1.

2. Materials and Methods

2.2. Research Methods

A research model was designed for this study as shown in Figure 2. First, the concept and characteristics of autonomous ships were analyzed based on previous studies, and then bibliographic data on autonomous ships were collected from the Web of Science database. The collected data was refined by unifying terms, removing unused terms, and filtering with a dictionary.

2.2.1. Co-occurrence Analysis

Co-occurrence word analysis is a way of visually representing topics in a set of documents by using the co-occurrence frequency of major keywords or classification codes, and it is used to identify topic trends and temporal changes in various fields [37]. In the case of n-gram analysis, which is often used in text mining, it has the advantage of being useful for identifying the relationship between adjacent keywords but has a disadvantage that the correlation between the keywords cannot be confirmed if the keywords that co-occur frequently in the same document do not appear one after another. Therefore, in this study, co-occurrence network analysis was conducted to check the correlation between co-occurring keywords in a document.

A network in co-occurrence network analysis comprises nodes and connections (links) between nodes, with each node representing a keyword and each link representing a relationship between keywords. To visually represent the relationship between keywords, each keyword is represented by a dot, and the link between keywords is represented by a line, and the thickness of the line varies depending on how often the two keywords are used together [38]. If a network analysis is conducted for all keywords, it becomes difficult to grasp the meaning due to too many nodes and connections, hence the range of nodes to be included in the analysis must be determined [39]. In this study, the top 20 keywords were subjected to a pathfinder network (PFNet), and the complex network represented by the input matrix was reduced by leaving only important links for each node so that the overall structure could be grasped at a glance [40].

2.2.2. LDA Topic Modeling

Depending on the calculation method used, various types of algorithms, including individual analysis techniques, can be used for topic modeling. In this study, the latent Dirichlet allocation (LDA) technique was used. LDA is commonly used when subject areas across articles have a strong tendency to be reduced to a single contact point; it is also used to derive topic modeling results for information technology articles containing specific domain information such as papers, news, and patents [41,42]. To derive analysis results using topic modeling, the parameter values of alpha, beta, and the number of topics must be arbitrarily specified in the topic modeling analysis settings. However, both alpha and beta range from 0.01 to 0.99 and can be divided into thousands of digits [43]. Furthermore, determining the number of cases, calculating the number of topics, and finding the best topic organization can be challenging. These challenges can be overcome by using the coherence coefficient to determine the appropriate number of topics and keywords within topics [44].

The coherence coefficient is a virtual proxy for the number of cases with the above-mentioned alpha, beta, and number of topic settings, and it uses the number of cas-es with coherence values close to 1. To assess the best number of topics and parameters for topic modeling results, the number of cases for alpha and beta values in this study was set to 0.01 - 0.99. Considering the total number of articles, the number of topics ranged from two to ten, and the consistency index was calculated for the number of all cases. Therefore, by comparing the topic composition according to the number of topics, core topics that show a high density of keyword composition in any set-ting and variable topics that indicate changes depending on the setting were derived, and the meaning and implications of the subject area implied by each topic were proposed. Figure 3 shows the conceptual model of LDA topic modeling used in this study.

The most challenging and trial-and-error aspect of LDA topic analysis is determining the number of topics [45]. Therefore, the coherence score is often used as a reference index for determining the number of topics. In this study, topic cohesion is used to determine the number of topics, and the ‘Cv’ metric is applied, which is rated as having excellent performance among several similar indices. Topic cohesion is a technique for determining the interpretability of an extracted topic from the words comprising that topic, reflecting the way humans interpret text, and assumes that the higher the average of the pairwise similarity between words in an extracted topic’s word set, the higher the topic’s cohesion [46]. ‘Cv’ is a method proposed by Röder et al. [47] that depicts a keyword as a context vector expressing its co-occurrence frequency with surrounding terms, and arithmetic averages pairwise similarity between keywords by calculating cosine similarity [47]. It is a more accurate measure of similarity because it takes into account the relationship with neighboring words rather than calculating the similarity between direct words [47].

3. Results

3.1. Descriptive Statistics Analysis Results

3.1.1. Status by Country

Looking at the status of publications by country, most countries are seeing an increase in the number of autonomous ship research articles. As shown in Table 3, China has the highest proportion (560 articles as of 2022), followed by the United States (95 articles) and Korea (76 papers). In particular, in 2018, the number of articles from China, which ranked first, and the United States, which ranked second, was only approximately 1.4 times, but in 2022, the difference increased substantially to 5.9 times. China is rapidly increasing Maritime Autonomous Surface Ships research with an average annual growth rate of 47%.

