Browsing by Author "Bu, Honggang"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Scheduling Smart Home Appliances using Goal Programming with Priority
    (North Dakota State University, 2016) Bu, Honggang
    Driven by the advancement of smart electrical grid technologies, automated home energy management systems are being increasingly and extensively studied, developed, and widely accepted. A system like this is indispensable for and symbolic of a smart home. Mixed integer linear programming (MILP) together with dynamic electricity tariff and smart home appliances is a common way to developing energy management systems capable of automatically scheduling appliance operation and greatly saving monetary cost. This study transformed an existing plain MILP model to a goal programming model with priority to better address the conflict among each single appliance cost saving objective and user time preference objective. Constraints regarding the delays between pairs of closely related appliances are either extended or newly introduced. Numerical experiments on five appliances under different situations justify the validness of the proposed framework. Besides, the influences of key parameters on model performance are also investigated.
  • No Thumbnail Available
    Item
    Yield and Quality Prediction Using Satellite Passive Imagery and Ground-Based Active Optical Sensors in Sugar Beet, Spring Wheat, Corn, and Sunflower
    (North Dakota State University, 2014) Bu, Honggang
    Remote sensing is one possible approach for improving crop nitrogen use efficiency to save fertilizer costs, reduce environmental pollution, and improve crop yield and quality. Feasibility and potential of using remote sensing tools to predict crop yields and quality as well as to detect nitrogen requirements, application timing, rate, and places in season were investigated based on a two-year (2012-2013) and four-crop (corn, spring wheat, sugar beet, and sunflower) study. Two ground-based active optical sensors, GreenSeekerTM and Holland Scientific Crop CircleTM, and the RapidEyeTM satellite imagery were used to collect sensing data. Highly significant statistical relationships between INSEY (NDVI normalized by growing degree days) and crop yield and quality indices were found for all crops, indicating that remote sensing tools may be useful for managing in-season crop yield and quality prediction.