Search results for: tube-based robust MPC

Authors: Khamael M. Abualnaja, Lidija Šiller, Ben R. Horrocks

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Surface enhanced Raman spectroscopy (SERS) of Silicon nano crystals (SiNCs) were obtained using two different laser excitations: 488 nm and 514.5 nm. Silver nano particles were used as plasmonics metal nano particles due to a robust SERS effect that observed when they mixed with SiNCs. SiNCs have been characterized by scanning electron microscopy (SEM), high resolution transmission electron microscopy (HRTEM), atomic force microscopy (AFM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). It is found that the SiNCs are crystalline with an average diameter of 65 nm and FCC lattice. Silver nano particles (AgNPs) of two different sizes were synthesized using photo chemical reduction of AgNO3 with sodium dodecyl sulfate (SDS). The synthesized AgNPs have a polycrystalline structure with an average particle diameter of 100 nm and 30 nm, respectively. A significant enhancement in the SERS intensity was observed for AgNPs100/SiNCs and AgNPs30/SiNCs mixtures increasing up to 9 and 3 times respectively using 488 nm intensity; whereas the intensity of the SERS signal increased up to 7 and 2 times respectively, using 514.5 nm excitation source. The enhancement in SERS intensities occurs as a result of the coupling between the excitation laser light and the plasmon bands of AgNPs; thus this intense field at AgNPs surface couples strongly to SiNCs. The results provide good consensus between the wavelength of the laser excitation source and surface plasmon resonance absorption band of silver nano particles consider to be an important requirement in SERS experiments.

Keywords: silicon nanocrystals (SiNCs), silver nanoparticles (AgNPs), surface enhanced raman spectroscopy (SERS)

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Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

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The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

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Authors: Robert O. Abolade, Olumide O. Ajayi, Zacheaus K. Adeyemo, Solomon A. Adeniran

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Link adaptation is an important strategy for achieving robust wireless multimedia communications based on quality of service (QoS) demand. Scheme switching in multiple-input multiple-output (MIMO) systems is an aspect of link adaptation, and it involves selecting among different MIMO transmission schemes or modes so as to adapt to the varying radio channel conditions for the purpose of achieving QoS delivery. However, finding the most appropriate switching method in MIMO links is still a challenge as existing methods are either computationally complex or not always accurate. This paper presents an intelligent switching method for the MIMO system consisting of two schemes - transmit diversity (TD) and spatial multiplexing (SM) - using fuzzy logic technique. In this method, two channel quality indicators (CQI) namely average received signal-to-noise ratio (RSNR) and received signal strength indicator (RSSI) are measured and are passed as inputs to the fuzzy logic system which then gives a decision – an inference. The switching decision of the fuzzy logic system is fed back to the transmitter to switch between the TD and SM schemes. Simulation results show that the proposed fuzzy logic – based switching technique outperforms conventional static switching technique in terms of bit error rate and spectral efficiency.

Keywords: channel quality indicator, fuzzy logic, link adaptation, MIMO, spatial multiplexing, transmit diversity

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Authors: Muhammad Bilal, Zhongfeng Qiu

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Aerosols, suspended particles in the atmosphere, play an important role in the earth energy budget, climate change, degradation of atmospheric visibility, urban air quality, and human health. To fully understand aerosol effects, retrieval of aerosol optical properties such as aerosol optical depth (AOD) at high spatiotemporal resolution is required. Therefore, in the present study, hourly AOD observations at 500 m resolution were retrieved from the geostationary ocean color imager (GOCI) using the simplified aerosol retrieval algorithm (SARA) over the urban area of Beijing for the year 2016. The SARA requires top-of-the-atmosphere (TOA) reflectance, solar and sensor geometry information and surface reflectance observations to retrieve an accurate AOD. For validation of the GOCI retrieved AOD, AOD measurements were obtained from the aerosol robotic network (AERONET) version 3 level 2.0 (cloud-screened and quality assured) data. The errors and uncertainties were reported using the root mean square error (RMSE), relative percent mean error (RPME), and the expected error (EE = ± (0.05 + 0.15AOD). Results showed that the high spatiotemporal GOCI AOD observations were well correlated with the AERONET AOD measurements with a correlation coefficient (R) of 0.92, RMSE of 0.07, and RPME of 5%, and 90% of the observations were within the EE. The results suggested that the SARA is robust and has the ability to retrieve high-resolution spatiotemporal AOD observations over the urban area using the geostationary satellite.

