Ship feature extraction from maritime radar data

This paper describes a prototype tracker that incorporates artificial intelligence (AI) procedures to improve tracking performance. The specific application area is the vessel traffic services (VTS) systems. VTS systems are operated by experienced marine traffic regulators (MTRs) who must use a variety of information to resolve automatic tracker errors. It is shown that proper cueing by an expert system enables the tracker to manage situations that can lead to problems for trackers without AI. The detail of the architecture of the AI tracker is presented. The paper concludes by presenting some results on real-life data from an operational VTS radar.

Background/Objectives: Ship trajectories in Vessel Traffic Service (VTS) system are generated by integrating the Automatic Identification System (AIS) or Radar system. However, the AIS system has missing data section caused by AIS device problems, radio jamming, and so on. These data have been confusing ship navigators and VTS operators.Methods/Statistical analysis: In order to extract missing AIS data, time intervals of sequent points from each ship trajectory are calculated. The section with missing AIS data is above a threshold time limit defined by characteristics. Using k-means algorithm, missing AIS data were clustered into several clusters stored by ship’s ID and sailing direction. Using association rule mining analysis, meaningful association pattern were calculated by missing AIS dataset.Findings: As a result of the association rule mining, we found several missing AIS situation patterns. In case of the west route, the probability of missing AIS situation is high when they enter the east and passenger routes. Also, the probability of missing AIS situation of passing the passenger route is high when that ship enter the LNG, east and west routes.Improvements/Applications: These results can be used to predict the probability of missing AIS data in VTS system.

The Taiwan Strait, to the west of Taiwan, is rich in wind energy resources and has the greatest offshore wind power potential in the world. Therefore, Taiwan has been actively expanding its offshore wind power industry in this area in recent years and expects to achieve the total installed capacity to 15.6 GW by 2035. Due to the large vessel traffic flow in Western Taiwan’s sea area, wind farms will inevitably reduce the navigable space and shadow some existing marine aids to navigation, thus worsening navigation safety. An approach using a fault tree analysis was used to carry out analysis of collision risk between ship-to-ship and ship-to-turbine. The vessel density distribution and traffic flow within the open sea of offshore wind farms would further increase to curtail the available navigable space. The shadowing effects along navigation channels would thereafter be worsened to raise the probability of collision risks in the sea. The results of the fault tree analysis revealed that if the ship is out of control, the time allowed to provide assistance is rather short, leading to the increase of collision risk extent between ships and wind turbines. Moreover, the study also found that unfit functions of the Vessel Traffic Service System and navigation aids and frequently and arbitrarily crossing the navigation channel of fishery vessels are the main causes of ship collisions. In order to effectively improve the navigation safety, competitive strategies for navigation safety are investigated and evaluated in this study. These strategies include making a complete plan for utilizing the whole sea, integrating the offshore vessel traffic service and management system, providing remote pilotage services, and building salvage vessels. The above promising strategies would enhance the navigation safety within the open sea. Collision risk might occur once marine accident occurs and no salvage vessel is available.

Oil tanker traffic constitutes a vital part of the maritime operations in the High North and is associated with considerable risk to the environment. As a consequence, the Norwegian Coastal Administration (NCA) administers a number of vessel traffic services (VTS) centers along the Norwegian coast, one of which is located in the town of Vardo, in the extreme northeast part of Norway. The task of the operators at the VTS center in Vardo is to command a fleet of tug vessels patrolling the northern Norwegian coastline such that the risk of oil tanker drifting accidents is reduced. Currently, these operators do not use computer algorithms or mathematical models to solve this dynamic resource allocation problem but rely on their own knowledge and experience when faced with constantly changing weather and traffic conditions. We therefore propose a novel sustainable model called the receding horizon mixed integer programming (RHMIP) model for optimal dynamic allocation of patrol vessels to oil tankers. The model combines features from model predictive control and linear programming. Simulations run with real-world parameters highlight the performance and quality of our method. The developed RHMIP model can be implemented as an operational decision support tool to the NCA.

Primary obstacle in using unmanned aerial vehicles are adverse meteorological conditions. To allow more usage options in day-to-day life, it is neccesary to increase safety in such conditions. The topic of this paper is analysing possible solutions. First part discusses the current state of art and describes systems currently used. It defined meteorological conditions such as low temperature, wind, humidity and low visibility. It describes their influence on usage of remote controlled aircraft and change in their flight characteristics. Next part is devoted to analysing possible solutions and their applicability. The objective was to find out how to increase safety during adverse meteorological conditions and which technologies are best for achieving this result. Selected technologies have shown great potential and options for increasing safety are plentyfull.

This paper presents an intellectual decision making system for the vessel traffic service (VTS). Its aim is to increase the safety and efficiency of the traffic and the effectiveness of VTS. In general, the navigational decision making process in a traffic control system is based on radar and navigation information although other systems, such as automatic identification systems, GPS can be used with good effect. The application of an intellectual component during a decision making process increases safety and economic effects of the shipping as well as reduces the operator's workload.

