Seismic Interpretation of the Nam Con Son Basin and Its Implication for the Tectonic Evolution

DOI:10.17014/ijog.3.2.127-137The Nam Con Son Basin covering an area of circa 110,000 km2 is characterized by complex tectonic settings of the basin which has not fully been understood. Multiple faults allowed favourable migration passageways for hydrocarbons to go in and out of traps. Despite a large amount of newly acquired seismic and well data there is no significant update on the tectonic evolution and history of the basin development. In this study, the vast amount of seismic and well data were integrated and reinterpreted to define the key structural events in the Nam Con Son Basin. The results show that the basin has undergone two extentional phases. The first N - S extensional phase terminated at around 30 M.a. forming E - W trending grabens which are complicated by multiple half grabens filled by Lower Oligocene sediments. These grabens were reactivated during the second NW - SE extension (Middle Miocene), that resulted from the progressive propagation of NE-SW listric fault from the middle part of the grabens to the margins, and the large scale building up of roll-over structure. Further to the SW, the faults of the second extentional phase turn to NNE-SSW and ultimately N - S in the SW edge of the basin. Most of the fault systems were inactive by Upper Miocene except for the N - S fault system which is still active until recent time

On the energy efficiency of NOMA for wireless backhaul in multi-tier heterogeneous CRAN

This paper addresses the problem of wireless backhaul in a multi-tier heterogeneous cellular network coordinated by a cloud-based central station (CCS), namely heterogeneous cloud radio access network (HCRAN). A non-orthogonal multiple access (NOMA) is adopted in the power domain for improved spectral efficiency and network throughput of the wireless downlink in the HCRAN. We first develop a power allocation for multiple cells of different tiers taking account of the practical power consumption of different cell types and wireless backhaul. By analysing the energy efficiency (EE) of the NOMA for the practical HCRAN downlink, we show that the power available at the cloud, the propagation environment and cell types have significant impacts on the EE performance. In particular, in a large network, the cells located at the cloud edge are shown to suffer from a very poor performance with a considerably degraded EE, which accordingly motivates us to propose an iteration algorithm for determining the maximal number of cells that can be supported in the HCRAN. The results reveal that a double number of cells can be covered in the urban environment compared to those in the shadowed urban environment and more than 1.5 times of the number of microcells can be deployed over the macrocells, while only a half number of cells can be supported when the distance between them increases threefol

Fuzzy controller for better tennis ball robot

This paper aims at designing a tennis ball robot as a training facility for tennis players. The robot is built with fuzzy controller which provides proper techniques for the players to gain practical experience as well as technical skills; thus, it can effectively serve the community and train athletes in the high-performance sport. It is found that it is more economically efficient by using the sensorless fuzzy control algorithm to replace the high-resolution optical encoders traditionally used in two main servo motors. From our simulation and practical experiment, the tennis ball robot can provide accurate speed and various directions as expected

Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators.Comment: This is a short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, US

Effect of polydispersity on the transport and sound absorbing properties of three-dimensional random fibrous structures

Sophisticated numerical approaches can predict the properties of composite nonwovens. However, for polydisperse random fibrous media, we need to identify microstructural descriptors for accurate predictions. We manufactured polydisperse composite felts with different fibrous structures and characterized them using scanning electron microscope images. The images showed a wide distribution of fiber diameters and a decreasing standard deviation of the azimuthal angle of fibers with increasing compression rate. Current models could not capture the evolution of their transport properties with compression rate. Therefore, we developed a fiber network model for the transport processes of transversely isotropic random fibrous media. The model relates the main visco-thermal dissipation mechanisms to the largest channels within the fluid phase, while the smallest channels lead the inertial behaviors. We estimated the viscous and thermal permeabilities from a representative elementary volume (REV) with a volume weighted average diameter, and the viscous and thermal characteristic lengths from a REV with inverse volume weighted average diameter. A unified empirical model was proposed. The model predictions agree with the experimental results.Comment: 29 pages, 19 figure

AN OVERVIEW OF GEOINFORMATICS STATE-OF-THE-ART TECHNIQUES FOR LANDSLIDE MONITORING AND MAPPING

Abstract. Natural hazards such as landslides, whether they are driven by meteorologic or seismic processes, are constantly shaping Earth's surface. In large percentage of the slope failures, they are also causing huge human and economic losses. As the problem is complex in its nature, proper mitigation and prevention strategies are not straightforward to implement. One important step in the correct direction is the integration of different fields; as such, in this work, we are providing a general overview of approaches and techniques which are adopted and integrated for landslide monitoring and mapping, as both activities are important in the risk prevention strategies. Detailed landslide inventory is important for providing the correct information of the phenomena suitable for further modelling, analysing and implementing suitable mitigation measures. On the other hand, timely monitoring of active landslides could provide priceless insights which can be sufficient for reducing damages. Therefore, in this work popular methods are discussed that use remotely-sensed datasets with a particular focus on the implementation of machine learning into landslide detection, susceptibility modelling and its implementation in early-warning systems. Moreover, it is reviewed how Citizen Science is adopted by scholars for providing valuable landslide-specific information, as well as couple of well-known platforms for Volunteered Geographic Information which have the potential to contribute and be used also in the landslide studies. In addition to proving an overview of the most popular techniques, this paper aims to highlight the importance of implementing interdisciplinary approaches

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio