Journal articles: 'Maritime anomaly detection'

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Iphar, Clément, Cyril Ray, and Aldo Napoli. "Data integrity assessment for maritime anomaly detection." Expert Systems with Applications 147 (June 2020): 113219. http://dx.doi.org/10.1016/j.eswa.2020.113219.

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Kim, Donghyun, Gian Antariksa, Melia Putri Handayani, Sangbong Lee, and Jihwan Lee. "Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data." Sensors 21, no. 15 (July 31, 2021): 5200. http://dx.doi.org/10.3390/s21155200.

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In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning algorithm. SHAP enables us to measure the marginal contribution of each sensor variable to an anomaly. Thus, one can easily specify which sensor is responsible for the specific anomaly. To illustrate our framework, the actual sensor stream obtained from the cargo vessel collected over 10 months was analyzed. In this analysis, we performed hierarchical clustering analysis with transformed SHAP values to interpret and group common anomaly patterns. We showed that anomaly interpretation and segmentation using SHAP value provides more useful interpretation compared to the case without using SHAP value.

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Tserpes, Konstantinos, Konstantinos Chatzikokolakis, Dimitris Zissis, Giannis Spiliopoulos, and Ioannis Kontopoulos. "Real-time maritime anomaly detection: detecting intentional AIS switch-off." International Journal of Big Data Intelligence 7, no. 2 (2020): 85. http://dx.doi.org/10.1504/ijbdi.2020.10029526.

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Kontopoulos, Ioannis, Konstantinos Chatzikokolakis, Dimitris Zissis, Konstantinos Tserpes, and Giannis Spiliopoulos. "Real-time maritime anomaly detection: detecting intentional AIS switch-off." International Journal of Big Data Intelligence 7, no. 2 (2020): 85. http://dx.doi.org/10.1504/ijbdi.2020.107375.

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Sithiravel, Rajiv, Bhashyam Balaji, Bradley Nelson, Michael Kenneth McDonald, Ratnasingham Tharmarasa, and Thiagalingam Kirubarajan. "Airborne Maritime Surveillance Using Magnetic Anomaly Detection Signature." IEEE Transactions on Aerospace and Electronic Systems 56, no. 5 (October 2020): 3476–90. http://dx.doi.org/10.1109/taes.2020.2973866.

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Kazemi, Samira, Shahrooz Abghari, Niklas Lavesson, Henric Johnson, and Peter Ryman. "Open data for anomaly detection in maritime surveillance." Expert Systems with Applications 40, no. 14 (October 2013): 5719–29. http://dx.doi.org/10.1016/j.eswa.2013.04.029.

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Han, X., C. Armenakis, and M. Jadidi. "DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 455–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-455-2020.

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Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering method for route planning and trajectory anomalies detection, which are the essential part of auto-vessel system design and development. In this paper, we present the development of an enhanced density-based spatial clustering (DBSCAN) method that can be applied on historical or real-time Automatic Identification System (AIS) data, so that vessel routes can be modelled, and the trajectories’ anomalies can be detected. The proposed methodology is based on developing an optimized trajectory clustering approach in two stages. Firstly, to increase the attribute dimension of the vessel’s positioning data, therefore other characteristics such as velocity and direction are considered in the clustering process along with geospatial information. Secondly, the DBSCAN clustering model has been enhanced by introducing the Mahalanobis Distance metric considering the correlations of the position cluster points aiming to make the identification process more accurate as well as reducing the computational cost.

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Michałowska, Katarzyna, Signe Riemer-Sørensen, Camilla Sterud, and Ole Magnus Hjellset. "Anomaly Detection with Unknown Anomalies: Application to Maritime Machinery." IFAC-PapersOnLine 54, no. 16 (2021): 105–11. http://dx.doi.org/10.1016/j.ifacol.2021.10.080.

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Park, Jaemin, and Sungil Kim. "Maritime Anomaly Detection Based on VAE-CUSUM Monitoring System." Journal of the Korean Institute of Industrial Engineers 46, no. 4 (August 31, 2020): 432–42. http://dx.doi.org/10.7232/jkiie.2020.46.4.432.

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Lei, Po-Ruey. "A framework for anomaly detection in maritime trajectory behavior." Knowledge and Information Systems 47, no. 1 (May 19, 2015): 189–214. http://dx.doi.org/10.1007/s10115-015-0845-4.

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Zhao, Liangbin, and Guoyou Shi. "Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network." Journal of Navigation 72, no. 04 (February 8, 2019): 894–916. http://dx.doi.org/10.1017/s0373463319000031.

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Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. The results show that the proposed method can detect anomalous behaviours of a vessel regarding speed, course and route quickly.

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Osekowska, Ewa, Henric Johnson, and Bengt Carlsson. "Grid Size Optimization for Potential Field based Maritime Anomaly Detection." Transportation Research Procedia 3 (2014): 720–29. http://dx.doi.org/10.1016/j.trpro.2014.10.051.

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Wolsing, Konrad, Linus Roepert, Jan Bauer, and Klaus Wehrle. "Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches." Journal of Marine Science and Engineering 10, no. 1 (January 14, 2022): 112. http://dx.doi.org/10.3390/jmse10010112.

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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.

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Filipiak, Dominik, Milena Stróżyna, Krzysztof Węcel, and Witold Abramowicz. "Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers." Zeszyty Naukowe Akademii Marynarki Wojennej 215, no. 4 (December 1, 2018): 5–28. http://dx.doi.org/10.2478/sjpna-2018-0024.

