" are rapidly becoming an important asset to society. The last decade has seen the advent of an impressive number of new UAV types and applications. For many applications, the UAVs need to be safe, highly automated, and versatile. Safety is a prerequisite to allowing their use in...
doctoral thesis 2022
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de Graaf, Samantha (author)
Predicting functional outcome after intra-arterial treatment (IAT) in acute ischemic stroke (AIS) patients is an important aspect of treatment decision making and prognostics. Standard methods for functional outcome prediction after stroke combine baseline clinical (and radiological) parameters. In this study, we investigated to what extent...
master thesis 2022
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van den Akker, Daniel (author)
Multi-Layer Perceptron and Support Vector Machine have both been widely used in machine learning. In this research paper, these models have been applied to binary classification on an individual time series basis. The goal was to see whether they can predict earthquakes, using earthquakes measured at specific stations across New Zealand. As it...
bachelor thesis 2022
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Hashmi, Irtaza (author)
Earthquakes are one of the most dangerous natural disasters that occur worldwide. Predicting them is one of the unsolved problems in the field of science. In the past decade, there has been an increase in seismic monitoring stations worldwide, which has allowed us to design and implement data-driven and deep learning solutions. In this paper, we...
bachelor thesis 2022
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Kopbayev, Alibek (author), Khan, Faisal (author), Yang, M. (author), Halim, S. Zohra (author)
The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is...
journal article 2022
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Tseremoglou, I. (author), Bombelli, A. (author), Santos, Bruno F. (author)
In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received...
journal article 2022
Searched for: subject%3A%22Deep%255C%2Bneural%255C%2Bnetworks%22
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