Search

Finding a balance point between the energy consumption and user experience is one of the most important tasks in current lighting control systems. For achieving that goal, finding resonalble group configuration of sensors/luminaires is necessary. However, most of the current lighting control systems use fixed group configuration based on empirical knowledge from human experts. This thesis uses time-series clustering, a specific method in the unsupervised learning, for exploring possible solutions of building a model that can find that balance point automatically and dynamically. For evaluating the clutering result of our model, this thesis proposes three evaluation methods specifically designed for the lighting control system, since this topic is relatively new in this industry. After testing with difierent combinations of tools, this thesis builds a model that proved to be useful for solving the problem of finding proper group configurations for lighting control system.

By tailoring the dispersion curve of surface plasmons (SPs) of a thin metallic film surrounded by dielectric half-spaces, it is shown that the group velocity of the symmetric mode is always positive, while the group velocity of the anti-symmetric mode can be negative. Consequently, the forward and backward propagation of SPs, in which the energy flow is respectively parallel or antiparallel to the wave vector, can be realized. The physical origin of the intriguing backward SPs is given. Furthermore, schemes for the negative refraction and imaging of SPs are proposed by incorporating two plasmon modes with group velocities of opposite signs.