This work presents a comparative study of two signal processing methods for the estimation of the roll natural frequency towards the real-time transverse stability monitoring of fishing vessels. The first method is based on sequential application of the Fast Fourier Transform (FFT); the second method combines the Empirical Mode Decomposition (EMD) and the Hilbert-Huang Transform (HHT). The performance of the two methods is analysed using roll motion data of a stern trawler. Simulated time series from a one degree-of-freedom nonlinear model, and experimental time series obtained from towing tank tests are utilized for the evaluation. In both cases, beam waves are considered but, while irregular waves are adopted in the simulated data, the towing tank tests are made in regular waves. Based on the available data the performance of both estimation methods is comparable, but the EMD-HHT method turns out slightly better than the sequential FFT. Finally, the use of a statistical change detector, together with the EMD-HHT methodology, is proposed as a possible approach for the practical implementation of an onboard stability monitoring system.
Abstract: In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation. Graphical Abstract: [Figure not available: see fulltext.]
Hydrogen is believed as a promising energy carrier that contributes to deep decarbonization, especially for the sectors hard to be directly electrified. A grid-connected wind/hydrogen system is a typical configuration for hydrogen production. For such a system, a critical barrier lies in the poor cost-competitiveness of the produced hydrogen. Researchers have found that flexible operation of a wind/hydrogen system is possible thanks to the excellent dynamic properties of electrolysis. This finding implies the system owner can strategically participate in day-ahead power markets to reduce the hydrogen production cost. However, the uncertainties from imperfect prediction of the fluctuating market price and wind power reduce the effectiveness of the offering strategy in the market. In this paper, we proposed a decision-making framework, which is based on data-driven robust chance constrained programming (DRCCP). This framework also includes multi-layer perception neural network (MLPNN) for wind power and spot electricity price prediction. Such a DRCCP-based decision framework (DDF) is then applied to make the day-ahead decision for a wind/hydrogen system. It can effectively handle the uncertainties, manage the risks and reduce the operation cost. The results show that, for the daily operation in the selected 30 days, offering strategy based on the framework reduces the overall operation cost by 24.36%, compared to the strategy based on imperfect prediction. Besides, we elaborate the parameter selections of the DRCCP to reveal the best parameter combination to obtain better optimization performance. The efficacy of the DRCCP method is also highlighted by the comparison with the chance-constrained programming method.
Product-based companies worldwide attempt to integrate services into their offerings, embarking on “servitization” as a key strategy. These days, the acceleration of technological innovation (i.e., Industry 4.0) has triggered an emerging IT-driven business paradigm called digital servitization or smart product-service system (PSS) that embeds Industry 4.0 technologies. As a result of these developments, related literature has expanded across different disciplines in recent years. However, understanding and describing literature is not easy considering its volume and variety. Establishing common ground for central concepts is essential for science. Thus, to clarify important topics and research issues on servitization and PSSs in Industry 4.0, we carry out a comprehensive literature review by performing text mining of 419 journal articles. A machine learning approach is applied to learn and identify the specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics are identified, and the enabler–engineering–goal framework is developed. This study contributes to clarifying a systematized view of dispersed studies of servitization and PSSs in Industry 4.0 across multiple disciplines and encourages further academic discussions and industrial transformation.
The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast that has been the focus of considerable recent attention. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy has initiated a new IEA Wind Task with a much broader perspective, which includes prospective interaction with other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. In the new IEA Wind Task 51 (entitled "Foreacsting for the Weather Drive Energy System") the existing Work Packages (WPs) are complemented by work streams in a matrix structure. The Task is divided in three WPs according to the stakeholders: WP1 is mainly aimed at meteorologists, providing the weather forecast basis for the power forecasts. In WP2, the forecast service vendors are the main stakeholders, while the end users populate WP3. The new Task 51 started in January 2022. Planned activities include 4 workshops. The first will focus on the state of the art in forecasting for the energy system plus related research issues and be held during September 2022 in Dublin. The other three workshops will be held later during the 4-year Task period and address (1) seasonal forecasting with emphasis on Dunkelflaute, storage and hydro, (2) minute-scale forecasting, and (3) extreme power system events. The issues and conclusions of each of the workshops will be documented by a published paper. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective.
