DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

Industry 4.0 Technologies Applied to Inland Waterway Transport: Systematic Literature Review

The focus of this article is inland waterway transport. Different problems in this domain have been studied due to the increase in waterway traffic globally. Industry 4.0 technologies have become an alternative for the possible solution of these problems. For this reason, this paper aims to answer the following research questions: (1) What are the main problems in transporting cargo by inland waterway? (2) What technological strategies are being studied to solve these problems? (3) What technologies from Industry 4.0 are used within the technological strategies to solve the exposed problems? This study adopts a Systematic Literature Review (SLR) approach. For this work, were recovered 645 articles, 88 of which were eligible, from which we could identify five domains corresponding to (1) traffic monitoring, (2) smart navigation, (3) emission reduction, (4) analytics with big data, and (5) cybersecurity. The strategies currently being considered combine navigation technologies, such as AIS (Automatic Identification System), which offers a large amount of data, with Industry 4.0 tools and mainly machine learning techniques, to take advantage of data collected over a long time. This study is, to our knowledge, one of the first to show how Industry 4.0 technologies are currently being used to tackle inland waterway transport problems and current application trends in the scientific community, which is a first step for the development of future studies and more advanced solutions.

Evaluate the challenges of sustainable supply chain 4.0 implementation under the circular economy concept using new decision making approach

Industry 4.0 has the potential of growing industrialization and, on the other hand, disrupting the sustainability of prevailing manufacturing supply chains through inducing great challenges such as higher resource consumption that, in turn, results in global warming and climate change. As a result, researchers working in the area of sustainable supply chain 4.0 need to make deep evaluations on the challenges arising for manufacturing supply chains contemplating the improvement of their sustainability levels and having a digital transformation toward Industry 4.0. To fill this gap, the current paper designs an innovative framework on the basis of the Stepwise Weight Assessment Ratio Analysis (SWARA) technique and the Complex Proportional Assessment (COPRAS) approach to evaluate the challenges that may arise for supply chain 4.0 in the q-Rung Orthopair Fuzzy Sets (q-ROFSs) setting. The proposed method uses an extended SWARA process to determine the criteria importance degrees considering the experts’ preferences. The performance of the proposed method was assessed by conducting an empirical case study under the q-ROFSs condition. Further, a sensitivity analysis was executed to check whether the proposed method is stable enough to be relied on parameter values. Finally, the results obtained were compared to those of currently used methods to verify the obtained results’ reliability. As revealed by the comparative results, the framework proposed in this article was of higher consistency and strength compared to other prevailing approaches.

Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model

To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with sensor data exchange. In coastal areas, littoral AIS does not meet the expectations of operational continuity and system availability, and there are areas not covered by the system. Therefore, in this study, process models were designed to simulate the tracking of vessel trajectories, enabling system failure detection based on integrity monitoring. Three methods for system integrity monitoring, through hypotheses testing with regard to differences between model output and actual simulated vessel positions, were implemented, i.e., a Global Positioning System (GPS) ship position model, Dead Reckoning and RADAR Extended Kalman Filter (EKF)—Simultaneous localization and mapping (SLAM) based on distance and bearing to navigational aid. The designed process models were validated on simulated AIS dynamic data, i.e., in a simulated experiment in the area of Gdańsk Bay. The integrity of AIS information was determined using stochastic methods based on Markov chains. The research outcomes confirmed the usefulness of the proposed methods. The results of the research prove the high level (~99%) of integrity of the dynamic information of the AIS system for Dead Reckoning and the GPS process model, while the level of accuracy and integrity of the position varied depending on the distance to the navigation aid for the RADAR EKF-SLAM process model.

Industry 4.0: technology spillover impact on digital manufacturing industry

Purpose

Under the guidance of the concept of openness and development, the paper grasps the mechanism of technology spillover in developed countries and analyzes how to better absorb advanced manufacturing technology based on empirical analysis so as to point out the path for the transformation and development of China’s digital manufacturing industry.

Design/methodology/approach

The paper constructs the panel data model and further analyzes the impact of international technology spillovers on the transformation and development of the digital manufacturing industry.

Findings

This paper measures the level of technology spillover in the Yangtze River Delta region and finds that foreign direct investment (FDI) technology spillover and import trade technology spillover among four provinces and cities show a growth trend from 2010 to 2017. But after 2017, there is a certain degree of decline.

Originality/value

With the advent of industry 4.0, the digital manufacturing industry of all countries in the world is developing with a new attitude, the global technology spillover methods are diverse and the spillover channels have changed greatly, which will affect the transformation and upgrading of China's digital manufacturing industry.

Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data

Implementing AI in all fields is a solution to the complications that can be troublesome to solve for human beings and will be the key point of the advancement of those spheres. In the marine world, specialists also encounter some problems that can be revealed through addressing AI and machine learning algorithms. One of these challenges is determining the depth of the seabed with high precision. The depth of the seabed is utterly significant in the procedure of ships at sea occupying a safe route. Thus, it is considerably crucial that the ships do not sit in shallow water. In this article, we have addressed the fuzzy c-means (FCM) clustering algorithm, which is one of the vigorous unsupervised learning methods under machine learning to solve the mentioned problems. In the case study, crowdsourced data have been trained, which are gathered from vessels that have installed sound navigation and ranging (SONAR) sensors. The data for the training were collected from ships sailing in the south part of South Korea. In the training section, we segregated the training zone into the diminutive size areas (blocks). The data assembled in blocks had been trained in FCM. As a result, we have received data separated into clusters that can be supportive to differentiate data. The results of the effort show that FCM can be implemented and obtain accurate results on crowdsourced bathymetry.