Development and application of thermal mathematical model of iron ore pellet bed in grate

Based on the analysis of heat transfer mechanics, physical and chemical change of pellet drying and preheating process in grate, the mathematical model is established and solved by three-diagonal matrix algorithm. With Visual Basic 6.0 a simulation software is developed. The model is verified by measurements at a domestic pellet plant, and the temperature distribution of pellet bed is gained. Meanwhile, the influence of different operation parameters on the pellet thermal process is studied. The results can be taken as a basis of practical production control and the grate optimizing design.     

The precautionary principle in maritime affairs

Since the 1980s, the precautionary principle has been drawing more and more international attention. This became particularly clear during the series of Conferences on the Protection of the North Sea, which addressed marine environmental protection, and initiated the application of the principle into maritime affairs. Although a unified concept of the precautionary principle is lacking, some typical formulations have been concluded in regional treaties and international documents, and components of the principle can be summarised (Part I and Part II). The precautionary principle has been adopted by international documents involving different fields of law of the sea, and been addressed by international courts and tribunals (Part III). While the precautionary principle is still not part of customary international law (Part IV), its increasing level of worldwide acceptance suggests that it will become a customary international law in the future.     

Analysis of cost estimation disclosure in environmental impact statements for surface transportation projects

Reliability in cost estimates in publicly funded projects is more important today than ever with increased governmental funding to infrastructure projects and associated accountability requirements, but surface transportation projects, large and small, in the United States have a legacy of cost overruns. The problems with these overruns start with the cost estimation process before projects begin. Studies have shown that early cost estimations reflect best-case scenarios rather than realistic expectations, and have attributed technical, economic, psychological, and political reasons for underestimation. The primary objectives of this research were to determine how costs have been presented and updated in environmental impact statements, to identify endemic technical problems with cost estimation during the environmental review of surface transportation projects, and to develop recommendations for improving cost estimation and disclosure. Cost estimation methods were analyzed from the documents of 100 projects, including draft environmental impact statements, final environmental impact statements, and records of decision. The study concluded that the lack of guidance on how to include cost estimation in environmental review revealed itself in the level of detail and attention given to estimating costs. Agencies omitted or included various aspects of cost estimation randomly. Professional organizations should fill the gap of the need for specific guidance to cost estimation for environmental review so it can evolve as a state of the practice.     

Modeling adoption timing of autonomous vehicles: innovation diffusion approach

Autonomous vehicles (AVs) are expected to act as an economically-disruptive transportation technology offering several benefits to the society and causing significant changes in travel behavior and network performance. However, one of the critical issues that policymakers are facing is the absence of a sound estimation of their market penetration. This study is an effort to quantify the effect of different drivers on the adoption timing of AVs. To this end, we develop an innovation diffusion model in which individuals’ propensities to adopt a new technology such as AVs takes influence from a desire to innovate and a need to imitate the rest of the society. It also captures various sources of inter-personal heterogeneity. We found that conditional on our assumptions regarding the changes in market price of AVs over time, their market penetration in our study region (Chicago metropolitan area) will eventually reach 71.3%. Further, model estimation results show that a wide range of socio-demographic factors, travel pattern indicators, technology awareness, and perceptions of AVs are influential in people’s AV adoption timing decision. For instance, frequent long-distance travelers are found to make the adoption decision more innovatively while those who have experienced an accident in their lifetime are found to be more influenced by word of mouth.     

Development of knowledge based body structure concept design model

The purpose of this study is to propose a concept design process for an automotive body structure using technical information on the major joints and members of vehicles. First, in order to collect the technical information on major joints and members, 17 vehicles were selected using benchmark data. The collected technical information for the selected vehicles was the cross sectional shapes of each joint and member which were used for the analysis of joint stiffness, crashworthiness and static stiffness of the member to make a database along with cross section properties. This study applied a ‘What If Study’ technique to perform a concept design of an automotive body using the analyzed information and selected cross section meeting the design objectives. The criteria for the selection of the cross section were defined by subdividing the defined design objectives of an automotive body structure and constraints into members and joints. In order to configure an analysis model of an automotive body structure using the selected cross section, a shape parametric model was used and static stiffness, dynamic stiffness and crashworthiness were assessed to evaluate the configured automotive body structure. The evaluation result showed that the crashworthiness and static/dynamic stiffness were improved compared to an existing body structure. In addition, the weight of the body structure was reduced. Through this study, the process that can rapidly and effectively derive and evaluate the concept design of an automotive body structure was defined. It is expected that, henceforth, this process will be helpful for the study of automotive body structures.     

Coupled heat and mass transfer analysis of NH<Subscript>3</Subscript>-H<Subscript>2</Subscript>O falling film absorption on inner tube surface with low solution flow rates

NH₃-H₂O falling film absorption usually takes place with low solution flow rate in real absorption refrigeration system. An experimental study of inner vertical absorption is carried out for the consideration of air-cooling absorber. Variable working conditions are tested to evaluate the heat and mass transfer performances.The traditional evaluation method based on log-mean-temperature（concentration） difference is criticized for its lack of theoretical basis while simultaneous heat and mass transfer process occurs. A new method proposed by Kim and Infante Ferreira is modified to evaluate the experimental results with reasonable assumptions. The method is based on the derivation of coupled heat and mass transfer differential equations of NH₃-H₂O absorption process.The analysis of the same experimental data shows that the new method realizes better consistency with smaller error, especially in heat transfer aspect. Heat and mass transfer performance is enhanced with the increase of solution Reynolds number. Sub-cooling of inlet weak solution also has positive influence on the absorption process,which should be evaluated by the new method correctly. Two correlations are developed to evaluate both Nusselt and Sherwood numbers for the design of air-cooling absorber.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.     

Brittleness analysis and important nodes discovery in large time-evolving complex networks

The brittleness analysis and important nodes detection have been a hot spot in the complex networks. How to get the overall feature of the whole network and how to find out some important nodes are requisites to solve these problems. In this paper, we adopt the trace of the adjacency matrix and the centrality of the complex networks to give a quantitative and qualitative analysis of networks being studied. Results show that the k-shell plays a more important role than the degree centrality and the betweenness in finding important nodes, and it can also be used to give direction on the immunization and maintenance of complex networks.     

Long-term tracking based on spatio-temporal context

Aiming at the problem that the fast tracking algorithm using spatio-temporal context (STC) will inevitably lead to drift and even lose the target in long-term tracking, a new algorithm based on spatio-temporal context that integrates long-term tracking with detecting is proposed in this paper. We track the target by the fast tracking algorithm, and the cascaded search strategy is introduced to the detecting part to relocate the target if the fast tracking fails. To a large extent, the proposed algorithm effectively improves the accuracy and stability of long-term tracking. Extensive experimental results on benchmark datasets show that the proposed algorithm can accurately track and relocate the target though the target is partially or completely occluded or reappears after being out of the scene.     

Poisson noise removal based on nonlocal total variation with Euler’s elastica pre-processing

An enhancement-based Poisson denoising method for photon-limited images is presented. The noisy image is firstly pre-processed for enhancing incomplete object information, and then it is denoised while preserving the restored structural details. A variational regularization model based on Euler’s elastica (EE) is proposed for image enhancement pre-processing. A nonlocal total variation (NLTV) regularization model is then employed in the second stage of image denoising. The above two optimization problems are solved by the alternating direction method of multipliers (ADMM). For Poissonian images with low image peak values, experiments demonstrate the validity and efficiency of the proposed method for both restoring geometric structure and removing noise.