Alert button
Picture for Juan Zhao

Juan Zhao

Alert button

China Mobile Research Institute, Beijing, China

A Food Package Recognition and Sorting System Based on Structured Light and Deep Learning

Sep 07, 2023
Xuanzhi Liu, Jixin Liang, Yuping Ye, Zhan Song, Juan Zhao

Figure 1 for A Food Package Recognition and Sorting System Based on Structured Light and Deep Learning
Figure 2 for A Food Package Recognition and Sorting System Based on Structured Light and Deep Learning
Figure 3 for A Food Package Recognition and Sorting System Based on Structured Light and Deep Learning
Figure 4 for A Food Package Recognition and Sorting System Based on Structured Light and Deep Learning

Vision algorithm-based robotic arm grasping system is one of the robotic arm systems that can be applied to a wide range of scenarios. It uses algorithms to automatically identify the location of the target and guide the robotic arm to grasp it, which has more flexible features than the teachable robotic arm grasping system. However, for some food packages, their transparent packages or reflective materials bring challenges to the recognition of vision algorithms, and traditional vision algorithms cannot achieve high accuracy for these packages. In addition, in the process of robotic arm grasping, the positioning on the z-axis height still requires manual setting of parameters, which may cause errors. Based on the above two problems, we designed a sorting system for food packaging using deep learning algorithms and structured light 3D reconstruction technology. Using a pre-trained MASK R-CNN model to recognize the class of the object in the image and get its 2D coordinates, then using structured light 3D reconstruction technique to calculate its 3D coordinates, and finally after the coordinate system conversion to guide the robotic arm for grasping. After testing, it is shown that the method can fully automate the recognition and grasping of different kinds of food packages with high accuracy. Using this method, it can help food manufacturers to reduce production costs and improve production efficiency.

* Our paper has been accepted by JCRAI 2023 with Oral Presentation: https://www.jcrai.org/ Demo video: https://youtu.be/kxS1tPQEHx8 
Viaarxiv icon

Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction

Feb 28, 2023
Xing Wang, Kexin Yang, Zhendong Wang, Junlan Feng, Lin Zhu, Juan Zhao, Chao Deng

Figure 1 for Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
Figure 2 for Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
Figure 3 for Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
Figure 4 for Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction

Cellular traffic prediction is an indispensable part for intelligent telecommunication networks. Nevertheless, due to the frequent user mobility and complex network scheduling mechanisms, cellular traffic often inherits complicated spatial-temporal patterns, making the prediction incredibly challenging. Although recent advanced algorithms such as graph-based prediction approaches have been proposed, they frequently model spatial dependencies based on static or dynamic graphs and neglect the coexisting multiple spatial correlations induced by traffic generation. Meanwhile, some works lack the consideration of the diverse cellular traffic patterns, result in suboptimal prediction results. In this paper, we propose a novel deep learning network architecture, Adaptive Hybrid Spatial-Temporal Graph Neural Network (AHSTGNN), to tackle the cellular traffic prediction problem. First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers. Second, we implement a Temporal Convolution Module with multi-periodic temporal data input to capture the nonlinear temporal dependencies. In addition, we introduce an extra Spatial-Temporal Adaptive Module to conquer the heterogeneity lying in cell towers. Our experiments on two real-world cellular traffic datasets show AHSTGNN outperforms the state-of-the-art by a significant margin, illustrating the superior scalability of our method for spatial-temporal cellular traffic prediction.

* To be published in IEEE International Conference on Communications (ICC) 
Viaarxiv icon

Natural language processing to identify lupus nephritis phenotype in electronic health records

Dec 20, 2021
Yu Deng, Jennifer A. Pacheco, Anh Chung, Chengsheng Mao, Joshua C. Smith, Juan Zhao, Wei-Qi Wei, April Barnado, Chunhua Weng, Cong Liu, Adam Cordon, Jingzhi Yu, Yacob Tedla, Abel Kho, Rosalind Ramsey-Goldman, Theresa Walunas, Yuan Luo

Figure 1 for Natural language processing to identify lupus nephritis phenotype in electronic health records
Figure 2 for Natural language processing to identify lupus nephritis phenotype in electronic health records
Figure 3 for Natural language processing to identify lupus nephritis phenotype in electronic health records

Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data. We developed four algorithms: a rule-based algorithm using only structured data (baseline algorithm) and three algorithms using different NLP models. The three NLP models are based on regularized logistic regression and use different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components respectively. The baseline algorithm and the best performed NLP algorithm were external validated on a dataset from Vanderbilt University Medical Center (VUMC). Our best performing NLP model incorporating features from both structured data, regular expression concepts, and mapped CUIs improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.62 vs 0.96) datasets compared to the baseline lupus nephritis algorithm.

