Thematische Bibliographien / Approach-Avoidance Training

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Auswahl der wissenschaftlichen Literatur zum Thema „Approach-Avoidance Training“

Autor: Grafiati

Veröffentlicht am 23. November 2024

Zuletzt aktualisiert am 19. Juli 2025

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Zeitschriftenartikel zum Thema "Approach-Avoidance Training"

Van Dessel, Pieter, Sean Hughes, and Jan De Houwer. "Consequence-Based Approach-Avoidance Training: A New and Improved Method for Changing Behavior." Psychological Science 29, no. 12 (2018): 1899–910. http://dx.doi.org/10.1177/0956797618796478.

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The repeated performance of approach or avoidance actions in response to specific stimuli (e.g., alcoholic drinks) is often considered a most promising type of cognitive-bias modification that can reduce unwanted behavior (e.g., alcohol consumption). Unfortunately, approach-avoidance training sometimes fails to produce desired outcomes (e.g., in the context of unhealthy eating). We introduce a novel training task in which approach-avoidance actions are followed by affective consequences. Four experiments (total N = 1,547) found stronger changes in voluntary approach-avoidance behavior, implici

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Mertens, Gaëtan, Pieter Van Dessel, and Jan De Houwer. "The contextual malleability of approach-avoidance training effects: approaching or avoiding fear conditioned stimuli modulates effects of approach-avoidance training." Cognition and Emotion 32, no. 2 (2017): 341–49. http://dx.doi.org/10.1080/02699931.2017.1308315.

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Machulska, Alla, Mike Rinck, Tim Klucken, et al. "“Push it!” or “Hold it!”? A comparison of nicotine-avoidance training and nicotine-inhibition training in smokers motivated to quit." Psychopharmacology 239, no. 1 (2022): 105–21. http://dx.doi.org/10.1007/s00213-021-06058-5.

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Abstract Rationale Recently, experimental paradigms have been developed to strengthen automatic avoidance or inhibitory responses for smoking cues. However, these procedures have not yet been directly compared regarding their effectiveness and mechanisms of action. Objective This study compared the effects of avoidance vs. inhibitory training as an add-on to a brief smoking cessation intervention. The standard Approach-Avoidance-Task (AAT) was adapted for both training types and control conditions. Methods One hundred twenty-four smokers attended behavioral counseling for smoking cessation and

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Woud, Marcella L., Eni S. Becker, Wolf-Gero Lange, and Mike Rinck. "Effects of Approach-Avoidance Training on Implicit and Explicit Evaluations of Neutral, Angry, and Smiling Face Stimuli." Psychological Reports 113, no. 1 (2013): 199–216. http://dx.doi.org/10.2466/21.07.pr0.113x10z1.

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A growing body of evidence shows that the prolonged execution of approach movements towards stimuli and avoidance movements away from them affects their evaluation. However, there has been no systematic investigation of such training effects. Therefore, the present study compared approach-avoidance training effects on various valenced representations of one neutral (Experiment 1, N = 85), angry (Experiment 2, N = 87), or smiling facial expressions (Experiment 3, N = 89). The face stimuli were shown on a computer screen, and by means of a joystick, participants pulled half of the faces closer (

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Krishna, Anand, and Andreas B. Eder. "The influence of pre-training evaluative responses on approach-avoidance training outcomes." Cognition and Emotion 33, no. 7 (2019): 1410–23. http://dx.doi.org/10.1080/02699931.2019.1568230.

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Van Dessel, Pieter, Jan De Houwer, Anne Gast, and Colin Tucker Smith. "Instruction-Based Approach-Avoidance Effects." Experimental Psychology 62, no. 3 (2015): 161–69. http://dx.doi.org/10.1027/1618-3169/a000282.