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Abstract The paper presents results of spatial analysis of huge volume of AIS data with the goal to detect predefined maritime anomalies. The maritime anomalies analysed have been grouped into: traffic analysis, static anomalies, and loitering detection. The analysis was carried out on data describing movement of tankers worldwide in 2015, using sophisticated algorithms and technology capable of handling big data in a fast and efficient manner. The research was conducted as a follow-up of the EDA-funded SIMMO project, which resulted in a maritime surveillance system based on AIS messages enriched with data acquired from open Internet sources.

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Amro, Ahmed, Aybars Oruc, Vasileios Gkioulos, and Sokratis Katsikas. "Navigation Data Anomaly Analysis and Detection." Information 13, no. 3 (February 23, 2022): 104. http://dx.doi.org/10.3390/info13030104.

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Several disruptive attacks against companies in the maritime industry have led experts to consider the increased risk imposed by cyber threats as a major obstacle to undergoing digitization. The industry is heading toward increased automation and connectivity, leading to reduced human involvement in the different navigational functions and increased reliance on sensor data and software for more autonomous modes of operations. To meet the objectives of increased automation under the threat of cyber attacks, the different software modules that are expected to be involved in different navigational functions need to be prepared to detect such attacks utilizing suitable detection techniques. Therefore, we propose a systematic approach for analyzing the navigational NMEA messages carrying the data of the different sensors, their possible anomalies, malicious causes of such anomalies as well as the appropriate detection algorithms. The proposed approach is evaluated through two use cases, traditional Integrated Navigation System (INS) and Autonomous Passenger Ship (APS). The results reflect the utility of specification and frequency-based detection in detecting the identified anomalies with high confidence. Furthermore, the analysis is found to facilitate the communication of threats through indicating the possible impact of the identified anomalies against the navigational operations. Moreover, we have developed a testing environment that facilitates conducting the analysis. The environment includes a developed tool, NMEA-Manipulator that enables the invocation of the identified anomalies through a group of cyber attacks on sensor data. Our work paves the way for future work in the analysis of NMEA anomalies toward the development of an NMEA intrusion detection system.

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Zhen, Rong, Yongxing Jin, Qinyou Hu, Zheping Shao, and Nikitas Nikitakos. "Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier." Journal of Navigation 70, no. 3 (January 16, 2017): 648–70. http://dx.doi.org/10.1017/s0373463316000850.

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Maritime anomaly detection is a key technique in intelligent vessel traffic surveillance systems and implementation of maritime situational awareness. In this paper, we propose a method which combines vessel trajectory clustering and Naïve Bayes classifier to detect anomalous vessel behaviour in the maritime surveillance system. A similarity measurement between vessel trajectories is designed based on the spatial and directional characteristics of Automatic Identification System (AIS) data, then the method of hierarchical and k-medoids clustering are applied to model and learn the typical vessel sailing pattern within harbour waters. The Naïve Bayes classifier of vessel behaviour is built to classify and detect anomalous vessel behaviour. The proposed method has been tested and validated on the vessel trajectories from AIS data within the waters of Xiamen Bay and Chengsanjiao, China. The results indicate that the proposed method is effective and helpful, thus enhancing maritime situational awareness in coastal waters.

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Riveiro, Maria. "Evaluation of Normal Model Visualization for Anomaly Detection in Maritime Traffic." ACM Transactions on Interactive Intelligent Systems 4, no. 1 (April 2014): 1–24. http://dx.doi.org/10.1145/2591511.

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Freitas, Sara, Hugo Silva, José Miguel Almeida, and Eduardo Silva. "Convolutional neural network target detection in hyperspectral imaging for maritime surveillance." International Journal of Advanced Robotic Systems 16, no. 3 (May 1, 2019): 172988141984299. http://dx.doi.org/10.1177/1729881419842991.

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This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications.

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Yan, Ran, and Shuaian Wang. "Ship detention prediction using anomaly detection in port state control: model and explanation." Electronic Research Archive 30, no. 10 (2022): 3679–91. http://dx.doi.org/10.3934/era.2022188.

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<abstract><p>Maritime transport plays an important role in global supply chain. To guarantee maritime safety, protect the marine environment, and enhance the living and working conditions of the seafarers, international codes and conventions are developed and implemented. Port state control (PSC) is a critical maritime policy to ensure that ships comply with the related regulations by selecting and inspecting foreign visiting ships visiting a national port. As the major inspection result, ship detention, which is an intervention action taken by the port state, is dependent on both deficiency/deficiencies (i.e., noncompliance) detected and the judgement of the inspector. This study aims to predict ship detention based on the number of deficiencies identified under each deficiency code and explore how each of them influences the detention decision. We innovatively view ship detention as a type of anomaly, which refers to data points that are few and different from the majority, and develop an isolation forest (iForest) model, which is an unsupervised anomaly detection model, for detention prediction. Then, techniques in explainable artificial intelligence are used to present the contribution of each deficiency code on detention. Numerical experiments using inspection records at the Hong Kong port are conducted to validate model performance and generate policy insights.</p></abstract>

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Chen, Shuguang, Yikun Huang, and Wei Lu. "Anomaly Detection and Restoration for AIS Raw Data." Wireless Communications and Mobile Computing 2022 (March 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/5954483.

