In this article, a novel method based on interval type-3 fuzzy logic systems (IT3-FLSs) and an online learning approach is designed for power control and battery charge planing for photovoltaic (PV)/battery hybrid systems. Unlike the other methods, the dynamics of battery, PV and boost converters are considered to be fully unknown. Also, the effects of variation of temperature, radiation, and output load are taken into account. The robustness and the asymptotic stability of the proposed method is analyzed by the Lyapunov/LaSalle's invariant set theorems, and the tuning rules are extracted for IT3-FLS. Also, the upper bound of approximation error (AE) is approximated, and then a new compensator is designed to deal with the effects of dynamic AEs. The superiority of the proposed method is examined in several conditions and is compared with some other well-known methods. It is shown that the schemed method results in high performance under difficult conditions such as variation of temperature and radiation and abruptly changing in the output load.

Accurate diagnosis of corn crop diseases is a complex challenge faced by farmers during the growth and production stages of corn. In order to address this problem, this paper proposes a method based on K-means clustering and an improved deep learning model for accurately diagnosing three common diseases of corn leaves: gray spot, leaf spot, and rust. First, to diagnose three diseases, use the K-means algorithm to cluster sample images and then feed them into the improved deep learning model. This paper investigates the impact of various k values (2, 4, 8, 16, 32, and 64) and models (VGG-16, ResNet18, Inception v3, VGG-19, and the improved deep learning model) on corn disease diagnosis. The experiment results indicate that the method has the most significant identification effect on 32-means samples, and the diagnostic recall of leaf spot, rust, and gray spot disease is 89.24 %, 100 %, and 90.95 %, respectively. Similarly, VGG-16 and ResNet18 also achieve the best diagnostic results on 32-means samples, and their average diagnostic accuracy is 84.42% and 83.75%. In addition, Inception v3 (83.05%) and VGG-19 (82.63%) perform best on the 64-means samples. For the three corn diseases, the approach cited in this paper has an average diagnostic accuracy of 93%. It has a more significant diagnostic effect than the other four approaches and can be applied to the agricultural field to protect crops.

Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage.