Search results for: failure detection and prediction

Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Koichi Kurita

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In this study, an effective non-contact technique for the detection of human physical activity is proposed. The technique is based on detecting the electrostatic induction current generated by the walking motion under non-contact and non-attached conditions. A theoretical model for the electrostatic induction current generated because of a change in the electric potential of the human body is proposed. By comparing the obtained electrostatic induction current with the theoretical model, it becomes obvious that this model effectively explains the behavior of the waveform of the electrostatic induction current. The normal walking motions are recorded using a portable sensor measurement located in a passageway　of office building. The obtained results show that detailed information regarding physical activity such as a walking cycle can be estimated using our proposed technique. This suggests that the proposed technique which is based on the detection of the walking signal, can be successfully applied to the detection of human walking motion in a secured building.

Keywords: human walking motion, access control, electrostatic induction, alarm monitoring

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Authors: Caroline E. Mendes, Alberto C. Badino

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Pneumatic reactors have been widely employed in various sectors of the chemical industry, especially where are required high heat and mass transfer rates. This study aimed to obtain correlations that allow the prediction of gas hold-up (Ԑ) and volumetric oxygen transfer coefficient (kLa), and compare these values, for three models of pneumatic reactors on two scales utilizing Newtonian fluids. Values of kLa were obtained using the dynamic pressure-step method, while  was used for a new proposed measure. Comparing the three models of reactors studied, it was observed that the mass transfer was superior to draft-tube airlift, reaching  of 0.173 and kLa of 0.00904s-1. All correlations showed good fit to the experimental data (R2≥94%), and comparisons with correlations from the literature demonstrate the need for further similar studies due to shortage of data available, mainly for airlift reactors and high viscosity fluids.

Keywords: bubble column, internal loop airlift, gas hold-up, kLa

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Bilal Saoud

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In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition.

Keywords: bee colony, networks, modularity, normalized mutual information

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Authors: Manasvi Pinnaka, Brianna Chrisman

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Cystic fibrosis patients often develop lung infections that range anywhere in severity from mild to life-threatening due to the presence of thick and sticky mucus that fills their airways. Since many of these infections are chronic, they not only affect a patient’s ability to breathe but also increase the chances of mortality by respiratory failure. With a publicly available dataset of DNA sequences from bacterial species in the lung microbiome of cystic fibrosis patients, the correlations between different microbial species in the lung and the extent of deterioration of lung function were investigated. 16S sequencing technologies were used to determine the microbiome composition of the samples in the dataset. For the statistical analyses, referencing helped distinguish between taxonomies, and the proportions of certain taxa relative to another were determined. It was found that the Fusobacterium, Actinomyces, and Leptotrichia microbial types all had a positive correlation with the FEV1 score, indicating the potential displacement of these species by pathogens as the disease progresses. However, the dominant pathogens themselves, including Pseudomonas aeruginosa and Staphylococcus aureus, did not have statistically significant negative correlations with the FEV1 score as described by past literature. Examining the lung microbiology of cystic fibrosis patients can help with the prediction of the current condition of lung function, with the potential to guide doctors when designing personalized treatment plans for patients.

Keywords: bacterial infections, cystic fibrosis, lung microbiome, 16S sequencing

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Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

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Authors: Ramanjaneyulu Mogili, G.V.R.K. Acharyulu

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Indian organized retail sector is growing at a faster pace and gaining popularity. Indian Brand Equity Foundation (IBEF) reveals that the current market size of Indian retail industry is about US$ 520 billion with for growth rate 14 to 15 percent annually by 2018 the Indian retail sector is likely to grow at a CAGR of 13% to reach a size of US$ 950 billion. Developments in Information Technology have enabled online Retail sector that empowers customers to order products, conduct transactions without the need to interact physically with the retailers. In recent years, the online shopping industry has gained popularity to the point where certain categories of customers would consider buying electronic products online rather than visiting the stores. Conventionally the physical location of a store is seen as a source of competitive advantage. Online Retailing service sites provide virtual shopping space to the customers. Online Retail services are gaining momentum in India, with internet penetration improving in the country and smartphones becoming affordable along with changing lifestyles and preferences of customers. Although online shoppers prefer the convenience and choice available in online shopping, certain issues raised due to the occurrence of service failure. The proposed study attempts to measure the service recovery and failure process of electronic goods in Indian retail channels.