3.1.2. Status by Institution

The institutional analysis, as detailed in Table 6, reveals a consistent upward trend in research activity across various organizations. Notably, in China, prominent entities like the Chinese Academy of Sciences and the University of Chinese Academy of Sciences, both governmental institutions, lead in this domain. This trend indicates a government-driven expansion in autonomous ship research, emphasizing the significant role of state-led research and development efforts.

Table 4. Status by Institution.

Table 4. Status by Institution.

Status	2018	2019	2020	2021	2022	2018~2022)
1	CHINESE ACADEMY OF SCIENCES (19 case)	CHINESE ACADEMY OF SCIENCES (33 case)	CHINESE ACADEMY OF SCIENCES (38 case)	CHINESE ACADEMY OF SCIENCES (50 case)	CHINESE ACADEMY OF SCIENCES (65 case)	CHINESE ACADEMY OF SCIENCES (205 case)
2	UNIVERSITY OF CHINESE ACADEMY OF SCIENCE (14 case)	UNIVERSITY OF CALIFORNIA SYSTEM (25 case)	WUHAN UNIVERSITY (30 case)	DALIAN MARITIME UNIVERSITY (46 case)	DALIAN MARITIME UNIVERSITY (47 case)	DALIAN MARITIME UNIVERSITY (132 case)
3	HELMHOLTZ ASSOCIATIO (13 case)	HELMHOLTZ ASSOCIATIO (24 case)	DALIAN MARITIME UNIVERSITY (21 case)	WUHAN UNIVERSITY (29 case)	UNIVERSITY OF CHINESE ACADEMY OF SCIENCE (40 case)	UNIVERSITY OF CHINESE ACADEMY OF SCIENCE (115 case)
4	CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (11 case)	UNIVERSITY OF CHINESE ACADEMY OF SCIENCE (16 case)	UNIVERSITY OF CHINESE ACADEMY OF SCIENCES (20 case)	XIDAN UNIVERSITY (26 case)	WUHAN UNIVERSITY (38 case)	WUHAN UNIVERSITY (111 case)
5	NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY CHINA (11 case)	WUHAN UNIVERSITY (16 case)	SEOUL NATIONAL UNIVERSITY (13 case)	UNIVERSITY OF CHINESE ACADEMY OF SCIENCE (25 case)	XIDAN UNIVERSITY (33 case)	XIDAN UNIVERSITY (86 case)

3.2. Results of Co-Occurrence Analysis

A word network was created to convert the bimodal network between words and articles into an unimodal network between words and words to analyze the relationship between the main keywords. The top 20 main keywords were selected based on the number of articles and converted into a network structure with a co-occurrence of words and words, resulting in 19 links.

Table 5. Results of Co-occurrence Analysis.

Table 5. Results of Co-occurrence Analysis.

Status	Main keywords (frequency of occurrence)	Status	Main keywords (frequency of occurrence)
1	detection (3,428)	11	time (1,178)
2	datum (2,991)	12	water (1,166)
3	image (2,876)	13	area (1,101)
4	system (2,590)	14	surface (1,096)
5	SAR (1,901)	15	control (991)
6	target (1,708)	16	accuracy (976)
7	network (1,653)	17	radar (950)
8	algorithm (1,625)	18	dataset (944)
9	sea (1,430)	19	object (929)
10	performance (1,383)	20	sensing (882)

Because converting the binary mode between thesis and word to unimodal mode between word and n-word may result in too many links, Path-Finer Network Scaling (PFnet) was used to visually represent the network between words by leaving only highly relevant relationships between words, as Im et al. [48] had done previously. The result of reducing the network to 19 links with a high correlation between words is shown in Figure 4. The wider the circle, the more frequently the keywords are used based on the number of articles, and the thicker the connection line, the stronger the link strength [37].

