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MEGA SILK WAY SHOPPING MALL MOBILE APPLICATION
No full textNavigating the Mega Silk Way shopping mall is not an easy task with available applications, especially for unfamiliar visitors. Our mobile application written in Flutter and incorporating image and audio recognition machine learning techniques allows users to easily determine their location, browse through hundreds of shops within their categories, and provides the shortest possible path to their desired destination
SAMPLING, MODELING AND ANALYSIS OF DIESEL PARTICULATE MATTER DISTRIBUTION IN UNDERGROUND POLYMETALLIC MINES
No full textThis thesis presents a comprehensive study on the modeling and analysis of Diesel Particulate Matter (DPM) distribution in underground polymetallic mines, integrating experimental sampling with Computational Fluid Dynamics (CFD) to enhance the accuracy of DPM dispersion models. Conducted at the Dolinnyy Mine, this research aimed to refine our understanding of the spatial and temporal distribution of particulate matter and assess its impacts on miners' health.
The methodology included detailed real-time experimental sampling of air parameters such as PM1 concentrations, airflow velocity, and temperature. This data was analyzed to identify patterns and correlations, forming the basis for subsequent CFD simulations performed using ANSYS FLUENT. These simulations attempted to model the complex environmental dynamics observed within the mine. Additionally, a comprehensive risk analysis using ‘Palisade@Risk’ software assessed the variability and predictability of DPM concentrations and airflow velocity, further supporting the CFD findings.
Despite successfully predicting airflow velocities close to the actual measurements (approximately 1.3 m/s), the CFD model significantly underestimated DPM concentrations. The simulated values averaged around 465 μg/m3, which was about four times lower than the observed values of approximately 1560 μg/m3. This substantial discrepancy underscores the need for further refinement of the CFD models to enhance their predictive accuracy.
The findings indicate a critical need for improving both sampling strategies and modeling techniques to bolster the accuracy and reliability of DPM assessments in underground mining environments. By addressing these issues, the research supports the development of more effective ventilation and monitoring systems, ultimately aiming to improve worker safety by reducing health risks associated with prolonged exposure to particulate matter. This thesis advocates for continued advancements in monitoring, risk analysis, and modeling approaches as crucial steps towards ensuring safer mining operations
INVESTIGATION OF RHEOLOGICAL PROPERTIES OF STAINLESS-STEEL POWDER AND MECHANICAL PERFORMANCES OF SLM PRINTED PARTS.
No full textThis study focuses on the rheological properties of the atomized stainless-steel powder and
the mechanical performances of the specimens obtained by selective laser melting (SLM).
Rheological properties of atomized stainless-steel powders provided by two suppliers
were characterized using the FT4 Rheometer, scanning electron microscopy (SEM), Hall
flowmeter, Tap density measurer, and the Mastersizer 3000. The mechanical performance
of the SLM printed specimens was investigated with the help of tensile, fatigue, hardness,
and corrosion tests. This study provides a characterization of the influence of the build
orientation and heat treatment on mechanical properties of SLM printed parts. Additionally,
a literature review was prepared for the current state of research on each of the relevant
properties of SLM printed 316L stainless steel parts. These comprehensive quantitative
and qualitative analyses are beneficial to anticipate the failure conditions of stainless-steel
specimens. From the rheological analysis, it was observed that the atomized powder
provided by supplier 1 had better flowability and rheological properties. However, it
was found that both commercial powders were deemed free-flowing and sufficiently
appropriate for 3D printing, which is why a mixture of both was used in this study. Build
orientation was found to have an effect on fatigue, hardness, and corrosion properties,
while tensile performance was not significantly affected. Additionally, SEM imaging
revealed a presence of defects, such as lack of fusion, voids, and unmelted powder particles
in as-built specimens. Heat treatment proved to mitigate the number of defects present,
leading to noticeable improvements in the properties of SLM printed parts. However, a
negative effect was noted for the corrosion resistance, stemming from the increase of the
number of grain boundaries, which are most susceptible to corrosion
MACHINE LEARNING-DRIVEN PREDICTION OF FLUORESCENT PROBE PROPERTIES: BRIDGING THE GAP BETWEEN PREDICTION AND EXPERIMENTATION
No full textThe development of organic fluorescent materials needs quick and precise predictions
of photophysical characteristics for techniques like high-throughput virtual screen ing. However, there is a challenge caused by the constraints of quantum mechanical
computations, experiments, and time. This thesis investigates the field of machine learning-assisted fluorescence probe design to answer this difficulty. The main part
of this investigation is the utilization of a substantial database of optical properties
of organic compounds that was collected from various scientific papers. One of the
complicating factors of this database is the presence of missing data which stems from
the collection from various sources, and this inconsistency is examined with the use
of a range of imputation methods. Furthermore, the thesis aims to construct predic tive models that can forecast properties that are inherent to fluorescent compounds
such as quantum yield, absorption and emission spectra, among others. This research
aims to pave the way for a more efficient and targeted approach to fluorescent probe
design
EFFECTS OF SCANNING TRAJECTORY AND PARAMETERS ON THE IMAGE QUALITIES OF MAGNETIC PARTICLE IMAGING
No full textToday, scanning methods are getting more popular and becoming an important part of many devices like microelectromechanical systems (MEMS), light detection and ranging (LiDAR) [1], atomic force microscopy (AFM) [2], medical imaging techniques (MRI [3]–[6] and MPI [7]–[11]), and mapping and surveying mechanisms [12], frequency modulated gyroscopes [13]. However, even though scanning techniques have many uses, one of the most important is in medical imaging. These pictures are important because they can be used to see inside the body without needing surgery. They help doctors diagnose, keep track of, stop, and treat many different illnesses [14], [15]. These techniques are used to look at the patient's field of vision and take a picture to study later to understand how the patient is doing. Choosing the right scanning path is very important to get the correct results. By picking the best path, we can scan faster and make the pictures clearer to help diagnose better. This means that the way a scan is done is very important for helping patients [16].