This paper presents a new approach to attain estimates of the sea state based on short-time sequences of wave-induced ship responses. The present sea state estimation method aims at reconstructing the incident wave profiles in time domain. In order to identify phase components of the incident waves, the Prolate Spheroidal Wave Functions (PSWF) are employed. The use of PSWF offers an explicit expression of phase components in the measured responses and incident waves, indicating that estimations can be efficiently attained. A method to estimate the relative wave heading angle based on the response measurements and pre-computed transfer functions of the responses is also proposed. The method is tested with numerical simulations and experimental measurements of ship motions, i.e. heave, pitch, and roll, together with vertical bending moment and local pressure in a post-panamax size containership. Validation is made by comparing the reconstructed wave profiles with the incident waves. The accuracy and efficiency of the present approach are promising. At the same time, it is shown that the use of responses, which are more broad-banded in their frequency characteristics, is an effective means to cope with high frequency noise in reconstructed waves.
This paper presents a study focused on wave spectrum estimation in practical scenarios where multiple ships operate in the same geographical area, potentially forming a network of wave recorders. A novel methodology is proposed to improve the accuracy and precision of the wave spectrum estimates, by combining sea state estimation methods and techniques for tuning the wave-to-motion transfer functions. The framework of the wave buoy analogy is used to derive estimates for each ship through the use of measured ship motion data and available initial estimates of transfer functions. Simultaneously, the wave-to-motion transfer functions of the individual ship are tuned by utilizing a weighted version of the wave data inferred on board the other ships in the network. The overall architecture of the procedure is modular, in the sense that various approaches may be implemented for obtaining sea state estimates and tuned transfer functions. The methodology is demonstrated through two case studies, one based on simulated vessel responses, and the other using model test data of ship motions in a wave tank. Both case studies consider a network of three ships in long-crested waves equipped with a dynamic positioning system. It is shown that the procedure provides good wave spectrum estimates, and leads to reduced uncertainty in the estimates via tuning of the vessel transfer functions.
With the current revolution in Shipping 4.0, a tremendous amount of data is accumulated during vessel operations.
Data quality (DQ) is becoming more and more important for the further digitalization and effective decision-making
in shipping industry. In this study, a practical DQ assessment method for raw data in vessel operations is proposed.
In this method, specific data categories and data dimensions are developed based on engineering practice and existing
literature. Concrete validation rules are then formed, which can be used to properly divide raw datasets. Afterwards,
a scoring method is used for the assessment of the data quality. Three levels, namely good, warning and alarm,
are adopted to reflect the final data quality. The root causes of bad data quality could be revealed once the internal
dependency among rules has been built, which will facilitate the further improvement of DQ in practice. A case study
based on the datasets from a Danish shipping company is conducted, where the DQ variation is monitored, assessed
and compared. The results indicate that the proposed method is effective to help shipping industry improve the quality
of raw data in practice. This innovation research can facilitate shipping industry to set a solid foundation at the early
stage of their digitalization journeys.
This paper studies the Quay Crane Scheduling Problem (QCSP). The QCSP determines how a number of quay cranes should be scheduled in order to service a vessel with minimum makespan. Previous work considers the QCSP to be a combinatorially hard problem. For that reason, the focus has been on developing efficient heuristics. Our study shows, however, that the QCSP is tractable in the realistic setting, where quay cranes can share the workload of bays. We introduce a novel linear time algorithm that solves the QCSP and prove its correctness.
The estimation of the thrust deduction fraction is generally conducted in ideal weather conditions. However, the presence of waves considerably alters the magnitude of this propulsive coefficient. The increased load of the propeller could be the main cause for the variation of the thrust deduction fraction in realistic operating conditions. In this work, load-varying self-propulsion model-scale numerical simulations in calm water conditions for the same ship speed are performed to investigate the influence of the propeller loading on the thrust deduction fraction. The single screw model-scale KVLCC2 tanker is selected as the case study. The results reveal a non-linear inverse correlation between the thrust deduction fraction and the propeller loading. A comparison with model-testing conducted on the KVLCC2 tanker in regular head waves suggests that the propeller loading is the main factor influencing the magnitude of the thrust deduction fraction in waves for the considered case vessel.