Keywords: AEORNET, AOD, SARA, GOCI, Beijing

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Authors: Mimi Roy, Sriroop Chaudhuri

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Sustainable water supply is among the basic needs for human development, especially in the rural areas of the developing nations where safe water supply and basic sanitation infrastructure is direly needed. In light of the above, we propose a simple methodology to develop a composite water sustainability index (WSI) to assess the collective performance of the existing rural water supply services (RWSS) in India over time. The WSI will be computed by summarizing the details of all the different varieties of water supply schemes presently available in India comprising of 40 liters per capita per day (lpcd), 55 lpcd, and piped water supply (PWS) per household. The WSI will be computed annually, between 2010 and 2016, to elucidate changes in holistic RWSS performances. Results will be integrated within a robust geospatial framework to identify the ‘hotspots’ (states/districts) which have persistent issues over adequate RWSS coverage and warrant spatially-optimized policy reforms in future to address sustainable human development. Dataset will be obtained from the National Rural Drinking Water Program (NRDWP), operating under the aegis of the Ministry of Drinking Water and Sanitation (MoDWS), at state/district/block levels to offer the authorities a cross-sectional view of RWSS at different levels of administrative hierarchy. Due to simplistic design, complemented by spatio-temporal cartograms, similar approaches can also be adopted in other parts of the world where RWSS need a thorough appraisal.

Keywords: rural water supply services, piped water supply, sustainability, composite index, spatial, drinking water

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Authors: M. Rutkowski, J. Cwiklak, M. Grzegorzewski, M. Adamski

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All class A Airplanes have to be equipped with Mod. S transponder for ATC surveillance purposes. This technology was designed to provide a robust and dependable solution to localize, identify and exchange data with the airplane. The purpose of this paper is to analyze potential hazards that are a result of lack of any security or encryption on a design level. Secondary Surveillance Radars rely on an active response from an airplane. SSR radar installation is broadcasting a directional interrogation signal to the planes in range on 1030MHz frequency with DPSK modulation. If the interrogation is correctly received by the transponder located on the plane, a proper answer is sent on 1090MHz with PPM modulation containing plane’s SQUAWK, barometric altitude, GPS coordinates and 24bit unique address code. This technology does not use any kind of encryption. All of the specifications from the previous chapter can be found easily on the internet. Since there is no encryption or security measure to ensure the credibility of the sender and message, it is highly hazardous to use such technology to ensure the safety of the air traffic. The only thing that identifies the airplane is the 24-bit unique address. Most of the planes have been sniffed by aviation enthusiasts and cataloged in web databases. In the moment of writing this article, The PoFung Technologies has announced that they are planning to release all band SDR transceiver – this device would be more than enough to build your own Mod. S Transponder. With fake transponder, a potential terrorist can identify as a different airplane. By replacing the transponder in a poorly controlled airspace, hijackers can enter another airspace identifying themselves as another plane and land in the desired area.

Keywords: flight safety, hijack, mod S transponder, security analysis

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Authors: Udayakumar Vee, Franck Quero

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Zwitterionic polymers generally have been viewed as a new class of antimicrobial and non-fouling materials. They offer a broad versatility for chemical modification and hence great freedom for accurate molecular design, which bear an equimolar number of homogenously distributed anionic and cationic groups along their polymer chains. This study explores the effectiveness of the auxetic zwitterion carboxylate/sulfonate hydrogel in the diabetic-induced mouse model. A series of silver metal-doped auxetic zwitterion carboxylate/sulfonate/vinylaniline copolymer hydrogels is designed via a 3D printer. Zwitterion monomers have been characterized by FT-IR and NMR techniques. The effect of changing the monomers and different loading ratios of Ag over zwitterion on the final hydrogel materials' antimicrobial properties and biocompatibility will be investigated in detail. The synthesized auxetic hydrogel has been characterized using a wide range of techniques to help establish the relationship between molecular level and macroscopic properties of these materials, including mechanical and antibacterial and biocompatibility and wound healing ability. This work's comparative studies and results provide new insights and guide us in choosing a better auxetic structured material for a broad spectrum of wound healing applications in the animal model. We expect this approach to provide a versatile and robust platform for biomaterial design that could lead to promising treatments for wound healing applications.

