Golf Green Slope Estimation Using a Cross Laser Structured Light System and an Accelerometer

Duy Duong Pham,Quoc Khanh Dang,Young Soo Suh 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.2

In this paper, we propose a method combining an accelerometer with a cross structured light system to estimate the golf green slope. The cross-line laser provides two laser planes whose functions are computed with respect to the camera coordinate frame using a least square optimization. By capturing the projections of the cross-line laser on the golf slope in a static pose using a camera, two 3D curves’ functions are approximated as high order polynomials corresponding to the camera coordinate frame. Curves’ functions are then expressed in the world coordinate frame utilizing a rotation matrix that is estimated based on the accelerometer’s output. The curves provide some important information of the green such as the height and the slope’s angle. The curves estimation accuracy is verified via some experiments which use OptiTrack camera system as a ground-truth reference.

Golf Green Slope Estimation Using a Cross Laser Structured Light System and an Accelerometer

Pham, Duy Duong,Dang, Quoc Khanh,Suh, Young Soo The Korean Institute of Electrical Engineers 2016 Journal of Electrical Engineering & Technology Vol.11 No.2

In this paper, we propose a method combining an accelerometer with a cross structured light system to estimate the golf green slope. The cross-line laser provides two laser planes whose functions are computed with respect to the camera coordinate frame using a least square optimization. By capturing the projections of the cross-line laser on the golf slope in a static pose using a camera, two 3D curves’ functions are approximated as high order polynomials corresponding to the camera coordinate frame. Curves’ functions are then expressed in the world coordinate frame utilizing a rotation matrix that is estimated based on the accelerometer’s output. The curves provide some important information of the green such as the height and the slope’s angle. The curves estimation accuracy is verified via some experiments which use OptiTrack camera system as a ground-truth reference.

Walking Monitoring for Users of Standard and Front-Wheel Walkers

Pham, Duy Duong,Duong, Huu Toan,Suh, Young Soo Institute of Electrical and Electronics Engineers 2017 IEEE transactions on instrumentation and measureme Vol.66 No.12

<P>In this paper, a walker movement monitoring system is proposed for a standard walker and a front-wheeled walker. The walking parameters (step length, step time, step speed, and total walking distance) are accurately estimated from the movement of walker to provide a statistical analysis of rehabilitation assessment. First, a movement classification algorithm is proposed to identify walking styles (continuous rolling, step-by-step rolling, two back tips lifting, and complete lifting). Based on this classification, the movement of a walker can be estimated using either two encoders (only for front-wheeled walker) or an inertial measurement unit. The accuracy of walking parameter estimation is verified through five subjects' experiments. In 20-m straight walking, the walking distance root mean square error is 0.1-0.3 m depending on walking styles.</P>

A Statistical Data-Filtering Method Proposed for Short-Term Load Forecasting Models

Duong Minh Bui,Phuc Duy Le,Tien Minh Cao,Hung Nguyen,Trang Thi Pham,Duy Anh Pham 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5

Reliability assessment of the SCADA-system based load data is necessary for improving accuracy of short-term load forecasting (STLF) methods in a distribution network (DN). Specifi cally, the reliability evaluation of the load data is to properly eliminate noise/outliers caused by random power consumption behaviors or the sudden change in load demand from industrial and residential customers in the DN. Thus, this paper proposes a novel statistical data-fi ltering method, working at an input data pre-processing stage, which will evaluate the reliability of input load data by analyzing all possible data confi dence levels in order to fi lter-out the noise/outliers for accuracy improvement of diff erent short-term load forecasting models. The proposed statistical data-fi ltering method is also compared to other existing data-fi ltering methods (such as Kalman Filter, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA)). Moreover, several case studies of short-term load forecasting for a typical 22 kV distribution network in Vietnam are conducted with an Artifi cial Neural Network (ANN) model, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model, a combined model of Long Short-Term Memory Network and Convolutional Neural Network (LSTM-CNN), and a conventional Autoregressive Integrated Moving Average (ARIMA) model to validate the statistical data-fi ltering method proposed. The achieved results demonstrate which the STLF using ANN, LSTM-RNN, LSTMCNN, and ARIMA models with the statistical data-fi ltering method can all outperform those with the existing data-fi ltering methods. Additionally, the numerical results also indicate that in case the SCADA-based load data is normally distributed, time-series forecasting models should be more preferred than neural network models; otherwise, when the SCADA-based load data contains multiple normally distributed sub-datasets, neural network-based prediction models are highly recommended.

Influence of Overconfidence and Cash Flow on Investment in Vietnam

NGUYEN, Duy Van,DANG, Duong Quy,PHAM, Giang Hoang,DO, Du Kim Korea Distribution Science Association 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.2

CEOs Overconfidence can bring potentially risky early decisions to businesses, along with large enterprise free cash flow that can bring different investment decisions with CEOs Overconfidence. Especially in the context of Vietnamese enterprises, CEOs are often influenced by behavioral psychology about overconfidence in investment decisions (due to individual cultural characteristics as well as operating financial markets also depend on many factors outside the market). Therefore, the authors study the impact of overconfidence and cash flow on investment in Vietnamese to find the internal relationship between these three factors in the financial environment in Vietnam. With 480 companies listed on the Vietnam Stock Exchange from 2014 to 2018 (companies have continuous reports), the regression analysis results with panel data (FEM, GLS models, correction of robust and GMM dealing with endogenous problems) have shown Overconfidence has a positive impact on investment. At the same time, the results also indicated that enterprises with overconfident CEOs and large cash flows tend to invest less than enterprises with low cash flow. The results of this study have shown the behavioral behavior of CEOs in Vietnamese enterprises that exist under both prospect theory and effective market theory.