Keywords: service recovery, customer satisfaction, e-shopping, service failure

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Authors: Majid Khan

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Introduction: In this era of microsurgery, free flap holds a remarkable spot in reconstructive surgery. A free flap is well suited for composite defects as it provides sufficient and well-vascularized tissue for coverage. We report our experience with the use of the free flaps for the reconstruction of composite defects. Methods: This is a retrospective case series (chart review) of patients who underwent reconstruction of composite defects with a free flap at Aga Khan University Hospital, Karachi (Pakistan) from January 01, 2015, to December 31, 2019. Data were collected for patient demographics, size of the defect, size of flap, recipient vessels, postoperative complications, and outcome of the free flap. Results: Over this period, 532 free flaps are included in this study. The overall success rate is 95.5%. The mean age of the patient was 44.86 years. In 532 procedures, there were 448 defects from tumor ablation of head and neck cancer. The most frequent free flap was the anterolateral thigh flap in 232 procedures. In this study, the risk factor hypertension (p=0.004) was found significant for wound dehiscence, preop radiation/chemotherapy (p=0.003), and malnutrition (p=0.005) were found significant for fistula formation. Malnutrition (p=0.02) and use of vein grafts (p=0.025) were significant factors for flap failure. Conclusion: Free tissue transfer is a reliable option for the reconstruction of large and composite defects. Hypertension, malnutrition, and preoperative radiotherapy can cause significant morbidity.

Keywords: free flap, free flap failure, risk factors for flap failure, free flap outcome

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Authors: Gonzalez Carlos, Martinez Fransisco

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The creation of a seismic prediction model that considers all the regional variations and perfectly adjusts its results to the response spectra is very complicated. To achieve statistically acceptable results, it is necessary to process a sufficiently robust data set, and even if high efficiencies are achieved, this model will only work properly in this region. However, when using it in other regions, differences are found due to different parameters that have not been calibrated to other regions, such as the site effect. The fact that impedance contrasts, as well as other factors belonging to the site, have a great influence on the local response is well known, which is why this work, using the residual method, is intended to establish a regional calibration of the corresponding parameters site effect for the Spain region in the global GMPM BSSA 14.

Keywords: GMPM, seismic prediction equations, residual method, response spectra, impedance contrast

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

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Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

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Authors: Lae-Jeong Park

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The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: pedestrian detection, color segmentation, false positive, feature extraction

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Authors: Yagahira E. Castro-Sesquen, Chloe Kim, Robert H. Gilman, David J. Sullivan, Peter C. Searson

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Diagnosis of severe malaria is particularly important in highly endemic regions since most patients are positive for parasitemia and treatment differs from non-severe malaria. Diagnosis can be challenging due to the prevalence of diseases with similar symptoms. Accurate diagnosis is increasingly important to avoid overprescribing antimalarial drugs, minimize drug resistance, and minimize costs. A nanoparticle-based assay for detection and quantification of Plasmodium falciparum histidine-rich protein 2 (HRP2) in urine and serum is reported. The assay uses magnetic beads conjugated with anti-HRP2 antibody for protein capture and concentration, and antibody-conjugated quantum dots for optical detection. Western Blot analysis demonstrated that magnetic beads allows the concentration of HRP2 protein in urine by 20-fold. The concentration effect was achieved because large volume of urine can be incubated with beads, and magnetic separation can be easily performed in minutes to isolate beads containing HRP2 protein. Magnetic beads and Quantum Dots 525 conjugated to anti-HRP2 antibodies allows the detection of low concentration of HRP2 protein (0.5 ng mL-1), and quantification in the range of 33 to 2,000 ng mL-1 corresponding to the range associated with non-severe to severe malaria. This assay can be easily adapted to a non-invasive point-of-care test for classification of severe malaria.

Keywords: HRP2 protein, malaria, magnetic beads, Quantum dots

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Authors: Chang Seok Lee, Sang Whan Han, Girbo Ko, Debbie Kim

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Existing reinforced concrete structures are designed and built without considering seismic loads. The columns in such buildings generally exhibit widely spaced transverse reinforcements without using seismic hooks. Due to the insufficient reinforcement details in columns, brittle shear failure is expected in columns that may cause pre-mature building collapse mechanism during earthquakes. In order to retrofit those columns, the accurate seismic behavior of the columns needs to be predicted with proper analytical models. In this study, an analytical model is proposed for accurately simulating the cyclic behavior of shear critical columns. The parameters for pinching and cyclic deterioration in strength and stiffness are calibrated using test data of column specimens failed by shear.

