Optimally leveraging depth features to enhance segmentation of recyclables from cluttered construction and demolition waste streams.

This paper addresses the critical environmental issue of effectively managing construction and demolition waste (CDW), which has seen a global surge due to rapid urbanization. With the advent of deep learning-based computer vision, this study focuses on improving intelligent identification of valuable recyclables from cluttered and heterogeneous CDW streams in material recovery facilities (MRFs) by optimally leveraging both visual and spatial features (depth). A high-quality CDW RGB-D dataset was curated to capture MRF stream complexities often overlooked in prior studies, and comprises over 3500 images for each modality and more than 160,000 dense object instances of diverse CDW materials with high resource value. In contrast to former studies which directly concatenate RGB and depth features, this study introduces a new depth fusion strategy that utilizes computationally efficient convolutional operations at the end of the conventional waste segmentation architecture to effectively fuse colour and depth information. This avoids cross-modal interference and maximizes the use of distinct information present in the two different modalities. Despite the high clutter and diversity of waste objects, the proposed RGB-DL architecture achieves a 13% increase in segmentation accuracy and a 36% reduction in inference time when compared to the direct concatenation of features. The findings of this study emphasize the benefit of effectively incorporating geometrical features to complement visual cues. This approach helps to deal with the cluttered and varied nature of CDW streams, enhancing automated waste recognition accuracy to improve resource recovery in MRFs. This, in turn, promotes intelligent solid waste management for efficiently managing environmental concerns.

A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete.

This paper proposes a new intelligent recognition method for concrete ultrasonic detection based on wavelet packet transform and a convolutional neural network (CNN). To validate the proposed data-based method, a case study is presented where the K-fold cross-validation was adopted to produce the performance analysis and classification experiments. Moreover, three evaluation indicators, precision, recall, and F-score, are calculated for analyzing the classification performance of the trained models. As a result, the obtained four-classifying CNN reaches more than 99% detection accuracy while the lowest recognition accuracy is not less than 92.5% on the testing dataset for the six-classifying CNN model. Compared with the existing stochastic configuration network (SCN) models, the presented method achieves the design objective with better recognition performance. The calculation results of the six-classifying and five-classifying models and related research clearly indicate the remaining challenging tasks for intelligent recognition algorithms in extracting features and classifying mass data from various concrete defects precisely and efficiently.

Small Target Recognition and Tracking Based on UAV Platform.

Target recognition and tracking based on multi-rotor UAVs have the advantages of low cost and high flexibility. It can monitor low-altitude targets with high intensity. It has great application prospects in national defense, military, and civil fields. The existing algorithms for aerial small target recognition and tracking have the disadvantages of slow speed, low accuracy, poor robustness, and insufficient intelligence. Aiming at the problems of existing algorithms, this paper first makes a lightweight improvement for the YOLOv4 network recognition algorithm suitable for small target recognition and tests it on the VisDrone dataset. The accuracy of the improved algorithm is increased by 1.5% and the speed is increased by 3.3 times. Then, by analyzing the response value, the KCF tracking situation is judged, and the template update of the adaptive learning rate is realized. When the tracking fails, the target is re-searched and tracked based on the recognition results and the similarity judgment. Finally, experiments are carried out on the multi-rotor UAV, and the adaptive zoom tracking strategy is designed to track pedestrians, cars, and UAVs. The results show that the proposed algorithm can achieve stable tracking of long-distance small targets.

[Research on Comprehensive Safety Monitoring System for Elderly Care Based on Artificial Intelligence and Information Fusion].

Nowadays, China has entered into an aging society; how to ensure safety in elderly care has drawn social attention. Through artificial intelligence and multi-information fusion research, combined with the applications of machine learning algorithms, internet of things devices and cloud computing, this paper presents a comprehensive, intelligent safety monitoring system for the elderly in the community and at home. The system collects the daily life data of the elderly through a series of sensors in an all-round, all-time, and non-intrusive manner, and realizes intelligent alarms for high-risk states such as falls, acute illness, abnormal personnel, and gas smoke for the elderly. Through the innovative research of human pose estimation and behavior recognition, and application of multi-sensor information fusion, the system can greatly reduce the occurrence or injury caused by safety incidents in senior care, bringing safe and healthy living environment for the elderly at homes and communities.

Research on intelligent forecasts of flight actions based on the implemented bi-LSTM.

Rapid identification of flight actions by utilizing flight data is more realistic so the quality of flight training can be objectively assessed. The bidirectional long short-term memory (bi-LSTM) algorithm is implemented to forecast the flight actions of aircraft. The dataset containing the flight actions is structured by collecting tagged flight data when real flight training is exercised. However, the dataset needs to be preprocessed and annotated with expert rules. One of the deep learning (DL) methods, called the bi-LSTM algorithm, is implemented to train and test, and the pivotal parameters of the algorithm are optimized. Finally, the constructed model is applied to forecast the flight actions of aircraft. The training's accuracy and loss rates are computed. The duration is kept between 1 through 3 h per session. Thus, the development of training the model is continued until an accuracy rate above 85% is achieved. The word-run inference time is kept under 2 s. Finally, the proposed algorithm's specific characteristics, which are short training time and high recognition accuracy, are achieved when complex rules and large sample sizes exist.

Machine learning-assisted melamine-Cu nanozyme and cholinesterase integrated array for multi-category pesticide intelligent recognition.