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Prior research suggests that repeatedly approaching or avoiding a certain stimulus changes the liking of this stimulus. We investigated whether these effects of approach and avoidance training occur also when participants do not perform these actions but are merely instructed about the stimulus-action contingencies. Stimulus evaluations were registered using both implicit (Implicit Association Test and evaluative priming) and explicit measures (valence ratings). Instruction-based approach-avoidance effects were observed for relatively neutral fictitious social groups (i.e., Niffites and Luupit

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Li, Wenqian, Peiqiao Shang, and Jing Huang. "An Obstacle Avoidance Approach Based on Naive Bayes Classifier." Journal of Autonomous Intelligence 3, no. 1 (2020): 27. http://dx.doi.org/10.32629/jai.v3i1.139.

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Obstacle avoidance plays an important role in mobile robot. However, the traditional methods of obstacle avoidance have difficulty in distinguishing multiple obstacles by edge detection. In this paper, the traditional obstacle avoidance methods are improved to realize the function of multi-obstacle avoidance. Regarding the implementation process, the LiDAR is used instead of the RGBD camera, which reduces the difficulty of handling image noise and achieves reliable obstacle detection. It can accurately detect the borders of the nearest obstacle even in complex environments and perform obstacle

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Van Dessel, Pieter, Andreas B. Eder, and Sean Hughes. "Mechanisms underlying effects of approach-avoidance training on stimulus evaluation." Journal of Experimental Psychology: Learning, Memory, and Cognition 44, no. 8 (2018): 1224–41. http://dx.doi.org/10.1037/xlm0000514.

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Kakoschke, Naomi, Rowan Page, Barbora de Courten, Antonio Verdejo-Garcia, and Jon McCormack. "Brain training with the body in mind: Towards gamified approach-avoidance training using virtual reality." International Journal of Human-Computer Studies 151 (July 2021): 102626. http://dx.doi.org/10.1016/j.ijhcs.2021.102626.

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Sun, Zhe, Yunsheng Fan, and Guofeng Wang. "An Intelligent Algorithm for USVs Collision Avoidance Based on Deep Reinforcement Learning Approach with Navigation Characteristics." Journal of Marine Science and Engineering 11, no. 4 (2023): 812. http://dx.doi.org/10.3390/jmse11040812.

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Many achievements toward unmanned surface vehicles have been made using artificial intelligence theory to assist the decisions of the navigator. In particular, there has been rapid development in autonomous collision avoidance techniques that employ the intelligent algorithm of deep reinforcement learning. A novel USV collision avoidance algorithm based on deep reinforcement learning theory for real-time maneuvering is proposed. Many improvements toward the autonomous learning framework are carried out to improve the performance of USV collision avoidance, including prioritized experience repl

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Dissertationen zum Thema "Approach-Avoidance Training"

Kaczmarek, Nicolas. "Différentes voies pour favoriser le changement comportemental." Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILH035.

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Plusieurs modèles soulignent l'importance de la motivation et de la volition pour favoriser le changement comportemental. Cette thèse a pour originalité de combiner certaines stratégies motivationnelles et/ou volitionnelles afin d'identifier les mécanismes générés et les potentiels modérateurs, et in fine, cerner comment maximiser leur efficacité. Cinq stratégies sont considérées : le contraste mental, le soutien à l'autodétermination de la motivation, le cadrage hiérarchique de buts (stratégies motivationnelles), l'entraînement à l'approche-évitement, et l'intention d'implémenter (stratégies

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Reibert, Evelyne [Verfasser], and Oliver [Akademischer Betreuer] Pogarell. "Evaluation des Approach-Avoidance Task Trainings als Zusatzintervention zu einer Standardbehandlung bei Tabakabhängigkeit / Evelyne Reibert ; Betreuer: Oliver Pogarell." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1211957624/34.

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Struwig, Gillian Anne. "Psychosocial factors and susceptibility to the common cold in distance runners." Thesis, 2004. http://hdl.handle.net/10500/1872.

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This study investigated the relationship between specific psychosocial factors and susceptibility to the common cold in a sample of 124 distance runners. A cross-sectional survey design was used to assess the role of life events, coping, hardiness, training workload and competition frequency in the athlete's risk of infection. Using correlational statistical techniques, it was found that the magnitude of recent life changes and the avoidance coping strategy of denial were positively related to self-reported symptoms of the common cold. Furthermore, a significant inverse correlation was observe

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Bücher zum Thema "Approach-Avoidance Training"

Office, General Accounting. Aviation Safety: Undeclared air shipments of dangerous goods and DOT's enforcement approach : report to the Ranking Minority Member, Subcommittee on Aviation, Committee on Transportation and Infrastructure, House of Representatives. U.S. General Accounting Office, 2003.