Looking at the network structure of the top highly relevant keywords, it can be seen that there is a high concentration of keywords related to situational awareness, which is a key technology for autonomous ships, such as ‘datum,’ ‘image,’ and ‘detection’. ‘Datum’ is connected to ‘sea,’ ‘condition,’ ‘surface,’ ‘area,’ etc., and can be seen as including all types of data such as sea currents and areas. Furthermore, in the case of ‘image,’ it seems that deep learning algorithms such as CNN (Convolutional Neural Network) are used based on SAR (Synthetic Aperture Radar) images to train the computer with various image data situation recognition technologies. In the case of ‘detection,’ it is clear that researchers mainly conducted ‘performance’ tests about detecting and locating all the various objects that already exist in the water. Conversely, deep learning technology is being used by researchers in the field of autonomous ships, to learn SAR images while collecting various marine data.

3.3. Results of LDA Topic Modeling

3.3.1. Coherence Score Measurement Results

To determine the ideal number of topics, α was set to 0.1, β to 0.01, and Iteration to 1,000, according to W. Zhao et al. [49]. The number of topics was then increased from 2 to 10 to simulate the coherence score, and the eight topics with the highest score (0.629) were determined as shown in Figure 5.

Following that, the top 10 keywords with the highest frequency for each topic were summarized, as shown in Table 6, and a comprehensive topic name was assigned by reviewing the topic’s keywords and the abstracts from research bibliographies. We received assistance in determining the topic names through collaboration with researchers who have worked in maritime public institutions for more than five years and professors who have conducted text mining research.

Table 6. Summary of Topic Modelling Analysis Results.

Table 6. Summary of Topic Modelling Analysis Results.

Topic 1 (11.98%)			Topic 2 (9.28%)			Topic 3 (7.94%)			Topic 4 (13.83%)
Rank	Keyword	Prob	Rank	Keyword	Prob	Rank	Keyword	Prob	Rank	Keyword	Prob
1	detection	0.035	1	datum	0.037	1	ice	0.031	1	system	0.033
2	target	0.034	2	measurement	0.019	2	concentration	0.02	2	control	0.026
3	image	0.03	3	satellite	0.018	3	water	0.015	3	navigation	0.015
4	SAR	0.028	4	wave	0.017	4	sea	0.014	4	algorithm	0.015
5	radar	0.027	5	surface	0.016	5	Arctic	0.012	5	collision	0.013
6	sea	0.02	6	water	0.016	6	region	0.011	6	datum	0.012
7	datum	0.018	7	ocean	0.015	7	emission	0.01	7	motion	0.008
8	algorithm	0.015	8	wind	0.014	8	cloud	0.009	8	simulation	0.008
9	wake	0.011	9	observation	0.012	9	surface	0.009	9	surface	0.008
10	clutter	0.011	10	sensing	0.011	10	particle	0.009	10	path	0.008
Situational awareness technology research			Ocean observation and sensing research			Arctic navigation research			Navigation decision-making and control technology research
Topic 5 (14.87%)			Topic 6 (8.15%)			Topic 7 (19.60%)			Topic 8 (14.36%)
Rank	Keyword	Prob	Rank	Keyword	Prob	Rank	Keyword	Prob	Rank	Keyword	Prob
1	system	0.022	1	area	0.021	1	detection	0.059	1	system	0.014
2	energy	0.017	2	oil	0.018	2	image	0.049	2	technology	0.012
3	design	0.013	3	datum	0.013	3	network	0.031	3	shipping	0.011
4	power	0.013	4	water	0.012	4	SAR	0.028	4	datum	0.01
5	structure	0.01	5	marine	0.01	5	object	0.02	5	service	0.008
6	condition	0.009	6	activity	0.009	6	dataset	0.02	6	port	0.007
7	performance	0.008	7	monitoring	0.008	7	target	0.018	7	development	0.007
8	process	0.008	8	fishing	0.007	8	performance	0.015	8	industry	0.007
9	fuel	0.008	9	spill	0.007	9	classification	0.015	9	management	0.007
10	load	0.008	10	island	0.007	10	accuracy	0.014	10	time	0.006
Research on energy and high-efficiency navigation technology			Research on the derivation of autonomous ships			Research on image analysis and classification			Research on port connectivity

The topics are ① Situational awareness technology research (11.98%), ② Ocean observation and sensing research (9.28%), ③ Arctic route research (7.94%), ④ Route decision and control technology research (13.83%), ⑤ Energy and high-efficiency navigation technology research (14.87%), ⑥ Autonomous ship derivation research (8.17%), ⑦ Image analysis and classification research (19.6%), and ⑧ Port connectivity research (14.36%). Similar to the results of the co-occurrence analysis, ⑦ image analysis and classification research (19.6%) were found to have the highest proportion.

