An Application of Vector Autoregressive Model for Analyzing the Impact of Weather And Nearby Traffic Flow On The Traffic Volume

This paper aims to predict the traffic flow at one road segment based on nearby traffic volume and weather conditions. Our team also discover the impact of weather conditions and nearby traffic volume on the traffic flow at a target point. The analysis results will help solve the problem of traffic flow prediction and develop an optimal transport network with efficient traffic movement and minimal traffic congestion. Hourly historical weather and traffic flow data are selected to solve this problem. This paper uses model VAR(36) with time trend and constant to train the dataset and forecast. With an RMSE of 565.0768111 on average, the model is considered appropriate although some statistical tests implies that the residuals are unstable and non-normal. Also, this paper points out some variables that are not useful in forecasting, which helps simplify the data-collecting process when building the forecasting system.Comment: International Conference on Computing and Communication Technologies (RIVF2022

ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese

Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several significant drawbacks, including imbalanced data and noisy data on social media platforms. Imbalanced and noisy are two essential issues that need to be addressed in Vietnamese social media texts. Graph Convolutional Networks can address the problems of imbalanced and noisy data in text classification on social media by taking advantage of the graph structure of the data. This study presents a novel approach based on contextualized language model (PhoBERT) and graph-based method (Graph Convolutional Networks). In particular, the proposed approach, ViCGCN, jointly trained the power of Contextualized embeddings with the ability of Graph Convolutional Networks, GCN, to capture more syntactic and semantic dependencies to address those drawbacks. Extensive experiments on various Vietnamese benchmark datasets were conducted to verify our approach. The observation shows that applying GCN to BERTology models as the final layer significantly improves performance. Moreover, the experiments demonstrate that ViCGCN outperforms 13 powerful baseline models, including BERTology models, fusion BERTology and GCN models, other baselines, and SOTA on three benchmark social media datasets. Our proposed ViCGCN approach demonstrates a significant improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized Language Models, including multilingual and monolingual, on three benchmark datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our integrated model ViCGCN achieves the best performance compared to other BERTology integrated with GCN models

An in-Depth Survey of Visible Light Communication Based Positioning Systems

While visible light communication (VLC) has become the candidate for the wireless technology of the 21st century due to its inherent advantages, VLC based positioning also has a great chance of becoming the standard approach to positioning. Within the last few years, many studies on VLC based positioning have been published, but there are not many survey works in this field. In this paper, an in-depth survey of VLC based positioning systems is provided. More than 100 papers ranging from pioneering papers to the state-of-the-art in the field were collected and classified based on the positioning algorithms, the types of receivers, and the multiplexing techniques. In addition, current issues and research trends in VLC based positioning are discussed

The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication

In vehicular optical camera communication (VOCC) systems, LED panels are used to transmit visible light signals which are captured by cameras. The logic bits 1 and 0 are represented by the On and Off status of the LEDs in the panel. The bit error rate (BER) of the system is directly proportional to the distinguishability of the On and Off LEDs in the received LED panel images. The signal quality is commonly believed in telecommunications to improve with a higher transmitted power. Therefore, one might expect to get a lower BER in VOCC systems by simply using more powerful LED lights. However, this is not the case with VOCC systems. This paper shows that the LED distinguishability is simultaneously determined by two factors: The LED extinction ratio and LED interference. The former needs to be kept high and the latter kept low for better LED distinguishability. The problem is that both the extinction ratio and interference increase with the ratio of LED light to ambient light (L2A). Consequently, an optimal L2A ratio exists to achieve the optimal balance between the positive impact of the extinction ratio and the negative impact of the interference. This can bring about the lowest BER without changing the system’s data rate. In addition, this paper shows that the optimal L2A ratio varies according to the interval between the LEDs in a panel. We analyze the effect of the L2A ratio and LED interval on LED distinguishability. We then formulate a constrained optimization problem to find the optimal L2A ratios corresponding to different LED intervals. The simulation results verify the necessity of LED to ambient light ratio optimization as it can bring about the lowest BER without scarifying other aspects of the VOCC system

An in-Depth Survey of Visible Light Communication Based Positioning Systems

While visible light communication (VLC) has become the candidate for the wireless technology of the 21st century due to its inherent advantages, VLC based positioning also has a great chance of becoming the standard approach to positioning. Within the last few years, many studies on VLC based positioning have been published, but there are not many survey works in this field. In this paper, an in-depth survey of VLC based positioning systems is provided. More than 100 papers ranging from pioneering papers to the state-of-the-art in the field were collected and classified based on the positioning algorithms, the types of receivers, and the multiplexing techniques. In addition, current issues and research trends in VLC based positioning are discussed

Performance Analysis of Visible Light Communication Using CMOS Sensors

This paper elucidates the fundamentals of visible light communication systems that use the rolling shutter mechanism of CMOS sensors. All related information involving different subjects, such as photometry, camera operation, photography and image processing, are studied in tandem to explain the system. Then, the system performance is analyzed with respect to signal quality and data rate. To this end, a measure of signal quality, the signal to interference plus noise ratio (SINR), is formulated. Finally, a simulation is conducted to verify the analysis

A Probability-Based Algorithm Using Image Sensors to Track the LED in a Vehicle Visible Light Communication System

This paper proposes a probability-based algorithm to track the LED in vehicle visible light communication systems using a camera. In this system, the transmitters are the vehicles’ front and rear LED lights. The receivers are high speed cameras that take a series of images of the LEDs. ThedataembeddedinthelightisextractedbyfirstdetectingthepositionoftheLEDsintheseimages. Traditionally, LEDs are detected according to pixel intensity. However, when the vehicle is moving, motion blur occurs in the LED images, making it difficult to detect the LEDs. Particularly at high speeds, some frames are blurred at a high degree, which makes it impossible to detect the LED as well as extract the information embedded in these frames. The proposed algorithm relies not only on the pixel intensity, but also on the optical flow of the LEDs and on statistical information obtained from previous frames. Based on this information, the conditional probability that a pixel belongs to a LED is calculated. Then, the position of LED is determined based on this probability. To verify the suitability of the proposed algorithm, simulations are conducted by considering the incidents that can happen in a real-world situation, including a change in the position of the LEDs at each frame, as well as motion blur due to the vehicle speed

Effects of Green Supply Chain Management Practices on Sustainability Performance: A Systematic Literature Review and Directions for Future Research

In the context that the world is increasingly paying attention to sustainable development, green supply chain management becomes the optimal solution to help balance the three effects of sustainability: economic, environmental, and social. This paper provides a research overview of sustainability performance and green supply chain management practices. Thence, the authors propose a research model on the impact of green supply chain management on the sustainability performance of small and medium enterprises in Vietnam. Keywords:Green Supply Chain Management (GSCM), sustainability performance, Small and Medium Enterprises. DOI: 10.7176/JESD/13-10-01 Publication date:May 31st 202