Keywords: auxetic, zwitterion, carboxylate, sulfonate, polymer, wound healing

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Authors: Udayakumar Veerabagu, Franck Quero

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Zwitterion carboxylate and sulfonate polymers generally have been viewed as a new class of antimicrobial and non-fouling materials. They offer a broad versatility for chemical modification and hence great freedom for accurate molecular design, which bear an equimolar number of homogenously distributed anionic and cationic groups along their polymer chains. This study explores the effectiveness of the auxetic zwitterion carboxylate/sulfonate hydrogel in the diabetic-induced mouse model. A series of silver metal-doped auxetic zwitterion carboxylate/sulfonate/vinylaniline copolymer hydrogels is designed via a 3D printer. Zwitterion monomers have been characterized by FT-IR and NMR techniques. The effect of changing the monomers and different loading ratios of Ag over zwitterion on the final hydrogel materials' antimicrobial properties and biocompatibility will be investigated in detail. The synthesized auxetic hydrogel has been characterized using a wide range of techniques to help establish the relationship between molecular level and macroscopic properties of these materials, including mechanical and antibacterial and biocompatibility and wound healing ability. This work's comparative studies and results provide new insights and guide us in choosing a better auxetic structured material for a broad spectrum of wound healing applications in the animal model. We expect this approach to provide a versatile and robust platform for biomaterial design that could lead to promising treatments for wound healing applications.

Keywords: auxetic, zwitterion, carboxylate, sulfonate, polymer, wound healing

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Authors: Naoum Abderrahmane, Boumehed Meriem, Alshaqaqi Belal

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Background subtraction algorithm is a larger used technique for detecting moving objects in video surveillance to extract the foreground objects from a reference background image. There are many challenges to test a good background subtraction algorithm, like changes in illumination, dynamic background such as swinging leaves, rain, snow, and the changes in the background, for example, moving and stopping of vehicles. In this paper, we propose an efficient and accurate background subtraction method for moving object detection in video surveillance. The main idea is to use a developed fast-independent component analysis (ICA) algorithm to separate background, noise, and foreground masks from an image sequence in practical environments. The fast-ICA algorithm is adapted and adjusted with a matrix calculation and searching for an optimum non-quadratic function to be faster and more robust. Moreover, in order to estimate the de-mixing matrix and the denoising de-mixing matrix parameters, we propose to convert all images to YCrCb color space, where the luma component Y (brightness of the color) gives suitable results. The proposed technique has been verified on the publicly available datasets CD net 2012 and CD net 2014, and experimental results show that our algorithm can detect competently and accurately moving objects in challenging conditions compared to other methods in the literature in terms of quantitative and qualitative evaluations with real-time frame rate.

Keywords: background subtraction, moving object detection, fast-ICA, de-mixing matrix

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Authors: Jeremy Faludi, Zhongyin Hu, Shahd Alrashed, Christopher Braunholz, Suneesh Kaul, Leulekal Kassaye

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Environmental impacts of six 3D printers using various materials were compared to determine if material choice drove sustainability, or if other factors such as machine type, machine size, or machine utilization dominate. Cradle-to-grave life-cycle assessments were performed, comparing a commercial-scale FDM machine printing in ABS plastic, a desktop FDM machine printing in ABS, a desktop FDM machine printing in PET and PLA plastics, a polyjet machine printing in its proprietary polymer, an SLA machine printing in its polymer, and an inkjet machine hacked to print in salt and dextrose. All scenarios were scored using ReCiPe Endpoint H methodology to combine multiple impact categories, comparing environmental impacts per part made for several scenarios per machine. Results showed that most printers’ ecological impacts were dominated by electricity use, not materials, and the changes in electricity use due to different plastics was not significant compared to variation from one machine to another. Variation in machine idle time determined impacts per part most strongly. However, material impacts were quite important for the inkjet printer hacked to print in salt: In its optimal scenario, it had up to 1/38th the impacts coreper part as the worst-performing machine in the same scenario. If salt parts were infused with epoxy to make them more physically robust, then much of this advantage disappeared, and material impacts actually dominated or equaled electricity use. Future studies should also measure DMLS and SLS processes / materials.