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With the wide application of location detection sensors in maritime surveillance, a large amount of raw automatic identification system (AIS) data is produced by many moving ships. Anomaly detection and restoration of the big AIS data are important issues in marine data mining, because they offer a reliable support to users to mining the behaviors of ships. This paper develops a novel approach to detect anomaly AIS data based on the ships’ maneuverability, such as the maximum acceleration, the minimum acceleration, the maximum distance, and the maximum angular displacement, which were designed to detect the anomaly AIS data. Furthermore, the performance of the developed approach is compared with that of Daiyong-Zhang’s method and Behrouz-Haji-Soleimani’s method to assess its detection efficiency. The results show that the proposed approach can be applied to easily extract the abnormal data. Finally, based on the developed approach to detect the anomaly data and cubic spline interpolation method to restore the AIS data, experiments are conducted on the AIS data of Xiamen Port of Fujian Province, China, that prove to be effective for marine intelligence research.

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Huang, Jie, Fengwei Zhu, Zejun Huang, Jian Wan, and Yongjian Ren. "Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment." Mobile Information Systems 2021 (May 4, 2021): 1–15. http://dx.doi.org/10.1155/2021/5598988.

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Fishing vessel monitoring systems (VMSs) play an important role in ensuring the safety of fishing vessel operations. Traditional VMSs use a cloud centralized computing model, and the storage, processing, and visualization of all fishing vessel data are completed in the monitoring center. Due to the limitation of maritime communications, the data generated by fishing vessels cannot be fully utilized, and communication delays lead to inadequate warnings in cases of fishing vessel abnormalities. In this paper, we present a real-time anomaly detection model (RADM) for fishing vessels based on edge computing. The model runs in the edge layer, making full use of the information of moving edge nodes and nearby nodes, and combines a historical trajectory extraction detection model with an online anomaly detection model to detect anomalies. The detection model of historical trajectory extraction mines frequent patterns in historical trajectories through multifeature clustering and identifies trajectories that are different from the frequent patterns as anomalies. Online anomaly detection algorithms detect anomalous behavior in specific scenarios based on the spatiotemporal neighborhood similarity and reduce the impact of anomaly evolution. Experiments show that RADM was more effective than traditional methods in real-time anomaly detection of fishing vessels, which provides a new method for upgrading the technology of traditional VMS.

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Xu, Gangyan, Chun-Hsien Chen, Fan Li, and Xuan Qiu. "AIS data analytics for adaptive rotating shift in vessel traffic service." Industrial Management & Data Systems 120, no. 4 (March 8, 2020): 749–67. http://dx.doi.org/10.1108/imds-01-2019-0056.

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PurposeConsidering the varied and dynamic workload of vessel traffic service (VTS) operators, design an adaptive rotating shift solution to prevent them from getting tired while ensuring continuous high-quality services and finally guarantee a benign maritime traffic environment.Design/methodology/approachThe problem of rotating shift in VTS and its influencing factors are analyzed first, then the framework of automatic identification system (AIS) data analytics is proposed, as well as the data model to extract spatial–temporal information. Besides, K-means-based anomaly detection method is adjusted to generate anomaly-free data, with which the traffic trend analysis and prediction are made. Based on this knowledge, strategies and methods for adaptive rotating shift design are worked out.FindingsIn VTS, vessel number and speed are identified as two most crucial factors influencing operators' workload. Based on the two factors, the proposed data model is verified to be effective on reducing data size and improving data processing efficiency. Besides, the K-means-based anomaly detection method could provide stable results, and the work shift pattern planning algorithm could efficiently generate acceptable solutions based on maritime traffic information.Originality/valueThis is a pioneer work on utilizing maritime traffic data to facilitate the operation management in VTS, which provides a new direction to improve their daily management. Besides, a systematic data-driven solution for adaptive rotating shift is proposed, including knowledge discovery method and decision-making algorithm for adaptive rotating shift design. The technical framework is flexible and can be extended for managing other activities in VTS or adapted in diverse fields.

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Venskus, Julius, Povilas Treigys, Jolita Bernatavičienė, Gintautas Tamulevičius, and Viktor Medvedev. "Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding." Sensors 19, no. 17 (August 31, 2019): 3782. http://dx.doi.org/10.3390/s19173782.

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The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.

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Tyasayumranani, Widiastuti, Taewoong Hwang, Taemin Hwang, and Ik-Hyun Youn. "Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation." Journal of International Maritime Safety, Environmental Affairs, and Shipping 6, no. 4 (October 2, 2022): 224–35. http://dx.doi.org/10.1080/25725084.2022.2154116.

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Abreu, Fernando H. O., Amilcar Soares, Fernando V. Paulovich, and Stan Matwin. "A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics." ISPRS International Journal of Geo-Information 10, no. 6 (June 15, 2021): 412. http://dx.doi.org/10.3390/ijgi10060412.

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With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable.

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Yan, Zhenguo, Xin Song, Hanyang Zhong, Lei Yang, and Yitao Wang. "Ship Classification and Anomaly Detection Based on Spaceborne AIS Data Considering Behavior Characteristics." Sensors 22, no. 20 (October 11, 2022): 7713. http://dx.doi.org/10.3390/s22207713.

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With the establishment of satellite constellations equipped with ship automatic identification system (AIS) receivers, the amount of AIS data is continuously increasing, and AIS data have become an important part of ocean big data. To further improve the ability to use AIS data for maritime surveillance, it is necessary to explore a ship classification and anomaly detection method suitable for spaceborne AIS data. Therefore, this paper proposes a ship classification and anomaly detection method based on machine learning that considers ship behavior characteristics for spaceborne AIS data. In view of the characteristics of different types of ships, this paper introduces the extraction and analysis of ship behavior characteristics in addition to traditional geometric features and discusses the ability of the proposed method for ship classification and anomaly detection. The experimental results show that the classification accuracy of the five types of ships can reach 92.70%, and the system can achieve better results in the other classification evaluation metrics by considering the ship behavior characteristics. In addition, this method can accurately detect anomalous ships, which further proves the effectiveness and feasibility of the proposed method.