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Board, United States National Transportation Safety. Aircraft accident report: Flight into terrain during missed approach, USAir flight 1016, DC-9-31, N954VJ, Charlotte/Douglas International Airport, Charlotte, North Carolina, July 2, 1994. National Transportation Safety Board, 1995.

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Beninger, Richard J. Dopamine and the elements of incentive learning. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824091.003.0003.

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Dopamine and the elements of incentive learning explains how, in lever pressing for food tasks, incentive learning produces a gradient of attractiveness of environment stimuli: during magazine training, food activates dopaminergic neurons and the click and food cup become conditioned incentive stimuli, acquiring the ability to elicit approach and other responses; during lever-press training, the click activates dopaminergic neurons and the lever and lever-related stimuli become conditioned incentive stimuli. In conditioned place preference, amphetamine enhances dopaminergic neurotransmission a

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Buchteile zum Thema "Approach-Avoidance Training"

Mehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.

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AbstractWe show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking controller from a centralized MPC-based one, we achieve the best of both worlds: the neural controllers have high performance (on par with the MPC controllers) and high efficiency. Our experimental results demonstrate the sophisticated nature of the distributed controllers we learn. In particular, the neural controllers are capable of achieving myriad flocking-oriented control objectives, including flocking formation, collision avoidance, obstacle avoidance, predator avoidance, and target seeking. Moreover, they generalize the behavior seen in the training data to achieve these objectives in a significantly broader range of scenarios. In terms of verification of our neural flocking controller, we use a form of statistical model checking to compute confidence intervals for its convergence rate and time to convergence.

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Yan, Liang, Xiaodong Wu, and Hangyu Lu. "Human-Centered Collaborative Decision-Making and Steering Control with Reinforcement Learning." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_105.

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AbstractThis paper presents a novel human-centered collaborative driving scheme using model-free reinforcement learning (RL) approach. The human-machine cooperation is achieved in both decision-making and steering control levels to improve driving safety while leaving space for human freedom as much as possible. A Markov decision process is firstly derived from the collaborative driving problem, then a RL agent is developed and trained to cooperatively control the vehicle steering under the guidance of a heuristic reward function. Twin delayed deep deterministic policy gradient (TD3) is conducted to attain the optimal control policy. In addition, two extended algorithms with distinct agent action definitions and training patterns are also devised. The effectiveness of the RL-based copilot system is finally validated in an obstacle avoidance scenario by simulation experiments. Driving performance and training efficiency of different RL agents are measured and compared to demonstrate the superiority of the proposed method.

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Silvia, Stephen J. "Nissan North America." In The UAW's Southern Gamble. Cornell University Press, 2023. http://dx.doi.org/10.7591/cornell/9781501769696.003.0005.

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This chapter examines the organizing attempts at Nissan plants: two in Smyrna, Tennessee, in 1989 and 2001, and one in Canton, Mississippi, in 2017. There was also an organizing attempt at the Smyrna plant in 1997 that was substantial enough to count as a case but did not culminate with an election. The United Auto Workers' (UAW) approach to organizing changed with successive cases. Union officials learned from previous mistakes and integrated new tactics and technologies. Nissan managers also innovated. They pioneered a number of measures that subsequently became standard tactics in the union-avoidance playbook, including taking a team approach that minimized distinctions between management and line employees, cross-training employees to do multiple jobs, paying wages that were slightly lower than union contracts but markedly higher than the area rate, offering a wider range of benefits to line employees, avoiding layoffs whenever possible, using in-plant monitors to broadcast pro-management and anti-union messages, hiring a law firm that specializes in union avoidance, and cultivating close community relations. These measures proved effective in keeping the share of employees willing to vote for union representation well below a majority.