Keywords: 3D printing, additive manufacturing, sustainability, life-cycle assessment, design for environment

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Authors: Houria Rezala, Jose Luis Valverde, Amaya Romero, Alessandra Molinari, Andrea Maldotti

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A pillared montmorillonite containing iron doped titania (Fe/Ti-PILC) has been prepared from a natural clay. This material has been characterized by X-ray diffraction, nitrogen adsorption, temperature programmed desorption of ammonia, inductively coupled plasma atomic emission spectroscopy, atomic absorption, and diffuse reflectance UV-VIS spectroscopy. The layer structure of Fe/Ti-PILC resulted to be ordered with an insertion of pillars, which caused a slight increase in the basal spacing of the clay. Its specific surface area was about three times larger than that of the parent Na-montmorillonite due principally to the creation of a remarkable microporous network. The doped material was a robust photocatalyst able to oxidize liquid alkyl aromatics to the corresponding carbonylic derivatives, using O2 as the oxidizing species, at mild pressure and temperature conditions. Accumulation of valuable carbonylic derivatives was possible since their over-oxidation to carbon dioxide was negligible. Fe/Ti-PILC was able to discriminate between toluene and cyclohexane in favor of the aromatic compound with an efficiency that is about three times higher than that of titanium pillared clays (Ti-PILC). It is likely that the addition of iron favored the formation of new acid sites able to interact with the aromatic substrate. Iron doping caused a significant TiO2 visible light-induced activity (wavelength > 400 nm) with only minor negative effects on its performance under UV-light irradiation (wavelength > 290 nm).

Keywords: alkyl aromatics oxidation, heterogeneous photocatalysis, iron doping, pillared clays

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Authors: Iván Rivet, Narges Dialami, Miguel Cervera, Michele Chiumenti

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Process parameters in Additive Manufacturing (AM) play a critical role in the mechanical performance of the final component. In order to find the input configuration that guarantees the optimal performance of the printed part, the process-performance relationship must be found. Fused Filament Fabrication (FFF) is the selected demonstrative AM technology due to its great popularity in the industrial manufacturing world. A material model that considers the different printing patterns present in a FFF part is used. A voxelized mesh is built from the manufacturing toolpaths described in the G-Code file. An Adaptive Mesh Refinement (AMR) based on the octree strategy is used in order to reduce the complexity of the mesh while maintaining its accuracy. High-fidelity and cost-efficient Finite Element (FE) simulations are performed and the influence of key process parameters in the mechanical performance of the component is analyzed. A robust optimization process based on appropriate failure criteria is developed to find the printing direction that leads to the optimal mechanical performance of the component. The Tsai-Wu failure criterion is implemented due to the orthotropy and heterogeneity constitutive nature of FFF components and because of the differences between the strengths in tension and compression. The optimization loop implements a modified version of an Anomaly Detection (AD) algorithm and uses the computed metrics to obtain the optimal printing direction. The developed methodology is verified with a case study on an industrial demonstrator.

Keywords: additive manufacturing, optimization, printing direction, mechanical performance, voxelization

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Authors: Muhammad Saleem

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Tourism stands as one of the swiftly expanding sectors globally, contributing to 10% of the overall worldwide GDP. It holds a vital role in generating income, fostering employment opportunities, alleviating poverty, facilitating foreign exchange earnings, and advancing intercultural understanding. This industry encompasses a spectrum of activities, encompassing transportation, communication, hospitality, catering, entertainment, and advertising. The objective of this study is to assess the evolution and obstacles encountered by sports tourism in Pakistan. In pursuit of this objective, relevant literature has been scrutinized, while data has been acquired from 60 respondents, employing a simple random sampling approach for analysis. The survey comprised close-ended inquiries directed towards all participants. Analytical tools such as mean, mode, median, graphs, and percentages have been employed for data analysis. The findings revealed through robust analysis, indicate that the mean, mode, and median tools consistently yield results surpassing the 70% mark, underscoring that heightened development within sports tourism significantly augments its progress. Effective governance demonstrates a favorable influence on sports tourism, with increased government-provided safety and security potentially amplifying its expansion, thus attracting a higher number of tourists and consequently propelling the growth of the sports tourism sector. This study holds substantial significance for both academic scholars and industry practitioners within Pakistan's tourism landscape, as previous explorations in this realm have been relatively limited.