3.3.1. Topic Classification Results

To interpret the results of the topic classification results, we organized the paragraphs as follows. First, the main keywords for each topic were presented and the reason for the topic name was explained. Next, we introduced the main articles on the topic.

The first topic, titled ‘Situational Awareness Technology Research’ contains the keywords ‘detection,’ ‘target,’ ‘image,’ ‘SAR,’ and ‘radar’. Situational awareness technology is a system that detects and recognizes maritime fixtures and floating objects by fusing onboard data such as AIS, radar, and SAR images to provide risk warnings, and is a core technology for autonomous ship operation [30], and it is estimated that research has been conducted on it. Lee et al. (2018) is the representative study, and if you look at the details, ship monitoring using satellite SAR imagery is classified into three stages: ship detection, ship discrimination, and ship identification. However, because there are many studies on ship detection and identification worldwide, but only a few studies on ship identification, this study applies recent identification concepts that have shown high performance in existing airborne SAR target identification to OpenSARShip database to perform ship identification, and then analyze the performance to investigate the utility of OpenSARShip database [50].

The main keywords of the second topic are ‘datum,’ ‘measurement,’ ‘wave,’ ‘water,’ ‘ocean,’ and ‘wind’ and is titled ‘Ocean Observation and Sensing Research’. The process of observing and predicting the state of the marine environment is essential for the operation of autonomous ships. Furthermore, considering ship communication, it is necessary to produce marine environment information so that decisions can be made by analyzing only the information onboard the ship [51], and it appears that research has been conducted on this. Yucheng Zhou et al. (2021) is an example of a representative study. In general, the authors point out that in existing studies, external factors such as wind, waves, and currents are not considered in the route planning of autonomous ships, which affects navigation safety, and propose a PSO optimization algorithm that considers external factors such as wind, waves, and currents [52].

The third topic titled ‘Arctic Route Research’ has the following main keywords: ‘ice,’ ‘concentration,’ and ‘Arctic’. The Arctic Ocean Route emerged as a game-changer in the Fourth Industrial Revolution because of its high economic efficiency when used as a regular route for logistics transport, as it reduces the distance by 4,000 to 7,000 km and shortens the voyage by approximately 13 days compared to the existing Southern Sea Route via the Suez Canal [53]. However, unlike other oceans, the Arctic Ocean is characterized by a high probability of ship accidents and casualties because of iceberg collisions and difficulty in rescuing people because of the harsh climate, so many shipping companies currently tend to avoid Arctic routes. However, autonomous ships are expected to use the Arctic route in the future because they can ensure crew safety and use optimized routes that can be traveled with minimal energy. Ziaul Haque Munim et al. (2022) proposed that the three most important criteria for deploying autonomous ships on Arctic routes are operating costs, operational aspects, and environmental protection. Later, the International Maritime Organization’s (IMO’s) Polar Code states that “autonomous ships are not included in the category of ships that can operate in polar waters, hence they cannot yet operate, and further discussion is needed [54].

The fourth topic is titled ‘Research on Navigation Decision-making and Control Technology’ and contains the following keywords: ‘system,’ ‘control,’ ‘navigation,’ and ‘collision’. Route decision-making and control technology research is a key indicator for determining the autonomy level of autonomous ships, as it enables autonomous ships to find autonomous and economical routes in consideration of various situations (traffic conditions, weather, routes, etc.) and determine the best route that can be operated autonomously [30], and it can be inferred that research has been conducted on it. Xinyu Zhang et al. (2021) is a representative article that reviews the main trends in the MASS R&D industry and collision avoidance navigation technology from academia to industry. It also analyzed the composition of collision avoidance navigation, cognitive navigation using the brain, and e-navigation technologies to systematically reveal the mechanisms and principles of their efficient operation in a typical maritime environment, thereby analyzing the trend of maritime collision avoidance navigation systems [55].

