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The first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), which is onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS), was successfully launched in June of 2010. GOCI has a local area coverage of the western Pacific region centered at around 36°N and 130°E and covers ~2500 × 2500 km(2). GOCI has eight spectral bands from 412 to 865 nm with an hourly measurement during daytime from 9:00 to 16:00 local time, i.e., eight images per day. In a collaboration between NOAA Center for Satellite Applications and Research (STAR) and Korea Institute of Ocean Science and Technology (KIOST), we have been working on deriving and improving GOCI ocean color products, e.g., normalized water-leaving radiance spectra (nLw(λ)), chlorophyll-a concentration, diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), etc. The GOCI-covered ocean region includes one of the world's most turbid and optically complex waters. To improve the GOCI-derived nLw(λ) spectra, a new atmospheric correction algorithm was developed and implemented in the GOCI ocean color data processing. The new algorithm was developed specifically for GOCI-like ocean color data processing for this highly turbid western Pacific region. In this paper, we show GOCI ocean color results from our collaboration effort. From in situ validation analyses, ocean color products derived from the new GOCI ocean color data processing have been significantly improved. Generally, the new GOCI ocean color products have a comparable data quality as those from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. We show that GOCI-derived ocean color data can provide an effective tool to monitor ocean phenomenon in the region such as tide-induced re-suspension of sediments, diurnal variation of ocean optical and biogeochemical properties, and horizontal advection of river discharge. In particular, we show some examples of ocean diurnal variations in the region, which can be provided effectively from satellite geostationary measurements.

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A machine is taught by finding the minimum value of the cost function which is induced by learning data. Unfortunately, as the amount of learning increases, the non-liner activation function in the artificial neural network (ANN), the complexity of the artificial intelligence structures, and the cost function’s non-convex complexity all increase. We know that a non-convex function has local minimums, and that the first derivative of the cost function is zero at a local minimum. Therefore, the methods based on a gradient descent optimization do not undergo further change when they fall to a local minimum because they are based on the first derivative of the cost function. This paper introduces a novel optimization method to make machine learning more efficient. In other words, we construct an effective optimization method for non-convex cost function. The proposed method solves the problem of falling into a local minimum by adding the cost function in the parameter update rule of the ADAM method. We prove the convergence of the sequences generated from the proposed method and the superiority of the proposed method by numerical comparison with gradient descent (GD, ADAM, and AdaMax).

Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a model that estimates atmospheric changes using deep learning. In addition, we developed an efficient algorithm to determine the optimal observation period for accurate air quality prediction. Experimental results with real-world data demonstrate the feasibility of our approach.

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