Keywords: analytical model, cyclic deterioration, existing reinforced concrete columns, shear failure

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Authors: M. V. Chidananda Murthy, M. Z. Kurian, H. S. Guruprasad

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Image restoration in the process of communication is one of the emerging fields in the image processing. The motion analysis processing is the simplest case to detect motion in an image. Applications of motion analysis widely spread in many areas such as surveillance, remote sensing, film industry, navigation of autonomous vehicles, etc. The scene may contain multiple moving objects, by using motion analysis techniques the blur caused by the movement of the objects can be enhanced by filling-in occluded regions and reconstruction of transparent objects, and it also removes the motion blurring. This paper presents the design and comparison of various motion detection and enhancement filters. Median filter, Linear image deconvolution, Inverse filter, Pseudoinverse filter, Wiener filter, Lucy Richardson filter and Blind deconvolution filters are used to remove the blur. In this work, we have considered different types and different amount of blur for the analysis. Mean Square Error (MSE) and Peak Signal to Noise Ration (PSNR) are used to evaluate the performance of the filters. The designed system has been implemented in Matlab software and tested for synthetic and real-time images.

Keywords: image enhancement, motion analysis, motion detection, motion estimation

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Authors: A. J. Babajanyan, T. A. Abrahamyan, H. A. Minasyan, K. V. Nerkararyan

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Optical radiation emitted from a metal-coated fiber tip apex at liquid-air interface was measured. The intensity of the output radiation was strongly depending on the relative position of the tip to a liquid-air interface and varied with surface fluctuations. This phenomenon permits in-situ real-time investigation of nano-metric vibrations of the liquid surface and provides a basis for development of various origin ultrasensitive vibration detecting sensors. The described method can be used for detection of week seismic vibrations.

Keywords: fiber-tip, liquid-air interface, nano vibration, opto-mechanical sensor

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Authors: Kenneth Fon Mbah, Arash Habibi Lashkari, Ali A. Ghorbani

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Phishing e-mails are a security issue that not only annoys online users, but has also resulted in significant financial losses for businesses. Phishing advertisements and pornographic e-mails are difficult to detect as attackers have been becoming increasingly intelligent and professional. Attackers track users and adjust their attacks based on users’ attractions and hot topics that can be extracted from community news and journals. This research focuses on deceptive Phishing attacks and their variants such as attacks through advertisements and pornographic e-mails. We propose a framework called Phishing Alerting System (PHAS) to accurately classify e-mails as Phishing, advertisements or as pornographic. PHAS has the ability to detect and alert users for all types of deceptive e-mails to help users in decision making. A well-known email dataset has been used for these experiments and based on previously extracted features, 93.11% detection accuracy is obtainable by using J48 and KNN machine learning techniques. Our proposed framework achieved approximately the same accuracy as the benchmark while using this dataset.

Keywords: phishing e-mail, phishing detection, anti phishing, alarm system, machine learning

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Authors: Yassir M. Abbas, Mohammad Iqbal Khan, Galal Fare

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In the present work, the structural responses of 12 ultrahigh-performance concrete (UHPC) beams to four-point loading conditions were experimentally and analytically studied. The inclusion of a fibrous system in the UHPC material increased its compressive and flexural strengths by 31.5% and 237.8%, respectively. Based on the analysis of the load-deflection curves of UHPC beams, it was found that UHPC beams with a low reinforcement ratio are prone to sudden brittle failure. This failure behavior was changed, however, to a ductile one in beams with medium to high ratios. The implication is that improving UHPC beam tensile reinforcement could result in a higher level of safety. More reinforcement bars also enabled the load-deflection behavior to be improved, particularly after yielding.

Keywords: ultrahigh-performance concrete, moment capacity, RC beams, hybrid fiber, ductility

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Authors: Yang Jian-Hong, Zhang Ren-Cheng, Huang Li

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Arc fault is one of the main inducements of electric fires. Arc Fault Detection Device (AFDD) can detect arc fault effectively. Arc fault detections and unhooking standards are the keys to AFDD practical application. First, an arc fault continuous production system was developed, which could count the arc half wave number. Then, Combining with the UL1699 standard, ignition probability curve of cotton and unhooking time of various currents intensity were obtained by experiments. The combustion degree of arc fault could be expressed effectively by arc area. Experiments proved that electric fires would be misjudged or missed only using arc half wave number as AFDD unhooking basis. At last, Practical tests were carried out on the self-developed AFDD system. The result showed that actual AFDD unhooking time was the sum of arc half wave cycling number, Arc wave identification time and unhooking mechanical operation time And the first two shared shorter time. Unhooking time standard depended on the shortest mechanical operation time.