The fifth topic, titled ‘Research on Energy and High-Efficiency Navigation Technology’ has the following keywords: ‘system.’ ‘energy,’ ‘power,’ and ‘condition’. Autonomous ships not only use optimized routes that can be navigated with minimum energy, but also have the advantage of integrated on-board data management which allows for energy optimization by managing all energy systems (fuel storage, fuel supply, power generation, energy storage, and energy consumption systems) installed on the ship in an integrated manner for their efficient use [30]. Konstantinos et al. (2018), propose a system that automates the energy management of propulsion engines, the largest energy producer on ships [56].

The sixth topic titled ‘Autonomous Ship Derivative Research’ has ‘area,’ ‘oil,’ ‘fishing,’ and ‘monitoring’. Deep learning, the basic technology of autonomous ships, can be applied not only to ships but also to many other marine fields. For example, FNN (Fully-connected Neural Network), and CNN (Convolutional Neural Network), etc. are used to identify red tides, oil spills, and illegal fishing boats, etc. that are deadly to fisheries and aquaculture, and Linear Regression, RNN (Recurrent Neural Network)/LSTM, etc. are used to estimate seaweed, sea water temperature, and salinity in the sea surface. Choi (2021) proposes a deep learning-based methodology to predict the occurrence of anomalous high-temperature phenomena using LSTM models [57].

The seventh topic is titled ‘Image Analysis and Classification Research’ with the following keywords ‘detection,’ ‘image,’ ‘network,’ and ‘SAR’. Perception of the surrounding environment, including detection and classification of objects is one of the essential skills required for autonomous ships [58]. Although computer vision research for ship image classification has been steadily progressing, it suffers from the lack of properly labeled databases for ship classification, which seems to have been improved. José (2022) is a sample article that uses Mask-CNN and OMRCNN-SHD techniques for small vessel detection in autonomous ship technology. Data augmentation was conducted to solve the problem of a limited number of real image samples of small ships [59].

The eighth topic is titled ‘Research related to Port Connectivity’ and contains the following keywords: ‘system,’ ‘technology,’ ‘shipping,’ ‘service,’ and ‘port’. When autonomous ships are unmanned, advanced port infrastructure that can be linked to port management, fueling, optimal loading and unloading, etc. is required, and it seems that research has been conducted on this. Anastasia Tsvetkova (2022) is a sample article that analyzes how autonomous ships will create value and for whom, and how various actors in the maritime logistics ecosystem can monetize or otherwise benefit from the innovation. Interviews were conducted with port logistics experts and relevant innovation leaders to analyze different aspects of value creation in autonomous ships [60].

3.3.2. Topic Analysis by Year

The number of articles in all eight topics increased at a compound annual growth rate (CAGR) of 33.2% from 2018 to 2022. In particular, ⑦ image analysis and classification research increased the most at 65.4%, followed by ④ route decision and control technology research at 52.4%, and ⑤ energy and high-efficiency navigation technology research at 35.1%. On the other hand, ③ research on Arctic routes increased the least at 3.3%. For Arctic routes, reliable sea ice information detection technology is needed to analyze the size, thickness, and ice concentration of drift ice during navigation [61]. However, data collection in this area is relatively difficult compared to other areas of the world, which is why research has stagnated.

