The recirculating aquaculture system (RAS) is a land-based aquaculture facility, either open-air or indoors, that minimizes water consumption by filtering, adapting, and reusing water. Solid organic matter from fish waste and food waste directly becomes waste that needs to be eliminated because it is a source of increasing total ammonia nitrogen (TAN), total suspended solids (TSS), total dissolved solids (TDS), and also has an impact on reducing dissolved oxygen (DO). RAS requires a water level control system so the fish tank does not experience water shortages or floods, disrupting the aquatic aquaculture ecosystem. In this study, small-scale RAS is modeled using a 3-coupled tanks system approach with a tank configuration that follows the most straightforward RAS water recirculation process (fish tank, mechanic filter, biofilter). Clean water from the reservoir flows into the fish tank through a protein skimmer. This study applies the fuzzy logic controller (FLC) to control the water level in the protein skimmer and biofilter tanks by controlling the position of several valves where the placement positions of the valves have been determined according to system requirements. The study results show that the tuned single-input FLC has the best average output response characteristics with t<sub>s</sub>=50, h<sub>1ss</sub>=49.98, e<sub>ss</sub>=0.02 in protein skimmer and t<sub>s</sub>=4700, h<sub>1ss</sub>=39.75, e<sub>ss</sub>=0.25 in the tank system.
In principle, a video codec is built by implementing various algorithms and their development. The next generation of codecs involves more artificial intelligence applications and their development. DCNN (Deep Convolutional Neural Network) is a multi-layer NN concept with a deep learning approach in the field of artificial intelligence development. This study has proposed a DCNN with three hidden layers for intra-frame-based video compression. DCT and fractal methods were used to compare the performance of the proposed method. The training image (obtained from the average of all down-sampled frames) is divided into several square blocks using the square block shift operation until all parts of the image are fulfilled. All pixels in each block act as input data patterns. After the training process, the trained proposed DCNN was then used to construct the feature and sub-feature image obtained through the max function operation in the feature bank and sub-feature bank. These feature and sub-feature images were then a spatial redundancy minimizer with specific manipulation techniques and simultaneously a quantizer without converting the frame's pixels to a bit-stream. The result of this process is a compressed image. Experiments on the entire dataset resulted in AAPR (Average Approximate Performance Ratio) of 147.71%, or an average of 1.5 times better than other methods. For further studies, the performance improvement of the proposed DCNN is performed by modifying its structure so that the output is direct in the form of feature and sub-feature images. Another way is to combine it with the DCT or fractal method to improve the performance of the result.
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