An Artificial Intelligence Model Considering Data Imbalance for Ship Selection in Port State Control Based on Detention Probabilities

Yan, Ran; Wang, Shuaian; Peng, Chuansheng

doi:10.1016/j.jocs.2020.101257

Cited by 45 publications

(25 citation statements)

References 50 publications

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“…Thus, SVR has received wide attention in SOH estimation. Although convex optimization problems [44] are able to solve large-scale practical engineering problems [9], random forest algorithm [53], not only has its unique merit in reducing the overfitting problems [55], but also has been widely used in feature screening [22]. In considering the nature and structure of the health of lithium-ion batteries, random forests are employed in this study.…”

Section: Literature Reviewmentioning

confidence: 99%

A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR

Zhi

Wang

2022

Complex Intell. Syst.

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State of health (SOH) is the ratio of the currently available maximum capacity of the battery to the rated capacity. It is an important index to describe the degradation state of a pure electric vehicle battery and has an important reference value in evaluating the health level of the retired battery and estimating the driving range. In this study, the random forest algorithm is first used to find the most important health factors to lithium-ion batteries based on the dataset released by National Aeronautics and Space Administration (NASA). Then the support vector regression (SVR) algorithm is developed to predict the SOH of a lithium-ion battery. The genetic algorithm-particle swarm optimization (GA-PSO) algorithm is brought forward to optimize the parameter values of the SVR, which could improve the estimation accuracy and convergence speed. The proposed SOH estimation method is applied to four batteries and gets a root mean square error (RMSE) of 0.40% and an average absolute percentage error (MAPE) of 0.56%. In addition, the method is also compared with genetic algorithm-support vector regression (GA-SVR) and particle swarm optimization-support vector regression (PSO-SVR), respectively. The results show that (i) compared with the PSO-SVR method, the proposed method can decrease the average RMSE by 0.10%, and the average MAPE by 0.17%; (ii) compared with the GA-PSO method, number of iterations under the proposed method can be reduced by 7 generations.

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Section: Literature Reviewmentioning

confidence: 99%

A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR

Zhi

Wang

2022

Complex Intell. Syst.

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“…A highly skewed speed-density dataset was processed using reproducible sample generation and the least squares method to obtain accurate traffic flow fundamental diagrams for various traffic flow conditions [23]. Inspection records for port state control have been used to predict the number of deficiencies each inspector can identify for each ship [24] and the ship detention probability [25]. Zhong et al [26] introduced RFID-Cuboids to represent the logistics information and mined the frequent trajectory from the cuboids.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Support System for Complicated Logistics Location Integrating Big Data

Cheng

Pan

2021

Advances in Civil Engineering

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Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two-stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set-covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self-organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

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“…The research results reveal that ship's safety condition related deficiencies as well as technical features of the inspected ship itself are among the most influential factors concerning PSC inspections and ship detention. Yan et al [14] proposed a binary classification machine learning model to predict ship detention in port state control inspection considering data imbalance. Due to the inspection historical factors before an inspection is conducted is not a trivial task as the low detention rate leads to a highly imbalanced inspection records.…”

Section: Psc Related Researchmentioning

confidence: 99%

“…However, in the analysis process, the elements of each level will inevitably affect each other, resulting in a hierarchical structure without independence [1,16]. Taking this study as an example, although PSC currently divides ship deficiencies into 18 deficiency categories, the deficiencies among these 18 categories will affect each other [9,13,14]. For example, in the fire safety (07000) and alarms (08000) deficiency categories, some deficiencies overlap.…”

Section: Risk Assessment Scalementioning

confidence: 99%

“…In addition, when performing data analysis, it is not possible to directly analyze the 18 deficiency categories of ships, as classified by PSC. These 18 deficiency categories are related, and direct data analysis will produce a lot of errors [9,13,14]. Therefore, given that the deficiencies of ships may affect the analysis results, this study believes that MCDM is more appropriate.…”

Section: Introductionmentioning

confidence: 96%

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Ship Deficiency Data of Port State Control to Identify Hidden Risk of Target Ship

Shen

Liu

Chang

et al. 2021

JMSE

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In the new inspection regime (NIR) of port state control (PSC), the criteria for being judged as a standard risk ship (SRS) is too broad. Some ships are classified as SRS even though they have a large number of ship deficiencies. This paper develops a selection system to identify the hidden risk of target ships in the SRS category using PSC inspection records. This system allows the target ship to be used to help reduce cases of flags being greylisted or blacklisted, which can cause huge shipping losses. This study analyzes ship deficiency data in the Tokyo memorandum of understanding (Tokyo MoU) database. It adopts the multiple criteria decision making (MCDM) model as a data processing technique to build a risk assessment scale. It uses fuzzy importance performance analysis (F-IPA) and technology for order preference by similarity to the ideal solution (TOPSIS) for its analysis. Subsequently, the weights of F-IPA and TOPSIS are adopted into the MCDM model. This article also consulted the Tokyo MoU database. It has been verified that the next time PSC inspection, the system hits 83.3% of the hidden risk ships in the SRS category. Thus, this system will help inspectors be more insightful for target ships.

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An Artificial Intelligence Model Considering Data Imbalance for Ship Selection in Port State Control Based on Detention Probabilities

Cited by 45 publications

References 50 publications

A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR

A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR

Design of a Support System for Complicated Logistics Location Integrating Big Data

Ship Deficiency Data of Port State Control to Identify Hidden Risk of Target Ship

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