Comparing Performance of Deep Convolution Networks in Reconstructing Soliton Molecules Dynamics from Real-Time Spectral Interference

Deep neural networks have enabled the reconstruction of optical soliton molecules with more complex structures using the real-time spectral interferences obtained by photonic time-stretch dispersive Fourier transformation (TS-DFT) technology. In this paper, we propose to use three kinds of deep convolution networks (DCNs), including VGG, ResNets, and DenseNets, for revealing internal dynamics evolution of soliton molecules based on the real-time spectral interferences. When analyzing soliton molecules with equidistant composite structures, all three models are effective. The DenseNets with layers of 48 perform the best for extracting the dynamic information of complex five-soliton molecules from TS-DFT data. The mean Pearson correlation coefficient (MPCC) between the predicted results and the real results is about 0.9975. Further, the ResNets in which the MPCC achieves 0.9906 also has the better ability of phase extraction than VGG which the MPCC is about 0.9739. The general applicability is demonstrated for extracting internal information from complex soliton molecule structures with high accuracy. The presented DCNs-based techniques can be employed to explore undiscovered mechanisms underlying the distribution and evolution of large numbers of solitons in dissipative systems in experimental research.

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Preliminary Outcomes Following Application Of The Real-Time Navigation System Combined With Intraoperative Three-Dimensional C-Arm Computed Tomography For Zygomatico-Orbital Fracture Reconstruction

10.21203/rs.3.rs-798592/v1 ◽

2021 ◽

Author(s):

Yu-Ying Chu ◽

Jia-Ruei Yang ◽

Han Tsung Liao ◽

Bo-Ru Lai

Keyword(s):

Computed Tomography ◽

Real Time ◽

Navigation System ◽

Surgical Navigation ◽

Three Dimensional ◽

Orbital Fracture ◽

The Real ◽

Orbital Volume ◽

Surgical Navigation System ◽

The Mean

Abstract This study analyzed the outcomes of zygomatico-orbital fracture reconstruction using the real-time navigation system with intraoperative three-dimensional (3D) C-arm computed tomography (CT). Fifteen patients with zygomatico-orbital or isolated orbital/zygoma fractures were enrolled in this prospective cohort. For zygoma reduction, the displacement at five key sutures and the differences between preoperative and intraoperative CT images were compared. For orbital reconstruction, the bilateral orbital volume differences in the anterior, middle, and posterior angles over the medial transitional buttress were measured. Two patients required implant adjustment once after the intraoperative 3D C-arm assessment. On comparing the preoperative and postoperative findings for the zygoma, the average sum of displacement was 19.48 (range, 5.1–34.65) vs. ±1.96 (0–3.95) mm (P < 0.001) and the deviation index was 13.56 (10–24.35) vs. 2.44 (0.6–4.85) (P < 0.001). For the orbit, the mean preoperative to postoperative bilateral orbital volume difference was 3.93 (0.35–10.95) vs. 1.05 (0.12–3.61) mm3 (P <0.001). The mean difference in the bilateral angles at the transition buttress was significantly decreased postoperatively at the middle and posterior one-third. The surgical navigation system with the intraoperative 3D C-arm can effectively improve the accuracy of zygomatico-orbital fracture reconstruction and decrease implant adjustment times.

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Recursive evaluation of the real-time path integral for dissipative systems. The spin-boson model

Chemical Physics Letters ◽

10.1016/0009-2614(95)00257-5 ◽

1995 ◽

Vol 236 (4-5) ◽

pp. 445-450 ◽

Cited By ~ 27

Author(s):

M. Winterstetter ◽

W. Domcke

Keyword(s):

Real Time ◽

Path Integral ◽

Dissipative Systems ◽

The Real ◽

Time Path ◽

Boson Model ◽

Recursive Evaluation

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Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation (Preprint)

10.2196/preprints.24285 ◽

2020 ◽

Author(s):

Yuanyuan Peng ◽

Xinjian Chen ◽

Yibiao Rong ◽

Chi Pui Pang ◽

Xinjian Chen ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Correlation Coefficient ◽

Pearson Correlation ◽

Google Trends ◽

Pearson Correlation Coefficient ◽

Spearman Correlation ◽

Time Prediction ◽

Search Volume ◽

The Mean

BACKGROUND Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. OBJECTIVE We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. METHODS Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. RESULTS Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs <10 and 187 (87.0%) had Pearson correlation coefficients >0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). CONCLUSIONS By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.

