Stage Predictions of Landslide and Subsidence from an Once-Through Cycle

In this paper both processes of landslide and subsidence are considered to be limited systems. Each of these systems in nature might be regarded as an organism. Generally their lifespan must develop with common ecological characteristics, including several evolutional stages, such as initiation, growth, maturation, decline and death. Among these stages, maturation is emphasized so as to find the occurring or thriving date of both systems. An once-through cycle of both landslide and subsidence is established and is accurately predicted by a developed, mathematic model of the Poisson cycle. The Weibull distribution is cited for a landslide example. Both fundamentals are discussed. Stage predictions of landslide and subsidence are performed for several examples. Back analysis of landslides that have already happened are studied with the same model. And when compared with results from the biological mathematic model and with practical results, it is found that they correspond. Stage prediction of subsidences is also researched by the principle of the Poisson cycle.

Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

Fruit processing devices,that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture.Recently,based on deep learning,many attempts have been made in computer image processing,to monitor the ripening stage of fruits.However,it is timeconsuming to acquire images of the various ripening stages to be used for training,and it is difficult to measure the ripening stages of fruits accurately with a small number of images.In this paper,we propose a prediction system that can automatically determine the ripening stage of fruit by a combination of deep neural networks(DNNs)and machine learning(ML)that focus on optimizing them in combination on several image datasets.First,we used eight DNN algorithms to extract the color feature vectors most suitable for classifying them from the observed images representing each ripening stage.Second,we applied seven ML methods to determine the ripening stage of fruits based on the extracted color features.Third,we propose an automatic prediction system that can accurately determine the ripeness in images of various fruits such as strawberries and tomatoes by a combination of the DNN and ML methods.Additionally,we used the transfer learning method to train the proposed system on few image datasets to increase the training speed.Fourth,we experimented to find out which of the various combinations of DNN and ML methods demonstrated excellent classification performance.From the experimental results,a combination of DNNs and multilayer perceptron,or a combination of DNNs and support vector machine or kernel support vector machine generally exhibited excellent classification performance.Conversely,the combination of various DNNs and statistical classification models shows that the overall classification rate is low.Second,in the case of using tomato images,it was found that the classification rate for the combination of various DNNs and ML methods was generally similar to the results obtained for strawberry images.