CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention.

The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network-long short-term memory (CNN-LSTM) residual blocks attention (PCLRA) anomaly detection model with engine sensor data. To our knowledge, this is the first time that parallel CNN-LSTM-based networks have been used in the field of CHP engine anomaly detection. In PCLRA, spatiotemporal features are extracted via CNN-LSTM in parallel and the information loss is compensated using the residual blocks and attention mechanism. The performance of PCLRA is compared with various hybrid models for 15 cases. First, the performances of serial and parallel models are compared. In addition, we evaluated the contributions of the residual blocks and attention mechanism to the performance of the CNN-LSTM hybrid model. The results indicate that PCLRA achieves the best performance, with a macro f1 score (mean ± standard deviation) of 0.951 ± 0.033, an anomaly f1 score of 0.903 ± 0.064, and an accuracy of 0.999 ± 0.002. We expect that the energy efficiency and safety of CHP engines can be improved by applying the PCLRA anomaly detection model.

PlantInfoCMS: Scalable Plant Disease Information Collection and Management System for Training AI Models.

In recent years, the development of deep learning technology has significantly benefited agriculture in domains such as smart and precision farming. Deep learning models require a large amount of high-quality training data. However, collecting and managing large amounts of guaranteed-quality data is a critical issue. To meet these requirements, this study proposes a scalable plant disease information collection and management system (PlantInfoCMS). The proposed PlantInfoCMS consists of data collection, annotation, data inspection, and dashboard modules to generate accurate and high-quality pest and disease image datasets for learning purposes. Additionally, the system provides various statistical functions allowing users to easily check the progress of each task, making management highly efficient. Currently, PlantInfoCMS handles data on 32 types of crops and 185 types of pests and diseases, and stores and manages 301,667 original and 195,124 labeled images. The PlantInfoCMS proposed in this study is expected to significantly contribute to the diagnosis of crop pests and diseases by providing high-quality AI images for learning about and facilitating the management of crop pests and diseases.

Intracerebral hemorrhage CT scan image segmentation with HarDNet based transformer.

Although previous studies conducted on the segmentation of hemorrhage images were based on the U-Net model, which comprises an encoder-decoder architecture, these models exhibit low parameter passing efficiency between the encoder and decoder, large model size, and slow speed. Therefore, to overcome these drawbacks, this study proposes TransHarDNet, an image segmentation model for the diagnosis of intracerebral hemorrhage in CT scan images of the brain. In this model, the HarDNet block is applied to the U-Net architecture, and the encoder and decoder are connected using a transformer block. As a result, the network complexity was reduced and the inference speed improved while maintaining the high performance compared to conventional models. Furthermore, the superiority of the proposed model was verified by using 82,636 CT scan images showing five different types of hemorrhages to train and test the model. Experimental results showed that the proposed model exhibited a Dice coefficient and IoU of 0.712 and 0.597, respectively, in a test set comprising 1200 images of hemorrhage, indicating better performance compared to typical segmentation models such as U-Net, U-Net++, SegNet, PSPNet, and HarDNet. Moreover, the inference time was 30.78 frames per second (FPS), which was faster than all en-coder-decoder-based models except HarDNet.

Region-aggregated attention CNN for disease detection in fruit images.

BACKGROUND: Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images. METHODS: Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps. RESULTS: The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN. CONCLUSIONS: The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.

Changes in handwashing and hygiene product usage patterns in Korea before and after the outbreak of COVID-19.

BACKGROUND: The World Health Organization declared COVID-19, the disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a global pandemic on March 11, 2020. Non-pharmaceutical interventions such as social distancing, handwashing, using hand sanitizer, and wearing facial masks are recommended as the first line of protection against COVID-19. Encouraging hand hygiene may be one of the most cost-effective means of reducing the global burden of disease. METHODS: This study uses a web-based questionnaire to evaluate the usage patterns and consumer perceptions of the effectiveness and health safety of bar soap, liquid hand soap, and hand sanitizer products before and after the spread of COVID-19. RESULTS: The results show that since the outbreak of COVID-19, the number of consumers who primarily use bar soap has decreased from 71.8 to 51.4%, the number of those who primarily use liquid hand soap has increased from 23.5 to 41.3%, and the number of those who use and carry hand sanitizer has increased. The frequency of use, duration of use, and amount used of all three products have increased significantly since the COVID-19 outbreak. Finally, consumer perception of the products' preventive effect against COVID-19 is higher for liquid hand soap and hand sanitizer than it is for bar soap. CONCLUSIONS: Because use of hand sanitizers has increased, public health guidelines must address the potential risks associated them. Our data also show that the public is abiding by the recommendations of the regulatory authorities. As handwashing has become important in preventing COVID-19 infections, the results of our study will support the development of better handwashing guidelines and a public health campaign. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12302-021-00517-8.

Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning.

Past studies of plant disease and pest recognition used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. To address this issue, there is a need for a system that suggest several candidate results and allow the user to make the final decision. In this study, we propose a method for diagnosing plant diseases and identifying pests using deep features based on transfer learning. To extract deep features, we employ pre-trained VGG and ResNet 50 architectures based on the ImageNet dataset, and output disease and pest images similar to a query image via a k-nearest-neighbor algorithm. In this study, we use a total of 23,868 images of 19 types of hot-pepper diseases and pests, for which, the proposed model achieves accuracies of 96.02 and 99.61%, respectively. We also measure the effects of fine-tuning and distance metrics. The results show that the use of fine-tuning-based deep features increases accuracy by approximately 0.7-7.38%, and the Bray-Curtis distance achieves an accuracy of approximately 0.65-1.51% higher than the Euclidean distance.