Risk Factors Analysis Modeling for Ship Collision Accident in Inland River Based on Text Mining

Under the backdrop of the robust growth of the global economy, the water transport industry is experiencing rapid development, resulting in an increase in ship collisions and a critical water traffic safety situation. This study uses text mining techniques to gather a corpus of data. The corpus includes human factors, ship factors, natural environmental factors, and management factors, which are used as target data to obtain a high-dimensional sparse original feature vector space set comprising eigenvalues and eigenvalue weight attributes. Chi-square statistics are utilised to reduce dimensionality, resulting in a final set of 33-dimensional text feature items that determine the causal factors of ship collision risk. Taking the four steps involved in the collision process as the primary focus, a Bayesian network structure for ship collision risk is constructed based on the “human-ship-environment-management” system. By incorporating existing ship collision accident/danger reports, conditional probability tables are computed for each node in the Bayesian network structure, enabling the modelling of ship collision risk. The model is validated through an example, revealing that, under relevant conditions, the probability of collision exceeds 90 %. This finding demonstrates the validity of the model and allows one to deduce the primary cause of ship collision accidents.

With the development of society, international trade is playing an important role. Maritime transport has become the main mode of transport in international trade with its advantages of low transport price, large transport volume and low environmental pollution. However, the rapid development of maritime transport is accompanied by the risk of maritime accidents. Studies show that ship collision accident has always been the main type of maritime accidents, and ship collision caused by human factors accounts for more than 90% of the total accidents. It has become a key issue in the research field of ship navigation safety to analyze the causes of ship collision and explore the mechanism of human factors. By analyzing the ship collision accident reports, this paper summarizing the main human factors of ship collision, and through analyzing the causal chain of ship collision accident to determine the Bayesian network structure. Finally, this paper using the accident reports into the model to illustrate the effectiveness of the Bayesian network model; meanwhile, by analyzing the cause of the Bayesian network model, this paper gets most likely cause chain that cause collision accident happens, so as to providing safety management countermeasures for the crew, ship company and ship safety management department.

In the process of ship collision avoidance, the occurrence of human errors and its probability are stochastic and fuzzy. It is difficult to use the traditional Fault Tree Analysis method to carry out the quantitative analysis. To solve the problem, the fuzzy fault tree of ship collision from human errors was established by using fuzzy numbers to represent the occurrence probability of basic events and top event. And the grey relational analysis method was applied to the qualitative and quantitative analysis of fuzzy fault tree. The fuzzy significance set of the basic events was taken as a reference sequence, while the characteristic matrix made up of minimal cut sets was taken as a comparative sequence. The failure mode of human factor which may greatly influence the occurrence of ship collision was obtained by calculating the relational degrees between the fault mode of human factor represented by the minimal cut set and ship collision accident, which provides a reference for decision making to reduce the impact of human errors on ship collision accident..

ABSTRACTOn 16 January 2015, about 10 miles east of Busan, South Korea, Liberian container carrier, M/V Ernest Hemingway collided with Korean fishing boat, Geunyang-ho, and Geunyang-ho sank and two Korean nationals perished and about 600 liters of fuel oil on board Geunyang-ho spilt into the sea. M/V Ernest Hemingway’s crew escaped from the site without carrying out duties of rescue after the collision. The Busan District Prosecutor’s Office arrested and prosecuted M/V Hemingway’s second officer and an able seaman, Philippine nationals, for the criminal facts subject to judgement, violation of the Act on Aggravated Punishment, etc., of Specific Crimes, Professional Negligence resulting in ship sinking, and violation of the Marine Environment Management Act. Busan District Court pleaded guilty and sentenced fine for the violation of the Marine Environment Management Act, on the other hand, sentenced dismissal of public prosecution for the violation of the Act on Aggravated Punishment, etc., of Specific Crimes and professional negligence resulting in ship sinking due to lack of jurisdiction based on UNCLOS Article 97(1). Looking at the rulings and the annotations on this case, the main issues were whether South Korea has jurisdiction on ship collision incidents on the high seas as the state of the alleged victim’s nationality, and whether South Korea may exercise criminal jurisdiction over marine pollution caused by a ship collision on the high seas. This paper examines the decisions by the Courts and the main issues related with the case. Here is a summary of what has been reviewed in this paper: First, UNCLOS Article 97(1) is applicable for all criminal penalties of the captain and crew members concerning ship collision and other navigational accidents, without consideration on the intention, and can also be applied to intentions or attempts of illegal activities. Therefore, it can be applied to the events causing loss of life or serious injury (professional negligence resulting in deaths, running away after professional negligence resulting in deaths, etc.) and physical damage (professional negligence resulting in ship sinking, or ship damage, etc.) that were caused by ship collision, allowing priority over criminal jurisdiction exercised by the flag state and the state of the alleged offender’s nationality. Second, it is appropriate to interpret that the exclusive jurisdiction of the flag state on high seas as having a superior position against the jurisdiction of other states, not as having jurisdiction that excludes all other states. Therefore, in the M/V Hemingway’s case, it would be reasonable for our country to exercise complementary jurisdiction if the flag state and the state of the alleged offender’s nationality did not exercise criminal jurisdiction.Third, marine pollution accidents occurring in the high seas (EEZ of South Korea) should apply the relevant provisions of UNCLOS Part 12 regardless of the cause. The coastal state can institute proceedings when there are cases of marine pollution occurring in territorial seas or EEZ of the state and occurring outside of its EEZ but causing or being likely to cause damages to the state. For the lawsuit regarding the pollution accidents that occurred outside of national territories shall be suspended if the flag state institute proceedings on the same allegation within six months of the commencement of the lawsuit, and when the flag state ends the lawsuit, the suspended lawsuit is also automatically terminated. South Korea is eligible to file a lawsuit on the M/V Hemingway’s case, and because the flag state did not file a suit within six months, the judgement of the South Korean Court is considered to be valid. Fourth, while the Marine Environment Management Act of South Korea prescribes its jurisdiction based on the flag state of the violated vessel and location of the spills, UNCLOS prescribes coastal state’s jurisdiction based on the severity of pollution damage and compliance on procedures. Therefore, some supplements on related laws need to be made.

