With the popularity of electrical vehicles, the lithium-ion battery industry is developing rapidly. To ensure battery safe usage and to reduce its average lifecycle cost, accurate state of charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper classifies the recently proposed online SOC estimation methods into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model-based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.

An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

Design of compliant mechanisms using continuum topology optimization: A review

Degradation and mineralization of ibuprofen (IBU) were investigated using Ultraviolet (UV) Light Emitting Diodes (LEDs) in TiO 2 photocatalysis. Samples of ultrapure water (UP) and a secondary treated effluent of a municipal wastewater treatment plant (WWTP), both spiked with IBU, as well as a highly concentrated IBU (230 mg L −1 ) pharmaceutical industry wastewater (PIWW), were tested in the TiO 2 /UV-LED system. Three operating parameters, namely, pH, catalyst load and number of LEDs were optimized. The process efficiency was evaluated in terms of IBU removal using high performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Additionally, the mineralization was investigated by determining the dissolved organic carbon (DOC) content. The chemical structures of transformation products were proposed based on the data obtained using liquid chromatography with a high resolution mass spectrometer ion trap/time-of-flight (LC-MS-IT-TOF). A possible pathway of IBU degradation was accordingly suggested. Bioassays were performed using the marine bacterium Vibrio fischeri to evaluate the potential acute toxicity of original and treated wastewaters. TiO 2 heterogeneous photocatalysis was efficient to remove IBU from UP and from PIWW, and less efficient in treating the wastewater from the municipal WWTP. The acute toxicity decreased by ca. 40% after treatment, regardless of the studied matrix.

Heterogeneous Photocatalytic Degradation of Ibuprofen in Ultrapure Water, Municipal and Pharmaceutical Industry Wastewaters Using a TiO2/UV-LED System

High-efficiency removal of dyes from wastewater by fully recycling litchi peel biochar.

Photocatalysis holds great promise as an efficient and sustainable oxidation technology for application in wastewater treatment. Rapid progress developing novel materials has propelled photocatalysis to the forefront of sustainable wastewater treatments. This review presents the latest progress on applications of photocatalytic wastewater treatment. Our focus is on strategies for improving performance. Challenges and outlooks in this promising field are also discussed. We hope this review will help researchers design low-cost and high-efficiency photocatalysts for water treatment.

Recent Advances of Photocatalytic Application in Water Treatment: A Review

Rachis detection and three-dimensional localization of cut off point for vision-based banana robot

Banana detection based on color and texture features in the natural environment

The detection of banana fruits is an important part of intelligent management in the banana plantation. To detect the banana fruit quickly and accurately in the complex orchard environment, this article proposes a method based on the latest deep learning algorithm to detect the banana fruit. Using a monocular camera, we applied the YOLOv4 neural network algorithm to extract the deep features of banana fruits, realizing accurate detection of different banana sizes. The detection algorithm achieved a 99.29% detection rate, the average execution time was 0.171s, the shortest execution time was 0.135s, and the AP was 0.9995. Moreover, the detection results were discussed with the YOLOv3 algorithm and the machine learning algorithm. Compared with the machine learning algorithm, deep learning algorithm was superior to both detection accuracy and detection time. YOLOv4 had higher detection confidence and higher detection rate than YOLOv3. The results show that the proposed method could realize the fast detection of different varieties and different maturity in banana plantations, under different illumination and occlusion conditions, and provide information for banana picking, maturity and yield estimation.

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Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard.

/pdf/yolo-banana-a-lightweight-neural-network-for-rapid-detection-t25484dj.pdf

Zhou Yang

Papers

Rachis detection and three-dimensional localization of cut off point for vision-based banana robot

Banana detection based on color and texture features in the natural environment

Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

Ligand-metal charge transfer mechanism enhances TiO2/Bi2WO6/rGO nanomaterials photocatalytic efficient degradation of norfloxacin under visible light