4. Discussion

5. Conclusions

References

1. Aslam, S.; Michaelides, M.P.; Herodotou, H. Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet Things J. 2020, 7, 9714–9727. [Google Scholar] [CrossRef]
2. Wróbel, K.; Gil, M.; Montewka, J. Identifying research directions of a remotely-controlled merchant ship by revisiting her system-theoretic safety control structure. Saf. Sci. 2020, 129, 104797. [Google Scholar] [CrossRef]
3. Mauro, F.; Kana, A. Digital twin for ship life-cycle: A critical systematic review. Ocean Eng. 2023, 269, 113479. [Google Scholar] [CrossRef]
4. Hwang, H.; Joe, I. Enhancing IoT Connectivity and Services for Worldwide Ships through Multi-Region Fog Cloud Architecture Platforms. Electronics 2023, 12, 4250. [Google Scholar] [CrossRef]
5. Ganchev, I.; Ji, Z.; O’droma, M. Horizontal IoT Platform EMULSION. Electronics 2023, 12, 1864. [Google Scholar] [CrossRef]
6. Ramos, M.A.; Utne, I.B.; Vinnem, J.E.; Mosleh, A. Accounting for Human Failure in Autonomous Ship Operations. In Safety and Reliability—Safe Societies in a Changing World; Haugen, S., Barros, A., van Gulijk, C., Kongsvik, T., Vinnem, J.E., Eds.; CRC Press: London, UK, 2018; pp. 1–10. [Google Scholar]
7. Rodseth, O.J. From Concept to Reality: Unmanned Merchant Ship Research in Norway. In Proceedings of the Underwater Technology (UT), Busan, Korea, 21–24 February 2017; p. 10. [Google Scholar]
8. Chang, Y.; Zhang, C.; Wang, N. The International Legal Status of Unmanned Maritime Vehicles. Marine Policy 2020, 113, 103830. [Google Scholar] [CrossRef]
9. Schmitt, M.N.; Goddard, D.S. International Law and the Military Use of Unmanned Maritime Systems. Int. Rev. Red Cross 2016, 98, 567–592. [Google Scholar] [CrossRef]
10. Kim, D.; Lee, C.; Park, S.; Lim, S. Potential Liability Issues of AI-Based Embedded Software in Maritime Autonomous Surface Ships for Maritime Safety in the Korean Maritime Industry. J. Mar. Sci. Eng. 2022, 10, 498. [Google Scholar] [CrossRef]
11. Utne, I.B.; Sorensen, A.J.; Schjolberg, I. Risk Management of Autonomous Marine Systems and Operations. In Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering, Hamburg, Germany, 5–10 June 2017; pp. 1–10. [Google Scholar]
12. Fan, C.; Wróbel, K.; Montewka, J.; Gil, M.; Wan, C.; Zhang, D. A Framework to Identify Factors Influencing Navigational Risk for Maritime Autonomous Surface Ships. Ocean Eng. 2020, 202, 107188. [Google Scholar] [CrossRef]
13. Wrobel, K.; Montewka, J.; Kujala, P. Towards the Assessment of Potential Impact of Unmanned Vessels on Maritime Transportation Safety. Reliab. Eng. Syst. Saf. 2017, 165, 155–169. [Google Scholar] [CrossRef]
14. Web of Science. Available online: https://www.webofscience.com (accessed on 22 November 2023).
15. Ananiadou, S.; Rea, B.; Okazaki, N.; Procter, R.; Thomas, J. Supporting Systematic Reviews Using Text Mining. Soc. Sci. Comput. Rev. 2009, 27, 509–523. [Google Scholar] [CrossRef]
16. Babić, D.; Kalić, M. Modeling the Selection of Airline Network Structure in a Competitive Environment. J. Air Transp. Manag. 2018, 66, 42–52. [Google Scholar] [CrossRef]
17. Jun, S.; Park, S.S.; Jang, D.S. Document Clustering Method Using Dimension Reduction and Support Vector Clustering to Overcome Sparseness. Expert Syst. Appl. 2014, 41, 3204–3212. [Google Scholar] [CrossRef]
18. Eroglu, Y. Text Mining Approach for Trend Tracking in Scientific Research: A Case Study on Forest Fire. Fire 2023, 6, 33. [Google Scholar] [CrossRef]
19. Rødseth, Ø.J.; Burmeister, H.C. Developments Toward the Unmanned Ship. Proceedings of International Symposium Information on Ships–ISIS 2012, August 2012; Vol. 201; pp. 30–31. [Google Scholar]
20. Kim, Tae-eun; Perera, Lokukaluge Prasad; Sollid, Magne-Petter; Batalden, Bjørn-Morten; Sydnes, Are Kristoffer. Safety Challenges Related to Autonomous Ships in Mixed Navigational Environments. WMU Journal of Maritime Affairs 2022, 21, 141–159. [Google Scholar] [CrossRef]
