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In the case of the global energy crisis and the higher sound of energy saving and emission reduction, how to take effective management measures of ship energy efficiency to achieve the goal of energy saving and emission reduction, put forward a new challenge for the development of shipping technology. The application of big data technology provides a new idea for the research of ship energy efficiency optimization management. The energy efficiency management level of the operating ship can be improved by the analysis and mining of the big data. In this paper, a big data analysis platform for ship energy efficiency management based on the widely used Hadoop platform architecture is designed. Afterward, due to the huge amount of involved data on the energy efficiency management which has exceeded the processing ability of traditional solutions, the big data analysis method is used to achieve the route division according to environmental factors, thus to lay the foundation for speed optimization in different segments of a route. Finally, a simple decision-making method of optimal engine speed based on the result of route division is proposed, which could improve ship energy efficiency and hence reduce CO2 emission.

The study aims to investigate the mechanical system optimum design based on collaborative theory. Due to the complexity of the modern machinery, mechanical systems are readily to damage when unexpected failures occur on important components. It is therefore, critical to monitor the machine state for preventing the impending faults. The key issues to realize the feasible and reliable mechanical condition monitoring is information acquisition, which relies on the available design of the detection devices. Literature review indicates that an extensive attention has been put on the so called Monitorability in the systematic design of mechanical systems. Monitorability is emphasized that in the original design of mechanical systems one should consider available information acquisition property. Moreover, monitorability-based design is known as a design attribute of mechanical system worldwide. However, less work has been done in this field. In this study, a novel method based on collaborative theory is proposed for the monitorability design. The connotation and application of collaborative theory for monitorability design are discussed in details. The information synergy model and organization framework of monitorability-based design are established by using computer technology and network technology. The experiments demonstrate the effectiveness of the proposed monitorability design system for a more powerful optimum design of mechanical systems and show a promising future for the industrial applications.

Ports are rally points and hubs of water and land transportation, important nodes of the internet of things as well, which are inevitably susceptible to surrounding meteorological environment. To address this issue, a comprehensive environment monitoring system with early warning and linkage control is presented in this paper. First, our system can monitor surrounding meteorological environment data in real-time, which ensures to provide early warming if there encounters any exception. Second, our system can be used to forecast future environment, which significantly benefits the operations and schedules of ports. The last but not the least, through linkage control with other relevant systems, our system enables to meet the needs of green port development. The framework of our system, together with the design of some main software modules, are described in details. Furthermore, several main characteristics and functionalities of our system are further thoroughly introduced.

Many researches indicate that a great number of failures occur in the tribological system which will reduce the reliability of the marine diesel engine. Therefore, it is necessary to monitor the condition and identify the fault mode of the engine. In this paper, remote fault diagnostic technology is developed to take full advantage of the online oil monitoring system and the laboratory analysis for the tribological systems. To increase the efficiency of fault diagnosis, a two-level fault diagnostic model based on self-organizing map (SOM) was established with the oil parameters from the online oil system and the experimental data from the laboratory. Based on the component map of SOM network, the attributes of the feature vector in the second level were reduced to simplify the model and the trajectory of the samples was tracked during the application of the system. The diagnostic result indicates that the remote fault diagnosis technology benefits the full acquirement of the information reflecting the engine condition and the two level fault diagnostic model can be well applied in fault diagnosis for the tribological systems in marine diesel engine with satisfactory result.

To provide a reference for management decision-making, evaluation index system and evaluation method are proposed to measure the level and degree of operation coordination between rail transit and bus transit. The idea of object-oriented is adopted to construct evaluation index system for operation coordination. The indexes for three objects are determined finally by modification, which are passenger-oriented evaluation indexes, enterprise-oriented evaluation indexes and government-oriented evaluation indexes. Among them, passenger-oriented evaluation indexes include convenience of transfer, economy of transfer, continuity of transfer and comfort of transfer; enterprise-oriented evaluation indexes include financial benefit, market competitiveness and coordination of income distribution; government-oriented evaluation indexes include transport efficiency and financial sustainability. On the basis of the above study, statistical indexes reflecting the general characteristics of operation coordination are analyzed, that is, the level of operation coordination and the degree of operation coordination. The non-linear weighted model is used to calculate the composite indicators of two statistical indexes.

Driver's route choice has become a hot issue in the field of traffic research in the last years. However, most of the researches didn't pay attention to the relationship between the driver's route choice and the traffic control signal. Obviously, driver's dynamical route choice is notable affected by delay due to crossing signal control in his running course. On the other hand, the familiarities of the route can also affect driver's route choice. Basing on inquiries to drivers, driver's route choice behavior is studied. How driver makes choice among those routes with different familiarities influenced by urban traffic control system is analyzed. As a result of signal control strategy, orientation of driver's transferring from familiar route to unfamiliar one is discussed by using multinomial Logit Model. In the first part, this study introduces the necessity of the integration between traffic signal control system and navigation system. On the basis of building pertinent Logit Model and travel time estimation model, typical route choice model is presented then. At last, route familiarity, as a parameter of the model, is confirmed by processing test data. Investigation argues that driver's route choice can effectively described by Logit Model with variables of traffic signal split and route familiarity.

In data fusion approaches, Dempster-Shafer (D-S) evidence theory offers an interesting tool to combine data from multi-sensor. The decision-level fusion based on Dempster-Shafer (D-S) evidence theory can process non-commensurate data and has robust operational performance, reduces ambiguity, increases confidence, and improves system reliability. This paper describes mainly a decision-level data fusion technique for fault diagnosis for electronically controlled spark ignition engines. A D-S evidence theory fault diagnosis model is founded, and the feature selection and extraction of fault signal is conducted. Experiments on a 462 mini engine show that the data fusion technique provides good engine fault diagnosis method.