Keywords: arc fault detection device, arc area, arc half wave, unhooking time, arc fault

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Authors: K. Kaouther, D. Hafedh, A. Ben Cheikh Larbi

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Various structures and compositions thin films were deposited on 100C6 (AISI 52100) steel substrate by PVD magnetron sputtering system. The morphological proprieties were evaluated using an atomic force microscopy (AFM). Vickers microindentation tests were performed with a Shimadzu HMV-2000 hardness testing machine. Hardness measurement was carried out using Jonsson and Hogmark model. The results show that the coatings topography was dominated by domes and craters. Mechanical behavior and failure modes under microindentation were depending of coatings structure and composition. TiAlN multilayer showed exception in the microindentation resistance compared to TiN single layer and TiAlN/TiAlN nanolayer. Piled structure provides an increase of failure resistance and a decrease in cracks propagation.

Keywords: PVD thin films, multilayer, microindentation, cracking, damage mechanisms

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Authors: Ravneil Nand

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Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

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Authors: Jing Nan

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The air breakdown voltage in compact circuit breakers is a critical factor in the design and reliability of electrical distribution systems. This voltage determines the threshold at which the air insulation between conductors will fail or 'break down,' leading to an arc. This phenomenon is highly sensitive to the conditions within the breaker, such as the temperature and the distance between electrodes. Typically, air breakdown voltage models have been reliable for predicting failure under standard operational temperatures. However, in conditions post-arcing, where temperatures can soar above 2000K, these models face challenges due to the complex physics of ionization and electron behaviour at such high-energy states. Building upon the foundational understanding that the breakdown mechanism is initiated by free electrons and propelled by electric fields, which lead to ionization and, potentially, to avalanche or streamer formation, we acknowledge the complexity introduced by high-temperature environments. Recognizing the limitations of existing experimental data, a notable research gap exists in the accurate prediction of breakdown voltage at elevated temperatures, typically observed post-arcing, where temperatures exceed 2000K.To bridge this knowledge gap, we present a method that integrates gap distance and high-temperature effects into air breakdown voltage assessment. The proposed model is grounded in the physics of ionization, accounting for the dynamic behaviour of free electrons which, under intense electric fields at elevated temperatures, lead to thermal ionization and potentially reach the threshold for streamer formation as Meek's criterion. Employing the Saha equation, our model calculates equilibrium electron densities, adapting to the atmospheric pressure and the hot temperature regions indicative of post-arc temperature conditions. Our model is rigorously validated against established experimental data, demonstrating substantial improvements in predicting air breakdown voltage in the high-temperature regime. This work significantly improves the predictive power for air breakdown voltage under conditions that closely mimic operational stressors in compact circuit breakers. Looking ahead, the proposed methods are poised for further exploration in alternative insulating media, like SF6, enhancing the model's utility for a broader range of insulation technologies and contributing to the future of high-temperature electrical insulation research.

Keywords: air breakdown voltage, high-temperature insulation, compact circuit breakers, electrical discharge, saha equation

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Authors: Afsal Mohammed Kadavilpparampu, Haider A. J. Al Lawati, Fakhr Eldin O. Suliman, Salma M. Z. Al Kindy

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In this work, we evaluated the appropriateness of various oxidants that can be used potentially with Ru(bipy)32+ CL system while performing CL detection in a microfluidic device using eight common active pharmaceutical ingredients- ciprofloxacin, hydrochlorothiazide, norfloxacin, buspirone, fexofenadine, cetirizine, codeine, and dextromethorphan. This is because, microfludics have very small channel volume and the residence time is also very short. Hence, a highly efficient oxidant is required for on-chip CL detection to obtain analytically acceptable CL emission. Three common oxidants were evaluated, lead dioxide, cerium ammonium sulphate and ammonium peroxydisulphate. Results obtained showed that ammonium peroxydisulphate is the most appropriate oxidant which can be used in microfluidic setup and all the tested analyte give strong CL emission while using this oxidant. We also found that Ru(bipy)33+ generated off-line by oxidizing [Ru(bipy)3]Cl2.6H2O in acetonitrile under acidic condition with lead dioxide was stable for more than 72 hrs. A highly sensitive microbore HPLC- CL method using ammonium peroxydisulphate as an oxidant in a microfluidic on-chip CL detection has been developed for the analyses of fixed-dose combinations of pseudoephedrine (PSE), fexofenadine (FEX) and cetirizine (CIT) in biological fluids and pharmaceutical formulations with minimum sample pre-treatment.