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Real-Time Detection and Location of Potted Flowers Based on a ZED Camera and a YOLO V4-Tiny Deep Learning Algorithm

Horticulturae ◽

10.3390/horticulturae8010021 ◽

2021 ◽

Vol 8 (1) ◽

pp. 21

Author(s):

Jizhang Wang ◽

Zhiheng Gao ◽

Yun Zhang ◽

Jing Zhou ◽

Jianzhi Wu ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

Point Cloud ◽

Learning Algorithm ◽

Absolute Error ◽

Frame Rate ◽

3D Point Cloud ◽

The Real ◽

Deep Learning Algorithm ◽

The Mean

In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches.

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Estimation of Change Point in Two-Stage Processes Subject to Step Change and Linear Trend

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539316500078 ◽

2016 ◽

Vol 23 (02) ◽

pp. 1650007 ◽

Cited By ~ 1

Author(s):

Amirhossein Amiri ◽

Samaneh Zolfaghari

Keyword(s):

Real Time ◽

Control Chart ◽

Linear Trend ◽

Likelihood Estimator ◽

Two Stage ◽

Mean Values ◽

The Real ◽

Stage Process ◽

Clustering Approach ◽

The Mean

When a control chart signals, it shows the process parameters have changed due to assignable cause(s). However, control chart signal is not the real time of a change in the process. Knowing the real time of change would simplify the detection and elimination of the assignable causes of variation. In this paper, a two-stage process is considered when the mean values of quality characteristics are changed under step shift and linear drift. First, a control chart based on the discriminant analysis (DA) is utilized to monitor the process. Then, when the out-of-control signal is received, the maximum likelihood estimator (MLE) based on the DA statistics, and clustering approach based on Mahalanobis distance of residuals are developed to estimate the real time of the change. The performances of the proposed estimators under different shifts are evaluated through numerical examples and a real case. The results indicate the better performance of the clustering approach rather than the MLE in most cases under both step shift and drift.

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Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project

PsycEXTRA Dataset ◽

10.1037/e307182005-005 ◽

2004 ◽

Cited By ~ 3

Author(s):

Michael M. Wagner ◽

J. Espino ◽

F-C. Tsui ◽

P. Gesteland ◽

W. Chapman ◽

...

Keyword(s):

Real Time ◽

Disease Surveillance ◽

Chief Complaint ◽

Outbreak Detection ◽

The Real

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The Real-Time Neural Genesis of a Punchline

PsycEXTRA Dataset ◽

10.1037/e528942014-019 ◽

2014 ◽

Author(s):

Irving Biederman ◽

Ori Amir

Keyword(s):

Real Time ◽

The Real

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Real-Time System for Sending Data from Three Clients to One Server with MATLAB

ACMIT Proceedings ◽

10.33555/acmit.v2i1.7 ◽

2015 ◽

Vol 2 (1) ◽

pp. 35-41

Author(s):

Rivan Risdaryanto ◽

Houtman P. Siregar ◽

Dedy Loebis

Keyword(s):

Information System ◽

Real Time ◽

Time System ◽

Real Time System ◽

The Real ◽

Performance Time ◽

Data Process ◽

Effectiveness And Efficiency

The real-time system is now used on many fields, such as telecommunication, military, information system, evenmedical to get information quickly, on time and accurate. Needless to say, a real-time system will always considerthe performance time. In our application, we define the time target/deadline, so that the system should execute thewhole tasks under predefined deadline. However, if the system failed to finish the tasks, it will lead to fatal failure.In other words, if the system cannot be executed on time, it will affect the subsequent tasks. In this paper, wepropose a real-time system for sending data to find effectiveness and efficiency. Sending data process will beconstructed in MATLAB and sending data process has a time target as when data will send.

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