It's important to note that all the mentioned methods are still being worked on by researchers to make them better. Even though the field is getting bigger, the main issue with current scanning methods is that it's hard to accurately estimate the size of the pixels for different scanning settings. For instance, we don't know how big each pixel will be in the scan, with a particular way of scanning a certain area and set of scanning settings. Remember that the size of the pixels you choose will affect how good the image looks after it's scanned. So, it's really important to understand how the scanner moves and works in the area it's focused on, including how dense the scanning is, how much time is spent scanning, the quality of the signal compared to the background noise, and any mistakes in each small area. It is important to think about the right size of the pixels and the space between the pattern and how it is spread out in the FOV. It is important to tell apart the ideas of image resolution and spatial resoution. The sharpness of an image depends on how many tiny dots are in the picture, and how big each dot is. For example, an image with lots of small dots instead of a few big ones will have a clear picture. So, the quality of the image is affected by the size of the pixels. The image resolution decides how much detail and sharpness you can see in the picture. On the other hand, spatial resolution means the smallest detail you can see in a picture, which determines how much detail a camera or sensor can show. Spatial resolution is how small of a thing you can see. It can be measured in millimeters, micrometers, or even nanometers. Usually, to see small details in a picture, the picture needs to have a higher resolution than the spatial resolution [17]. This means the pixels should be much smaller than the spatial resolution. This paper focuses on how the quality of images is affected by the way they are scanned, and the scanning settings used.Any system that scans a particular area has a scanning point that moves in a specific pattern [6], [18]. The quality of the scanned images can change depending on the path chosen, which can also affect how long it takes to scan them and how clear they are [8]. Therefore, it's important for system operators to be able to measure image resolution using pixel size and understand how it's related to scanning parameters [19]. This work aims to create a theory that can figure out the smallest image resolution or biggest pixel size by using all the points where paths cross in the entire view. For a range of paths that may be used in biomedical imaging, the image resolution and its effect on the quality of the reconstructed image are also assessed. These days, a variety of scanning trajectories are accessible, such as spiral, radial, unidirectional, bidirectional Cartesian (BC), triangular Lissajous (TL), sinusoidal Lissajous (SL), and radial Lissajous (RL), as well as different enhanced and unidirectional trajectories. BC, TL, SL, and RL—will be the focus of this thesis because of their high scan resolution, reasonably regular pattern generation, and capacity to provide high-quality reconstructed images with isotropic resolution [8], [20].
Also, the influence of scanning repetition on the quality of reconstructed images in Magnetic Particle Imaging (MPI) systems is thoroughly examined in this research. In order to reconstruct images of various phantoms, were investigated using MATLAB simulations.
Simulations were methodically carried out with different numbers of repetitions - 1, 2, 4, and 8 - to obtain a more detailed understanding. The trade-offs between trajectory accuracy, precision, and uniformness were well-explained by this investigation. Using performance indicators such as Normalized Root Mean Square Error (NRMSE), Normalized Total Square Error (NTSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), a thorough analysis was conducted in the post-reconstruction phase to compare scanning trajectories. Finding the trajectory that provided the most exact and accurate image reconstruction was the goal
LATENCY REDUCTION IN 5G COMMUNICATION: A CHANNEL ESTIMATION ALGORITHM
No full textThe evolution of wireless communication illustrates tremendous expansion in terms of data rate, latency, coverage, and other network performances. From the humble beginning of the first generation of communication, which had about 10 kbps, it has progressed to the fifth generation (5G) of communication that can easily achieve 10 Gbps of data rate. Several cutting-edge technologies lie behind the 5G wireless communication to improve the connection quality of the network. For instance, mmWave uses high frequencies to increase bandwidth, while beamforming focuses the spread signal in a single direction. Additionally, Massive MIMO, which stands for Multiple Input Multiple Output, enhances energy and spectral efficiency. However, using all these outstanding technologies leads to signal degradation or reception issues despite these impressive advancements in the wireless communication field. To address these challenges, proper channel estimation is required to obtain the right signal. Numerous channel estimation techniques are offered by scientists. In this thesis work, different channel estimation techniques and types are investigated, and channel estimation algorithms are represented. Additionally, the impact of channel estimation parameters to the latency of the communication network is explored.
