Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches

The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations.

Potential uses of AI for perioperative nursing handoffs: a qualitative study.

Objective: Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are key to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews and direct observations to better understand how AI could work in this context. Materials and Methods: 58 handoffs were observed of patients entering and leaving the post-anesthesia care unit at a single center. 11 nurses participated in semi-structured interviews. Mixed inductive-deductive thematic analysis extracted major themes and subthemes around roles for AI supporting postoperative nursing. Results: Four themes emerged from the interviews: (1) Nurse understanding of patient condition guides care decisions, (2) Handoffs are important to nurse situational awareness; problem focus and information transfer may be improved by AI, (3) AI may augment nurse care decision making and team communication, (4) User experience and information overload are likely barriers to using AI. Key subthemes included that AI-identified problems would be discussed at handoff and team communications, that AI-estimated elevated risks would trigger patient re-evaluation, and that AI-identified important data may be a valuable addition to nursing assessment. Discussion and Conclusion: Most research on postoperative handoff communication relies on structured checklists. Our results suggest that properly designed AI tools might facilitate postoperative handoff communication for nurses by identifying elevated risks faced by a specific patient, triggering discussion on those topics.

User-Requirements Analysis on Augmented Reality-based Maintenance in Manufacturing

Maintenance of technical equipment in manufacturing is inevitable for sustained productivity with minimal downtimes. Elimination of unscheduled interruptions as well as real-time monitoring of equipment health can potentially benefit from adopting augmented reality (AR) technology. How best to employ this technology in maintenance demands a fundamental comprehension of user requirements for production planners. Despite augmented reality applications being developed to assist various manufacturing operations, no previous study has examined how these user requirements in maintenance have been fulfilled and the potential opportunities that exist for further development. Reviews on maintenance have been general on all industrial fields rather than focusing on a specific industry. In this regard, a systematic literature review was performed on previous studies on augmented reality applications in the maintenance of manufacturing entities from 2017 to 2021. Specifically, the review examines how user requirements have been addressed by these studies and identifies gaps for future research. The user requirements are drawn from the challenges encountered during AR-based maintenance in manufacturing following a similar approach to usability engineering methodologies. The needs are identified as ergonomics, communication, situational awareness, intelligence sources, feedback, safety, motivation, and performance assessment. Contributing factors to those needs are cross-tabulated with the requirements and their results presented as trends, prior to drawing insights and providing possible future suggestions for the made observations.

Quantification and Forecasting of Cumulonimbus (Cb) Clouds Direction, Nebulosity and Occurrence With Autoregression Using 2018-2020 Dataset From Yaounde-nsimalen

This works reports the quantification and forecasting of Cumulonimbus (Cb) clouds direction, nebulosity and occurrence with auto regression using 2018-2020 dataset from Yaoundé –Nsimalen of Cameroon. Data collected for October 2018-2020 consisted of 2232 hourly observations. Codes were written to automatically align, multi-find and replace data points in excel to facilitate treating big datasets. The approach included quantification of direction generating time series from data and determination of model orders using the correlogram. The coefficients of the SARIMA model were determined using Yule-Walker equations in matrix form, the Augmented Dickey Fuller test (ADF) was used to check for stationarity assumption, Portmanteau test to check for white noise in residuals and Shapiro-Wilk test to check normality assumptions. After writing several algorithms to test different models, an Autoregressive Neural Network (ANN) was fitted and used to predict the parameters over window of 2 weeks. Autocorrelation Function (ACF) shows no correlation between residuals, with p ≤ 0.05, fitting the stationarity assumption. Average performance is 80%. A stationary wavelike occurrence of the direction has been observed, with East as the most frequented sector. Forecast of Cb parameters is important in effective air traffic management, creating situational awareness and could serve as reference for future research. The method of decomposition could be made applicable in future research to quantify/forecast cloud directions.

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

This brief highlights research advances on cooperative techniques for localization and communication. These two macro trends are investigated in the general context of mobile multi-agent networks for situational awareness applications, where time-varying agents of unknown locations are asked to fulfill positioning and information sharing tasks. Cooperative localization is conceived for both active and passive agents, i.e., targets to be detected and localized, and it is analyzed in vehicular and maritime environments. Communication is investigated for vehicular scenarios, where vehicles are requested to share massive data in the perspective development of connected and automated mobility systems. Both research areas rely on the integration of heterogeneous sensors and communication. Specifically, it is studied how to improve localization by exploring communication techniques as well as how to enhance communication performances by extracting information from perception sensors. The dynamic environment of multi-agent systems calls for robust, flexible and adaptive techniques, capable of profitably fuse different types of information, and the outcomes of these researches show how a statistical approach based on cooperation guarantees higher resilience, reliability and confidence.