Search results for: bag of feature

Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

Procedia PDF Downloads 378

Authors: Warda Nasir, M. N. S. Qureshi

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Whistler waves are right handed circularly polarized waves and are frequently observed in space plasmas. The Low frequency branch of the Whistler waves having frequencies nearly around 100 Hz, known as Lion roars, are frequently observed in magnetosheath. Another feature of the magnetosheath is the observations of flat top electron distributions with single as well as two electron populations. In the past, lion roars were studied by employing kinetic model using classical bi-Maxwellian distribution function, however, could not be justified both on quantitatively as well as qualitatively grounds. We studied Whistler waves by employing kinetic model using non-Maxwellian distribution function such as the generalized (r,q) distribution function which is the generalized form of kappa and Maxwellian distribution functions by employing kinetic theory with single or two electron populations. We compare our results with the Cluster observations and found good quantitative and qualitative agreement between them. At times when lion roars are observed (not observed) in the data and bi-Maxwellian could not provide the sufficient growth (damping) rates, we showed that when generalized (r,q) distribution function is employed, the resulted growth (damping) rates exactly match the observations.

Keywords: kinetic model, whistler waves, non-maxwellian distribution function, space plasmas

Procedia PDF Downloads 318

Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

Procedia PDF Downloads 293

Authors: Fahimeh Palizban, Farshad Noravesh, Amir Hossein Saeidian, Mahya Mehrmohamadi

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In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the blood and contribute to a pool of circulating fragments called cell-free DNA. Accordingly, identifying the tissue origin of these DNA fragments from the plasma can result in more accurate and fast disease diagnosis and precise treatment protocols. Open chromatin regions are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. There have been several studies in the area of cancer liquid biopsy that integrate distinct genomic and epigenomic features for early cancer detection along with tissue of origin detection. However, multimodal analysis requires several types of experiments to cover the genomic and epigenomic aspects of a single sample, which will lead to a huge amount of cost and time. To overcome these limitations, the idea of predicting OCRs from WGS is of particular importance. In this regard, we proposed a computational approach to target the prediction of open chromatin regions as an important epigenetic feature from cell-free DNA whole genome sequence data. To fulfill this objective, local sequencing depth will be fed to our proposed algorithm and the prediction of the most probable open chromatin regions from whole genome sequencing data can be carried out. Our method integrates the signal processing method with sequencing depth data and includes count normalization, Discrete Fourie Transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of the clustering (open chromatin region+, open chromatin region-) with previously validated open chromatin regions related to human blood samples of the ATAC-DB database. The percentage of overlap between predicted open chromatin regions and the experimentally validated regions obtained by ATAC-seq in ATAC-DB is greater than 67%, which indicates meaningful prediction. As it is evident, OCRs are mostly located in the transcription start sites (TSS) of the genes. In this regard, we compared the concordance between the predicted OCRs and the human genes TSS regions obtained from refTSS and it showed proper accordance around 52.04% and ~78% with all and the housekeeping genes, respectively. Accurately detecting open chromatin regions from plasma cell-free DNA-seq data is a very challenging computational problem due to the existence of several confounding factors, such as technical and biological variations. Although this approach is in its infancy, there has already been an attempt to apply it, which leads to a tool named OCRDetector with some restrictions like the need for highly depth cfDNA WGS data, prior information about OCRs distribution, and considering multiple features. However, we implemented a graph signal clustering based on a single depth feature in an unsupervised learning manner that resulted in faster performance and decent accuracy. Overall, we tried to investigate the epigenomic pattern of a cell-free DNA sample from a new computational perspective that can be used along with other tools to investigate genetic and epigenetic aspects of a single whole genome sequencing data for efficient liquid biopsy-related analysis.