A dense, pinholes-free pure cubic phase CsPbBr3 nanocrystals film for high-performance photodetector

Thanh-Tung Duong,Phuong-Nam Tran,Tuan-Pham Van,Duy-Hung Nguyen,Van-Dang Tran 대한금속·재료학회 2024 ELECTRONIC MATERIALS LETTERS Vol.20 No.2

This study demonstrates a simple centrifugal coating method to prepare high-quality pure cubic phase CsPbBr 3 nanocrystalfi lm. The resultant perovskite layers possess a uniform and dense 500 nm-thick, with a bandgap of 2.38 eV, a low trap-statedensity of 6.9 × 10 − 15 cm − 3 , and carrier mobility of approximately 19.8 cm 2 V − 1 s − 1 . Furthermore, CsPbBr 3 NCs-basedself-powered photodetectors with high charge carriers’ charge transfer are fabricated. The device shows a low dark currentdensity of 1.93 × 10 − 7 A/cm 2 at room temperature. Such photodetectors show the highest responsivity of 3.0 AW − 1 ,specifi c detectivity of 1.2 × 10 13 Jones, and external quantum effi ciency (EQE) of 920% at zero bias voltage. The proposedmethod shows signifi cant promise for use in the lab fabrication of optoelectronic devices based on thin fi lms of nanocrystalperovskite materials.

Applying the IoT platform and green wave theory to control intelligent traffic lights system for urban areas in Vietnam

Phan, Cao Tho,Pham, Duy Duong,Tran, Hoang Vu,Tran, Trung Viet,Huu, Phat Nguyen Korean Society for Internet Information 2019 KSII Transactions on Internet and Information Syst Vol.13 No.1

This paper proposes an intelligent system performing an application with assistance of an Internet of Things (IoT) platform to control a traffic lights system. In our proposed systems, the traffic lights can be remotely controlled through the Internet. Based on IoT platform, the traffic conditions at different intersections of roads are collected and the traffic lights are controlled in a central manner. For the software part, the algorithm is designed based on the green wave theory to maximize the green bandwidth of arterial roads while addressing a challenging issue: the rapid changes of parameters including cycle time, splits, offset, non-fixed vehicles' velocities and traffic flow along arterial roads. The issue typically happens at some areas where the transportation system is not well organized like in Vietnam. For the hardware part, PLC S7-1200 are placed at the intersections for two purposes: to control traffic lights and to collect the parameters and transmit to a host machine at the operation center. For the communication part, the TCP/IP protocol can be done using a Profinet port embedded in the PLC. Some graphical user interface captures are also presented to illustrate the operation of our proposed system.

Applying the IoT platform and green wave theory to control intelligent traffic lights system for urban areas in Vietnam

( Cao Tho Phan ),( Duy Duong Pham ),( Hoang Vu Tran ),( Trung Viet Tran ),( Phat Nguyen Huu ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.1

This paper proposes an intelligent system performing an application with assistance of an Internet of Things (IoT) platform to control a traffic lights system. In our proposed systems, the traffic lights can be remotely controlled through the Internet. Based on IoT platform, the traffic conditions at different intersections of roads are collected and the traffic lights are controlled in a central manner. For the software part, the algorithm is designed based on the green wave theory to maximize the green bandwidth of arterial roads while addressing a challenging issue: the rapid changes of parameters including cycle time, splits, offset, non-fixed vehicles’ velocities and traffic flow along arterial roads. The issue typically happens at some areas where the transportation system is not well organized like in Vietnam. For the hardware part, PLC S7-1200 are placed at the intersections for two purposes: to control traffic lights and to collect the parameters and transmit to a host machine at the operation center. For the communication part, the TCP/IP protocol can be done using a Profinet port embedded in the PLC. Some graphical user interface captures are also presented to illustrate the operation of our proposed system.

3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining 3 Classes-EM and Bayesian Method

Nguyen Ho Minh Duy,Tran Anh Tuan,Nguyen Hai Duong,Tran Anh Tuan,Nguyen Kim Dao,Atsuo Yoshitaka,Jin Young Kim,Seung Ho Choi,Pham The Bao 한국정보기술학회 2016 한국정보기술학회논문지 Vol.14 No.5

MRI and CT images are the most popular formats assisting a doctor in diagnosis and treatment, but highly accurate segmentation is a challenging problem due to intensity inhomogeneity and environmental noises. In this paper, we introduce an appropriate and effective automatic approach to facilitate this problem in two stages. In the first stage, skull region is removed from the brain by morphological active contour and level set process. Moreover, in level set process, some AI rules are defined on slice positions of brain to increase the accuracy. In the second stage, a modified EM method is performed on the resultant skull-stripping image to identify some candidate main regions of CSF (cerebro-spinal fluid), GM (gray matter), and WM (white matter). The candidate regions are then re-estimated into the proper CSF, GM, and WM through a Bayesian Estimation Process. The experimental results show that the proposed approach obtains a robust segmentation for IBSR, OASIS and Korean Hospital database. With the proposed AI-rules, the level set method gets good skull-stripping images regardless of MRI slice position in bran. Also, Bayesian postprocessing can improve the segmentation performance by 10~15% in CSF, GM and WM ratios compared the basic EM algorithm.

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam

Ha-Linh Quach,Thai Quang Pham,Ngoc-Anh Hoang,Dinh Cong Phung,Viet-Cuong Nguyen,Son Hong Le,Thanh Cong Le,Dang Hai Le,Anh Duc Dang,Duong Nhu Tran,Nghia Duy Ngu,Florian Vogt,Cong-Khanh Nguyen 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.4

Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020. Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts. Results: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33–0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07–0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23–2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20–76.70) or unverified (OR = 5.03; 95% CI, 1.66–15.24). Conclusions: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online “infodemics” to inform public health responses.