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Stróżyna, Milena, Jacek Małyszko, Krzysztof Węcel, Dominik Filipiak, and Witold Abramowicz. "Architecture of Maritime Awareness System Supplied with External Information." Annual of Navigation 23, no. 1 (December 1, 2016): 135–49. http://dx.doi.org/10.1515/aon-2016-0009.

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Abstract In this paper, we discuss a software architecture, which has been developed for the needs of the System for Intelligent Maritime Monitoring (SIMMO). The system bases on the state-of-the-art information fusion and intelligence analysis techniques, which generates an enhanced Recognized Maritime Picture and thus supports situation analysis and decision- making. The SIMMO system aims to automatically fuse an up-to-date maritime data from Automatic Identification System (AIS) and open Internet sources. Based on collected data, data analysis is performed to detect suspicious vessels. Functionality of the system is realized in a number of different modules (web crawlers, data fusion, anomaly detection, visualization modules) that share the AIS and external data stored in the system’s database. The aim of this article is to demonstrate how external information can be leveraged in maritime awareness system and what software solutions are necessary. A working system is presented as a proof of concept.

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Katsamenis, Iason, Nikolaos Bakalos, Eleni Eirini Karolou, Anastasios Doulamis, and Nikolaos Doulamis. "Fall Detection Using Multi-Property Spatiotemporal Autoencoders in Maritime Environments." Technologies 10, no. 2 (March 29, 2022): 47. http://dx.doi.org/10.3390/technologies10020047.

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Man overboard is an emergency in which fast and efficient detection of the critical event is the key factor for the recovery of the victim. Its severity urges the utilization of intelligent video surveillance systems that monitor the ship’s perimeter in real time and trigger the relative alarms that initiate the rescue mission. In terms of deep learning analysis, since man overboard incidents occur rarely, they present a severe class imbalance problem, and thus, supervised classification methods are not suitable. To tackle this obstacle, we follow an alternative philosophy and present a novel deep learning framework that formulates man overboard identification as an anomaly detection task. The proposed system, in the absence of training data, utilizes a multi-property spatiotemporal convolutional autoencoder that is trained only on the normal situation. We explore the use of RGB video sequences to extract specific properties of the scene, such as gradient and saliency, and utilize the autoencoders to detect anomalies. To the best of our knowledge, this is the first time that man overboard detection is made in a fully unsupervised manner while jointly learning the spatiotemporal features from RGB video streams. The algorithm achieved 97.30% accuracy and a 96.01% F1-score, surpassing the other state-of-the-art approaches significantly.

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Sun, Jiaqi, Jiarong Wang, Zhicheng Hao, Ming Zhu, Haijiang Sun, Ming Wei, and Kun Dong. "AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM." Remote Sensing 14, no. 13 (July 4, 2022): 3221. http://dx.doi.org/10.3390/rs14133221.

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Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.

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Liu, Lei, Yong Zhang, Yue Hu, Yongming Wang, Jingyi Sun, and Xiaoxiao Dong. "A Hybrid-Clustering Model of Ship Trajectories for Maritime Traffic Patterns Analysis in Port Area." Journal of Marine Science and Engineering 10, no. 3 (March 1, 2022): 342. http://dx.doi.org/10.3390/jmse10030342.

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A hybrid-clustering model is presented for the probabilistic characterization of ship traffic and anomaly detection. A hybrid clustering model was proposed to increase the efficiency of trajectory clustering in the port area and analyze the maritime traffic patterns in port. The model identified dissimilarities between trajectories based on characteristics, using K-Means and the density-based spatial clustering of applications with noise algorithm (DBSCAN). Firstly, the ship’s trajectory characteristics are constructed based on real ship trajectories considering static characteristics and dynamic characteristics of ship trajectories to calculate the characteristic dissimilarity between trajectories. Simultaneously, the spatial dissimilarity could be quantified using the Hausdorff algorithm. Then, the ship trajectory is clustered initially based on the departure and destination characteristics using K-Means algorithms to obtain various sub-trajectories. However, there are still different types of trajectories in each sub-trajectory. Thus, the DBSCAN algorithm is adopted to cluster the sub-trajectory based on the analysis of the different trajectory characteristics. Finally, the proposed model is applied to the characterization of the Zhanjiang Port, and the results show that the hybrid-clustering method can effectively cluster ship trajectory and present probabilistic characterization of ship traffic and anomaly detection. This lays a solid theoretical foundation for the supervision and risk control of intelligent ships.

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d'Afflisio, Enrica, Paolo Braca, and Peter Willett. "Malicious AIS Spoofing and Abnormal Stealth Deviations: A Comprehensive Statistical Framework for Maritime Anomaly Detection." IEEE Transactions on Aerospace and Electronic Systems 57, no. 4 (August 2021): 2093–108. http://dx.doi.org/10.1109/taes.2021.3083466.

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Roberts, Steven Andrew. "A Shape‐Based Local Spatial Association Measure (LISShA): A Case Study in Maritime Anomaly Detection." Geographical Analysis 51, no. 4 (November 19, 2018): 403–25. http://dx.doi.org/10.1111/gean.12178.

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Guo, Shaoqing, Junmin Mou, Linying Chen, and Pengfei Chen. "An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation." Journal of Marine Science and Engineering 9, no. 6 (June 1, 2021): 609. http://dx.doi.org/10.3390/jmse9060609.

