Research on probability distribution of port cargo throughput

In order to more accurately examine developing trends in gross cargo throughput, we have modeled the probability distribution of cargo throughput. Gross cargo throughput is determined by the time spent by cargo ships in the port and the operating efficiency of handling equipment. Gross cargo throughput is the sum of all compound variables determining each aspect of cargo throughput for every cargo ship arriving at the port. Probability distribution was determined using the Wald equation. The results show that the variability of gross cargo throughput primarily depends on the different times required by different cargo ships arriving at the port. This model overcomes the shortcoming of previous models： inability to accurately determine the probability of a specific value of future gross cargo throughput. Our proposed model of cargo throughput depends on the relationship between time required by a cargo ship arriving at the port and the operational capacity of handling equipment at the port. At the same time, key factors affecting gross cargo throughput are analyzed. In order to test the efficiency of the model, the cargo volume of a port in Shandong Province was used as an example. In the case study the actual results matched our theoretical analysis.     

How remote is remote pilotage?

“Today, the idea of allowing aircraft pilots to operate as they see fit, without any control from the land, is unthinkable. Will the same things apply to ships in twenty to thirty years’ time?”Current maritime pilotage regimes are clearly seen in some quarters as too restrictive and old fashioned. Change is “in the air” and VTS, pilotage, and the relationship between them cannot be immune to it. Subject to the attainment of the appropriate standards by both ships and their crews there is no practical reason why more flexible Vessel Traffic Management cannot be introduced. The required technology is largely available and, with the introduction of AIS, the required dynamic data can be available. Technical problems foreseen are believed capable of solution. The more significant obstacles to implementation are, therefore, largely political, commercial, organisational, and cultural. These issues are discussed in this paper taking into account the viewpoints of major stakeholders.     

结构动力学基于摄动法的高阶非线性随机有限元方法研究(英文)

Several algorithms were proposed relating to the development of a framework of the perturbation-based stochastic finite element method (PSFEM) for large variation nonlinear dynamic problems. For this purpose, algorithms and a framework related to SFEM based on the stochastic virtual work principle were studied. To prove the validity and practicality of the algorithms and framework, numerical examples for nonlinear dynamic problems with large variations were calculated and compared with the Monte-Carlo Simulation method. This comparison shows that the proposed approaches are accurate and effective for the nonlinear dynamic analysis of structures with random parameters.     

An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

Improving the coexistence of offshore wind farms and shipping: an international comparison of navigational risk assessment processes

The continued growth and evolution of the offshore wind industry, and the emergence of other novel marine uses such as wave and tidal generators, have upped the ante for spatial planners, as well as consenting and approval authorities in various coastal states. These stakeholders rely on processes such as navigational risk assessments (NRAs) to balance safety and efficiency requirements and to make optimal decisions over use of space. Given the increasingly complex and crowded seascape, however, there are some apprehensions about potential shortcomings in these NRA processes. There is also some concern that these inadequacies may lead to unsafe or inefficient marine spatial use. To understand how NRA processes can be improved further, a literature review is conducted, followed by a survey of respondents who are involved in the planning, consenting and/or approval of offshore wind farms across seven different countries. A summary of the NRA processes in these seven countries is presented, and several shortcomings are identified. Based on the findings of the survey, a list of recommendations is presented to enhance existing NRA processes—and to improve the coexistence of shipping and offshore wind farms (OWFs).     

Five-axis interpolation of continuous short linear trajectories for 3[PP]S-<Emphasis Type="Italic">XY</Emphasis> hybrid mechanism by dual Bezier blending

A novel five-axis real-time interpolation algorithm for 3[PP]S-XY hybrid mechanism is proposed in this paper. In the algorithm, the five-axis tool path for controlling this hybrid mechanism is separated into two sub-paths. One sub-path describes the movement of 3[PP]S parallel kinematic mechanism module, and the other one describes the movement of XY platform. A pair of cubic Bezier curves is employed to smooth the corners in those two sub-paths. Based on the homogenous Jacobian matrix of 3[PP]S mechanism, a relationship between the position errors of every driving joint in hybrid mechanism and the position deviation of the tool tip center point at the moving platform is established. This relationship is used to estimate the approximation error for the corners smoothing according to the accuracy requirement of tool tip center in interpolation. Due to the high computational efficiency of this corner smoothing method, it is integrated into the look-ahead module of computer numerical control (CNC) system to perform online tool path smoothing. By performing the speed planning based on a floating window scheme, a jerk limited S-shape speed profile can be generated efficiently. On this basis, a realtime look-ahead scheme, which is comprised of path-smoothing and feedrate scheduling, is developed to acquire a speed profile with smooth acceleration. A monotonic cubic spline is employed for synchronization between those two smoothed sub-paths in tool path interpolation. This interpolation algorithm has been integrated into our own developed CNC system to control a 3PRS-XY experimental instrument (P, R and S standing for prismatic, revolute and spherical, respectively). A club shaped trajectory is adopted to verify the smoothness and efficiency of the five-axis interpolator for hybrid mechanism control.     

