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Experimental characterisation of on-body radio channel for ultrawideband (UWB) wireless active tags is reported in this paper. The aim of this study is to investigate the performance of the commercially available wireless tags on the UWB on-body radio channel characterisation. Measurement campaigns are performed in the chamber and in an indoor environment. Statistical path loss parameters of nine different on-body radio channels for static and dynamic cases are shown and analyzed. Results demonstrated that lognormal distribution provides the best fits for on-body propagation channels path loss model. The path loss was modeled as a function of distance for 34 different receiver locations for propagation along the front part of the body. A reduction of 11.46% path loss exponent is noticed in case of indoor environment as compared to anechoic chamber. In addition, path loss exponent is also extracted for different body parts (trunk, arms, and legs). Second-order channel parameters as fade probability (FP), level crossing rate (LCR), and average fade duration (AFD) are also investigated.

Author(s): Zixuan Zhu [1]; Xiaoyan Lin [1]; Hao Yang (corresponding author) [1] In the article titled “Booming with Speed: High-Speed Rail and Regional Green Innovation” [1], the authors included incorrect [...]

Exploiting China's high-speed rail (HSR) as a quasi-natural experiment, we examine the relationship between the HSR connection and green innovation. The opening of HSR can promote green innovation by facilitating the flow of innovation factors. Using the multiperiod difference-in-differences (DID) model, we find that the regional green innovation performance significantly becomes better following the opening of HSR in the local city. Moreover, in examining the specific mechanisms at work, we find evidence that HSR stimulates green patents through increased labor mobility and research capital mobility. Further analyses show that the facilitating effect of HSR is heterogeneous among cities. Our paper sheds new light on the effects of HSR on social welfare in the case of sustainable economy., Author(s): Zixuan Zhu [1]; Xiaoyan Lin [1]; Hao Yang (corresponding author) [1] 1. Introduction As one of the major technological innovations in the history of railway transportation, high-speed rail (HSR) [...]

In order to make safety risk assessment more accurately and more reasonably for high-speed railway station in China, this paper analyzes risk factors of fault tree and transfers the fault tree of risk accident into fuzzy petri net and then builds the FPN-FTA model by combining the dynamic weighting fuzzy petri net (FPN) and fault tree analysis (FTA) based on the latter. This paper simulates the FTA-FPN model with Stateflow of Matlab software. Then, it builds up a bi-objective risk control model, making the minimum safety risk level and minimum necessary cost as the objectives, and it designs discrete particle swarm optimization algorithm to solve the risk control model. Finally, this paper selects stampede accident of Shijiazhuang high- speed railway station as an example in case study for assessing stampede risk level and gets the risk control schemes for this station. The results verify the feasibility and validity of the model and algorithm., 1. Introduction The pressure on railway transport safety management has increased constantly with the rapid development of railway. Therefore, safety risk control is the key to secure railway transport. Safety [...]

Based on the 'Internet +' technology, this paper establishes a logistics information platform for the container terminal. Under the premise of the scheduled arrival time and quantity of the truck, this paper aims at minimizing the working time of the loading and unloading equipment and the stay time of the train and truck in the station, and develops a scheduling optimization of loading and unloading model combing equilibrium assignment and flexible scheduling to realize the seamless transfer between rail and road transportation in container terminal. In order to solve the model, a multi-layer coding genetic algorithm with chromosome feasibility is designed to obtain the optimal scheduled time for the truck, and the optimal operation sequence of the gantry crane. Referring to China's container station, this paper takes Takoradi container terminal of Ghana as a case to verify the accuracy and effectiveness of the model and algorithm and provides the medium or long term planning for Ghana's development., 1. Introduction The globalization of economy and trade has improved the necessity of multimodal transport research and laid a good foundation for container transportation. Recently, the study of railway container [...]

Various devices and sensors of cyber‐physical systems interact with each other in time and space, and the generated multiple time series have implicit correlations and highly nonlinear relationships. Determining how to model the multiple time series and capture dependencies through extracting features is the key to anomaly detection. In this paper, we propose a graph‐based Bayesian network conditional normalizing flows model for multiple time series anomaly detection, Bayesian network conditional normalizing flows (BNCNF). It applies a Bayesian network to model the causal relationships of multiple time series and introduces a spectral temporal dependency encoder to obtain the representations of interdependency between multiple time series. The representations are introduced as conditional information into the normalizing flows for density estimation, and the data corresponding to low density is judged as anomalies. The experiment are conducted on SWaT, WADI, and SMD datasets, the F1 score reaches 0.95, 0.92, and 0.97 on the three datasets, respectively. The results show that BNCNF has better performance in anomaly detection compared with the current mainstream methods. [ABSTRACT FROM AUTHOR]

To elucidate the puffing characteristics of the berry slice under continuous microwave conditions, heat and mass transfer and deformation of slices were investigated based on the models of coupled multi‐physics fields involving electromagnetic heating, heat and mass transfer and solid mechanics. The results indicated that transmission mode of microwaves in a microwave cavity determined the electric field distribution inside slices with interior focusing phenomenon described by higher temperature and lower moisture content at the central region of slices than that of edge. The berry slice volume showed a "invisible‐rapid‐maintained" deformation under the synergistic effects of temperature, moisture and internal pressure with sample movement. This study may provide guidance for the elucidation of microwave puffing process of the moving slice and the underlying applications such as technology optimization and quality control of slices like berry. Novelty impact statement: This work investigates the puffing characteristics of the moving berry slices under a continuous microwave dryer, and elucidates the heat and mass transfer and deformation process of berry slices by means of experiment and simulation. The distribution of alternating electric field profiles with high and low levels determine the absorption of microwave energy, heat and mass transfer in berry slices to induce the volume deformation and uneven distribution. The volume of berry slices laid on moving conveyor belt under continuous microwave puffing undergoes three deformation trends as invisible change followed by rapid expansion till desired shape. [ABSTRACT FROM AUTHOR]

Managing and estimating the availability information of parking lots is of great importance to travelers and managers. However, the task is very challenging since the occupancy rate is affected by various factors, including spatial‐temporal features, parking lot attributes features, and environmental changes. Previous studies mostly focus on the short‐term prediction by capturing the historical sequential dependencies among inputs and outputs, which leads to low estimation accuracy for long‐term prediction and limited scalability for real deployment in parking lots. To address the challenges, a comprehensive framework for real‐time Smart Parking Data Management and Prediction (SPDMP) system is proposed. Three types of data sources, including historical sequential data, real‐time sequential data, and attributes category data, are sufficiently integrated into a customized Parking Availability Prediction (PAP) neural network by representation learning and heterogeneous feature embedding. Specifically, instead of using parking lot property information and environmental data directly, the authors design an Attributes Tensor Embedding Component (ATEC) to integrate the intra‐class affection and interclass representations and correlations by two steps of customized embedding process. To balance the impact of the indiscriminate features for various prediction targets, this study proposes a multi‐factor attention mechanism to learn the weights and help the PAP achieve a stable performance for time‐various tasks. From extensive experiments on two real‐world large‐scale data sets collected in China and USA (including 19 urban parking and 2 truck parking lots), PAP achieves superior prediction results on both urban parking (6.69% average MAPE) and truck parking prediction (7.63% average MAPE) for five prediction time slots (5 min, 10 min, 30 min, 1 h, and 2 h). Furthermore, even with limited training data, PAP still shows better transferability and adaptability for various types of lots. The proposed SPDMP is selected and used as a pilot test bed by the Washington State Department of Transportation (WSDOT) for truck parking information promotion. [ABSTRACT FROM AUTHOR]