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With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

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Bombara, Giuseppe, and Calin Belta. "Offline and Online Learning of Signal Temporal Logic Formulae Using Decision Trees." ACM Transactions on Cyber-Physical Systems 5, no. 3 (July 2021): 1–23. http://dx.doi.org/10.1145/3433994.

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In this article, we focus on inferring high-level descriptions of a system from its execution traces. Specifically, we consider a classification problem where system behaviors are described using formulae of Signal Temporal Logic (STL). Given a finite set of pairs of system traces and labels, where each label indicates whether the corresponding trace exhibits some system property, we devised a decision-tree-based framework that outputs an STL formula that can distinguish the traces. We also extend this approach to the online learning scenario. In this setting, it is assumed that new signals may arrive over time and the previously inferred formula should be updated to accommodate the new data. The proposed approach presents some advantages over traditional machine learning classifiers. In particular, the produced formulae are interpretable and can be used in other phases of the system’s operation, such as monitoring and control. We present two case studies to illustrate the effectiveness of the proposed algorithms: (1) a fault detection problem in an automotive system and (2) an anomaly detection problem in a maritime environment.

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Sfyridis, A., T. Cheng, and M. Vespe. "DETECTING VESSELS CARRYING MIGRANTS USING MACHINE LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 53–60. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-53-2017.

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Political instability, conflicts and inequalities result into significant flows of people worldwide, moving to different countries in search of a better life, safety or to be reunited with their families. Irregular crossings into Europe via sea routes, despite not being new, have recently increased together with the loss of lives of people in the attempt to reach EU shores. This highlights the need to find ways to improve the understanding of what is happening at sea. This paper, intends to expand the knowledge available on practices among smugglers and contribute to early warning and maritime situational awareness. By identifying smuggling techniques and based on anomaly detection methods, behaviours of interest are modelled and one class support vector machines are used to classify unlabelled data and detect potential smuggling vessels. Nine vessels are identified as potentially carrying irregular migrants and refugees. Though, further inspection of the results highlights possible misclassifications caused by data gaps and limited knowledge on smuggling tactics. Accepted classifications are considered subject to further investigation by the authorities.

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ÇALIŞKAN, Ufuk Yakup, and Burak ZİNCİR. "Tracking Liquefied Natural Gas Fuelled Ship’s Emissions via Formaldehyde Deposition in Marine Boundary Layer." Marine Science and Technology Bulletin 11, Early View (December 31, 2022): 384–96. http://dx.doi.org/10.33714/masteb.1159477.

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One of the reasons that anthropogenic greenhouse gas emissions estimation is imprecise is the uncertainty of aerosol impacts on cloud properties. Maritime transportation is slowly changing fuel preferences. With the policy framework changing regulations, the shipping business is going in a direction that emits less sulfur dioxide and black carbon, which are the compounds that cause linear cloud formations known as ship tracks. Aside from their effects on the total radiative forcing of a transportation mean, this phenomenon enables the detection of ships via satellite imagery sensors. The rapidly increasing trend of shifting propulsion of maritime transportation from conventional heavy fuel oil and distillate marine fuels to liquefied natural gas causes enormous hikes in methane emissions. Therefore, oxidation of the volatile organic compound in the marine boundary layer by the hydroxyl radical in the troposphere makes significant deposition of formaldehyde which causes human effects, ecosystem damage, and climate impact. The primary triggering substance among the compounds in the ship plume is methane. This paper discusses methods to assess near real time tracking of anomalies and the deposition of the short lived substance in different seasons in one of the main occurring areas, shipping corridors. The study also employs anomaly map analysis for June and December 2010 and 2020. Several global tracking methods are available with satellites, monitoring experiments, and other satellite tracking tools. Apart from a few areas the results are not indicative since the formaldehyde formations caused by LNG fueled ships are not widespread enough alongside with overall LNG fueled fleet. On the other hand, the analysis and method are promising for the follow-up of the emissions in the future.

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Steidel, Matthias, Jan Mentjes, and Axel Hahn. "Context-Sensitive Prediction of Vessel Behavior." Journal of Marine Science and Engineering 8, no. 12 (December 4, 2020): 987. http://dx.doi.org/10.3390/jmse8120987.

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Research in the field of maritime anomaly detection and vessel behavior prediction primarily focuses on developing methods for extracting typical vessel movement patterns from historical traffic data. However, contextual information is currently not considered during pattern extraction by existing research. Combining contextual information with historical traffic data has the potential to produce both more accurate traffic patterns and more precise predictions of vessel behavior. This paper investigates the benefit of incorporating contextual information during the extraction of vessel behavior and the prediction of the most probable vessel behavior. A method is presented that combines historical vessel traffic data with information about the course of waterways. Typical behavior patterns are extracted by applying kernel density estimation, which are subsequently used for predicting the most probable vessel behavior. Using this approach, we were able to predict in which area the vessel is most likely to sail, as well as the actual track for a sailing time of 2:35 h. Additional potential applications of our approach can be derived from the results, which, in addition to behavior prediction, can also be used to detect anomalous vessel behavior.

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Oka Widyantara, I. Made, I. Putu Noven Hartawan, Anak Agung Istri Ngurah Eka Karyawati, Ngurah Indra Er, and Ketut Buda Artana. "Automatic identification system-based trajectory clustering framework to identify vessel movement pattern." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (March 1, 2023): 1. http://dx.doi.org/10.11591/ijai.v12.i1.pp1-11.

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<span lang="EN-US">Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.</span>

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Wang, Yitao, Lei Yang, Xin Song, Quan Chen, and Zhenguo Yan. "A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data." Applied Sciences 11, no. 21 (November 3, 2021): 10336. http://dx.doi.org/10.3390/app112110336.

