モデルベース予測制御技術に基づく支援型移動ロボットの運動制御

要約のみTohoku University小菅一弘課

Exploring curriculum policy implementation through the relationship between policy and practice: case studies of modern languages in primary schools in Scotland and Shanghai

In the world of educational policy, there are often gaps between the promises inherent in the policy rhetoric and how policy emerges in practice. This study explored the relationship between policy initiatives and practices in the policy implementation process. As teachers are key to the enactment of policy, this study explored the relationship between policy and practice through the lens of teachers’ beliefs about education policy. Modern Languages is a key policy for schools and governments internationally; it is also one of the most challenging policies, especially in traditionally monolingual countries or regions like Shanghai and Scotland. In Modern Languages education, there is a widely held view that early Modern Language learning will offer children the best start in life, will contribute to wider success in education and will offer better future life chances. This study explored these assumptions by examining the implementation of Modern Languages policy through the eyes of policy makers and practitioners involved in the teaching of Modern Languages in primary schools in Scotland and Shanghai. The study included 9 practitioners from four Case Study Schools and 3 policy makers from the two research sites. Using a qualitative research methodology, the study explored the relationship between policy makers aspirations and teachers’ practices in Modern Languages. The findings of this study revealed that in both research sites, despite the distinctive differences between Scottish and Shanghai’s educational systems, there were many common themes. In both locations, there was a need to enhance communication between policy makers and practitioners; in addition, there was a need for greater Modern Languages proficiency requirements for teachers. The findings challenged the assumption that the earlier children begin to learn a Modern Language, the better their language accomplishment. However, there was an indication that earlier Modern Languages learning had advantages beyond linguistic proficiency. These wider cognitive, cultural, societal and literacy benefits emerging from Modern Languages learning, need to become more influential in shaping expectations of the benefits of Modern Languages learning and should become a more central part of the primary languages curriculum

Dynamic Extra Buses Scheduling Strategy in Public Transport

This paper presents a dynamic extra buses scheduling strategy to improve the transit service of transit routes. In this strategy, in order to decide when to dispatch an extra bus, the service reliability of transit route is assessed firstly. A model aimed at maximizing the benefit of the extra buses scheduling strategy is constructed to determine how many stops extra buses need to skip from the terminal to accommodate passengers at the following stops. A heuristic algorithm is defined and implemented to estimate the service reliability of transit route and to optimize the initial stop of extra buses scheduling strategy. Finally, the strategy is tested on two examples: a simple and a real-life transit route in the Dalian city in China. The results show that the extra buses scheduling strategy based on terminal stops with a reasonable threshold can save 8.01% waiting time of passengers

Measurement of functional resilience of transport network:The case of the Beijing subway network

Resilience is an important concept for measuring a system\u27s ability to cope with various disruptions. This study proposes an application-oriented framework for measuring the dynamic functional resilience (FR) of a transport network responding to supply and demand disruptions without external interventions. On the conceptual side, three complementary capacity-related dimensions, namely, robustness, adaptability, and recoverability, are incorporated in the single FR framework from the perspective of physical laws. On the applied side, we suggest a measurement model given certain network indices and apply it to the Beijing subway network (BSN). The results indicate the measurement model can capture the dynamics of network performances, identify the time-varying bottlenecks, and predict the influence of the dynamic capacity expansions on network resilience. The findings are useful for policy-making regarding the dynamic design, operation, and reconstruction of the transport infrastructure

ABatRe-Sim: A Comprehensive Framework for Automated Battery Recycling Simulation

With the rapid surge in the number of on-road Electric Vehicles (EVs), the amount of spent lithium-ion (Li-ion) batteries is also expected to explosively grow. The spent battery packs contain valuable metal and materials that should be recovered, recycled, and reused. However, only less than 5% of the Li-ion batteries are currently recycled, due to a multitude of challenges in technology, logistics and regulation. Existing battery recycling is performed manually, which can pose a series of risks to the human operator as a consequence of remaining high voltage and chemical hazards. Therefore, there is a critical need to develop an automated battery recycling system. In this paper, we present ABatRe-sim, an open-source robotic battery recycling simulator, to facilitate the research and development in efficient and effective battery recycling au-omation. Specifically, we develop a detailed CAD model of the battery pack (with screws, wires, and battery modules), which is imported into Gazebo to enable robot-object interaction in the robot operating system (ROS) environment. It also allows the simulation of battery packs of various aging conditions. Furthermore, perception, planning, and control algorithms are developed to establish the benchmark to demonstrate the interface and realize the basic functionalities for further user customization. Discussions on the utilization and future extensions of the simulator are also presented

The effect of public target on the public-private partnership (PPP) residential development

A growing importance of public-private partnership (PPP) in public housing projects has drawn much attention. This paper presents a theoretical analysis exploring the effect of the public target on the private’s optimal strategy in a PPP housing project. An option-based model is established to show that an increase in the proportion of public housing will delay the project development. It indicates that the government needs to consider the trade-off between the waiting time and the supply of public housing. On the other hand, due to the delay effect, the expected project value would rise because the private developer is willing to wait for a better environment in the presence of a rise in public housing. Both private and public sector can benefit from this accurate evaluation model and its implications

Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors

Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating the physical relationship of the studied system and the Hankel matrix update from the generalized behavior representation to a particular one, we develop a new Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle human driver diversities. In particular, a power consumption term is added to the DeePC cost function to reduce the holistic energy consumption of both CAVs and HDVs. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and addressing the complex dynamics of mixed traffic flows, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.83% and 9.16% when compared to the benchmark algorithms