Seminar

8 March 2016 (Tuesday)

9:00 - 13:00

HW - 828

With the continuance of global warming in these years, carbon emission management has become a global issue. Governments and international organizations are taking efforts to control the carbon emission produced by human activities. Since industrial activities release huge amount of greenhouse gases, optimizing industrial processes would be an effective way to restrict the carbon emission. Hence my research will focus on the production process optimization, and mathematical model will be established based on a two stage push-pull production system. To control the carbon emission by the production process, carbon emission constraints will be included in the model by adding carbon emission cost for each unit of greenhouse gases released during the production processes. By using this mathematical model, we can study the influence of carbon emission management on production optimization. The presentation will include four main parts. In the first part, an introduction of the global warming phenomenon will be given. Then it comes to a review of important global carbon emission management issues in the history. A brief introduction of the research content is also included in the introduction part. The literature review of carbon emission policies and production system will be the second part of the presentation. Literatures of carbon emission management policies and strategies and related mathematical models are described and analysed in this section. The third part introduces the research progress and a primary mathematical model of the research topic would be illustrated here. Last part comes to a conclusion for the presentation. A brief review of the presentation will be conducted, and the plan of future work will be introduced as the end.

As a typical Returnable Transport Items (RTIs) that is frequently used in manufacturing activities, pallet is one of the fatal elements in developing smooth logistics in supply chains. Despite of its importance, insufficient attention has been given to enhance the operations of pallets and the management processes are inefficient, knowing little about when, where, why, how and by who pallets are utilized. These limitations result in low responsibilities and poor managements of pallets in involved parties. As a consequence, high loss and breakage rates are always prone to pallets, which increases the total cost of involved parties. Thanks to the emergence of Internet of Things (IoT), there appeared a possible solution to achieve real-time visibility and traceability, as well as information sharing in the management of pallets. My research proposes an IoT-enabled pallet hub in industrial park for enhancing the effectiveness and efficiency of sharing pallets and related services. Firstly, the concept of cloud pallet is proposed based on cloud computing, mobile agent, and various smart devices, such as RFID, sensors, GPS and etc. Secondly, an information infrastructure is put forward in order to achieve the characteristic of visibility, traceability and information sharing in industrial park. Thirdly, future research is discussed along several dimensions including IoT infrastructure, decision models, synchronization mechanisms.

Financial systems are extremely complex because of their compositions of different types of financial institutions and the complicated relationships among them. The increasing frequency and scope of financial crises has made global financial stability one of the major concerns of economic policy and decision makers. Under this highly complex environment, supervision of the financial system has to be thought of as a systemic task, focusing not only on the strength of the institutions but also on the interdependent relations among them. The opaque nature of financial systems poses a considerable challenge for the analysis of systemic resilience. My research focus on the measurement and improvement of financial system resilience.

To efficiently manage financial systems, modelling of financial systems is necessary. Network science has grown exponentially as a novel tool for the study of complex systems. Some empirical and theoretical study has been done on the risk contagion in financial system. The objective of my study is to model the financial system on the basis of the existed network as well as take the influence of the asset side on the balance sheet into account, which explore the systemic risk from two channels, direct interbank liability linkages between financial institutions and contagion via changes in asset values. By changing the variable which represents the common economy wide shock, we can explore how large macroeconomic shock needs to be in order to tip the financial system into a systemic crisis, on the basis of which, we can give some measurements and improvements on the financial system resilience.

Title: Monocular SLAM and moving object tracking

Simultaneous localization and mapping (SLAM) is a basic problem of robot mapping which constructs and updates a map of an unknown environment while simultaneously keeping track of the robot’s pose and position within it. The state-of-the-art methods get great results in the static environment, but there are still many problems of detecting and tracking moving object.

In this talk, a research plan of moving object tracking is provided. To tracking the moving object, firstly every frame of the video will be segment into grids and a AdaBoost based training algorithms could then tell which grids related to potential moving objects. Then these selected grids will be tracking and analysis in the next frames to finally estimates if they are moving objects. Some experimental design will be show in this presentation.

Previous studies have identified long waiting time as a huge problem in our health care systems that had major impacts on patient satisfaction and thus greatly affected the quality of care. Service time, namely the time spent with health care professionals, has also been widely viewed as an important predictor of patient satisfaction. Our study objective was to provide insights into the relationship among both objective and perceived time factors (waiting time, and service time) together with various aspects of patient satisfaction in a typical Chinese medical setting. This was a cross-sectional study conducted in the endocrinology outpatient department of a large tertiary hospital in China. Objective time outcomes were measured and recorded real-timely. Subjective outcomes including perceived time outcomes and satisfaction were collected by conducting questionnaire survey at the end of each patient visits. We found that objective waiting time was negatively related to patients’ satisfaction toward waiting time, convenience of location, and phone accessibility, but not significantly correlated with overall satisfaction. In contrast, there was no significant correlation between objective service time and patients’ perception of service time or patients’ perception of service providers’ level of patience. Perceived outcomes of both time factors were significantly correlated with patients’ satisfaction toward the socio-culture atmosphere and identity-oriented aspects of the service provided, and thus significantly related to patients’ overall satisfaction. We inferred from the statistical analysis results that long objective waiting time negatively affected patients’ perceived accessibility of health care service, while perceived service time might relate more to the quality than to the quantity of time spent.  Both perceived waiting time and perceived service time might be managed by improvements in patient-centered aspects (i.e. clearer and more transparent information system, more empathy and respect shown by care providers, private and comfortable space provided, etc.).

Title: Optimal Decisions on Service Quality, Price and Resources Allocation in Community-based Elderly Service Supply Chain in China

The community-based elderly service mode (CESM) enjoys a popularity in China and abroad practically and theoretically. However, its performance actually is unsatisfied in China because of the contradiction between the elderly service operational system and the trend of elderly service industrialization, and the contradiction between the elderly service level and the service demand. On one hand, the CESM is lack of an integrated and coordinated system managed by a third-party rather than the government; on the other hand, as the lifeline of CESM development, the service level cannot meet the elderly’s demand. Thus, changing the system of CESM into an innovative counterpart has been one of the key issues. Also, optimizing the elderly service level is an important guarantee of the CESM sustainable development. Considering the above issues, the big data technology proves a more scientific and in-depth analysis methodology than statistical analysis, and it can help to address the problem between the elderly service level and the service demand by mining the characteristics and regularity among the elderly and services, which is beneficial to decision-making. Besides, the service supply chain (SSC) provides a new brand direction to improve the system and management of CESM, and its related research on CESM has not been conducted. Therefore, this research will first identify the key factors influencing the elderly service level and analyze the relationship between the demand preferences and the service level by using the big data technology. With the above demonstration, the research will then propose an innovative CESM based on the elderly service supply chain (ESSC), and build models to make optimal decisions on the elderly service level considering the elderly’s demand. Finally, numerical examples will be employed to verify the feasibility of models.