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A Contactless Glucose Solution Concentration Measurement System Based on Improved High Accurate FMCW Radar algorithm

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SENSORS 2022年第11期22卷 4126-4126页

作者： Liu, Chengjie Du, Yuan Du, Li Nanjing Univ Sch Elect Sci & Engn Nanjing 210000 Peoples R China

To reduce the pain and the probability of cross-infection caused by the invasive blood glucose testing instruments, the ex vivo glucose measurement is of high significance. The electrical property of blood varies with the density of the glucose, which can be sensed by measuring its reflected coefficient in millimeter-wave. In this article, we built a contactless glucose solution concentration measurement system based on 77-GHz FMCW radar. Several preliminary signal processing algorithms are cascaded with a deep neural network to improve the accuracy of glucose solution concentration measurement. Our experiment shows that the resolution of this ex vivo glucose measurement can achieve up to 0.1 mg/mL.

关键词： FMCW radar wavelet transform deep learning algorithm PSD sparrow search algorithm convolutional neural network

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Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 256卷 108382-108382页

作者： Wei, Jin Xu, Yupeng Wang, Hanying Niu, Tian Jiang, Yan Shen, Yinchen Su, Li Dou, Tianyu Peng, Yige Bi, Lei Xu, Xun Wang, Yufan Liu, Kun Shanghai Jiao Tong Univ Shanghai Engn Ctr Precise Diag & Treatment Eye Dis Shanghai Engn Ctr Visual Sci & Photomed Dept OphthalmolShanghai Gen HospSch MedNatl Cli 100 Haining Rd Shanghai 20080 Peoples R China Shanghai Jiao Tong Univ Inst Translat Med Natl Ctr Translat Med Shanghai 20080 Peoples R China Shanghai Jiao Tong Univ Shanghai Gen Hosp Dept Endocrinol & Metab Sch Med Shanghai 200080 Peoples R China

Objective: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling peripheral blood from the patient, which is an invasive procedure and not conducive to routine monitoring. Therefore, we developed deep learning algorithm to detect noninvasively hyperuricemia from retina photographs and metadata of patients with diabetes and evaluated performance in multiethnic populations and different subgroups. Materials and methods: To achieve the task of non-invasive detection of hyperuricemia in diabetic patients, given that blood uric acid metabolism is directly related to estimated glomerular filtration rate(eGFR), we first performed a regression task for eGFR value before the classification task for hyperuricemia and reintroduced the eGFR regression values into the baseline information. We trained 3 deep learning models: (1) metadata model adjusted for sex, age, body mass index, duration of diabetes, HbA1c, systolic blood pressure, diastolic blood pressure;(2) image model based on fundus photographs;(3)hybrid model combining image and metadata model. Data from the Shanghai General Hospital Diabetes Management Center (ShDMC) were used to develop (6091 participants with diabetes) and internally validated (using 5-fold cross-validation) the models. External testing was performed on an independent dataset (UK Biobank dataset) consisting of 9327 participants with diabetes. Results: For the regression task of eGFR, in ShDMC dataset, the coefficient of determination (R2) was 0.684+0.07 (95 % CI) for image model, 0.501+0.04 for metadata model, and 0.727+0.002 for hybrid model. In external UK Biobank dataset, a coefficient of determination (R2) was 0.647+0.06 for image model, 0.627+0.03 for metadata model, and 0.697+0.07 for hybrid model. Our method was demonstrably superior to previous methods. For the

关键词： deep learning algorithm Hyperuricemia Fundus photographs Noninvasively screening

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STNILM: Switch Transformer based Non-Intrusive Load Monitoring for short and long duration appliances

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2024年 37卷

作者： Varanasi, L. N. Sastry Karri, Sri Phani Krishna Natl Inst Technol Andhra Pradesh NIT AP Tadepalligudem India

Non-Intrusive Load Monitoring (NILM) is a technique used by contemporary energy management systems to predict and optimize appliance load distribution in real time. The real-time reduction of energy consumption and improvement of electricity efficiency are two major benefits of energy disaggregation. Transformer models have made NILM far better at forecasting device power values. Due to the absence of inductive bias in the local context, transformers may not be able to capture local signal patterns in sequence-to-point settings. In this work, we present a Switch Transformer based Non-Intrusive Load Monitoring (STNILM). STNILM utilizes switching and routing layers by replacing the vanilla transformer final layers to accurately estimate the power signals of short and long duration domestic appliances. It also uses self attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. STNILM works with minimal dataset pre-processing and unbalanced. With extensive experiments and quantitative analysis, we demonstrate the efficiency and effectiveness of the proposed STNILM with considerable improvements in terms of accuracy and F1-score compared to state-of-the-art baselines.

