Internet of Things (IoT) Innovation and Application to Intelligent Governance Systems: A Case Study on DISHUB for Transport Vehicles

Azis, Abdul; Krisbiantoro, Dwi; Riyanto, -

doi:10.30630/joiv.7.1.1282

Cited by 3 publications

(5 citation statements)

References 19 publications

(19 reference statements)

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“…Data Integration: Integrating machine learning algorithms with data infrastructure is complex. Marine transportation operations generate vast amounts of data from sensors, IoT devices, marine databases, and legacy systems [126], [127]. Combining this data into a framework machine learning algorithms can understand is difficult.…”

Section: ) Integration Challenges With Existing Infrastructurementioning

confidence: 99%

“…Retrofitting obsolete systems with machine learning while retaining compatibility and data integrity is tough. Machine learning models in marine transport applications often require realtime data processing to improve decision-making [127], [130]. However, existing infrastructure may struggle to handle real-time data streams' volume, velocity, and variety.…”

Section: ) Integration Challenges With Existing Infrastructurementioning

confidence: 99%

See 1 more Smart Citation

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

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Section: ) Integration Challenges With Existing Infrastructurementioning

confidence: 99%

Section: ) Integration Challenges With Existing Infrastructurementioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Overloading of vehicles is a common occurrence in developing countries. Road damage caused by overloaded vehicles is a significant cause of motor vehicle accidents in Indonesia [5]. According to [6], approximately 79 % of the overall damage was attributed to the degradation of roads due to overload.…”

Section: Introductionmentioning

confidence: 99%

“…According to [6], approximately 79 % of the overall damage was attributed to the degradation of roads due to overload. Moreover, [5] states that vehicle accidents can also be attributed to brake failure resulting from the overloading of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Revealing the impact of losses on flexible pavement due to vehicle overloading

Miftahulkhair,

Arifin,

Sutikno

2024

EEJET

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Pavement overload has become a significant concern in Indonesia due to its damaging effects on the pavement system, where most roads use flexible pavements. Meanwhile, overloading is also typical in other developing countries, causing everything from damage to accidents to the detriment of road users and operators. The current problem is that there is no definite knowledge of handling these overloaded vehicles, which has adverse effects. Therefore, this study aims to reveal the impact of losses caused by overloaded vehicles on flexible pavement. This research uses vehicle traffic load data to examine actual conditions in the field, which are converted to axle load values for each vehicle and used to determine pavement condition using the truck factor approach, mechanistic-empirical design, pavement smoothness level, and the impact of overloading due to fuel consumption by vehicles on flexible pavement. The results show that overloaded vehicles harm the flexible pavement. The increase in truck factor values for all vehicles in overloaded vehicles beyond the maximum allowable load results in more damage to the flexible pavement with an increase in truck factor values up to 83 %. The effect of overloading can be mitigated by increasing the thickness of the asphalt layer and the modulus of asphalt, so it is essential to pay attention to the quality of asphalt in anticipation of this overloading. To anticipate the overload phenomenon, it was found that the thickness of the asphalt overlay in the range of 170–205 mm could mitigate this detrimental effect. In addition, overloading affects the flexible pavement roughness and vehicle fuel consumption. Increased roughness and fuel consumption will increase road maintenance costs and affect driving comfort and safety. In addition, excessive fuel consumption can pollute the surrounding environment

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“…Traditional maritime simulation systems have limitations that make it difficult to simulate complex scenarios accurately [6]. These systems rely on predefined rules and data, which may not be accurate in real-life situations [7]. Another limitation of traditional maritime simulation systems is that they are not adaptive [8].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Khanh

Thao

2023

JOIV : Int. J. Inform. Visualization

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Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.

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Internet of Things (IoT) Innovation and Application to Intelligent Governance Systems: A Case Study on DISHUB for Transport Vehicles

Cited by 3 publications

References 19 publications

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Revealing the impact of losses on flexible pavement due to vehicle overloading

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

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