The paper presents a broad comparison of various channel estimation methods. Also, the performance of several pilot-based channel estimation methods in terms of accuracy and time execution is explored. The outcomes of the thesis contribute to the development of channel estimation algorithms focused on a reduction of latency in 5G communication
PHYSICAL LAYER SECURITY IN RIS-ASSISTED V2V COMMUNICATION
No full textPhysical layer security (PLS) aims to ensure the confidentiality and authenticity of transmit- ted data by capitalizing on the inherent randomness of wireless channels. Owing to the popularity of intelligent transportation systems (ITS), PLS research has sparked renewed interest in the wireless research community. This paper investigates the performance of secure communication in a vehicle-to-vehicle (V2V) communication scenario using a reconfigurable intelligent surface (RIS). Additionally, we introduce the concept of non-orthogonal multiple access (NOMA) to enhance communication efficiency in V2V networks. This study aims to comprehensively analyze secrecy performance, including parameters such as secrecy outage probability (SOP) and probability of non-zero secrecy capacity (PNZSC). Our research aims to demonstrate the effectiveness of RIS in providing secure and reliable communication within V2V NOMA networks. Ultimately, our study contributes to advancing secure communication protocols in ITS
SOIL DENSITY IMPACT ON SOIL-WATER CHARACTERISTIC CURVE AND PORE-SIZE DISTRIBUTION
No full textThe mechanical and hydraulic characteristics of unsaturated soil mechanics are defined by the soil-water characteristic curve (SWCC). The pore structure, mineral varieties, and physical features of a soil sample all have a significant impact on the parameters and shape of the SWCC. This research examined how varying percentages of water content are affected by soil density in relation to suction by means of SWCC and variance in porosity from pore-size distributions (PSD) of unsaturated soil. Two different soil types engineered soil samples from Astana, Kazakhstan and different sand-kaolin mixtures, purchased from manufacturer were tested in this study. Tested samples were compacted at optimum water content (OWC), wet of optimum and dry of optimum. SWCCs were generated using Tempe cell with suction range from 0 to 100 kPa and WP4C with suction range from 0 to 300 MPa. The PSDs were estimated using the differentiated equation of Fredlund and Xing (1994) and examined using the Scanning electron microscope (SEM) analysis. The mineral composition of soil samples was defined using the X-ray diffraction (XRD) analysis.
The SWCC testing results indicated that sample compacted at OWC and dry of optimum likely to have bimodal SWCC, while sample compacted wet of optimum likely to have unimodal SWCC. Dry density of soil increased as the AEV decreased. In addition, this resulted in decreasing water content. Dominant pore sizes with maximum frequency increased with the decreasing dry density of soil, which resulted in increased matric suction. The soil samples of SWCC lied in the range of created envelope to evaluate the obtained results. However, the PSD values including maximum pore size and frequency didn’t fit well the given range. In summary, the laboratory findings may be applied practically and are able to represent the hydraulic behavior of tested samples
PREDICTIVE MODELING OF PROPPED FRACTURE CONDUCTIVITY IN SHALE GAS RESERVOIRS
No full textHydraulic fracturing is a well completions technique that induces a network of flow channels in a reservoir. These channels are characterized by fracture conductivity, a measure of how easily a liquid or gas flows through the fracture. Fracture conductivity is influenced by several variables including proppant size, proppant concentration, and hydraulic fracture characteristics. The purpose of this research is to present a unique process that incorporates machine learning neural networks in order to predict the fracture conductivity of multi-stage fractured horizontal well in shale gas reservoirs. To accurately predict fracture conductivity using fracture parameters such as width, height, length and orientation, a robust model is necessary. In this study, predictive ability of Multilayer Perceptron algorithm was used in forecasting fracture conductivity. The findings revealed the R-squared value of 0.82, which show a good correlation of these values with the previously conducted researches. Secondly, during validation of algorithm, CMG calculated fracture conductivity at 4.6 md.ft, although the machine learning model came closest at 4.43. Overall, values and other input variable parameters are near, indicating good model performance. Lastly, enhancing the cumulative gas output has been shown to be significantly aided by the process of fine-tuning fracture parameters, which are fracture length, height, and width, inside the CMG program. The obtained results can be used as references in the future examination of parameters that affect fracture conductivity
CONTACT GRAPH ROUTING FOR INTER SATELLITE NETWORKING
No full textThis study tends to improve communication effectiveness among satellite through contact graph routing for inter-satellite networking. For deep space mission, the research systematically ad- dresses the problem of extreme latency which is as a result of the vast distance between space objects, also it addresses the issue of intermittent connectivity and low goodput by reviewing literatures, setting up of a conceptual framework, developing an algorithm, simulating and comparing with existing algorithms.
The result shows comparisons of incremental forwarding and batch forwarding whilst using a bundle aggregation technique proving that contact graph routing can be optimized upon arrival with the implementation of the Licklider Transmission Protocol Convergence Layer (LTPCL). The research states that DTN protocols are important in terms of routing efficiency and also, it was observed that the implementation of a bundle aggregation techniques contributes greatly to the development of better space communication systems