Viaarxiv icon

Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

Nov 01, 2021
Xing Wang, Juan Zhao, Lin Zhu, Xu Zhou, Zhao Li, Junlan Feng, Chao Deng, Yong Zhang

Figure 1 for Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
Figure 2 for Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
Figure 3 for Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
Figure 4 for Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

Mobile network traffic forecasting is one of the key functions in daily network operation. A commercial mobile network is large, heterogeneous, complex and dynamic. These intrinsic features make mobile network traffic forecasting far from being solved even with recent advanced algorithms such as graph convolutional network-based prediction approaches and various attention mechanisms, which have been proved successful in vehicle traffic forecasting. In this paper, we cast the problem as a spatial-temporal sequence prediction task. We propose a novel deep learning network architecture, Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Networks (AMF-STGCN), to model the traffic dynamics of mobile base stations. AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting. Experiments on four real-world datasets from two different domains consistently show AMF-STGCN outperforms the state-of-the-art methods.

* To be published in IEEE GLOBECOM 
Viaarxiv icon

Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search

May 03, 2021
Jindi Lv, Qing Ye, Yanan Sun, Juan Zhao, Jiancheng Lv

Figure 1 for Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
Figure 2 for Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
Figure 3 for Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
Figure 4 for Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search

Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. However, manually evaluating ECG signals is a complicated and time-consuming task. With the application of convolutional neural networks (CNNs), the evaluation process has been accelerated and the performance is improved. It is noteworthy that the performance of CNNs heavily depends on their architecture design, which is a complex process grounded on expert experience and trial-and-error. In this paper, we propose a novel approach, Heart-Darts, to efficiently classify the ECG signals by automatically designing the CNN model with the differentiable architecture search (i.e., Darts, a cell-based neural architecture search method). Specifically, we initially search a cell architecture by Darts and then customize a novel CNN model for ECG classification based on the obtained cells. To investigate the efficiency of the proposed method, we evaluate the constructed model on the MIT-BIH arrhythmia database. Additionally, the extensibility of the proposed CNN model is validated on two other new databases. Extensive experimental results demonstrate that the proposed method outperforms several state-of-the-art CNN models in ECG classification in terms of both performance and generalization capability.

Viaarxiv icon

FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing

Apr 22, 2019
Peng Zhang, Fuhao Zou, Zhiwen Wu, Nengli Dai, Skarpness Mark, Michael Fu, Juan Zhao, Kai Li

Figure 1 for FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Figure 2 for FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Figure 3 for FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Figure 4 for FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing

Face Anti-spoofing gains increased attentions recently in both academic and industrial fields. With the emergence of various CNN based solutions, the multi-modal(RGB, depth and IR) methods based CNN showed better performance than single modal classifiers. However, there is a need for improving the performance and reducing the complexity. Therefore, an extreme light network architecture(FeatherNet A/B) is proposed with a streaming module which fixes the weakness of Global Average Pooling and uses less parameters. Our single FeatherNet trained by depth image only, provides a higher baseline with 0.00168 ACER, 0.35M parameters and 83M FLOPS. Furthermore, a novel fusion procedure with ``ensemble + cascade'' structure is presented to satisfy the performance preferred use cases. Meanwhile, the MMFD dataset is collected to provide more attacks and diversity to gain better generalization. We use the fusion method in the Face Anti-spoofing Attack Detection Challenge@CVPR2019 and got the result of 0.0013(ACER), 0.999(TPR@FPR=10e-2), 0.998(TPR@FPR=10e-3) and 0.9814(TPR@FPR=10e-4).

* 10 pages;6 figures 
Viaarxiv icon