Keywords: obstacles-spots, evolution-tourism, sports-pakistan, sports-obstacles-pakistan

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Authors: Suraj Sharma

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Green infrastructure (GI) offers a contemporary approach for reducing the risk of flooding, improve water quality, and harvesting stormwater for sustainable use. GI promotes landscape planning to enhance sustainable development and urban resilience. However, the existing literature is lacking in ensuring the comprehensive assessment of GI performance in terms of ecosystem function and services for social, ecological, and economical system resilience. We propose a robust indicator set and fuzzy comprehensive evaluation (FCE) for quantitative and qualitative analysis for sustainable water management to assess the capacity of urban resilience. Green infrastructure in urban resilience water management system (GIUR-WMS) supports decision-making for GI planning through scenario comparisons with urban resilience capacity index. To demonstrate the GIUR-WMS, we develop five scenarios for five sectors of Chandigarh (12, 26, 14, 17, and 34) to test common type of GI (rain barrel, rain gardens, detention basins, porous pavements, and open spaces). The result shows the open spaces achieve the highest green infrastructure urban resilience index of 4.22/5. To implement the open space scenario in urban sites, suitable vacant can be converted to green spaces (example: forest, low impact recreation areas, and detention basins) GIUR-WMS is easy to replicate, customize and apply to cities of different sizes to assess environmental, social and ecological dimensions.

Keywords: green infrastructure, assessment, urban resilience, water management system, fuzzy comprehensive evaluation

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Authors: J. Constanzo, B. Paquette, G. Charest, L. Masson-Côté, M. Guillot

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Targeted and whole-brain irradiation in humans can result in significant side effects causing decreased patient quality of life. To adequately investigate structural and functional alterations after stereotactic radiosurgery, preclinical studies are needed. The first step is to establish a robust standardized method of targeted irradiation on small regions of the rat brain. Eleven euthanized male Fischer rats were imaged in a stereotactic bed, by computed tomographic (CT), to estimate positioning variations regarding to the bregma skull reference point. Using a rat brain atlas and the stereotactic bregma coordinates assessed from CT images, various regions of the brain were delimited and a treatment plan was generated. A dose of 37 Gy at 30% isodose which corresponds to 100 Gy in 100% of the target volume (X = 98.1; Y = 109.1; Z = 100.0) was set by Leksell Gamma Plan using sectors number 4, 5, 7, and 8 of the Gamma Knife unit with the 4-mm diameter collimators. Effects of positioning accuracy of the rat brain on the dose deposition were simulated by Gamma Plan and validated with dosimetric measurements. Our results showed that 90% of the target volume received 110 ± 4.7 Gy and the maximum of deposited dose was 124 ± 0.6 Gy, which corresponds to an excellent relative standard deviation of 0.5%. This dose deposition calculated with the Gamma Plan was validated with the dosimetric films resulting in a dose-profile agreement within 2%, both in X- and Z-axis,. Our results demonstrate the feasibility to standardize the irradiation procedure of a small volume in the rat brain using a Gamma Knife.

Keywords: brain irradiation, dosimetry, gamma knife, small-animal irradiation, stereotactic radiosurgery (SRS)

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Authors: Waleed Baloch

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This paper investigates the problems and challenges in social-economic research in the case study of the province of Sindh after the COVID-19 pandemic; the pandemic has significantly impacted various aspects of society and the economy, necessitating a thorough examination of the resulting implications. The study also investigates potential strategies and solutions to mitigate these challenges, ensuring the continuation of robust social and economic research in the region. Through an in-depth analysis of data and interviews with key stakeholders, the study reveals several significant findings. Firstly, researchers encountered difficulties in accessing primary data due to disruptions caused by the pandemic, leading to limitations in the scope and accuracy of their studies. Secondly, the study highlights the challenges faced in conducting fieldwork, such as restrictions on travel and face-to-face interactions, which impacted the ability to gather reliable data. Lastly, the research identifies the need for innovative research methodologies and digital tools to adapt to the new research landscape brought about by the pandemic. The study concludes by proposing recommendations to address these challenges, including utilizing remote data collection methods, leveraging digital technologies for data analysis, and establishing collaborations among researchers to overcome resource constraints. By addressing these issues, researchers in the social economic field can effectively navigate the post-COVID-19 research landscape, facilitating a deeper understanding of the socioeconomic impacts and facilitating evidence-based policy interventions.

