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This paper presents a path loss model based on path profile in urban propagation environments for 5G systems. Although deep learning approaches are indeed powerful in tasks involving prediction or classification, they often lack transparency and suffer from high computational complexity. The proposed model combines the log‐distance path loss model for line‐of‐sight propagation scenarios and a machine‐learning‐based model for non‐line‐of‐sight (NLOS) cases. This paper uses the principal component analysis algorithm to extract relevant features out of some selected attributes of the path profile for NLOS cases. Then, the path loss model can be constructed based on the approach of polynomial regression. Simulation results show that the proposed model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by about 22.2–37.2% dB when compared to the conventional models. Furthermore, the prediction performance was also evaluated in a non‐standalone 5G New Radio network in the urban environment of Taipei city. The real‐world measurements show that the standard deviation of prediction error can be reduced by 3.33–6.13 dB when compared to the conventional models. [ABSTRACT FROM AUTHOR]

This paper applies a deep learning approach to model the mechanism of path loss based on the path profile in urban propagation environments for 5G cellular communication systems. The proposed method combines the log-distance path loss model for line-of-sight propagation scenarios and a deep-learning-based model for non-line-of-sight cases. Simulation results show that the proposed path loss model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by 34% when compared to the conventional models. To explain the internal behavior of the proposed deep-learning-based model, which is a black box in nature, eight relevant features were selected to model the path loss based on a linear regression approach. Simulation results show that the accuracy of the explanatory model reached 72% when it was used to explain the proposed deep learning model. Furthermore, the proposed deep learning model was also evaluated in a non-standalone 5G New Radio network in the urban environment of Taipei City. The real-world measurements show that the standard deviation of prediction error can be reduced by 30–43% when compared to the conventional models. In addition, the transparency of the proposed deep learning model reached 63% in the realistic 5G network. [ABSTRACT FROM AUTHOR]

Scholars have long sought to identify key city locations that have a pronounced effect on the flow of people under various conditions. Identifying key locations makes it possible for government and/or enterprises to obtain accurate estimates as to the flow of people and thereby formulate reasonable management strategies. Note however that much of the previous research based on professional knowledge or machine learning fails to provide accurate results. Some researchers have employed CNN-based models to predict the flow of people, claiming that those models can extract freatures from multiple locations to improve prediction accuracy. Theoretically, the features extracted using CNN-based models could be used themselves as key locations for specific problems; however, the fact that the features are consolidated via multiple operations often renders interpretation difficult when applied to a real-world setting. In the current study, we developed a novel approach to the identification of key locations based on the results of a CNN-based model and the Grad-Cam kit. The structure of a CNN-based models can have a profound effect on the results of the Grad-Cam kit; therefore, we compared the results obtained using three state-of-art models as well as our CNN-LSTM. Actual flow patterns based on telecommunication data in Taipei were used to verify the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Dr Chien-Yu Chen is a Professor at the Department of Biomechatronics Engineering at National Taiwan University (NTU) in Taipei. She received a PhD degree in Computer Science and Information Engineering from NTU and has been leading her lab since 2005. Dr Chen develops machine learning and deep learning models to study multi-omics data, including genomes, transcriptomes, epigenomes, and proteomes. [ABSTRACT FROM AUTHOR]

In this study, we propose a volume‐to‐point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location‐specific precipitations. In contrast to QPE methods based on the Z–R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013–2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume‐to‐point framework performed comparably well in general cases and excelled in detecting heavy‐rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios. Plain Language Summary: Quantitative precipitation estimation (QPE) is a method of approximating the amount of rain that has fallen at a location or across a region. In most use cases, weather service providers use radar signals to estimate the amount of precipitation through the formula describing the relationship between radar reflectivity and the size of raindrop particles, Z–R relation. The state‐of‐the‐art QPE methods with adjusted Z–R relation are robust and accurate in general. Yet, they consider only the radar signal at a given location, which represented a point‐to‐point framework. In this study, we proposed a volume‐to‐point alternative that estimates the amount of rain by considering the signals in a broader spatial region and a longer time‐span. By using a deep neural network to process the large data volume, we demonstrated that the proposed method performed comparably well in general cases and excelled in detecting heavy‐rainfall events. This method could improve advanced warning for flash flooding and make water resource management more effective. In ongoing research, we seek to extend our approach to integrate the heterogeneous data sources and to be applied to precipitation forecasting. Key Points: We proposed a novel deep learning approach for estimating precipitation from a large data volume and demonstrated it with QPE from aggregated radar dataIn comparison to the operational QPE scheme, the proposed framework performed comparably well in general cases and excelled in detecting heavy‐rainfall eventsThe proposed framework can be further extended to integrate heterogeneous data sources and potentially improve the QPF in extreme precipitation scenarios [ABSTRACT FROM AUTHOR]