关键词： Non-Intrusive Load Monitoring (NILM) Energy disaggregation Transformer model deep learning algorithm Sequence-to-sequence learning

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A novel oil pipeline leakage detection method based on the sparrow search algorithm and CNN

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MEASUREMENT 2022年 204卷

作者： Li, Qi Shi, Yaru Lin, Ruiqi Qiao, Wenxu Ba, Wei Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116023 Peoples R China Dalian Univ Technol Key Lab Intelligent Control Optimizat Ind Equipment Minist Educ Dalian 116023 Peoples R China Dalian Jiaotong Univ Coll Elect & Informat Engn Dalian 116023 Peoples R China

A novel sparrow search algorithm and convolutional neural network (SSA-CNN) method is proposed for oil pipeline leakage detection. Firstly, the proposed SSA-CNN method converts the input data from time series to two-dimensional matrix, and the classification conditions of different convolution kernel sizes and different pooling sizes are compared. Then, the SSA algorithm is used to optimize the parameters of the CNN. The simulation results show that, compared with the traditional machine learning method, using two-dimensional data as input can enhance the neural network's extraction of eigenvalues. The proposed SSA-CNN method was able to accurately classify 148 sample points in 150 test sets with an accuracy rate of 98.67%, which is not only higher than traditional machine learning methods, but also further improves the classification capability of CNN, while the SSA-CNN method can use the parameters already learned in the test set to ensure real-time detection.

关键词： Oil pipeline leakage deep learning algorithm Convolutional neural network Fault diagnosis Sparrow search algorithm

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Review of CNN in aerial image processing

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The Journal of Photographic Science 2023年第1期71卷 1-13页

作者： Xinni Liu Kamarul Hawari Ghazali Fengrong Han Izzeldin Ibrahim Mohamed a School of Information Xi’an University of Finance and Economics Xi’an People’s Republic of China b Faculty of Electrical and Electronics Engineering University Malaysia Pahang Pekan Malaysia c School of Computer Baoji University of Arts and Sciences Baoji People’s Republic of China

ABSTRACTABSTRACTIn recent years, deep learning algorithm has been used in many applications mainly in image processing of object detection and classification. The use of image processing techniques is becoming more interesting with the existence of drone technology with the employ of deep learning in aerial view image processing because of the high resolution and heaps of images taken. This paper aims to review neural networks specifically on the aerial view image by drones and to discuss the work principles and classic architectures of convolutional neural networks, its latest research trend and typical models along with target detection in object detection, image classification and semantic segmentation. In addition, this study also provided a specific application in the aerial image. Finally, the limitations of the convolutional network and expected future development trends were also discussed. Based on the findings, the deep learning algorithm was observed to provide high accuracy, it outperformed other generally image processing-based techniques.

关键词： Aerial image review image processing drone technology object detection image classification deep learning algorithm convolutional neural networks

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The Recognition algorithm of Two-Phase Flow Patterns Based on GoogLeNet+5 Coord Attention

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MICROMACHINES 2023年第2期14卷 462-462页

作者： Zhang, Jinsong Wei, Xinpeng Wang, Zhiliang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Shanghai Univ Sch Mech & Engn Sci Shanghai 200444 Peoples R China

The two-phase flow in a microchannel consists of liquid-liquid and gas-liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of flow patterns in the two-phase flow images. The different convolutional kernels in the GoogLeNet algorithm extracted the image features with different scales. In order to strengthen the important channel and spatial features, this paper proposes the combined five-layer Coord attention and GoogLeNet algorithm to enhance the accuracy of the new algorithm. The optimized algorithm model was derived from image datasets with different liquid-liquid two-phase flows (NaAlg-Oil, GaInSn-Water), and its accuracy was 95.09% in training and 98.12% in testing. This new model was also applied to predict the flow patterns, with a recognition accuracy of more than 97% in both the liquid-liquid and gas-liquid two-phase flows (water-soybean oil, water-lubricating oil, and argon-water).

关键词： deep learning algorithm two-phase flow image pattern recognition attention mechanism

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MFCANN: A feature diversification framework based on local and global attention for human activity recognition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Yang, Zhixuan Li, Kewen Huang, Zongchao China Univ Petr East China Inst Software Coll Comp Sci & Technol 66 Changjiang West Rd Qingdao 266580 Shandong Peoples R China

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://***/RangerHeart/MFCANN.

