The state of the problem in the field of ocean engineering is that all the existing parametric or non-parametric models can not accurately and efficiently predict the sea state parameter probability ...distribution tails. Therefore, a research motivation has been developed to propose a new method to forecast in the tail region. In this paper the work objectives and scope are to propose a new adaptive kernel density estimation method formulated on the theory of linear diffusion processes and to utilize this method for predicting the sea state parameter probability distribution tails and for calculating the 50-year extreme Power-Take-Off heaving force value. The key findings are that our proposed new method is robust and can forecast well the 50-year extreme design force values for offshore sustainable energy systems.
•A new adaptive KDE method is proposed.•It is based on linear diffusion processes.•It can accurately calculate the sea state parameter distribution tails.•It can accurately calculate environmental contour lines.•It can forecast well the 50-year extreme design force values for offshore sustainable energy systems.
Here, we present a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization ...problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this paper, a data-driven minimum entropy design is presented without pre-knowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.
This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific ...time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 − 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi's weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities.
This article presents a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization ...problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this article, a data-driven minimum entropy design is presented without preknowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.
Kernel density estimates are important for a broad variety of applications. Their construction has been well-studied, but existing techniques are expensive on massive datasets and/or only provide ...heuristic approximations without theoretical guarantees. We propose randomized and deterministic algorithms with quality guarantees which are orders of magnitude more efficient than previous algorithms. Our algorithms do not require knowledge of the kernel or its bandwidth parameter and are easily parallelizable. We demonstrate how to implement our ideas in a centralized setting and in MapReduce, although our algorithms are applicable to any large-scale data processing framework. Extensive experiments on large real datasets demonstrate the quality, efficiency, and scalability of our techniques.
A photovoltaic power prediction and uncertainty analysis method, which is based on time-sharing, multi-objective slime mould optimization algorithm (MOSMA), support vector machine (SVM) and ...nonparametric kernel density estimation, has been proposed in this study. Furthermore, the high-resolution solar irradiance forecast value of a certain area is obtained through WRF model. The weather forecast value of local temperature and wind speed is crawled, which is matched to the same type of historical data in the corresponding period for processing. A new algorithm that MOSMA-SVM is also put forward to improve the power prediction accuracy. In addition, the probability density of the power prediction error is calculated using the nonparametric kernel density estimation method, and the confidence interval is established according to the probability density distribution. On this basis, the uncertainty of the photovoltaic power prediction is analyzed, and the prediction model is applied to the Yulala photovoltaic power plant in central Australia. In comparison to the particle swarm optimization support vector machine, whale algorithm optimization support vector machine, and three deep learning algorithms, the average absolute percentage error (MAPE) of the proposed algorithm is reduced by 0.25%-27.13%, indicating that the proposed algorithm has higher accuracy.
Smart cities are attracting much interest in terms of future development. As new technologies come on stream, ordinary towns are reshaping themselves as smart cities, where technology is used to ...improve connections between all elements of the town. The technology can be embedded everywhere and can harvest data for dedicated smart city applications. Smart cities will have a huge number of different devices running these applications. There will be a substantial amount of data associated with these devices. In the interlinked smart city environment, many different messages could be shared between them. Such devices will be associated with many security risks and privacy issues, as many of the shared statistics could also hold personal data. A substantial review of research has been recently undertaken to ensure that data will be safe in the smart city environment. This review has included all the latest research in the area and is intended to ensure that all the data required to run green smart cities and the devices required for them will remain secure and confidential.
Urban areas around the world are experiencing a sharp growth of shared micro-mobility services mainly because of the introduction of shared dockless bikes and, more recently, of e-scooters. Besides ...understanding who uses these services and why, more studies are needed to understand when and where these services are used and whether their usage patterns differ. This study aims to expand the current state of knowledge about the usage of micro-mobility services by comparing the spatiotemporal usage patterns of a dockless bike sharing (BS) service and an e-scooter service both operating in the city of Turin (Italy). Both visual and statistical approaches are used to analyze and contrast the temporal usage patterns of such services. Usage hotspots are detected by using spatial analysis and contextualized by considering the land use destination.
Results indicate that both micro-mobility services are used to perform short trips, which are mainly occurring on weekdays in the afternoon. Usage peaks suggest that both services primarily fulfill the demand for non-commute related travel, in line with previous studies in other countries. Nevertheless, morning usage peaks of dockless BS service show that the service might also be used for commuting trips to and from university. Usage hotspot detected near to a university district only during weekdays supports this finding. On the other hand, e-scooter trips are mainly concentrated in the city center and in proximity of railway and metro stations, suggesting that, among other purposes, the service is used as a first and last-mile connection to public transport.
The cutting force spectrum of the CNC lathe is the basic data for the reliability design, reliability test, and reliability evaluation of the CNC lathe and its components. Due to the complex and ...changeable turning conditions and different cutting processes, the cutting load presents multi-peak characteristics. At the same time, grouping the counted load cycles when parameter modeling will produce certain errors. As a result, the parameter distribution model cannot meet the modeling requirements. Thus, a compilation method based on kernel density estimation (KDE) of goodness-smoothness comprehensive evaluation (G-SCE) is proposed. The KDE is used to establish the dynamic cutting force distribution of the CNC lathe in which grouping the counted load cycles is not needed. For the bandwidth-determining methods, the rule of thumb method (ROT) and the least-squares cross-validation method (LCV) do not take into account the influence of different bandwidths on the goodness estimation and the smoothness of the estimated curve, and the G-CSE for KDE is proposed to determining the optimal bandwidth. It includes the estimation accuracy test method based on multiple goodness-of-fit tests and the smoothness test method based on the envelope curve, and the entropy method is used to comprehensively weights the estimated goodness index and the smoothness index to determine the optimal bandwidth. The results of the case analysis indicate that the method proposed can solve the problem of too large estimation error of parameter distribution for multimodal distribution. At the same time, it can better comprehensively evaluate the KDE under different bandwidths. In short, a new method of optimal bandwidth selection is proposed in the original method.
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