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Kaushik, Rekha, Pritam Goyal, and Atharv Pandey. "NeuroVoice: Leveraging Neural Networks for Precise Gender Classification in Audio." In Computational Intelligence and Machine Learning. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-975670-5-6-5.

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In this paper, the model for gender recognition is developed with voice samples using various machine learning algorithms and acoustic parameters. It is divided at the beginning into the training and test data of the dataset. There then follows a number of key steps and techniques as part of the process that improves the performance of this model. The paper focuses on a holistic approach toward gender classification from audio data through various techniques of data preprocessing, augmentation, feature scaling, model development, and their performance evaluation. First, it encodes the class label (male/female) into a numerical format through label encoding. Then, it extracts critical features like MFCC, Chroma Features, Spectral Contrast, and Pitch to extract the most essential characteristics of the audio. Data augmentation with SMOTE avoids bias in the dataset by creating artificial samples. Features are scaled with Min-Max Scaling to enhance model convergence and performance. Several advanced neural network architectures like MLP with Batch Normalization and Dropout techniques have also been considered. Stratified K-Fold Cross-Validation ensures robustness and avoidance of bias in the evaluation. In this study, each model has an evaluation including performance metrics, such as accuracy, precision, recall, F1-score, confusion matrix, and classification report. Remarkably, this model has an accuracy of 99.7% against the test dataset, improving both in total accuracy and robustness. The findings could be of importance in telecommunication, human-computer interaction, and security systems where accurate gender recognition from voice is required.

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Robinson, Selina W. M. "Applications of VR (Virtual Reality) Technology for Detection, Investigation, and Rehabilitation." In Cases on Forensic and Criminological Science for Criminal Detection and Avoidance. IGI Global, 2024. http://dx.doi.org/10.4018/978-1-6684-9800-2.ch011.

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This chapter explores the extensive use of virtual reality (VR) technology within the UK's criminal justice system, focusing on its applications in investigation, correction, and rehabilitation. Driven by the need for efficient training solutions, VR is employed for crime scene reconstruction, inmate rehabilitation, and offender reintegration, as illustrated through case studies and scholarly literature. The chapter delves into the intersection of VR technology with various aspects of the criminal justice system, emphasizing its potential to improve investigative practices, enhance correctional outcomes, and support offender rehabilitation. It encompasses the technical capabilities of VR, its applications in criminal investigation, forensics, correctional facility management, and offender treatment programs, while also addressing ethical considerations and best practices for implementation. By harnessing the power of VR, the UK's criminal justice system can transform its approach to investigation, corrections, and rehabilitation.

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Ruskulis, Lilia, and Lidiia Aizikova. "SCIENTIFIC TEXT AS A MEANS FOR REALIZATION OF HIGHER EDUCATION INSTITUTION STUDENTS IN EDUCATIONAL AND SCIENTIFIC RESEARCH ACTIVITIES." In Trends of philological education development in the context of European integration. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-069-8-11.