This paper proposes a maritime Accident Dynamic Simulator (MADS) method from one ship’s perspective of ship collision accident. The MADS model is built based on discrete dynamic event trees (D-DET) to generate the time-dependent ship collision accident scenarios. According to the characteristics of maritime activities, such as high freedom of participation, the big influence of the environment, and the high uncertainty, the MADS method is modified, and the analyzed ship collision scenario is reasonably simplified. Both crews’ decision-making process and ship manoeuvre are fully discretized to generate new branches of the D-DET. The interactions between encountered ships are modelled with an information transfer model to form the uncertainties between two ships. The MADS consists of physical simulation engine, crew response and interaction model, reliability model of the system hardware, functional model of human-system interfaces and event sequence controlling model. The MADS starts with a pre-set initiating event under a specific situation. Event branches are discretely generated based on probable alternative state changes of operator response or system evolution. The output probability of an end state can be calculated in the same way with the conventional event tree after the whole scenario completed. At the end of the paper, an accident case study is used to verify the efficiency of the ADS.

This paper includes the following parts:• Ship Structure Optimization: methodology to perform ship scantling optimization, decreasing steel weight and keeping the production cost at an acceptable level. We first review the links between “Design” and “Optimization” and secondly define the place of “Ship Structure Optimization” within the general framework of a “Ship Optimization”.• Ship impacts (Collision), with applications to navigation lock and dry dock gates: these gates have to be designed taking into account accidental loads, such as ship collisions. A new simplified analytical method is proposed, based on the so-called super-element method. This method was developed to rapidly assess the crashworthiness of the collided structure and avoid high computational effort of numerical simulations.• Inland waterway Navigation and the development in South America of Inland Waterway Classifications.• EMSHIP, European ERASMUS MUNDUS education program (www.emship.eu): the unique master’s degree in Ship & Offshore Structures awarded by the prestigious European Erasmus Mundus Program. EMSHIP shares the outstanding experiences of educators, trainers, industrial partners and students in the rapidly developing areas of marine and offshore engineering industry.

