Browse Topic: Data exchange
We present a novel processing approach to extract a ship traffic flow framework in order to cope with problems such as large volume, high noise levels and complexity spatio-temporal nature of AIS data. We preprocess AIS data using covariance matrix-based abnormal data filtering, develop improved Douglas-Peucker (DP) algorithm for multi-granularity trajectory compression, identify navigation hotspots and intersections using density-based spatial clustering and visualize chart overlays using Mercator projection. In experiments with AIS data from the Laotieshan waters in the Bohai Bay, we achieve compression rate up to 97% while maintaining a key trajectory feature retention error less than 0.15 nautical miles. We identify critical areas such as waterway intersections and generate traffic flow heatmap for maritime management, route planning, etc.
To address the high security demands of HSR communication, this letter proposes a covert communication scheme using irregular intelligent transparent surfaces (ITSs) deployed on train windows. A joint optimization framework is developed to enhance covert rate under element constraints, involving ATS for topology design and NECE for beamforming and phase shift. Gradient descent is used to handle covert constraints. Simulations confirm that the proposed irregular ITS outperforms regular ITS in performance and robustness, offering a promising solution for future HSR covert communication.
Semi-trailer trains are the main force of highway freight. In a complex environment with multiple vehicles, accidents are easily caused by complex structures and driver operation problems. Intelligent technology is urgently needed to improve safety. In view of the shortcomings of existing research on its dedicated models and algorithms, this paper studies the intelligent decision-making and trajectory planning of semi-trailer trains under multiple vehicles. A local trajectory planning method based on global path planning and Frenet coordinate decoupling based on the improved A* algorithm is proposed. The smooth weight transition function and B-spline curve are introduced to optimize the global path. The polynomial function is combined with the acceleration rate to optimize the local trajectory. TruckSim, Prescan and Simulink are used to build a joint simulation platform for multi-condition verification. The simulation results show that the search efficiency of the improved A* algorithm
Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low
Type IV composite pressure (CP) vessels composed of a plastic liner and composite layers require special design attention to the dome region. The cylindrical portion of the composite cylinder is wrapped with composite layers consisting of the 900 hoop layers and low-angle helical layers, whereas the dome surface carries helical layers only. The winding angle of the helical layers being a constant over the cylindrical portion starts to vary from the cylinder-dome junction toward the boss at the top continuously. Along with the winding angle, the composite thickness also varies continuously resulting in a maximum thickness at the top crown region. The complete analysis and layer-wise stress prediction of Type IV composite cylinders for service pressures up to 70 MPa was analyzed by the Classical Lamination theory (CLT)-based MATLAB program. The MATLAB program developed in this work for the dome initially performs the dome profile generation through the numerical integration of the dome
This document establishes the Rotorcraft Industry Technology Association (RITA) Health and Usage Monitoring System Data Interchange Specification. The RITA HUMS Data Interchange Specification will provide information exchange within a rotorcraft HUMS and between a rotorcraft HUMS and external entities.
Modern military aircraft represent some of the most complex electronic environments ever engineered. These platforms integrate advanced avionics, radar systems, data links, and communication networks that must function seamlessly in hostile, high-frequency environments. In these mission-critical contexts, electromagnetic interference (EMI) poses a silent but serious threat that can degrade signal integrity, cause crosstalk between systems, or even lead to mission failure. The combination of increasing data rates, higher frequencies, and more complex electromagnetic environments demands shielding solutions that can deliver superior performance while contributing to overall system weight reduction. This challenge has driven innovation toward advanced materials that maintain electrical effectiveness while dramatically reducing mass.
Researchers developed wearable skin sensors that can detect what’s in a person’s sweat. Using the sensors, monitoring perspiration could bypass the need for more invasive procedures like blood draws and provide real-time updates on health problems such as dehydration or fatigue. The sensor design can be rapidly manufactured using a roll-to-roll processing technique that essentially prints the sensors onto a sheet of plastic.
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a
Items per page:
50
1 – 50 of 1342