Browsing by Author "Nguyen, Tam"

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  • Äänikäyttöliittymä
    (2011-12-28) Nguyen, Tam
     Perustieteiden korkeakoulu | Bachelor's thesis
  • Effect of chemical composition of tropical tree species on durability, usage and mechanical properties
    (2023-01-14) Nguyen, Tam
     Kemiantekniikan korkeakoulu | Bachelor's thesis
  • Forecasting Electricity Price With KNN and SARIMA Models
    (2024-04-26) Nguyen, Tam
     Perustieteiden korkeakoulu | Bachelor's thesis
    Over the past few decades, the combination of market deregulation and renewable energy integration into the electricity grid has made the Finnish electricity market more volatile and unpredictable than ever. This situation has raised the demand for accurate electricity price forecasts. Despite some studies on Finnish day-ahead electricity market prices forecasting, there is a notable gap in research regarding forecasting other Finnish electricity markets, especially the reserve markets. A classical SARIMA model and a machine learning KNN model are preliminarily chosen to study the reserve market prices forecasting. An exploratory example of two models on Finnish Automatic Frequency Restoration Reserve Upregulation (aFRR Up) reveals the complexity of forecasting reserve market prices and concludes that such simple models like SARIMA and KNN are not sufficient for the forecasting objectives. In addition, the example also provides some insights for the performance comparison between KNN and SARIMA in time-series forecasting: while SARIMA is more consistent over different time periods, KNN is much faster to train and can sometimes yield much better accuracy.
  • Median Filter on 16-bit Images
    (2017-06-12) Nguyen, Tam
     Perustieteiden korkeakoulu | Master's thesis
    This thesis utilizes high-performance computing to filter noisy images by various median filter algorithms. Since smartphones are widely available to consumers, customers take noisy images daily. Thus algorithms to filter noises are needed. Many new cameras support more than 8-bit depth for colors. Using 8-bit filtering algorithms on 16-bit images will typically result in color lost. The 8-bit and 16-bit filtering algorithms are benchmarked in order to observe how the execution changes when the number of bits increases. Median filter algorithms are the focus of this thesis. This work benchmarks the execution time of four median filter methods: constant time median filter (CTMF), naive, improved naive, and median heap. These algorithms are tested by varying the following parameters: image size, color depth, number of threads, filter window size, and different kind of images. If we use only one thread, CTMF outperforms the other methods for large images. However, it is more difficult to exploit multicore processors efficiently in CTMF. With a large number of threads, the other three algorithms will outperform the CTMF algorithm in 8-bit and 16-bit images. However, this is not the case when the filter window size increases and the image size is constant. The execution time of the CTMF algorithm remains steadily lower than others algorithms in 8-bit and 16-bit images.