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AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the types of ships from the massive space-based AIS data, we propose a multi-feature ensemble learning classification model (MFELCM). The method consists of three steps. Firstly, the static and dynamic information of the original data is preprocessed and features are then extracted in order to obtain static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. Secondly, four base classifiers, namely Random Forest, 1D-CNN (one-dimensional convolutional neural network), Bi-GRU (bidirectional gated recurrent unit), and XGBoost (extreme gradient boosting), are trained by the above four types of samples, respectively. Finally, the base classifiers are integrated by another Random Forest, and the final ship classification is outputted. In this paper, we use the global space-based AIS data of passenger ships, cargo ships, fishing boats, and tankers. The model gets a total accuracy of 0.9010 and an F1 score of 0.9019. The experiments prove that MFELCM is better than the base classifiers. In addition, MFELCM can achieve near real-time online classification, which has important applications in ship behavior anomaly detection and maritime supervision.

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Lumbangaol, A., I. M. Radjawane, and A. Furqon. "Linkages of Active and Weakening MJO events to Seasonal Variations over the Maritime Continent." IOP Conference Series: Earth and Environmental Science 925, no. 1 (November 1, 2021): 012004. http://dx.doi.org/10.1088/1755-1315/925/1/012004.

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Abstract The Madden-Julian Oscillation (MJO) is a large-scale phenomenon of air-sea intra-seasonal variability in the equatorial area, particularly in the Maritime Continent (MC). This research focused on the analysis of the MJO propagation process in association with rainfall events and sea surface temperature anomaly (SSTA) during seasonal variations, i.e., November, December, January February, and March (NDJFM), and May, June, July, August September (MJJAS). MJO events from 2010 to 2019 were classified as MJO active or MJO weakening according to propagation characteristics and amplitude changes in the RMM index. This research uses a dataset of 10-year series of daily Tropical Rainfall Measuring Mission (TRMM) (3B42 V7 derived) measurements for detecting rain rates. Daily OLR data from the NOAA Physical Sciences Laboratory and SSTA daily data from Physical Oceanography Distributed Active Archive Centre (PODAAC) NOAA are considered for analysing MJO propagation. Composites of outgoing longwave radiation (OLR) were also identified differences between the two events; active MJO events had consistently higher negative OLR anomalies than weakening MJO events. Active MJO events during NDJFM had a higher rain rate and positive SSTA than weakening MJO events. Furthermore, composite rain rates distribution over MC during NDJFM are mainly located in the south of the equator, contrarily when MJJAS are north of the equator.

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Yanti, Tia Novi, and Dahruji. "Window Dressing Detection in the Energy Sector Industry Listed on the Indonesian Sharia Stock Index." Jurnal Ekonomi Syariah Teori dan Terapan 9, no. 6 (November 30, 2022): 800–814. http://dx.doi.org/10.20473/vol9iss20226pp800-814.