Background: While it is commonly recognized that almost every work and research discipline utilize their own taxonomy, the language used within a specific discipline may also vary depending on numerous factors, including the desired effect of the information being communicated and the intended audience. Different audiences are reached through publication of information, including research results, in different types of publication outlets such as newspapers, newsletters, magazines, websites, and journals. Prior research has shown that students, both undergraduate and graduate, as well as faculty may have a difficult time locating information in different publication outlet types (e.g., magazines, newspapers, journals). The type of publication may affect the ease of understanding and also the confidence placed in the acquired information. A text analytics tool for classifying the source of research as a newsletter (used as a substitute for newspaper articles), a magazine, or an academic journal article has been developed to assist students, faculty, and researchers in identifying the likely source type of information and classifying their own writings with respect to these possible publication outlet types. Literature Review: Literature on information literacy is discussed as this forms the motivation for the reported research. Additionally, prior research on using text mining and text analytics is examined to better understand the methodology employed, including a review of the original Scale of Theoretical and Applied Research system, adapted for the current research. Research Questions: The primary research question is: Can a text mining and text analytics approach accurately determine the most probable publication source type with respect to being from a newsletter, magazine, or journal? Methodology: A text mining and text analytics algorithm, STAR’ (System for Text Analytics-based Ranking), was developed from a previously researched text mining tool, STAR (Scale of Theoretical and Applied Research), that was used to classify the research type of articles between theoretical and applied research. The new text mining method, STAR’, analyzes the language used in manuscripts to determine the type of publication. This method first mines all words from corresponding publication source types to determine a keyword corpus. The corpus is then used in a text analytics process to classify full newsletters, magazine articles, and journal articles with respect to their publication source. All newsletters, magazine articles, and journal articles are from the library and information sciences (LIS) domain. Results: The STAR’ text analytics method was evaluated as a proof of concept on a specific LIS organizational newsletter, as well as articles from a single LIS magazine and a single LIS journal. STAR’ was able to classify the newsletters, magazine articles, and journal articles with 100% accuracy. Random samples from another similar LIS newsletter and a different LIS journal were also evaluated to examine the robustness of the STAR’ method in the initial proof of concept. Following the positive results of the proof of concept, additional journal, magazine, and newsletter articles were used to evaluate the generalizability of STAR’. The second-round results were very positive for differentiating journals and newsletters from other publication types, but revealed potential issues for distinguishing magazine articles from other types of publications. Discussion: STAR’ demonstrates that the language used for transferring information within a specific discipline does differ significantly depending on the intended recipients of the research knowledge. Further work is needed to examine language usage specific to magazine articles. Conclusions: The STAR’ method may be used by students and faculty to identify the likely source of research or discipline-specific information. This may improve trust in the reliability of information due to different levels of rigor applied to different types of publications. Additionally, the STAR’ classifications may be used by students, faculty, or researchers to determine the most appropriate type of outlet and correspondingly the most appropriate type of audience for the reported information in their own manuscripts, thereby improving the chance for successful sharing of information to appropriate audiences who will deem the information to be reliable, through publication in the most relevant outlet type.

To introduce the research methods of computerized text mining and its possible applications in suicide research and to demonstrate the procedures of applying a specific text mining area, document classification, to a suicide-related study. A systematic search of academic papers that applied text mining methods to suicide research was conducted. Relevant papers were reviewed focusing on their research objectives and sources of data. Furthermore, a case of using natural language processing and document classification methods to analyze a large amount of suicide news was elaborated to showcase the methods. Eighty-six papers using text mining methods for suicide research have been published since 2001. The most common research objective (72.1%) was to classify which documents exhibit suicide risk or were written by suicidal people. The most frequently used data source was online social media posts (45.3%), followed by e-healthcare records (25.6%). For the news classification case, the top three classifiers trained for classification tasks achieved 84% or higher accuracy. Computerized text mining methods can help to scale up content analysis capacity and efficiency and uncover new insights and perspectives for suicide research.

The aim of the study was to examine the possibility of converting subjective textual data written in the free column space of the Mother and Child Handbook (MCH) into objective information using text mining and to compare any monthly changes in the words written by the mothers. Pregnant women without complications (n = 60) were divided into two groups according to State-Trait Anxiety Inventory grade: low trait anxiety (group I, n = 39) and high trait anxiety (group II, n = 21). Exploratory analysis of the textual data from the MCH was conducted by text mining using the Word Miner software program. Using 1203 structural elements extracted after processing, a comparison of monthly changes in the words used in the mothers' comments was made between the two groups. The data was mainly analyzed by a correspondence analysis. The structural elements in groups I and II were divided into seven and six clusters, respectively, by cluster analysis. Correspondence analysis revealed clear monthly changes in the words used in the mothers' comments as the pregnancy progressed in group I, whereas the association was not clear in group II. The text mining method was useful for exploratory analysis of the textual data obtained from pregnant women, and the monthly change in the words used in the mothers' comments as pregnancy progressed differed according to their degree of unease.

We recently released a plugin to Pathvisio to assist the curator with automatic integration of heterogeneous pathway knowledge. Pathway curation involves gathering this knowledge not only through discussions with peers, but also from biological databases and the scientific literature. Integrating this all into one pathway is challenging and to date still mainly a laborious manual process.More and more biological databases provide webservices (ie. REST or SOAP). The main objective of webservices is to provide an interface for other computer applications. Our plug-in uses these interfaces to gather binary relationships of a given pathway entity and present the relation in star shaped figures to the curator. The plug-in also uses functionality of Pathvisio to project and visualize research data on pathways. If research data exists, this can then be projected on to the suggested potential novel entities. The curator can then make an educated decision on extending pathways with database knowledge.Different text mining methods exist to process large quantities of available literature. Text mining results can be very valuable in pathway curation. Integrating them in a similar way as with biological databases is difficult. It requires a text-mining output standard. In our current plug-in we included text-mining support in our current version of the plug-in.We like to propose here a standard for text mining output. The output standard is a data object that captures the relationships found with text mining, the document identifiers, the sentences from where the results originate, the method used, and its metrics. Such an output standard would enable a user-oriented comparison of text mining results. Currently, text-mining results are mainly evaluated on their performance in finding relevant results. By enabling a text mining out standard, the results become available to the larger user community. The data is immediately accessible to a wide range of applications. If a text mining method supports our output standard, the pathway curation community can use it in Pathvisio through our plug-in.