Keywords: oxidants, microbore High Performance Liquid Chromatography, chemiluminescence, microfluidics

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Authors: Nadia Allouache

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The concept of second law is assumed to be important to optimize the energy losses in heat exchangers. The present study is devoted to the numerical prediction of entropy generation due to heat transfer and friction in a double tube heat exchanger partly or fully filled with a porous medium. The goal of this work is to find the optimal conditions that allow minimizing entropy generation. For this purpose, numerical modeling based on the control volume method is used to describe the flow and heat transfer phenomena in the fluid and the porous medium. Effects of the porous layer thickness, its permeability, and the effective thermal conductivity have been investigated. Unexpectedly, the fully porous heat exchanger yields a lower entropy generation than the partly porous case or the fluid case even if the friction increases the entropy generation.

Keywords: heat exchangers, porous medium, second law approach, turbulent flow

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Authors: Sai Snehitha Yadavalli, K. Sruthi, Swati Ghosh Acharyya

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Heavy metal ions are toxic to humans and all living species when exposed in large quantities or for long durations. Though Fe acts as a nutrient, when intake is in large quantities, it becomes toxic. These toxic heavy metal ions, when consumed through water, will cause many disorders and are harmful to all flora and fauna through biomagnification. Specifically, humans are prone to innumerable diseases ranging from skin to gastrointestinal, neurological, etc. In higher quantities, they even cause cancer in humans. Detection of these toxic heavy metal ions in water is thus important. Traditionally, the detection of heavy metal ions in water has been done by techniques like Inductively Coupled Plasma Mass Spectroscopy (ICPMS) and Atomic Absorption Spectroscopy (AAS). Though these methods offer accurate quantitative analysis, they require expensive equipment and cannot be used for on-site measurements. Anodic Stripping Voltammetry is a good alternative as the equipment is affordable, and measurements can be made at the river basins or lakes. In the current study, Square Wave Anodic Stripping Voltammetry (SWASV) was used to detect the heavy metal ions in water. Literature reports various electrodes on which deposition of heavy metal ions was carried out like Bismuth, Polymers, etc. The working electrode used in this study is a polyvinyl chloride (PVC) modified glassy carbon electrode (GCE). Ag/AgCl reference electrode and Platinum counter electrode were used. Biologic Potentiostat SP 300 was used for conducting the experiments. Through this work of simultaneous detection, four heavy metal ions were successfully detected at a time. The influence of modifying GCE with PVC was studied in comparison with unmodified GCE. The simultaneous detection of Cd⁺², Fe⁺², Co⁺², Pb⁺² heavy metal ions was done using PVC modified GCE by drop casting 1 wt.% of PVC dissolved in Tetra Hydro Furan (THF) solvent onto GCE. The concentration of all heavy metal ions was 0.2 mg/L, as shown in the figure. The scan rate was 0.1 V/s. Detection parameters like pH, scan rate, temperature, time of deposition, etc., were optimized. It was clearly understood that PVC helped in increasing the sensitivity and selectivity of detection as the current values are higher for PVC-modified GCE compared to unmodified GCE. The peaks were well defined when PVC-modified GCE was used.

Keywords: cadmium, cobalt, electrochemical sensing, glassy carbon electrodes, heavy metal Ions, Iron, lead, polyvinyl chloride, potentiostat, square wave anodic stripping voltammetry

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Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

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Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

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Authors: Chi Zhang, Hua-Peng Chen

Abstract:

With the increasingly demanding resources in the word, renewable and clean energy has been considered as an alternative way to replace traditional ones. Thus, one of practical examples for using wind energy is wind turbine, which has gained more attentions in recent research. Like most offshore structures, the blades, which is the most critical components of the wind turbine, will be subjected to millions of loading cycles during service life. To operate safely in marine environments, the blades are typically made from fibre reinforced composite materials to resist fatigue delamination and harsh environment. The fatigue crack development of blades is uncertain because of indeterminate mechanical properties for composite and uncertainties under offshore environment like wave loads, wind loads, and humid environments. There are three main delamination failure modes for composite blades, and the most common failure type in practices is subjected to mixed mode loading, typically a range of opening (mode 1) and shear (mode 2). However, the fatigue crack development for mixed mode cannot be predicted as deterministic values because of various uncertainties in realistic practical situation. Therefore, selecting an effective stochastic model to evaluate the mixed mode behaviour of wind turbine blades is a critical issue. In previous studies, gamma process has been considered as an appropriate stochastic approach, which simulates the stochastic deterioration process to proceed in one direction such as realistic situation for fatigue damage failure of wind turbine blades. On the basis of existing studies, various Paris Law equations are discussed to simulate the propagation of the fatigue crack growth. This paper develops a Paris model with the stochastic deterioration modelling according to gamma process for predicting fatigue crack performance in design service life. A numerical example of wind turbine composite materials is investigated to predict the mixed mode crack depth by Paris law and the probability of fatigue failure by gamma process. The probability of failure curves under different situations are obtained from the stochastic deterioration model for comparisons. Compared with the results from experiments, the gamma process can take the uncertain values into consideration for crack propagation of mixed mode, and the stochastic deterioration process shows a better agree well with realistic crack process for composite blades. Finally, according to the predicted results from gamma stochastic model, assessment strategies for composite blades are developed to reduce total lifecycle costs and increase resistance for fatigue crack growth.

Keywords: Reinforced fibre composite, Wind turbine blades, Fatigue delamination, Mixed failure mode, Stochastic process.

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Authors: Henok Marie Shiferaw, Barbara Schneider-Muntau

Abstract:

Test repeatability and data reproducibility are a concern in many geotechnical laboratory tests due to inherent soil variability, inhomogeneous sample preparation and measurement inaccuracy. Test results on comparable test specimens vary to a considerable extent. Thus, also the derived shear strength parameters from triaxial tests are affected. In this contribution, we present the reproducibility of effective shear strength parameters from consolidated undrained triaxial tests on plain soil and cement-treated soil specimens. Six remolded test specimens were prepared for the plain soil and for the cement-treated soil. Conventional three levels of consolidation pressure testing were considered with an effective consolidation pressure of 100 kPa, 200 kPa and 300 kPa, respectively. At each effective consolidation pressure, two tests were done on comparable test specimens. Focus was laid on the same mean dry density and same water content during sample preparation for the two specimens. The cement-treated specimens were tested after 28 days of curing. Shearing of test specimens was carried out at a deformation rate of 0.4 mm/min after sample saturation at a back pressure of 900 kPa, followed by consolidation. The effective peak and residual shear strength parameters were then estimated from regression analysis of 21 different combinations of the failure stresses from the six tests conducted for both the plain soil and cement-treated soil samples. The 21 different stress combinations were constructed by picking three, four, five and six failure tresses at once at different combinations. Results indicate that the effective shear strength parameters estimated from the regression of different combinations of the failure stresses vary. Effective critical friction angle was found to be more consistent than effective peak friction angle with a smaller standard deviation. The reproducibility of the shear strength parameters for the cement-treated specimens was even lower than that of the untreated specimens.

Keywords: shear strength parameters, test repeatability, data reproducibility, triaxial soil testing, cement improvement of soils

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Authors: Ashlesha Khanapure, Harsh Kashyap, Abhinav Anand, Sanjana Habib, Anupama Bidargaddi

Abstract:

Technologies that are developing quickly are being developed daily in a variety of disciplines, particularly the medical field. For the purpose of detecting bone fractures in X-ray pictures of different body segments, our work compares the ResNet-50 and MobileNetV3 architectures. It evaluates accuracy and computing efficiency with X-rays of the elbow, hand, and shoulder from the MURA dataset. Through training and validation, the models are evaluated on normal and fractured images. While ResNet-50 showcases superior accuracy in fracture identification, MobileNetV3 showcases superior speed and resource optimization. Despite ResNet-50’s accuracy, MobileNetV3’s swifter inference makes it a viable choice for real-time clinical applications, emphasizing the importance of balancing computational efficiency and accuracy in medical imaging. We created a graphical user interface (GUI) for MobileNet V3 model bone fracture detection. This research underscores MobileNetV3’s potential to streamline bone fracture diagnoses, potentially revolutionizing orthopedic medical procedures and enhancing patient care.

Keywords: CNN, MobileNet V3, ResNet-50, healthcare, MURA, X-ray, fracture detection

Procedia PDF Downloads 72