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ABSTRAK Penelitian ini mempunyai tujuan yaitu ingin menganalisis apakah terdapat indikasi window dressing dalam laporan keuangan dilihat dari nilai cash holding, financial leverage dan nilai perusahaan pada sektor energi yang terdaftar di Indeks Saham Syariah Indonesia (ISSI) Bursa Efek Indonesia selama periode 2017-2020. Metode penelitian yang diterapkan yakni uji Beda t-test menggunakan uji Mann Whitney. Sampel yang dipakai ada 22 perusahaan sektor energi di ISSI BEI untuk rentang waktu 2017 triwulan pertama sampai 2020 triwulan keempat. Berdasarkan hasil analisa dalam riset ini membuktikan kalau tidak ada perbedaan nilai cash holding, financial leverage serta nilai perusahaan pada triwulan IV terhadap nilai dalam triwulan I, triwuulan II serta triwulan III. Hal ini mengidentifikasi bahwa perusahaan tidak mengindikasikan gejala window dressing. Sehingga dapat ditarik kesimpulan bahwa beberapa perusahaan di sektor energi yang tertera di ISSI BEI pada tahun 2017 hingga 2020 tidak terindikasi melaksanakan praktik window dressing. Hal ini dapat digunakan sebagai dokumen evaluasi bagi perusahaan di sektor energi untuk lebih meningkatkan operasional mereka. Untuk menghindari adanya indikasi window dressing, perusahaan energi yang terdaftar di ISSI BEI harus memprioritaskan upaya strategis dalam meningkatkan kinerja keuangan perusahaan sehingga cash holding, financial leverage, dan nilai perusahaan dapat mencerminkan situasi perusahaan. Kata kunci: window dressing, cash holding, financial leverage, nilai perusahaan. ABSTRACT This study aimed to analyze whether there are indications of window dressing in financial statements seen from the value of cash holding, financial leverage and company value in the energy sector listed on the Indonesian Sharia Stock Index (ISSI) of the Indonesia Stock Exchange (IDX) during the 2017-2020 period. The research method applied was the Different t-test using the Mann Whitney test. The sample used is 22 energy sector companies on the ISSI IDX for the period of 2017 first quarter to 2020 fourth quarter. Based on the results of the analysis, it proves that there is no difference in the value of cash holding, financial leverage and the value of the company in the 4th quarter of the value in the 1st, 2nd and 3rd quarters. This indicates that the company has no indication of window dressing symptoms. So it can be concluded that several companies in the energy sector listed on the ISSI IDX in 2017 to 2020 are not indicated to carry out window dressing practices. It can be used as an evaluation document for companies in the energy sector to further improve their operations. To avoid any indication of window dressing, energy companies listed on the ISSI IDX must prioritize strategic efforts in improving the company's financial performance so that cash holding, financial leverage, and company value can reflect the company's situation. Keywords: window dressing, cash holding, financial leverage, firm value. REFERENCES Alandari, T. rohmadoni. (2016). Analisis window dressing pada reksa dana saham perusahaan sekuritas Indonesia tahun 2010-2015. 1–8. Skrupsi tidak dipublikasikan. Universitas Jember. Alteza, M. (2007). Efek hari perdagangan terhadap return saham: suatu telaah atas anomali pasar efisien. Jurnal Ilmu Manajemen, 3(1), 31–42. Aprillia, S. V. (2016). Analisis window dressing pada perusahaan BUMN yang terdaftar di bursa efek Indonesia Periode 2012-2014. Doctoral dissertation. STIE Perbanas Surabaya. BBPT. (2022). BBPT outlook energi Indonesia. Badan Pengkajian Dan Penerapan Teknologi. Retrieved from https://www.bppt.go.id/dokumen/outlook-energi BEI. (2022a). Indeks saham syariah. Bursa Efek Indonesia. Retrieved from https://www.idx.co.id/idx-syariah/indeks-saham-syariah/ BEI. (2022b). Klasifikasi sektor dan subsektor. Bursa Efek Indonesia. Retrieved from https://www.idx.co.id/produk/saham/#Klasifikasi Sektor dan Subsektor Bestari, W. A. (2014). Analisis window dressing pada perusahaan sektor industri barang konsumsi yang terdaftar di bursa efek Indonesia periode 2010-2013. Skripsi tidak dipublikasikan. Universitas Maritim Raja Ali Haji Tanjungpinang. Chandra, F. O., Sugiarto, B., & Biantara, D. (2022). Analisis window dressing pada perusahaan pertambangan yang terdaftar di bursa efek Indonesia periode 2018-2020. Accounting Cycle Journal, 3(2), 88–111. Christina, S. O., & Andadari, R. K. (2015). Praktek window dressing pada reksa dana saham di Indonesia tahun 2008-2012. Jurnal Studi Manajemen, 9(1), 57–75. Dahruji, D., & Muslich, A. A. (2022). Pengaruh profitabilitas terhadap financial distress pada bank umum syariah periode 2018 – 2020. Jurnal Ekonomi Syariah Teori Dan Terapan, 9(3), 388–400. https://doi.org/10.20473/vol9iss20223pp388-400 DEN. (2021). Laporan hasil analisis neraca energi nasional 2021. Dewan Energi Nasional. Retrieved from https://www.den.go.id/index.php/publikasi/documentread?doc=buku-neraca-energi-2021.pdf Fathurrahman, A., & Widiastuti, R. A. (2021). Determinan indeks saham syariah Indonesia. Islamic Banking: Jurnal Pemikiran Dan Pengembangan Perbankan Syariah, 7(1), 179–194. https://doi.org/10.36908/isbank.v7i1.309 Febriani, L., Wahyudi, I., & Olimsar, F. (2021). Pengaruh window dressing terhadap keputusan investasi pada perusahaan otomotif dan komponen yang terdaftar di bursa efek Indonesia (2016-2020). Jambi Accounting Review (JAR), 2(1), 112–127. Gill, A., & Shah, C. (2012). Determinants of corporate cash holdings: evidence from Canada. International Journal of Economics and Finance, 4(1), 70–79. https://doi.org/10.5539/ijef.v4n1p70 Harinaldi. (2005). Prinsip-prinsip statistik untuk teknik dan sains. Jakarta: Erlangga. Hartono, J. (2013). Teori portofolio dan analisis investasi edisi kedelapan. Yogyakarta: BPFE. Iramani, R., & Mahdi, A. (2006). Studi tentang pengaruh hari perdagangan terhadap return saham pada BEI. Jurnal Akuntansi Dan Keuangan, 8(2), 63–70. https://doi.org/10.9744/jak.8.2.pp.%2063-70 Khokhar, A. R. (2013). Three essays in empirical corporate. Unpublished Thesis. McMaster University. Mardhiyah, S. (2012). Pengaruh bulan perdagangan terhadap return saham: pengujian january effect di indeks harga saham liquidity 45 (studi pada perusahaan sektor keuangan perbankan yang tercatat di LQ45 selama periode 2004-2012). Jurnal Ilmiah Mahasiswa Fakultas Ekonomi Dan Bisnis, 2(1), 1–12. Murtini, U., & Ukru, M. J. (2021). Determinan cash holding bank yang terdaftar di bursa efek Indonesia. Jurnal Riset Manajemen Dan Bisnis, 16(1), 13–20. https://doi.org/10.21460/jrmb.2021.161.368 Nersiyanti, Usman, H., & Hapid. (2020). Pengaruh manajemen laba terhadap nilai perusahaan dengan mekanisme corporate governance sebagai variabel moderasi. http://repository.umpalopo.ac.id/560/ Prasetyorini, B. F. (2013). Pengaruh ukuran perusahaan, leverage, price earnings ratio dan profitabilitas terhadap nilai perusahaan. Jurnal Imu Manajemen, 1(1), 183–196. Primasari, N. S., & Tri Wahyuningtyas, E. (2021). Analisis F-score untuk pendeteksian window dressing dengan moderasi manajemen laba dan cash holding. E-Jurnal Akuntansi, 31(5), 1189–1200. https://doi.org/10.24843/eja.2021.v31.i05.p09 Primasari, N. S., & Wahyuningtyas, E. T. (2020). Earning management dan cash holding sebagai moderasi pendeteksian window dressing dengan F-score analysis. Accounting Global Journal, 4(2), 139–152. https://doi.org/10.24176/agj.v4i2.5095 Pujiningsih, Y., & Dahruji. (2021). Pengaruh tingkat inflasi dan indeks saham syariah indonesia (ISSI) terhadap pertumbuhan sukuk korporasi di Indonesia (periode tahun 2015-2020). Kaffa: Journal of Sharia Economic and Islamic Law, 2(4), 105–120. Putri, W. D., & Sari, S. P. (2022). Fenomena sell in May, window dressing, December effect dan January effect terhadap dinamika harga saham perbankan. 5th Prosiding Business and Economics Conference in Utilization of Modern Technology, 614–623. Rahmawati, H., Suparlinah, I., & Pratiwi, U. (2018). Analisis variabel cash holding, financial leverage, managerial ownership dan ukuran perusahaan dalam mendeteksi adanya praktik window dressing pada perusahaan sektor pertambangan yang terdaftar di bursa efek Indonesia periode 2013-2016. SAR (Soedirman Accounting Review): Journal of Accounting and Business, 3(2), 184–200. https://doi.org/10.20884/1.sar.2018.3.2.1217 Riswandi, P., & Yuniarti, R. (2020). Pengaruh manajemen laba terhadap nilai perusahaan. Pamator Journal, 13(1), 134–138. https://doi.org/10.21107/pamator.v13i1.6953 Riyanto, B. (2010). Dasar-dasar pembelajaran perusahaan. Yogyakarta: UGM Press. Rudiyanto. (2013). Sukses finansial dengan reksa dana. Jakarta: PT Elex Media Komputindo. Sari, D. N. (2019). Analisis praktik window dressing pada perusahaan yang terdaftar di LQ45 bursa efek Indonesia periode 2015-2017. Skripsi tidak dipublikasikan. Universitas Brawijaya. Sugiyono. (2008). Metode penelitian administrasi. Bandung: CV Alfabeta. Supriyono, R. A. (2018). Akuntansi keperilakuan. Yogyakarta: UGM PRESS. Suryahadi, A. (2022). Indeks sektor energi melesat, ini deretan saham yang bisa dicermati. Retrieved from https://investasi.kontan.co.id/news/indeks-sektor-energi-melesat-ini-deretan-saham-yang-bisa-dicermati Syaifullah, A. (2018). Analisis pengaruh financial leverage dan operating leverage terhadap stock return. INOVASI, 14(2), 53. https://doi.org/10.29264/jinv.v14i2.1928 Wulandari, M. (2013). Anomali pasar bulan perdagangan terhadap return saham dan abnormal return. Skripsi tidak dipublikasikan. Universitas Islam Negeri Syarif Hidayatullah Jakarta.