Keywords: social economic, sociology, developing economies, COVID-19

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Authors: Yehjune Heo

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Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

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Authors: Mohamed Asaad Abdelrazek, Amir Taher El-Sheikh, M. Zayan, A.M. Elhady

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The successful realization of complex systems is dependent not only on the technology issues and the process for implementing them, but on the management issues as well. Managing the systems development lifecycle requires technical management. Systems engineering management is the technical management. Systems engineering management is accomplished by incorporating many activities. The three major activities are development phasing, systems engineering process and lifecycle integration. Systems engineering management activities are performed across the system development lifecycle. Due to the ever-increasing complexity of systems as well the difficulty of managing and tracking the development activities, new ways to achieve systems engineering management activities are required. This paper presents a systematic approach used as a design management tool applied across systems engineering management roles. In this approach, Transdisciplinary System Development Lifecycle (TSDL) Model has been modified and integrated with Quality Function Deployment. Hereinafter, the name of the systematic approach is the Transdisciplinary Quality System Development Lifecycle (TQSDL) Model. The QFD translates the voice of customers (VOC) into measurable technical characteristics. The modified TSDL model is based on Axiomatic Design developed by Suh which is applicable to all designs: products, processes, systems and organizations. The TQSDL model aims to provide a robust structure and systematic thinking to support the implementation of systems engineering management roles. This approach ensures that the customer requirements are fulfilled as well as satisfies all the systems engineering manager roles and activities.

Keywords: axiomatic design, quality function deployment, systems engineering management, system development lifecycle

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Ali Alattabi, Clare Harris, Rafid Alkhaddar, Ali Alzeyadi

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Petroleum refinery wastewater (PRW) can be considered as one of the most significant source of aquatic environmental pollution. It consists of oil and grease along with many other toxic organic pollutants. In recent years, a new technique was implemented using different types of membranes and sequencing batch reactors (SBRs) to treat PRW. SBR is a fill and draw type sludge system which operates in time instead of space. Many researchers have optimised SBRs’ operating conditions to obtain maximum removal of undesired wastewater pollutants. It has gained more importance mainly because of its essential flexibility in cycle time. It can handle shock loads, requires less area for operation and easy to operate. However, bulking sludge or discharging floating or settled sludge during the draw or decant phase with some SBR configurations are still one of the problems of SBR system. The main aim of this study is to develop and innovative design for the SBR optimising the process variables to result is a more robust and efficient process. Several experimental tests will be developed to determine the removal percentages of chemical oxygen demand (COD), Phenol and nitrogen compounds from synthetic PRW. Furthermore, the dissolved oxygen (DO), pH and oxidation-reduction potential (ORP) of the SBR system will be monitored online to ensure a good environment for the microorganisms to biodegrade the organic matter effectively.

Keywords: petroleum refinery wastewater, sequencing batch reactor, hydraulic retention time, Phenol, COD, mixed liquor suspended solids (MLSS)

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Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

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This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

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Authors: Bernardo Cortez, Florian Peter, Thomas Lausenhammer, Paulo Oliveira

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Designing missiles’ autopilot controllers have been a complex task, given the extensive flight envelope and the nonlinear flight dynamics. A solution that can excel both in nominal performance and in robustness to uncertainties is still to be found. While Control Theory often debouches into parameters’ scheduling procedures, Reinforcement Learning has presented interesting results in ever more complex tasks, going from videogames to robotic tasks with continuous action domains. However, it still lacks clearer insights on how to find adequate reward functions and exploration strategies. To the best of our knowledge, this work is a pioneer in proposing Reinforcement Learning as a framework for flight control. In fact, it aims at training a model-free agent that can control the longitudinal non-linear flight dynamics of a missile, achieving the target performance and robustness to uncertainties. To that end, under TRPO’s methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Not only does this work enhance the concept of prioritized experience replay into BPER, but it also reformulates HER, activating them both only when the training progress converges to suboptimal policies, in what is proposed as the SER methodology. The results show that it is possible both to achieve the target performance and to improve the agent’s robustness to uncertainties (with low damage on nominal performance) by further training it in non-nominal environments, therefore validating the proposed approach and encouraging future research in this field.

Keywords: Reinforcement Learning, flight control, HER, missile autopilot, TRPO

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Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

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Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

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Authors: Simone Rondelli, Damiano Rondelli, Bishesh Poudyal, Juan Jose Cabrera-Lazarini

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One of the main obstacles for Social Entrepreneurship is to find a business model that is financially sustainable. In other words, the captured value generates enough cash flow to ensure business continuity and reinvestment for growth. Providing Health Services in poor countries for the uninsured population affected by a high-cost chronical disease is not the exception for this challenge. As a prime example, cancer has become a high impact on a global disease not only because of the high morbidity but also of the financial impact on both the patient family and health services in underdeveloped countries. Therefore, it is relevant to find a Social Entrepreneurship Model that provides affordable treatment for this disease while maintaining healthy finances not only for the patient but also for the organization providing the treatment. Using the methodology of Constructive Research, this paper applied a Positive Theory and four business models of Social Entrepreneurship to a case of a Private Foundation model whose mission is to address the challenge previously described. It was found that the Foundation analyzed, in this case, is organized as an Embedded Business Model and complies with the four propositions of the Positive Theory considered. It is recommended for this Private Foundation to explore implementing the Integrated Business Model to ensure more robust sustainability in the long term. It evolves as a scalable model that can attract investors interested in contributing to expanding this initiative globally.