The consequences of climate change on agriculture have generated concern among researchers and decision-makers, especially regarding the effects it will have on this sector. The phenomenon is expected to affect the productivity of livestock systems, even more so in grazing livestock, where cattle and pastures are directly impacted by climatic variables. This study evaluated the climatic influence on bulk tank milk production and quality in 38 tropical dairy farms, using Pearson’s partial correlation and canonical correlation analyses. Farm production level and milk quality traits were merged with meteorological information obtained from climatological stations distributed around the Valle del Cauca department, Colombia. According to farms’ milk production levels, four evaluation groups (EG) were established for the analysis, and within each group, the available information between 2.0 and 8.5 years was included. Pearson’s partial correlations among productive and climatic variables were scarce and low (r <± 0.10) within each EG. On the other hand, canonical correlation analyses between productive and climatic variable sets presented a linear increase since the establishment of EGs from 1 to 4 (r ranging from 0.39 to 0.59). The magnitude of canonical correlation coefficients depended on bulk tank dairy farm production levels, being the most productive systems the most susceptible, especially for meteorological variables related to temperature and relative humidity. As a consequence, dairy farms with higher performances must define mitigation strategies to reduce the weather effects. Multivariate correlations are recommended to evaluate the relationship between milk production, composition, and environmental variables in grazing dairy systems of tropical areas. Highlights Livestock farming in the tropics are more vulnerable to climate change. Heat stress has an impact on the productivity of dairy cows. Dairy production systems not intensive under grazing conditions and adapted breeds of ecology zones are not affected by adverse meteorological conditions.

Concise Encyclopedia of Traffic &amp; Transportation Systems

The paper proves the need for continuous dynamic measurements of increasing of fine dispersion dust concentration in the air under adverse weather conditions. It analyzes stages of process reduction of harmful fine-dispersion dust under adverse meteorological conditions. Special attention is paid to operation of industrial automated process control systems. Special software of these systems supports dynamic measurements and controls procedures decreasing harmful fine-dispersion dust emission. The paper proposes two methods for this emissions decrease. Conducted research enables estimate of impact of combined adverse meteorological conditions. Behavior options of an automated system controlling fine-dispersion dust emissions in atmospheric air are offered.

The United States Coast Guard is responsible for the design, procurement, installation, and operation of reference stations, broadcast facilities, monitor sites, and control stations to provide a Differential GPS (DGPS) service for coastal and other critical waterways of the United States. The DGPS service is intended to support a number of applications where system accuracy, reliability, and integrity are all critical to safe and efficient navigation. As part of the design effort, the Coast Guard has analyzed the accuracy, reliability, and integrity requirements of four DGPS applications: harbor and harbor approach navigation, vessel traffic service (VTS) surveillance, aids to navigation (ATON) positioning, and exclusive economic zone (EEZ) surveying. This paper provides an overview of the DGPS design efforts, and describes the analysis methodologies used to derive the DGPS reliability and integrity requirements.

Low fruit set in fruit trees has been traditionally related to weather conditions. In apricot, flowering takes place in winter or early spring, when adverse meteorological conditions can influence the pollination process and subsequent fruit set. The blooming period usually lasts one to two weeks in most cultivars and locations. Thus, external conditions can strongly vary over this period, and flowers in the same tree could have different conditions during their development depending on the time of anthesis. In order to evaluate whether the capacity of flowers to set fruits is affected by the time of anthesis, in this work flower development and fruit drop have been characterised in early and late flowers of the cv. Moniqui. Flowers from both populations were hand-pollinated with compatible pollen. A set of flowers were weekly counted in marked branches from anthesis to harvest. Another set of flowers were collected at anthesis, weighed and anatomically characterized. Fruit set in early flowers was considerably higher than in late flowers. These differences in the capacity of flowers to set fruits between early and late flowers were associated with differences in the size of different floral structures. Results are discussed in terms of the implications of floral differences at anthesis in the subsequent fruit set.

Unbalanced emission reductions and adverse meteorological conditions facilitate the formation of secondary pollutants during the COVID-19 lockdown in Beijing.

Adverse weather conditions pose a significant challenge to the widespread adoption of Autonomous Vehicles (AVs) by impacting sensors like LiDARs and cameras. Even though Collaborative Perception (CP) improves AV perception in difficult conditions, existing CP datasets lack adverse weather conditions. To address this, we introduce Adver-City, the first open-source synthetic CP dataset focused on adverse weather conditions. Simulated in CARLA with OpenCDA, it contains over 24 thousand frames, over 890 thousand annotations, and 110 unique scenarios across six different weather conditions: clear weather, soft rain, heavy rain, fog, foggy heavy rain and, for the first time in a synthetic CP dataset, glare. It has six object categories including pedestrians and cyclists, and uses data from vehicles and roadside units featuring LiDARs, RGB and semantic segmentation cameras, GNSS, and IMUs. Its scenarios, based on real crash reports, depict the most relevant road configurations for adverse weather and poor visibility conditions, varying in object density, with both dense and sparse scenes, allowing for novel testing conditions of CP models. Benchmarks run on the dataset show that weather conditions created challenging conditions for perception models, with CoBEVT scoring 58.30/52.44/38.90 (AP@30/50/70). The dataset, code and documentation are available at https://labs.cs.queensu.ca/quarrg/datasets/adver-city/.

Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions

Testing of advanced driver-assistance systems (ADAS) is complex, time-consuming and expensive. Therefore, new methods for the validation of such applications are required. A common solution is the use of virtual validation via environment simulation tools, but their reliability must first be confirmed. For this purpose, a comparison of a real and a simulated radar sensor under adverse weather conditions is performed in this work.To quantify the deviation between reality and the virtual test environment, a complex scenario with multiple traffic objects is set up on the proving ground and in the simulation. The data is measured for clear weather, rain and fog and afterwards compared to validate the performance of the sensor model under those non-ideal conditions.The implemented method shows that both sensors neglect the same traffic objects. Compared to the real radar, the variation of the measured parameters according to changing weather conditions in the simulation tends to be correct, but the values are not completely realistic.Though, the validation method is implemented successfully, in further work the comparison of different sensors is recommended. Furthermore, an in-depth examination of the impact due to varying intensity of rain and fog has to be undertaken.