Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

斜向妥当化(Cross-Validation)の必要と方法

Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.

https://www.mdpi.com/1424-8220/18/12/4169/pdf

Insect Detection and Classification Based on an Improved Convolutional Neural Network.

Intelligent intrusion detection system featuring a virtual fence, active intruder detection, classification, tracking, and action recognition

It is necessary to develop an automated insect classification method in order to create high added value of insects Recently, image classification work applying deep learning shows the best result of classification performance In this paper, we use convolutional neural network to classify insects and examine the effect of the classification performance with the varying number of kernels in the convolution layer and with the different image datasets Experiments were conducted on 27 insect classes of ImageNet dataset with AlexNet We confirmed that the classification performance of neural network improves where insects are well represented at the center of the image, and when the number of convolution kernels increases

Performance effect analysis for insect classification using convolutional neural network

The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.

https://www.mdpi.com/2227-9717/8/10/1225/pdf

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV industry is an essential renewable energy industry. However, the amount of power generated through PV systems is closely related to unpredictable and uncontrollable environmental factors such as solar radiation, temperature, humidity, cloud cover, and wind speed. Particularly, changes in temperature and solar radiation can substantially affect power generation, causing a sudden surplus or reduction in the power output. Nevertheless, accurately predicting the energy produced by PV power generation systems is crucial. This paper proposes a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) for stable power generation forecasting. The CNN classifies weather conditions, while the LSTM learns power generation patterns based on the weather conditions. The proposed model was trained and tested using the PV power output data from a power plant in Busan, Korea. Quantitative and qualitative evaluations were performed to verify the performance of the model. The proposed model achieved a mean absolute percentage error of 4.58 on a sunny day and 7.06 on a cloudy day in the quantitative evaluation. The experimental results suggest that precise power generation forecasting is possible using the proposed model according to instantaneous changes in power generation patterns. Moreover, the proposed model can help optimize PV power plant operations.

/pdf/solar-power-forecasting-using-cnn-lstm-hybrid-model-1d07uymi.pdf

Solar Power Forecasting Using CNN-LSTM Hybrid Model

Insect identification has the disadvantage that it is difficult for non-experts to carry out due to the specificity of insects. Therefore, it is necessary for the general user to use auxiliary tools such as books to identify insects for education such as ecological learning. In recent years, researches using Deep Learning in fields such as object detection, behavior recognition, voice recognition as well as cancer detection in medical field have been actively conducted and show excellent results. In this paper, we developed a classification application that can be used in mobile phones with high automation and portability to solve the above insect classification problems. Experiments were conducted on 30 insect species selected for observable insects irrespective of environmental factors such as habitat and season, and the transform learning were applied to ResNet, which showed excellent performance in ILSVRC to classify forest insect. Our system achieved an average insect classification accuracy of 94%, an insect classification speed of 0.03 sec, and an insect photo transmission of 0.5 sec to output this information. This has proven that non-experts provide the appropriate performance to use.

Su-Chang Lim

Papers

Intelligent intrusion detection system featuring a virtual fence, active intruder detection, classification, tracking, and action recognition

Performance effect analysis for insect classification using convolutional neural network

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Solar Power Forecasting Using CNN-LSTM Hybrid Model

Development of Application for Forest Insect Classification using CNN