Early kick detection using data-driven Bayesian network: model development and experimental testing

Safety is one of the keys to success for offshore oil and gas development projects. Drilling operation becomes more and more complicated when moving to deeper water and harsher conditions, which requires a higher level of safety. Kick is an event that happens when the hydrostatic pressure of drilling mud is lesser than the formation pressure which let the formation fluid enter the wellbore. Detection of a kick event as soon as it happens will spare drillers more time to make decision and take necessary actions. An uncontrolled or unaware kick event may lead to a well blowout which causes major damage to infrastructure, could kill people and costs lots of money. The conventional method of kick detection entails monitoring surface parameters such as stand-pipe pressure, mud pit volume, changing in flow rate and other drilling parameters can lead to the delay in detection. Some recent studies have successfully proved the ability to employ downhole parameters to realize kick. The new methods show the robust results in detection and the improvement in detection time. Besides, data-driven Bayesian network (BN) has shown to solve the problem in historical data, which is usually available, unlike expensive, and insufficient, expert knowledge. This work presents the application of data-driven BN using downhole parameters to early kick detection. The work includes three main parts: 1) creating and testing a data-based Bayesian network based on historical experiment data and synthetic data; 2) designing drilling sample, setting up and conducting experiments with the new large drilling simulator (LDS); 3) validating the data-based Bayesian network with the data from the LDS experiment. Upon the success of this work, the developed BN model will serve an efficient and effective way to detect kick early, which will enable appropriate corrective actions. The new setup of experiment with LDS can be used to conduct further experimentation to simulate more complicated kick scenarios during drilling operation

A robust diagnosis method for speed sensor fault based on stator currents in the RFOC induction motor drive

A valid diagnosis method for the speed sensor failure (SSF) is an essential requirement to ensure the reliability of Fault-Tolerant Control (FTC) models in induction motor drive (IMD) systems. Most recent researches have focused on directly comparing the measured and estimated rotor speed signal to detect the speed sensor fault. However, using that such estimated value in both the fault diagnosis and the controller reconfiguration phases leads to the insufficient performance of FTC modes. In this paper, a novel diagnosis-technique based on the stator current model combined with a confusion prevention condition is proposed to detect the failure states of the speed sensor in the IMD systems. It helps the FTC mode to separate between the diagnosis and reconfiguration phases against a speed sensor fault. This proposed SSF diagnosis method can also effectively apply for IMs’ applications at the low-speed range where the speed sensor signal often suffers from noise. MATLAB/Simulink software has been used to implement the simulations in various speed ranges. The achieved results have demonstrated the capability and effectiveness of the proposed SSF method against speed sensor faults

An Improved Current-Sensorless Method for Induction Motor Drives Applying Hysteresis Current Controller

A novel strategy based on the feed-forward field-oriented control (FOC) method is proposed for the Hysteresis Current technique to control the induction motor (IM) drive without current sensors (CSs). A control scheme is proposed to estimate stator currents from reference rotor flux, rotor flux angle, and state variables as a replacement for the feedback-signal of CSs used in the hysteresis current controller (HCC). Here the rotor flux angle component is extracted from the feed-forward FOC loop. MATLAB/Simulink is applied to implement the simulations under many different operating conditions. The simulation results demonstrated the feasibility of the proposed method to obtain high performance in controlling the IM drives without the current sensors

TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network

The electrocardiogram (ECG) is a valuable signal used to assess various aspects of heart health, such as heart rate and rhythm. It plays a crucial role in identifying cardiac conditions and detecting anomalies in ECG data. However, distinguishing between normal and abnormal ECG signals can be a challenging task. In this paper, we propose an approach that leverages anomaly detection to identify unhealthy conditions using solely normal ECG data for training. Furthermore, to enhance the information available and build a robust system, we suggest considering both the time series and time-frequency domain aspects of the ECG signal. As a result, we introduce a specialized network called the Multimodal Time and Spectrogram Restoration Network (TSRNet) designed specifically for detecting anomalies in ECG signals. TSRNet falls into the category of restoration-based anomaly detection and draws inspiration from both the time series and spectrogram domains. By extracting representations from both domains, TSRNet effectively captures the comprehensive characteristics of the ECG signal. This approach enables the network to learn robust representations with superior discrimination abilities, allowing it to distinguish between normal and abnormal ECG patterns more effectively. Furthermore, we introduce a novel inference method, termed Peak-based Error, that specifically focuses on ECG peaks, a critical component in detecting abnormalities. The experimental result on the large-scale dataset PTB-XL has demonstrated the effectiveness of our approach in ECG anomaly detection, while also prioritizing efficiency by minimizing the number of trainable parameters. Our code is available at https://github.com/UARK-AICV/TSRNet.Comment: Accepted at ISBI 202

SAM3D: Segment Anything Model in Volumetric Medical Images

Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominence, showcasing exceptional proficiency in processing medical imagery. Motivated by the Segment Anything Model (SAM)-a foundational model renowned for its remarkable precision and robust generalization capabilities in segmenting 2D natural images-we introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike current SAM-based methods that segment volumetric data by converting the volume into separate 2D slices for individual analysis, our SAM3D model processes the entire 3D volume image in a unified approach. Extensive experiments are conducted on multiple medical image datasets to demonstrate that our network attains competitive results compared with other state-of-the-art methods in 3D medical segmentation tasks while being significantly efficient in terms of parameters. Code and checkpoints are available at https://github.com/UARK-AICV/SAM3D.Comment: Accepted at ISBI 202

Factors Affecting Consumers’ Impulsive Purchasing Behavior in Circle K Convenience Stores in Hanoi, Vietnam

Impulsive purchasing behavior has been observed as one of the important studies conducted by marketers and researchers, as impulse buying has become a prevalent phenomenon in every retail format. The study was conducted to assess factors affecting consumers’ impulsive purchasing behavior in Circle K convenience stores in Hanoi, Vietnam. After reviewing a group of previous studies, the authors indicated 05 factors that affected consumers' impulsive purchasing behavior including impulsiveness, instant gratification, visual appeal, promotions and money availability. The study had selected 05 experts in the field of economics to conduct the expert interview. Moreover, the research team had also handed out the questionnaire and received 310 observations. Specifically, Impulsiveness had the strongest influence on the impulsive purchasing behavior of Circle K’s consumers in Hanoi. Keywords: factors, Impulsive purchasing behavior, Circle K convenience stores DOI: 10.7176/JESD/14-8-03 Publication date: April 30th 2023

On the Performance of the Relay Selection in Multi-hop Cluster-based Wireless Networks with Multiple Eavesdroppers Under Equally Correlated Rayleigh Fading

The performance of multi-hop cluster-based wireless networks under multiple eavesdroppers is investigated in the present work. More precisely, we derive the outage probability (OP) of the considered networks under two relay selection schemes: the channel-gain-based scheme and the random scheme. Although equally correlated Rayleigh fading is taken into consideration, the derived mathematical framework remains tractable. Specifically, we represent the exact expression of the OP under the channel-based scheme in series form, while the OP under the random scheme is computed in a closed-form expression. Additionally, we propose a novel power allocation for each transmitter that strictly satisfies the given intercept probability. Numerical results based on the Monte Carlo method are provided to verify the correctness of the derived framework. These results are also used to identify the influences of various parameters, such as the number of clusters, the number of relays per cluster, and the transmit power