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The article clarifies the peculiarities of the organization of educational and scientific research activity of higher education institution students, which is an important way to improve the quality of training and forming of specialists with an academic degree, able to creatively apply the latest achievements of scientific and technological progress, and opens opportunities for effective acquisition and use of knowledge; implements an individualized approach to learning; develops the ability to independently conduct research and summarize the investigation results; dominant tasks are characterized; the directions (educational research, scientific research and scientific organizational) and types of educational research activity of students (abstract, scientific reviews and articles, course and diploma papers) are analysed; the theoretical bases of stylistics and the concept of “style” are investigated; the substyles of the scientific style are analysed (proper scientific (academic, purely scientific); scientific and technical (production and technical); scientific humanitarian; scientific informative (scientific summarizing); scientific reference (reference-encyclopaedic). It is proved that the main purpose of scientific language is to create and produce the scientific text, by which we mean the highest communicative unit within scientific discourse, a holistic communicative block having a clear, logical structure and internally complete parts, saturated with relevant terminology, a set of constant text categories and a means of presenting scientific information, the results of scientific research. The levels of organization of the scientific text (linguistic-structural (operating with linguistic models); linguistic-cognitive (verbalized concepts in the text); communicative-rhetorical (means of persuasion in the scientific text); communicative-pragmatic (personal attitude of the one who produces a scientific text to the message) are investigated; the features of primary (monograph, dissertation, bachelor and master theses) and secondary (scientific article, abstracts, summaries, annotations, reviews, reports) scientific texts are characterized. The paper reveals the principles of compiling scientific texts: content saturation – innovativeness of the presented information, its cognitive value; professional core – the need for analysed information for a particular sphere; scientific informativeness – the author’s concept of the represented research; novelty of the scientific text – new observations and knowledge discoveries that can be implemented in practice; content completeness – the integrity of the presented statements; problematicity – coding of problematic issues; comprehensibility to a specialist in a particular field – apprehensibility of information and providing necessary conditions to understanding it; intertextuality – connection of the scientific text with other types of texts; text declarativeness – a clear comparative analysis of a particular process or phenomenon. The stages of work on the scientific text (organizational, research, generalization of research results) are studied. Requirements for the creation of scientific texts are defined: clear structure (division into chapters, sections, units, paragraphs and sentences that are closely related to each other), avoiding of repetitions (in particular, in conclusions to chapters and in final conclusions); deliberate use of graphic material; systemacity in the process of writing the text; avoidance of concepts that cannot be unambiguously interpreted; justified use of figures and facts; text coherence. The requirements to the structure of the scientific text (introduction, research part, conclusions) are covered.

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Konferenzberichte zum Thema "Approach-Avoidance Training"

Chamorro, William, José Zuniga, Jorge Medina, Viviana Moya, Andrea Pilco, and Diego Guffanti. "Neuro-Fuzzy Obstacle Avoidance: A Fast Training Dataset Generation Approach Using Low-Cost Sensors." In 2024 6th International Conference on Robotics and Computer Vision (ICRCV). IEEE, 2024. http://dx.doi.org/10.1109/icrcv62709.2024.10758599.

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Melsom, Steve, Linda Stiles, and Joe Welsh. "Corrosion Control Knowledge Sharing Network: Fighting the War on Corrosion from Multiple Fronts." In SSPC 2013 Greencoat. SSPC, 2013. https://doi.org/10.5006/s2013-00045.

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Abstract Corrosion is one of the leading maintenance costs across the United States Surface Navy, with approximately 25 cents of every dollar spent on maintenance going towards fighting or correcting corrosion. In addition to the huge financial burden, corrosion can limit the ability of our Navy’s ships to meet operational commitments and even prevent them from achieving their expected service life (ESL). In February of 2011, Surface Team One (ST1) stood up the Corrosion Control Knowledge Sharing Network (KSN) in order to streamline the efforts to fight and win the war against corrosion. The C

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Sajad, Allawati, and Saad Hiam. "Blended Approach to Stuck Pipe Avoidance Training." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216860-ms.

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Abstract The objectives of the project is to develop a blended training program for Stuck Pipe Avoidance using e-Learning, video learning and Virtual Reality, VR, to increase participants attention throughout the course and to enhance knowledge retention. PDO works in a complex, high-risk, high-consequence work environment where accidents can be extremely serious, causing damage to people, assets, and the environment. The traditional multiple day course content was reviewed and updated to incorporate VR technology into a blended training program for Stuck Pipe Avoidance with e-learning, 3D mod

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Lindenberg, K., and C. Ebner. "Eine app-basiertes Approach-Avoidance-Task-Training zur Reduktion von Symptomen bei Jugendlichen mit riskantem Mediengebrauch." In Deutscher Suchtkongress 2022. Georg Thieme Verlag, 2022. http://dx.doi.org/10.1055/s-0042-1756019.

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McEnroe, Patrick, Shen Wang, and Madhusanka Liyanage. "Towards Faster DRL Training: An Edge AI Approach for UAV Obstacle Avoidance by Splitting Complex Environments." In 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC). IEEE, 2024. http://dx.doi.org/10.1109/ccnc51664.2024.10454660.