Keywords: affordable treatment, global healthcare, social entrepreneurship theory, sustainable business model

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Authors: Gaurav Kumar Sinha

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The oil and gas industry is a critical component of the global economy, relying heavily on industrial control systems (ICS) to manage and monitor operations. However, these systems are increasingly becoming targets for cyber-attacks, posing significant risks to operational continuity, safety, and environmental integrity. This paper explores comprehensive cybersecurity strategies for protecting oil and gas industrial control systems. It delves into the unique vulnerabilities of ICS in this sector, including outdated legacy systems, integration with IT networks, and the increased connectivity brought by the Industrial Internet of Things (IIoT). We propose a multi-layered defense approach that includes the implementation of robust network security protocols, regular system updates and patch management, advanced threat detection and response mechanisms, and stringent access control measures. We illustrate the effectiveness of these strategies in mitigating cyber risks and ensuring the resilient and secure operation of oil and gas industrial control systems. The findings underscore the necessity for a proactive and adaptive cybersecurity framework to safeguard critical infrastructure in the face of evolving cyber threats.

Keywords: cybersecurity, industrial control systems, oil and gas, cyber-attacks, network security, IoT, threat detection, system updates, patch management, access control, cybersecurity awareness, critical infrastructure, resilience, cyber threats, legacy systems, IT integration, multi-layered defense, operational continuity, safety, environmental integrity

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Authors: Ali Mashayekh, Mahdiye Khorasani, Thomas Weyh

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The tendency to use the Battery-Electric vehicle (BEV) for the low and medium driving range or even high driving range has been growing more and more. As a result, higher safety, reliability, and durability of the battery pack as a component of electric vehicles, which has a great share of cost and weight of the final product, are the topics to be considered and investigated. Battery aging can be considered as the predominant factor regarding the reliability and durability of BEV. To better understand the aging process, offline battery characterization has been widely used, which is time-consuming and needs very expensive infrastructures. This paper presents the substitute method for the conventional battery characterization methods, which is based on battery Modular Multilevel Management (BM3). According to this Topology, the battery cells can be drained and charged concerning their capacity, which allows varying battery pack structures. Due to the integration of the power electronics, the output voltage of the battery pack is no longer fixed but can be dynamically adjusted in small steps. In other words, each cell can have three different states, namely series, parallel, and bypass in connection with the neighbor cells. With the help of MATLAB/Simulink and by using the BM3 modules, the battery string model is created. This model allows us to switch two cells with the different SoC as parallel, which results in the internal balancing of the cells. But if the parallel switching lasts just for a couple of ms, we can have a perturbation pulse which can stimulate the cells out of the relaxation phase. With the help of modeling the voltage response pulse of the battery, it would be possible to characterize the cell. The Online EIS method, which is discussed in this paper, can be a robust substitute for the conventional battery characterization methods.

Keywords: battery characterization, SoH estimation, RLS, BEV

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Authors: Swajeeth Panchangam, Arun Rajendran, Swarnim Gupta, Ahmed Zeouita

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Semiconductor processing chambers tend to operate in controlled but aggressive operating conditions (chemistry, plasma, high temperature etc.) Owing to this, the design of this equipment requires developing robust and reliable hardware and software. Any equipment downtime due to reliability issues can have cost implications both for customers in terms of tool downtime (reduced throughput) and for equipment manufacturers in terms of high warranty costs and customer trust deficit. A thorough reliability assessment of critical parts and a plan for preventive maintenance/replacement schedules need to be done before tool shipment. This helps to save significant warranty costs and tool downtimes in the field. However, designing a proper reliability test plan to accurately demonstrate reliability targets with proper sample size and test duration is quite challenging. This is mainly because components can fail in different failure modes that fit into different Weibull beta value distributions. Without apriori Weibull beta of a failure mode under consideration, it always leads to over/under utilization of resources, which eventually end up in false positives or false negatives estimates. This paper proposes a methodology to design a reliability test plan with optimal model size/duration/both (independent of apriori Weibull beta). This methodology can be used in demonstration tests and can be extended to accelerated life tests to further decrease sample size/test duration.

