Carbon flux in ice–ocean–plankton systems of the Bellingshausen Sea during a period of ice retreat

Most analyses of marine microbial systems in the seasonally ice covered areas of the Southern Ocean have been based on data from the major embayment areas of the Ross and Weddell Seas. In this study data were collected at stations covering a range of regimes from full ice cover through to open water in the Bellingshausen Sea. A major feature of the production system was a rapid retreat of the ice-edge, which uncoupled the marginal ice zone from a phytoplankton bloom which remained associated with a frontal system. This bloom was maintained, and probably initiated, in an unusual environment generated by the interaction between the marginal ice zone and the front. Size-based analyses of the microbial system were derived for ice-covered, recently ice-covered and open water sites. Estimates of standing stocks and key rate processes were combined to produce a single food web network for each station. The under-ice system was one of low production and low recycling but apparently high retention. As the ice retreated the microbial systems to the north began to develop, but these were constrained by grazing pressure. The bloom in the area appeared to be sustained even though estimated losses were far higher than production, although the high sedimentation losses expected were not observed. The carbon flow networks are discussed in relation to the environmental changes and the interaction of the marginal ice zone and the frontal system appears crucial to the phytoplankton. Microzooplankton grazing is implicated as a major controlling factor. The local microbial dynamics are strongly influenced by material which was produced at an earlier time and somewhere else in the Southern Ocean.     

Micro deformation of automobile hood in dipping process using stiffness

Technicians have been going through trial-and-error processes to solve very small or micro deformation on automobile hood during the painting process. In order to establish a systematic improvement procedure which can replace the time-consuming trial-and-error method to reduce defects, an accurate analysis of how micro deformation occurs during the painting process is needed. We have utilized a stiffness scanning method in automobile hood and reverse engineering to build up a reliable and accurate structural analysis and measurement procedures. We measured the load-stroke data at critical locations on automobile hood through stiffness scanning to determine material constants closest to the measurement by assuming several critical hood parts, such as sealer inside the hood, hemming part, spot weld part, and other uncertain joints, as virtual elastic materials. After setting the difference between analytical and measured load-stroke data as an objective function, we computed and minimized it by using the response surface method and partial differentiation of the object function. As a result, by obtaining the reliability was over 91%, which showed a strong correlation between analysis and measured results. By comparing the actual strain measured in real painting lines with calculated strain, we confirmed the validity of our structural analysis method. It was concluded that the proper analysis tool could be utilized in determination of optimal locations of supports during the painting process.     

Urban driving cycle for performance evaluation of electric vehicles

Nowadays, a number of environmental issues have seriously come to the fore. For this reason, the R & D spending on eco-friendly vehicles that use electric power has been gradually increasing. In general, fuel economy and pollutant emissions of both conventional and eco-friendly vehicles are measured through chassis dynamometer tests that are performed on a variety of driving cycles before an actual driving test. There are a number of driving cycles that have been developed for the for performance evaluation of conventional vehicles. However, there is a lack of research into driving cycle for EV. Because large differences exist between the drive system and driving charateristics of EV and that of CV, a study on driving cycle for EV should be conducted. In this study, the necessity of an urban driving cycle for the performance evaluation of electric vehicles is confirmed by developing the driving cycle. First, the Gwacheon-city Urban Driving Cycle for Electric Vehicles (GUDC-EV) is developed by using driving data obtained through actual driving experiments and statistical analysis. Second, GUDC-EV is verified by constructing EV simulators and performing simulations that use the actual driving data. The simulation results are then compared against existing urban driving cycles, such as FTP-72, NEDC, and Japan 10–15. These results confirm that GUDC-EV can be used as an urban driving cycle to evaluate the performance of electric vehicles and validate the necessity of development of the driving cycle for electric vehicles.     

Computer Aided Simulation on Precision Cold Forging of Universal Joint Cross

IntroductionThefiwtee1emenmethod(FEM)playsanextrmelyimPortantroleintheanalysesofengineeringproblems.ForalongtimethismethodhasmainlybeenaPPliedinthelinearfieldandshownitsgreatpower.HoweveealOtofnonlinearengineeringPrOblemsWhichcanneitherbesimPlyreducedtolinearonesnorrCPresentedbylinearlyanalogywhichshallchnsethePfacticalconditionsrelativelymuch,havechallengedfiniteelementmethod(FEM).RecentymuchProgresshasbeenmadebothinthemathematicalandmechanicaltheoryandthecomPulertechnologysothatevent…     

降低感应电机直接转矩控制的转矩脉动的控制器

基于磁滞比较器和选择表的感应电机直接转矩控制(DTC),其开关频率可变,且转矩脉动大,特别在低速时.这是因为转矩和磁通的变化率以及转矩和磁通达到其上带和下带所花的时间随运行工况变化.文中提出了一种新型的开关频率固定的转矩控制器.新型控制器结构非常简单,其输出类似于传统的三点式磁滞比较器.就提出的控制器介绍了近似频域分析法,作为控制器参数选择的指南.介绍了感应电机直接转矩控制采用该转矩控制器进行的仿真和实验结果.结果表明提出的控制器可以保持开关频率恒定,并降低了转矩脉动,尤其在低速时.     

铁路噪声对沿线居民影响的研究

分析了铁路沿线居民对噪声的反应，并比较了铁路与交通运输噪声主观反应。结果表明，无论是高干扰率阈值的比较，或是从高干拢率与声级的回归方程比较，铁路和交通这两种运输噪声对人的主观反应干扰基本一样。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

Demand management of congested public transport systems: a conceptual framework and application using smart card data

Transportation - Transportation demand management, long used to reduce car traffic, is receiving attention among public transport operators as a means to reduce congestion in crowded public...     

A decentralized control strategy for freeway regulation

A multilevel decentralized control scheme, the cascading technique, with application to the regulation of traffic on an urban freeway is presented. Performance of the decentralized system is compared to the performance of a centralized and a fixed time control structure. It is shown that the decentralized structure performs better than the centralized structure when incidents (lane closures) occur on the freeway. The freeway is modeled in terms of the aggregate variables section density and section speed, and is considered as a system of interconnected subsystems.