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Stops, L. (author), Leenhouts, Roel (author), Gao, Q. (author), Schweidtmann, A.M. (author)Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from...journal article 2022
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Chen, Zhijun (author), Lu, Zhe (author), Chen, Qiushi (author), Zhong, Hongliang (author), Zhang, Yishi (author), Xue, J. (author), Wu, Chaozhong (author)Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays an important role in traffic management. The graph convolution network (GCN) is widely used in traffic prediction models to efficiently handle the graphical structural data of road networks. However, the influence weights among different road...journal article 2022
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Cuperman, Rafael (author), Jansen, K.M.B. (author), Ciszewski, M.G. (author)Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...journal article 2022
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Marcelis, Paul J. (author), KOUVELAS, N. (author), Rao, V.S. (author), Venkatesha Prasad, Ranga Rao (author)Long-range wide-area network (LoRaWAN) is an energy-efficient and inexpensive networking technology that is rapidly being adopted for many Internet-of-Things applications. In this study, we perform extensive measurements on a new LoRaWAN deployment to characterise the spatio-temporal properties of the LoRaWAN channel. Our experiments reveal...journal article 2022
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Vatandaslar, Can (author), Narin, O.G. (author), Abdikan, Saygin (author)Key message: Despite showing a cost-effective potential for quantifying vertical forest structure, the GEDI and ICESat-2 satellite LiDAR missions fall short of the data accuracy standards required by tree- and stand-level forest inventories. Abstract: Tree and stand heights are key inventory variables in forestry, but measuring them manually...journal article 2022
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Kopbayev, Alibek (author), Khan, Faisal (author), Yang, M. (author), Halim, Syeda Zohra (author)Natural gas leakage can impose significant danger on a facility and its surrounding communities. Methods for early detection and diagnosis of such leakages have been developed and widely used for gas pipelines and storage tanks. Most techniques include inspection of sensor-aided mathematical models. Application of machine learning techniques to...journal article 2022
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He, Yuxin (author), Li, L. (author), Zhu, X. (author), Tsui, Kwok Leung (author)Short-term forecasting of passenger flow is critical for transit management and crowd regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven by other latent factors, and exogenous factors bring challenges to the short-term forecasts of passenger flow of urban rail transit networks. An innovative deep...journal article 2022
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Tapia, Estefania Alexandra (author), Colomé, Delia Graciela (author), Rueda, José L. (author)Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power...journal article 2022
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Cristiani, D.L.M. (author), Falcetelli, F. (author), Yue, N. (author), Sbarufatti, Claudio (author), Di Sante, Raffaella (author), Zarouchas, D. (author), Giglio, Marco (author)Machine learning (ML) methods for the structural health monitoring (SHM) of composite structures rely on sufficient domain knowledge as they typically demand to extract damage-sensitive features from raw data before training the ML model. In practice, prior knowledge is not available in most cases. Deep learning (DL) methods, on the other...journal article 2022
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Wang, J. (author), Li, Runlong (author), He, Yuan (author), Yang, Yang (author)In this article, the interference mitigation (IM) problem is tackled as a regression problem. A prior-guided deep learning (DL)-based IM approach is proposed for frequency-modulated continuous-wave (FMCW) radars. Considering the complex-valued nature of radar signals, a complex-valued convolutional neural network, which is different from the...journal article 2022
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Chang, X. (author), Wu, Jianjun (author), Correia, Gonçalo (author), Sun, Huijun (author), Feng, Ziyan (author)Carsharing has become a popular travel mode owing to its convenience of use, easy parking, and low cost of using a car by those who only need it occasionally. However, because of the inadequate location of carsharing stations (station-based systems) or vehicles (free-floating systems), effectively requiring expensive and complex relocation...journal article 2022
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Lager, I.E. (author), Stumpf, Martin (author), Vandenbosch, Guy A.E. (author), Antonini, Giulio (author)The late-time evaluation of electromagnetic (EM) field quantities yielded by convolution integrals that combine Green's functions available at discrete time samples and strictly causal excitations is critically revisited. A typical situation is used for tracing the causes of the divergent late-time behavior that is often experienced. A...journal article 2022
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Chang, Z. (author), Wan, Z. (author), Xu, Y. (author), Schlangen, E. (author), Šavija, B. (author)Extrusion-based 3D concrete printing (3DCP) results in deposited materials with complex microstructures that have high porosity and distinct anisotropy. Due to the material heterogeneity and rapid growth of cracks, fracture analysis in these air-void structures is often complex, resulting in a high computational cost. This study proposes a...journal article 2022
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Hou, Miaomiao (author), Hu, Xiaofeng (author), Cai, Jitao (author), Han, Xinge (author), Yuan, S. (author)Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial–temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to...journal article 2022
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Pasqualetto Cassinis, L. (author), Menicucci, A. (author), Gill, E.K.A. (author), Ahrns, Ingo (author), Sanchez-Gestido, Manuel (author)The estimation of the relative pose of an inactive spacecraft by an active servicer spacecraft is a critical task for close-proximity operations, such as In-Orbit Servicing and Active Debris Removal. Among all the challenges, the lack of available space images of the inactive satellite makes the on-ground validation of current monocular...journal article 2022
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Yang, Fan (author), Li, Xueyuan (author), Liu, Qi (author), Li, Z. (author), Gao, Xin (author)In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the...journal article 2022
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Nadi Najafabadi, A. (author), Sharma, Salil (author), van Lint, J.W.C. (author), Tavasszy, Lorant (author), Snelder, M. (author)This paper proposes a data-driven transport modeling framework to assess the impact of freight departure time shift policies. We develop and apply the framework around the case of the port of Rotterdam. Container transport demand data and traffic data from the surrounding network are used as inputs. The model is based on a graph convolutional...journal article 2022
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Hafner, Frank M. (author), Zeller, Matthias (author), Schutera, Mark (author), Abhau, Jochen (author), Kooij, J.F.P. (author)Customization of a convolutional neural network (CNN) to a specific compute platform involves finding an optimal pareto state between computational complexity of the CNN and resulting throughput in operations per second on the compute platform. However, existing inference performance benchmarks compare complete backbones that entail many...conference paper 2022
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Zhou, Yujue (author), Zheng, Yonglai (author), Liu, Yongcheng (author), Pan, Tanbo (author), Zhou, Y. (author)Vibration-based structural damage detection (SDD) has been a subject of intense research in structural health monitoring (SHM) for large civil engineering structures over the decades. The performance of the conventional SDD approaches predominantly relies on the rational choices of the damage feature and classifier. Hand-crafted features or...journal article 2022
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Eichinger, Matthias (author), Heinlein, A. (author), Klawonn, Axel (author)A convolution neural network (CNN)-based approach for the construction of reduced order surrogate models for computational fluid dynamics (CFD) simulations is introduced; it is inspired by the approach of Guo, Li, and Iori [X. Guo, W. Li, and F. Iorio, Convolutional neural networks for steady flow approximation, in Proceedings of the 22nd ACM...journal article 2022