Keywords: reliability, stochastics, preventive maintenance

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Authors: Ali El Ksimi

Abstract:

In today's digital academic environment, secure authentication methods are essential for managing sensitive user data, including that of students and faculty. The rise in cyber threats and data breaches has exposed the vulnerabilities of traditional authentication systems used in universities. Passwords, often the first line of defense, are particularly susceptible to hacking, phishing, and brute-force attacks. While multi-factor authentication (MFA) provides an additional layer of security, it can still be compromised and often adds complexity and inconvenience for users. As universities seek more robust security measures, blockchain technology emerges as a promising solution. Renowned for its decentralization, immutability, and transparency, blockchain has the potential to transform how user management is conducted in academic institutions. In this article, we explore a system that leverages blockchain technology specifically for managing user accounts within a university setting. The system enables the secure creation and management of accounts for different roles, such as administrators, teachers, and students. Each user is authenticated through a decentralized application (DApp) that ensures their data is securely stored and managed on the blockchain. By eliminating single points of failure and utilizing cryptographic techniques, the system enhances the security and integrity of user management processes. We will delve into the technical architecture, security benefits, and implementation considerations of this approach. By integrating blockchain into user management, we aim to address the limitations of traditional systems and pave the way for the future of digital security in education.

Keywords: blockchain, university, authentication, decentralization, cybersecurity, user management, privacy

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Authors: Alireza Rasekh, Peter Sergeant, Jan Vierendeels

Abstract:

This paper copes with the numerical simulation for convective heat transfer in the stator disk of an axial flux permanent magnet (AFPM) electrical machine. Overheating is one of the main issues in the design of AFMPs, which mainly occurs in the stator disk, so that it needs to be prevented. A rotor-stator configuration with 16 magnets at the periphery of the rotor is considered. Air is allowed to flow through openings in the rotor disk and channels being formed between the magnets and in the gap region between the magnets and the stator surface. The rotating channels between the magnets act as a driving force for the air flow. The significant non-dimensional parameters are the rotational Reynolds number, the gap size ratio, the magnet thickness ratio, and the magnet angle ratio. The goal is to find correlations for the Nusselt number on the stator disk according to these non-dimensional numbers. Therefore, CFD simulations have been performed with the multiple reference frame (MRF) technique to model the rotary motion of the rotor and the flow around and inside the machine. A minimization method is introduced by a pattern-search algorithm to find the appropriate values of the reference temperature. It is found that the correlations are fast, robust and is capable of predicting the stator heat transfer with a good accuracy. The results reveal that the magnet angle ratio diminishes the stator heat transfer, whereas the rotational Reynolds number and the magnet thickness ratio improve the convective heat transfer. On the other hand, there a certain gap size ratio at which the stator heat transfer reaches a maximum.

Keywords: AFPM, CFD, magnet parameters, stator heat transfer

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Authors: Mirjana Ruppel, Rajendra Persad, Amit Bahl, Sanja Dogramadzi, Chris Melhuish, Lyndon Smith

Abstract:

Multimodal image registration is a profoundly complex task which is why deep learning has been used widely to address it in recent years. However, two main challenges remain: Firstly, the lack of ground truth data calls for an unsupervised learning approach, which leads to the second challenge of defining a feasible loss function that can compare two images of different modalities to judge their level of alignment. To avoid this issue altogether we implement a generative adversarial network consisting of two registration networks GAB, GBA and two discrimination networks DA, DB connected by spatial transformation layers. GAB learns to generate a deformation field which registers an image of the modality B to an image of the modality A. To do that, it uses the feedback of the discriminator DB which is learning to judge the quality of alignment of the registered image B. GBA and DA learn a mapping from modality A to modality B. Additionally, a cycle-consistency loss is implemented. For this, both registration networks are employed twice, therefore resulting in images ˆA, ˆB which were registered to ˜B, ˜A which were registered to the initial image pair A, B. Thus the resulting and initial images of the same modality can be easily compared. A dataset of liver CT and MRI was used to evaluate the quality of our approach and to compare it against learning and non-learning based registration algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01 and is therefore comparable to and slightly more successful than algorithms like SimpleElastix and VoxelMorph.

Keywords: cycle consistency, deformable multimodal image registration, deep learning, GAN

Procedia PDF Downloads 131