Day-Ahead Solar Forecasting Based on Multi-level Solar Measurements

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and achieve better forecasts. The proposed model consists of four stages of data preparation, establishment of fitting model, model training, and forecasting. The model is tested under different weather conditions. Numerical simulations exhibit the acceptable performance of the model when compared to forecasting results obtained from two-level and single-level studies

Entropy-difference based stereo error detection

Stereo depth estimation is error-prone; hence, effective error detection methods are desirable. Most such existing methods depend on characteristics of the stereo matching cost curve, making them unduly dependent on functional details of the matching algorithm. As a remedy, we propose a novel error detection approach based solely on the input image and its depth map. Our assumption is that, entropy of any point on an image will be significantly higher than the entropy of its corresponding point on the image's depth map. In this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo depth estimates and a binary classification method to identify incorrect depths. Experiments on the Middlebury dataset show the effectiveness of our method. Our proposed stereo confidence measure outperforms 17 existing measures in all aspects except occlusion detection. Established metrics such as precision, accuracy, recall, and area-under-curve are used to demonstrate the effectiveness of our method

Development of computer aided design system based on artificial neural network for macular hole detection

Medical imaging is a technique used to identify or study disease in the body. In order to obtain the retinal images, clinical ophthalmology broadly used a non-invasive medical imaging named optical coherence tomography (OCT). OCT images lead to visualize retina’s inner layers, which was important to identify the retinal diseases at the early stage. Macular Hole is one of the retinal disease that should be treated early. A Graphical User Interface (GUI) was created to detect the Macular Hole disease. In this paper, Computer Aided Design (CAD) system to detect Macular Hole eye disease has been developed. This disease diagnosed and need to be treated at beginning state as it will lead to vision lost if it get severe. An algorithm is proposed in this study which performs Macular Hole detection. There are browse image, pre-processing, segmentation, feature extraction and lastly classification steps. This study successfully classified the Macular Hole and normal retinal images correctly using Artificial Neural Network (ANN) classification. The accuracy of the net produced after several trials of retrain is 90%. The other statistical parameters such as precision, specificity and sensitivity was obtained from this project. In a conclusion, there was a way obtained from this study to diagnosis the patients who were suffer from this Macular Hole disease

Antimycotic, antibiodeteriorative and antiaflatoxigenic potency of 2-​hydroxy-​4-​methoxybenzaldehyde isolated from Decalepis hamiltonii on fungi causing biodeterioration of maize and sorghum grains

We characterized the antimycotic, antibiodeteriorative and antiaflatoxigenic efficacy of the compd., 2-​hydroxy-​4-​methoxybenzaldehyde isolated from Decalepis hamiltonii Wight & Arn, against different species of fungi that cause biodeterioration of corn (Zea mays L.) and sorghum (Sorghum bicolor L.) grains during storage. Fungal species that cause biodeterioration were isolated from corn and sorghum grains by agar plating method and std. blotter method (SBM)​. Poisoned food technique was adopted to assess fungitoxicity of the compd. against fungal isolates. For different fungal species, the inhibitory concn. (IC50)​, minimal inhibitory concn. (MIC) and minimal fungicidal concn. (MFC) of 2-​hydroxy-​4-​methoxybenzaldehyde varied between 80 to 350μg​/mL, 350 to 800μg​/mL and 450 to 850μg​/mL, resp. Among the 14 fungal species tested, Aspergillus niger, A. columnaris and A. tamari were completely inhibited at higher concns., while Fusarium oxysporum, F. proliferatum, Drechslera tetramera and Aspergillus ochraceus were completely inhibited at low concns. The inhibitory effect of the compd. was of broad-​spectrum in activity and was concn.-​dependent. Comparative evaluation of the active compd. with the synthetic fungicides, Blitox and Thiram, at recommended dosages revealed that the antimycotic activity of 2-​hydroxy-​4-​methoxybenzaldehyde was superior than that of synthetic fungicides. In vivo evaluation of the compd., 2-​hydroxy-​4-​methoxybenzaldehyde at 0.5 g​/kg as seed treatment on corn and sorghum revealed that carbohydrates, water sol. proteins, lipids, aflatoxin B1 prodn. and dry matter losses (DML) were significantly conserved in treated compared with control untreated grains up to 120 days storage. D. hamiltonii being an edible plant can be exploited in the management of seed-​borne pathogenic fungi and in the prevention of biodeterioration of grains and mycotoxin prodn. during storage in an eco-​friendly way

Effect of Enzyme Concentration and Temperature on Viscosity and Betacyanin Content From Pitaya Waste Extract

As synthetic dye has shown up few hazards in contributing in food and textile industries, natural dye has gain its priority in those fields especially in textile sector. In this experiment, pitaya’s waste was selected as a source for natural dye. Thus, there is one obstacle that prevents the natural dye to fulfill the requirements needed in textile industries as the physical properties of the natural dye which is high in viscosity causing it not fasting on cloth which is believed, due to pectin content. In order to come over this problem, this research aim to reduce the viscosity of the natural dye using commercialize pectinase, in a small scale and study the effect of the enzyme concentration and temperature on the reduction of viscosity of the natural dye and also observe the difference in betacyanin content. This experiment was carried out by chemical and also by biological mechanism. Chemical mechanism refers to solvent extraction using water to extract the dye from the fruit, whereas, biological manner refers to usage of enzyme to reduce the viscosity of the natural dye. When enzyme concentration varies form 0.1 % to 5%, the viscosity reduced gradually until the enzyme concentration is 2.5% then the reduction is insignificant. Whereas, the temperature shown a similar result. The highest reduction in viscosity is when the reaction temperature is set at 50oC. It is because, when temperature increases, the rate of reaction will increase and at one point, the rate of reaction will decrease. It is because the enzymes will be denatured at high temperatures. From this research, it is recommended that further studies can be done to the pH and concentration of natural dye in order to obtain low viscosity and high betacyanin content