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Özge Sahin and Naci Caglar
The dynamic characteristics of buildings and their behavior under various dynamic loads play a crucial role in civil engineering applications, particularly for earthquake-resistant structural design. Employing a precise mathematical model of the structur...
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Gwan-Hee Son, Beom-Soo Kim, Seung-Je Cho and Young-Jun Park
As the demand for agricultural electric vehicles increases, it is becoming important to conduct noise reduction in consideration of the characteristics of an electric powertrain. This study was conducted to optimize the shape design of gearbox housing fo...
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Wancheng Zhang, Yanmin Luo, Zhi Chen, Yongzhao Du, Daxin Zhu and Peizhong Liu
Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and i...
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Kalliopi Giannakopoulou, Andreas Paraskevopoulos and Christos Zaroliagis
In this paper, a new model, known as the multimodal dynamic timetable model (DTM), is presented for computing optimal multimodal journeys in schedule-based public transport systems. The new model constitutes an extension of the dynamic timetable model (D...
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Wancheng Zhang, Yanmin Luo, Zhi Chen, Yongzhao Du, Daxin Zhu and Peizhong Liu
Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and i...
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Zhi Chen, Peizhong Liu, Yongzhao Du, Yanmin Luo and Wancheng Zhang
Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single chann...
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Stefano Parmeggiani, Jens Peter Kofoed and Erik Friis-Madsen
An overtopping model specifically suited for Wave Dragon is needed in order to improve the reliability of its performance estimates. The model shall be comprehensive of all relevant physical processes that affect overtopping and flexible to adapt to any ...
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Lingkai Yang, Sally McClean, Mark Donnelly, Kevin Burke and Kashaf Khan
Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data. Due to fact...
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Navid Jadidoleslam, Ricardo Mantilla and Witold F. Krajewski
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) ...
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Oscar D. Hurtado, Albert R. Ortiz, Daniel Gomez and Rodrigo Astroza
Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techni...
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Jianchen Zhang, Jiayao Wang and Heying Li
Incremental updating is an important technical method used to maintain the data of road networks. Topology conflict detection of multiscale road networks in incremental updating is an important link. Most of the previous algorithms focus on a single scal...
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Ilaria Capanna, Riccardo Cirella, Angelo Aloisio, Rocco Alaggio, Franco Di Fabio and Massimo Fragiacomo
Masonry towers, located in seismic zones, are vulnerable and prone to damages up to compromise their stability. The scatter of data on the mechanical properties of masonry, geometry and boundary conditions determine a lack of building knowledge on their ...
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Shimelis Asfaw Wakigari and Robert Leconte
Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own str...
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Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng and Chao Deng
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user?s dissatisfactio...
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Peng Hu, Junyu Tao, Aofei Ji, Wei Li and Zhiguo He
In this paper, a computationally efficient shallow water model is developed for sediment transport in the Yangtze Estuary by considering mixed cohesive and non-cohesive sediment transport. It is firstly shown that the model is capable of reproducing tida...
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Mona Laroussi,Alain Derycke
Pág. pp. 22 - 31
In this paper, we discuss the issue of adaptativity in mobile learning. To adapt a system, we need a learner model. We discuss the limits of learner model in the environment of mobility. We present LIP (Learner Information Package) and we propose an exte...
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Achilleas G. Samaras and Theophanis V. Karambas
This work presents a new model for surf and swash zone morphology evolution induced by nonlinear waves. Wave transformation in the surf and swash zones is computed by a nonlinear wave model based on the higher order Boussinesq equations for breaking and ...
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Fumin Zou, Yue Xing, Qiang Ren, Feng Guo, Zhaoyi Zhou and Zihan Ye
With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utili...
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Yang Chao, Chaoning Lin, Tongchun Li, Huijun Qi, Dongming Li and Siyu Chen
Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists ...
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Liangkun Yu, Xiang Sun, Rana Albelaihi and Chen Yi
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the co...
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