We recently released a plugin to Pathvisio to assist the curator with automatic integration of heterogeneous pathway knowledge. Pathway curation involves gathering this knowledge not only through discussions with peers, but also from biological databases and the scientific literature. Integrating this all into one pathway is challenging and to date still mainly a laborious manual process.More and more biological databases provide webservices (ie. REST or SOAP). The main objective of webservices is to provide an interface for other computer applications. Our plug-in uses these interfaces to gather binary relationships of a given pathway entity and present the relation in star shaped figures to the curator. The plug-in also uses functionality of Pathvisio to project and visualize research data on pathways. If research data exists, this can then be projected on to the suggested potential novel entities. The curator can then make an educated decision on extending pathways with database knowledge.Different text mining methods exist to process large quantities of available literature. Text mining results can be very valuable in pathway curation. Integrating them in a similar way as with biological databases is difficult. It requires a text-mining output standard. In our current plug-in we included text-mining support in our current version of the plug-in.We like to propose here a standard for text mining output. The output standard is a data object that captures the relationships found with text mining, the document identifiers, the sentences from where the results originate, the method used, and its metrics. Such an output standard would enable a user-oriented comparison of text mining results. Currently, text-mining results are mainly evaluated on their performance in finding relevant results. By enabling a text mining out standard, the results become available to the larger user community. The data is immediately accessible to a wide range of applications. If a text mining method supports our output standard, the pathway curation community can use it in Pathvisio through our plug-in.

Ship deployment problem with green technology adoption for an inland river carrier under non-identical streamflow and speed limits

Information could be power if most technological approach engaged in the era of IT. Web based text mining is one of the approach that could be analyzed in many ways through soft-computing methods and techniques. The analytical result has shown that different methods of text mining have several advantages with the gap of knowledge that is required to improve. This paper explores the performance of various methods and its impact on specific text mining field such as web based financial text analysis for stock prediction. Key research area of financial text mining is becoming one of the potential research field based on the source of online news, forums, blogs or social media.

With the expansion of water transportation networks and increasing traffic intensity, maritime accidents have become frequent, posing significant threats to safety and property. This study presents a knowledge graph-driven framework for maritime accident analysis, addressing the limitations of traditional risk analysis methods in extracting and organizing unstructured accident data. First, a standardized ontology for ship collision accidents is developed, defining core concepts such as event, spatiotemporal behavior, causation, consequence, responsibility, and decision-making. Advanced natural language processing models, including a lexicon-enhanced LEBERT-BiLSTM-CRF and a K-BERT-BiLSTM-CRF incorporating ship collision knowledge triplets, are proposed for named entity recognition and relation extraction, with F1-score improvements of 6.7% and 1.2%, respectively. The constructed accident knowledge graph integrates heterogeneous data, enabling semantic organization and efficient retrieval. Leveraging graph topological features, an accident severity classification model is established, where a graph-feature-driven LSTM-RNN demonstrates robust performance, especially with imbalanced data. Comparative experiments show the superiority of this approach over conventional models such as XGBoost and random forest. Overall, this research demonstrates that knowledge graph-driven methods can significantly enhance maritime accident knowledge extraction and severity classification, providing strong information support and methodological advances for intelligent accident management and prevention.

This paper aims to identify the trends in machine learning research using text mining. The researcharticles contain significant knowledge and research results. However, they are long and have many noisy results such that it takes a lot of human efforts to analyze them. Text mining can be used to analyze and extract useful information from a large number of research articles quickly and automatically. Text mining is the method of defining innovative, and unseen knowledge from unstructured, semi-structured and structured textual data. This knowledge contributed to very important information that can derive from textual data. In this paper, text mining methods are applied to detect trends of terms that occur in the research articles and how they varies over time. We collected 21,906 scientific papers from six top journals in the field of machine learning published in period 1988-2017 and analyzed them using text mining. Our result analysis shows a changing trend of various terms in Machine learning research in three decades. The analysis of our study helps the upcoming researchers to explore the significant research area of machine learning.