21. Arnsdorf, I. Roll-Royce Drone Ships Challenge $375 Billion Industry: Freight. Bloomberg Online, 2014. Available online: Bloomberg Website (accessed on 2014. 2).
22. Lim, Y.; Lee, Y.C. Issues of Autonomous Ships and Implications for Maritime Legislation under IMO Agreements. Legal Studies 2018, 18, 155–181. [Google Scholar]
23. Cui, K.; Lin, B.; Sun, W.; Sun, W. Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster. Electronics 2019, 8, 1287. [Google Scholar] [CrossRef]
24. Pritchett, P.W. Ghost Ships: Why the Law Should Embrace Unmanned Vessel Technology. Tulane Maritime Law Journal 2015, 40, 197. [Google Scholar]
25. Choi, J.; Lee, S.I. An Analytical Review on the Seaworthiness of Unmanned Ships. Maritime Policy Research 2018, 33, 171–191. [Google Scholar]
26. Yoon, H. Y.; Kim, J. H.; Choi, H. C. Analysis of Research Trends on Secondary Batteries Using Bibliographic. Information. Journal of Technology Innovation 2023, 26, 463–478. [Google Scholar]
27. Jin, S. A.; Heo, G. E.; Jung, Y. K.; Song, M. A Study on Tracking Topic Changes Using Network Analysis Based on Twitter Data. Journal of the Korean Society for Information Management 2013, 30, 285–302. [Google Scholar] [CrossRef]
28. Kim, N. G.; Lee, D. H.; Choi, H. C. Trends in Text Analysis Technology and Its Applications. Journal of the Korean Institute of Communications and Information Sciences 2017, 42, 471–492. [Google Scholar] [CrossRef]
29. Park, C. S.; Jeong, J. W. Text Network Analysis: A Case of Understanding Shared Meanings Among Policy Stakeholders through Socio-Cognitive Network Analysis. In Proceedings of the Summer Annual Conference of the Korean Association for Policy Studies; 2013; pp. 828–849. [Google Scholar]
30. Park, H.S.; Park, H.R. A Study on the Policy Directions related to the Introduction of Smart Maritime Autonomous Surface Ship (MASS). In Proceedings of the Spring Conference of the Korean Institute of Navigation and Port Research; 2019. [Google Scholar]
31. Munim, Z. H. Autonomous ships: A review, innovative applications and future maritime business models. In Proceedings of the Supply Chain Forum: An International Journal, Vol. 20, No. 4; 2019; pp. 266–279. [Google Scholar]
32. Lim, Y.; Lee, Y. C. Issues of Autonomous Ships and Implications for Maritime Legislation under IMO Agreements. Legal Studies 2018, 18, 155–181. [Google Scholar]
33. Ortiz de Rozas, J. M. The Production of Unmanned Vessels and its Legal Implications in the Maritime Industry. Master’s Thesis, [University of Oslo], [Oslo], 2014. [Google Scholar]
34. Pritchett, P. W. Ghost Ships: Why the Law Should Embrace Unmanned Vessel Technology. Tulane Maritime Law Journal 2015, 40, 197. [Google Scholar]
35. Choi, J.; Lee, S. I. An Analytical Review on the Seaworthiness of Unmanned Ships. Maritime Policy Research 2018, 33, 171–191. [Google Scholar]
36. Yoon, H. Y.; Kim, J. H.; Choi, H. C. Analysis of Research Trends on Secondary Batteries Using Bibliographic Information. Journal of Technology Innovation 2023, 26, 463–478. [Google Scholar]
37. Jin, S. A.; Heo, G. E.; Jung, Y. K.; Song, M. A Study on Tracking Topic Changes Using Network Analysis Based on Twitter Data. Journal of the Korean Society for Information Management 2013, 30, 285–302. [Google Scholar] [CrossRef]
38. Kim, N. G.; Lee, D. H.; Choi, H. C. Trends in Text Analysis Technology and Its Applications. Journal of the Korean Institute of Communications and Information Sciences 2017, 42, 471–492. [Google Scholar] [CrossRef]
39. Park, C. S.; Jeong, J. W. Text Network Analysis: A Case of Understanding Shared Meanings Among Policy Stakeholders through Socio-Cognitive Network Analysis. In Proceedings of the Summer Annual Conference of the Korean Association for Policy Studies; 2013; pp. 828–849. [Google Scholar]
40. Schvaneveldt, Roger W. Pathfinder Associative Networks: Studies in Knowledge Organization. Ablex Publishing.
41. Jelodar, H.; Wang, Y.; Yuan, C.; Feng, X. Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey. Multimed. Tools Appl. 2019, 78, 15169–15211. [Google Scholar] [CrossRef]