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Riveiro, Maria, Giuliana Pallotta, and Michele Vespe. "Maritime anomaly detection: A review." WIREs Data Mining and Knowledge Discovery 8, no. 5 (May 25, 2018). http://dx.doi.org/10.1002/widm.1266.

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Wei, Zhaokun, Xinlian Xie, and Xiaoju Zhang. "Maritime anomaly detection based on a support vector machine." Soft Computing, August 7, 2022. http://dx.doi.org/10.1007/s00500-022-07409-w.

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Wei, Zhaokun, Xinlian Xie, and Xiaoju Zhang. "Maritime anomaly detection based on a support vector machine." Soft Computing, August 7, 2022. http://dx.doi.org/10.1007/s00500-022-07409-w.

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Karataş, Gözde Boztepe, Pinar Karagoz, and Orhan Ayran. "Trajectory pattern extraction and anomaly detection for maritime vessels." Internet of Things, August 2021, 100436. http://dx.doi.org/10.1016/j.iot.2021.100436.

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Hu, Jia, Kuljeet Kaur, Hui Lin, Xiaoding Wang, Mohammad Mehedi Hassan, Imran Razzak, and Mohammad Hammoudeh. "Intelligent Anomaly Detection of Trajectories for IoT Empowered Maritime Transportation Systems." IEEE Transactions on Intelligent Transportation Systems, 2022, 1–10. http://dx.doi.org/10.1109/tits.2022.3162491.

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"Anomaly Detection in Vessel Tracking – A Bayesian Networks (BNs) Approach." International Journal of Maritime Engineering Part A3 2015 157, A3 (January 1, 2015): 145–52. http://dx.doi.org/10.3940/rina.ijme.2015.a3.316.

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"The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour."

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Handayani, D., W. Sediono, and A. Shah. "ANOMALY DETECTION IN VESSEL TRACKING – A BAYESIAN NETWORKS (BNs) APPROACH." International Journal of Maritime Engineering 157, A3 (December 13, 2021). http://dx.doi.org/10.5750/ijme.v157ia3.956.

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The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour.

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Forti, Nicola, Enrica d'Afflisio, Paolo Braca, Leonardo M. Millefiori, Peter Willett, and Sandro Carniel. "Maritime Anomaly Detection in a Real-World Scenario: Ever Given Grounding in the Suez Canal." IEEE Transactions on Intelligent Transportation Systems, 2021, 1–7. http://dx.doi.org/10.1109/tits.2021.3123890.

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Nguyen, Duong, Rodolphe Vadaine, Guillaume Hajduch, Rene Garello, and Ronan Fablet. "GeoTrackNet--A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection." IEEE Transactions on Intelligent Transportation Systems, 2021, 1–13. http://dx.doi.org/10.1109/tits.2021.3055614.

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Journal articles on the topic 'Maritime anomaly detection'

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