Expanding target pesticide species and intelligent pesticide recognition were formidable challenges for existing cholinesterase inhibition methods. To improve this status, multi-active Mel-Cu nanozyme with mimetic Cu-N sites was prepared for the first time. It exhibited excellent laccase-like and peroxidase-like activities, and can respond to some pesticides beyond the detected range of enzyme inhibition methods, such as glyphosate, carbendazim, fumonisulfuron, etc., through coordination and hydrogen bonding. Inspired by the signal complementarity of Mel-Cu and cholinesterase, an integrated sensor array based on the Mel-Cu laccase-like activity, Mel-Cu peroxidase-like activity, acetylcholinesterase, and butyrylcholinesterase was creatively constructed. And it could successfully discriminate 12 pesticides at 0.5-50 µg/mL, which was significantly superior to traditional enzyme inhibition methods. Moreover, on the basis of above array, a unified stepwise prediction model was built using classification and regression algorithms in machine learning, which enabled concentration-independent qualitative identification as well as precise quantitative determination of multiple pesticide targets, simultaneously. The sensing accuracy was verified by blind sample analysis, in which the species was correctly identified and the concentration was predicted within 10% error, suggesting great intelligent recognition ability. Further, the proposed method also demonstrated significant immunity to interference and practical application feasibility, providing powerful means for pesticide residue analysis.

A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification.

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.

Intelligent Recognition and Analysis of Negative Emotions of Undergraduates Under COVID-19.

Background: The outbreak and spread of COVID-19 has brought a tremendous impact on undergraduates' study and life, and also caused anxiety, depression, fear and loneliness among undergraduates. If these individual negative emotions are not timely guided and treated, it is easy to cause the amplification of social negative emotions, resulting in individual and collective irrational behavior, and ultimately destroy social stability and trust foundation. Therefore, how to strengthen the analysis and guidance of negative emotions of undergraduates has become an important issue to be urgently solved in the training of undergraduates. Method: This paper presents a weight and structure double-determination method. Based on this method, a Radial Basis Function Neural Networks (RBFNN) classifier is constructed for recognizing negative emotions of undergraduates. After classifying the input psychological crisis intervention scale samples by the RBFNN classifier, recognition of negative emotions for undergraduates are divided into normal, mild depression, moderate depression and severe depression. Experiments: Afterwards, we analyze negative emotions of undergraduates and give some psychological adjustment strategies. In addition, the experiment results demonstrate that the proposed method has a good performance in terms of classification accuracy, classification time and recognition rate of negative emotions among undergraduates.

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree.

Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.

Corrigendum on: A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree.

[This corrects the article DOI: 10.3389/fphys.2022.1008111.].

[Establishment of a deep learning-based visual model for intelligent recognition of Oncomelania hupensis].

OBJECTIVE: To establish a deep learning-based visual model for intelligent recognition of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, and evaluate the effects of different training strategies for O. hupensis image recognition. METHODS: A total of 2 614 datasets of O. hupensis snails and 4 similar snails were generated through field sampling and internet capture, and were divided into training sets and test sets. An intelligent recognition model was created based on deep learning, and was trained and tested. The precision, sensitivity, specificity, accuracy, F1 score and Youden index were calculated. In addition, the receiver operating characteristic (ROC) curve of the model for snail recognition was plotted to evaluate the effects of "new learning", "transfer learning" and "transfer learning + data enhancement" training strategies on the accuracy of the model for snail recognition. RESULTS: Under the "transfer learning + data enhancement" strategy, the precision, sensitivity, specificity, accuracy, Youden index and F1 score of the model were 90.10%, 91.00%, 97.50%, 96.20%, 88.50% and 90.51% for snail recognition, which were all higher than those under both "new learning" and "transfer learning" strategies. There were significant differences in the sensitivity, specificity and accuracy of the model for snail recognition under "new learning", "transfer learning" and "transfer learning + data enhancement" training strategies (all P values < 0.001). In addition, the area under the ROC curve of the model was highest (0.94) under the "transfer learning + dataenhancement" training strategy. CONCLUSIONS: This is the first visual model for intelligent recognition of O. hupensis based on deep learning, which shows a high accuracy for snail image recognition. The "transfer learning + data enhancement" training strategy is helpful to improve the accuracy of the model for snail recognition.

Establishment of a deep learning-based visual model for intelligent recognition of Oncomelania hupensis / 中国血吸虫病防治杂志

Objective To establish a deep learning-based visual model for intelligent recognition of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, and evaluate the effects of different training strategies for O. hupensis image recognition. Methods A total of 2 614 datasets of O. hupensis snails and 4 similar snails were generated through field sampling and internet capture, and were divided into training sets and test sets. An intelligent recognition model was created based on deep learning, and was trained and tested. The precision, sensitivity, specificity, accuracy, F1 score and Youden index were calculated. In addition, the receiver operating characteristic (ROC) curve of the model for snail recognition was plotted to evaluate the effects of “new learning”, “transfer learning” and “transfer learning + data enhancement” training strategies on the accuracy of the model for snail recognition. Results Under the “transfer learning + data enhancement” strategy, the precision, sensitivity, specificity, accuracy, Youden index and F1 score of the model were 90.10%, 91.00%, 97.50%, 96.20%, 88.50% and 90.51% for snail recognition, which were all higher than those under both “new learning” and “transfer learning” strategies. There were significant differences in the sensitivity, specificity and accuracy of the model for snail recognition under “new learning”, “transfer learning” and “transfer learning + data enhancement” training strategies (all P values < 0.001). In addition, the area under the ROC curve of the model was highest (0.94) under the “transfer learning + dataenhancement” training strategy. Conclusions This is the first visual model for intelligent recognition of O. hupensis based on deep learning, which shows a high accuracy for snail image recognition. The “transfer learning + data enhancement” training strategy is helpful to improve the accuracy of the model for snail recognition.