A relaxed-PPA contraction method for sparse signal recovery

Sparse signal recovery is a topic of considerable interest, and the literature in this field is already quite immense. Many problems that arise in sparse signal recovery can be generalized as a convex programming with linear conic constraints. In this paper, we present a new proximal point algorithm (PPA) termed as relaxed-PPA (RPPA) contraction method, for solving this common convex programming. More precisely, we first reformulate the convex programming into an equivalent variational inequality (VI), and then efficiently explore its inner structure. In each step, our method relaxes the VI-subproblem to a tractable one, which can be solved much more efficiently than the original VI. Under mild conditions, the convergence of the proposed method is proved. Experiments with l ₁ analysis show that RPPA is a computationally efficient algorithm and compares favorably with the recently proposed state-of-the-art algorithms.     

Catalytic Combustion of Lean Methane Assisted by Electric Field over Pd/Co<Subscript>3</Subscript>O<Subscript>4</Subscript> Catalysts at Low Temperature

A series of Pd/Co₃O₄ catalysts were prepared by Self-Propagating High-Temperature Synthesis (SHS) method in this study, and electric field was applied for catalytic combustion of lean methane over Pd/Co₃O₄ catalysts at low temperature. When electric field was applied, the catalytic combustion performance of Pd/Co₃O₄ catalysts was greatly improved, and the application of electric field could reduce the load of active element Pd to some extent while maintaining the same efficiency. Based on experimental tests and the analysis results of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), H₂-temperature-programmed reduction (H₂-TPR) and in-situ diffuse reflectance infrared Fourier transform spectroscopy (in-situ DRIFTS), the mechanism of catalytic oxidation of CH₄ over Pd/Co₃O₄ catalysts in electric field was proposed. The catalytic combustion of CH₄ occurs only when the temperature is higher than 250 °C normally, but when electric field was applied, the whole process of CH₄ oxidation was promoted significantly and the reaction temperature was reduced. Electric field could promote the reduction of the support Co₃O₄ to release the lattice oxygen, resulting in the increase of PdO_x and the surface chemisorbed oxygen, which could provide more active sites for the low-temperature oxidation of CH₄. Furthermore, electric field could accelerate the dehydroxylation of CoOOH to further enhance the activity of the catalysts.     

Automatic semantic analysis of software requirements through machine learning and ontology approach

Nowadays, software requirements are still mainly analyzed manually, which has many drawbacks (such as a large amount of labor consumption, inefficiency, and even inaccuracy of the results). The problems are even worse in domain analysis scenarios because a large number of requirements from many users need to be analyzed. In this sense, automatic analysis of software requirements can bring benefits to software companies. For this purpose, we proposed an approach to automatically analyze software requirement specifications (SRSs) and extract the semantic information. In this approach, a machine learning and ontology based semantic role labeling (SRL) method was used. First of all, some common verbs were calculated from SRS documents in the E-commerce domain, and then semantic frames were designed for those verbs. Based on the frames, sentences from SRSs were selected and labeled manually, and the labeled sentences were used as training examples in the machine learning stage. Besides the training examples labeled with semantic roles, external ontology knowledge was used to relieve the data sparsity problem and obtain reliable results. Based on the SemCor and WordNet corpus, the senses of nouns and verbs were identified in a sequential manner through the K-nearest neighbor approach. Then the senses of the verbs were used to identify the frame types. After that, we trained the SRL labeling classifier with the maximum entropy method, in which we added some new features based on word sense, such as the hypernyms and hyponyms of the word senses in the ontology. Experimental results show that this new approach for automatic functional requirements analysis is effective.