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Soares, Natália Souza, João Marcelo Xavier Natário Teixeira, and Veronica Teichrieb. "Robot training in virtual environments using Reinforcement Learning techniques." In Anais Estendidos do Simpósio de Realidade Virtual e Aumentada. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/svr_estendido.2020.12950.

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In this work, we propose a framework to train a robot in a virtual environment using Reinforcement Learning (RL) techniques and thus facilitating the use of this type of approach in robotics. With our integrated solution for virtual training, it is possible to programmatically change the environment parameters, making it easy to implement domain randomization techniques on-the-fly. We conducted experiments with a TurtleBot 2i in an indoor navigation task with static obstacle avoidance using an RL algorithm called Proximal Policy Optimization (PPO). Our results show that even though the trainin

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Zhou, Qi, Sikai Li, Jingbo Qu, Jin Wu, Haomiao Xu, and Youyi Bi. "An Adaptive Path Planning Approach for Digital Twin-Enabled Robot Arm Based on Inverse Kinematics and Deep Reinforcement Learning." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-113131.

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Abstract Efficient path planning methods for robot arms are crucial to ensure the quality and safety of their completing various tasks. Compared to traditional manual instruction, Reinforcement Learning (RL) based path planning methods show better adaptability for complex working scenarios. However, the training of RL is usually time-consuming with limited success rate. To tackle this problem, we propose an adaptive path planning approach for robot arm based on Inverse Kinematics (IK) and Deep Reinforcement Learning (DRL) in a pick-and-place context. A judgement mechanism is developed to adapt

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Arumugam, Vengatesan, Vasudevan Alagumalai, and Sundarakannan Rajendran. "Simulation Analysis of Multi-Objective Functions in Mobile Robot Navigation Based on Enhanced Deep Q-Network Algorithm." In Automotive Technical Papers. SAE International, 2024. https://doi.org/10.4271/2024-01-5110.

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<div class="section abstract"><div class="htmlview paragraph">This research, path planning optimization of the deep Q-network (DQN) algorithm is enhanced through integration with the enhanced deep Q-network (EDQN) for mobile robot (MR) navigation in specific scenarios. This approach involves multiple objectives, such as minimizing path distance, energy consumption, and obstacle avoidance. The proposed algorithm has been adapted to operate MRs in both 10 × 10 and 15 × 15 grid-mapped environments, accommodating both static and dynamic settings. The main objective of the algorithm is

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Punjabi, Preet, and Anshay Tomar. "Social Anxiety Disorder: Insights into Causes, Manifestations, and Therapeutic Approaches." In Transforming Knowledge: A Multidisciplinary Research on Integrative Learning Across Disciplines. The Bhopal School of Social Sciences, 2025. https://doi.org/10.51767/ic250434.

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Social Anxiety Disorder (SAD) is a prevalent and debilitating mental health condition characterized by intense fear and anxiety in social situations, often leading to avoidance behaviors and significant impairment in daily functioning. This case study examines the experiences of Anjali Arora, a 19-year-old college student diagnosed with SAD. Her symptoms include an overwhelming fear of social judgment, avoidance behaviors, and physical distress in social settings, which have severely impacted her academic performance and quality of life. A comprehensive methodology, including clinical intervie

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Noel, A., K. Shreyanka, K. G. S. Kumar, B. M. Shameem, and B. Akshar. "Autonomous Ship Navigation Methods: A Review." In International Conference on Marine Engineering and Technology Oman. IMarEST, 2019. http://dx.doi.org/10.24868/icmet.oman.2019.028.

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Autonomous navigation is achieved by training or programming the ship with the stored data about the vessel behavior in various sailing environment. The autonomous behaviour relies on intelligent analytics based on machine learning algorithms. As a major advance in machine learning, the deep learning approach is becoming a powerful technique for autonomy. The deep learning methodologies are applied in various fields in the maritime industry such as detecting anomalies, ship classification, collision avoidance, risk detection of cyber attacks, navigation in ports and so on. The present paper re

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