关键词： Human activity recognition Diverse feature extraction Attention mechanism deep learning algorithm Time series classification

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Workforce forecasting in the building maintenance and repair work: Evaluating machine learning and LSTM models

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JOURNAL OF BUILDING ENGINEERING 2024年 95卷

作者： Cao, Nan Sing, Michael C. P. Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China

Effective workforce forecasting is critical to strategic management in construction projects, particularly ensuring staffing is optimized for efficient and timely project completion. This aspect holds particular importance in densely populated urban environments, where the complexity and scale of high-rise building construction exacerbate the need for proactive maintenance strategies. This research aims to compare the efficacy of various machine learning models, including support vector machine (SVM), extreme gradient boosting (XGBoost), gradient-boosted decision trees (GBDT), and multi-layer perceptron (MLP) models, in predicting the workforce demand for building maintenance and repair work. Accordingly, 128 sets of data composed of seven indicators collected from the Hong Kong building maintenance and repair contractors were curated to serve as input-dependent variables to forecast the demands for different labor categories in addition to the overall workforce demands. The performance metrics for the four machine learning models were evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and determination coefficient (R2), with GridSearch employed to optimize their hyperparameters for enhanced predictive accuracy. The evaluation using MAE, MSE, RMSE, and R2 revealed that the XGBoost model achieved the most favorable results for predicting workforce demand on the testing sets (0.1790, 0.0582, 0.2412, 0.9915). Compared to MLP (0.1828, 0.0775, 0.2783, 0.8821) and GBDT (0.1828, 0.0828, 0.2878, 0.8739), XGBoost demonstrated a stronger performance in this specific task. Furthermore, the study also explored the potential of a long short-term memory (LSTM) model to forecast the gross value of building maintenance and repair work undertaken by main contractors, which strongly correlates with workforce demand. This approach aimed to capture the consistent growth trend observed across various maintenance activities. Consequently,

关键词： Workforce forecasting Machine learning techniques deep learning algorithm Predictive analytics

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Federated learning based privacy preserved English accent training ecosystem for people with Indian language accent

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ENTERTAINMENT COMPUTING 2023年第1期46卷

作者： Angurala, Mohit Khullar, Vikas Vellore Inst Technol Univ Chennai Tamil Nadu India Chitkara Univ Inst Engn & Technol Punjab India

Language refers to the system of communication used by a community, while accent refers to the distinct way in which an individual or group pronounces words or phrases within that language. This paper aims to design and develop a privacy-preserving federated learning-based intervention for individuals to identify a recommending system for the improvement of English accents. In methodology, the deep learning algorithm is trained and validated using the AccentDB dataset and further same model is implemented over Federated learning Ecosystem. Data in terms of identical and independent distribution is analyzed to ensure the productivity of the trained model in data constraint environments. The results section is focused to analyse the utilization of pro-posed implementations. Further, the trained model is applied to mobile and web applications for accessing levels and then training English accents to individuals. Similarly, to analyze incremental learning through federated learning, the implementation is analyzed on basis of parameters viz., accuracy, precision, recall, and loss. In the future, this proposed system could be embedded with various other applications for the overall enhancement of effective communication skills.

关键词： Language Federated learning deep learning algorithm AccentDB dataset Incremental learning

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Hybrid of deep recurrent network and long short term memory for rear-end collision detection in fog based internet of vehicles

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Almutairi, Mubarak S. Almutairi, Khalid Chiroma, Haruna Univ Hafr Al Batin Coll Comp Sci & Engn Hafar al Batin Saudi Arabia Univ Hafr Batin Appl Coll Mech Engn Technol Hafar Al Batin Saudi Arabia Univ Hafr Al Batin Appl Coll Comp Sci & Engn Technol Hafar al Batin Saudi Arabia

The development and use of intelligent transportation systems as an emerging trend in the application of computational intelligence within the concept of internet of vehicles (IoV) is attracting attention in the academia and industries. The computational intelligence algorithms such as the deep learning is playing a significant role in the IoV. The detection of rear end collision in the IoV is a critical aspect of the IoV because of safety. Previous research applied genetically optimized artificial neural network (ANN) (GA-ANN) for the detection of rear end collision in the IoV. However, GA-ANN has limitations such as stretching of the entire network leading to reduced flexibility, deteriorating performance as data increases to very large size, lack of generalization power resulting from over-fitting of the training data and calling for additional effort for regularization functions to monitor the model's complexity. In this research, as an alternative to GA-ANN for improving the accuracy of rear end collision detection in IoV, we propose a hybrid of deep Recurrent Neural Network (DRNN) and Long Short Term Memory (LSTM) (DRNN-LSTM) for rear end collision detection in the IoV. The scenario for the IoV is created and simulated to generate the dataset. The propose DRNN-LSTM is applied to detect the rear end collision in IoV. The performance of the proposed DRNN-LSTM is compared with the constituent algorithms of the DRNN-LSTM, ANN and GA-ANN. The results show that the proposed DRNN-LSTM detects rear end collision in IoV better than the GA-ANN, LSTM, DRNN and ANN. Thus, our propose approach has potential to improve safety, and effectiveness of the overall vehicle mobility in the IoV environment. Keywords: Collision Detection;Internet of Vehicles;Long Short Term Memory;deep Recurrent Neural Network;deep learning algorithm.

关键词： Collision detection Internet of Vehicles Long short term memory deep Recurrent Neural Network deep learning algorithm

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