42. Tian, C.; Zhang, J.; Liu, D.; Wang, Q.; Lin, S. Technological Topic Analysis of Standard-Essential Patents Based on the Improved Latent Dirichlet Allocation (LDA) Model. Technol. Anal. Strat. Manag. 2022, pp. 1–16.
43. Vorontsov, K.; Potapenko, A.; Plavin, A. Additive Regularization of Topic Models for Topic Selection and Sparse Factorization. In Proceedings of the Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, 20–23 April 2015; pp. 193–202. [Google Scholar]
44. O’Callaghan, D.; Greene, D.; Carthy, J.; Cunningham, P. An Analysis of the Coherence of Descriptors in Topic Modeling. Expert Syst. Appl. 2015, 42, 5645–5657. [Google Scholar] [CrossRef]
45. Durbin, J.; Watson, G.S. Testing for Serial Correlation in Least Squares Regression. III. Biometrika 1971, 58, 1–9. [Google Scholar] [CrossRef]
46. Seo, Y.; Kim, K.; Kim, J.-S. Trends of Nursing Research on Accidental Falls: A Topic Modeling Analysis. Int. J. Environ. Res. Public Health 2021, 18, 3963. [Google Scholar] [CrossRef]
47. Röder, Michael; Hinneburg, Alexander. Exploring the Space of Topic Coherence Measures. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 2015, pp. 399–408.
48. Im, Jeong-Yeon; Yoon, Ji-Young. Analysis of Trends in Domestic Workplace Women Research through Network Analysis. Asian Women 2018, 57, 201–236. [Google Scholar] [CrossRef]
49. Zhao, W.; Chen, J.; Zen, W. Best Practices in Building Topic Models with LDA for Mining Regulatory Textual Documents. CDER, 9th 15. 20 November.
50. Lee, Seung-Jae; Chae, Tae-Byeong; Kim, Kyung-Tae. Analysis of Ship Classification Performances Using Open-SARShip DB. Korean Journal of Remote Sensing 2018, 34, 801–810. [Google Scholar]
51. Um, Dae-Yong; Park, Bo-Seul; Lee, Bang-Hee. Real-time Observation-based AI Prediction Technology Verification for Autonomous Navigation Ship Support. Proceedings of the Korean Institute of Navigation and Port Research Academic Conference, 2022, 2022, 172–173.
52. Zhou, Yucheng, et al. An Algorithm for Path Planning of Autonomous Ships Considering the Influence of Wind and Wave. Journal of Physics: Conference Series, IOP Publishing, 2021.
53. Shin, Jae-Ha. Legal Study on the Commercialization of Autonomous Transport Vehicles - Prospects and Legal Challenges of Autonomous Shipping Operations. Legal Studies 2020, 20, 43–70. [Google Scholar]
54. Munim, Ziaul Haque. Autonomous Ships for Container Shipping in the Arctic Routes. Journal of Marine Science and Technology 2022, 27, 320–334. [Google Scholar] [CrossRef]
55. Zhang, Xinyu, et al. Collision-avoidance Navigation Systems for Maritime Autonomous Surface Ships: A State of the Art Survey. Ocean Engineering 2021, 235, 109380. [Google Scholar] [CrossRef]
56. 56. Konstantinos, Noutsos S. Performance Indicators and Competition Ranking in Women’s and Men’s World Handball Championship 2017. J Phys Educ Sport, 1766.
57. Choi HM; Kim MK; Yang H. Deep-learning Model for Sea Surface Temperature Prediction Near the Korean Peninsula. Deep Sea Research Part II: Topical Studies in Oceanography, April 1, 2023, 208, 105262.
58. Nam, Kwon-Woo; Noh, Myung-Il; Lee, Hye-Won; Lee, Won-Jae. Deep Learning-Based Ship Image Classification Method for Autonomous Navigation Ships. Journal of the Korean Society for Computer Engineering and Information Technology 2021, 26, 144–153. [Google Scholar]
59. Escorcia-Gutierrez, J.; Gamarra, M.; Beleño, K.; Soto, C.; Mansour, R.F. Intelligent Deep Learning-Enabled Autonomous Small Ship Detection and Classification Model. Computers and Electrical Engineering 2022, 100, 107871. [Google Scholar] [CrossRef]
60. Tsvetkova, Anastasia; Hellström, Magnus. Creating Value through Autonomous Shipping: An Ecosystem Perspective. Maritime Economics & Logistics 2022, 24, 255–277. [Google Scholar]
61. Yoo, Jin-Ho. Implementation of the International Maritime Organization Polar Code in Response to Global Warming and Future Challenges: Initiating Open Regulations in the Arctic and Antarctic and Expanding the Agenda of Eco-friendly Policy.