Keywords: open chromatin regions, cancer, cell-free DNA, epigenomics, graph signal processing, correlation clustering

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Authors: Mete Ulaş Aksoy

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In contemporary politics, the state of emergency has become a permanent and salient feature of politics. This study aims to clarify the anthropological and ontological dimensions of the permanent state of emergency. It pays special attention to the structural relation between the exception and play. Focusing on the play in the context of emergency and exception enables the recognition of the difference and sometimes the discrepancy between the exception and emergency, which has passed into oblivion because of the frequency and normalization of emergency situations. This study coins the term “ludic exception” in order to highlight the difference between the exceptions in which exuberance and paroxysm rule over the socio-political life and the permanent emergency that protects the authority with a sort of extra-legality. The main thesis of the study is that the ludic elements such as risk, conspicuous consumption, sacrificial gestures, agonism, etc. circumscribe the exceptional moments temporarily, preventing them from being routine and normal. The study also emphasizes the decline of ludic elements in modernity as the main factor in the transformation of the exceptions into permanent emergency situations. In the introduction, the relationship between play and exception is taken into consideration. In the second part, the study elucidates the concept of ludic exceptions and dwells on the anthropological examples of the ludic exceptions. In the last part, the decline of ludic elements in modernity is addressed as the main factor for the permanent emergency.

Keywords: emergency, exception, ludic exception, play, sovereignty

Procedia PDF Downloads 94

Authors: Hao Li, Guy Y. Cornejo Maceda, Yiqing Li, Jianguo Tan, Marek Morzynski, Bernd R. Noack

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We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich database of machine learning control (MLC) optimizing a feedback law for a cost function in the plant. The proposed methodology provides (1) insights into the control landscape, which maps control laws to performance, including extrema and ridge-lines, (2) a catalogue of representative flow states and their contribution to cost function for investigated control laws and (3) visualization of the dynamics. Key enablers are classification and feature extraction methods of machine learning. The analysis is successfully applied to the stabilization of a mixing layer with sensor-based feedback driving an upstream actuator. The fluctuation energy is reduced by 26%. The control replaces unforced Kelvin-Helmholtz vortices with subsequent vortex pairing by higher-frequency Kelvin-Helmholtz structures of lower energy. These efforts target a human interpretable, fully automated analysis of MLC identifying qualitatively different actuation regimes, distilling corresponding coherent structures, and developing a digital twin of the plant.

Keywords: machine learning control, mixing layer, feedback control, model-free control

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Authors: Muriel Canas-Walker

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Polygamy is a sensitive issue yet a surprisingly popular topic on television. In China, among other palace intrigues dramas, "The Legend of Zhen Huan" stands out in its harsh portrayal of sequestered concubines in the Forbidden City. In the United States the critically acclaimed "Big Love", set in the Mormon community, generated much discussion and controversy, both accademically and on social media. More recently "The Handmaid’s Tale", adapted from the famous novel by Canadian writer Margaret Atwood, also contributed to the topic. All three dramas feature the plight of women caught in a polygamy system and are particularly popular with female audiences. Using Foucault’s theory of power, visual anthropology, and feminist perspective this paper aims at analyzing the treatment of this sensitive topic in the media and its reception. From the seemingly happy sister wives in "Big Love", to the fiercely competitive concubines in "The Legend of Zhen Huan" and the tragically coerced handmaids in "The Handmaid’s Tale", the lives of women in a polygamy system are inspiring to modern audiences. This paper’s objective is to understand how the treatment of polygamy is relevant to these audiences.

Keywords: polygamy, michel foucault, feminism, visual anthropology

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Authors: Hanan M. Taleb

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Since buildings are a major consumer of energy, their potential impact on the environment is considerable. Therefore, expanding the application of low energy architecture is of the utmost importance. Designing with nature is also one of the most attractive methods of design for many architects and designers because it creates a pathway to sustainability. One feature of designing with nature is the use of green roofing which aims to cover the roof with vegetation either partially or completely. Appreciably, green roofing in a building has many advantages including absorbing rainwater, providing thermal insulation, enhancing the ecology, creating a peaceful retreat for people and animals, improving air quality and helping to offset the air temperature and heat island effect. The aim of this paper is to monitor energy saving in the residential buildings of Dubai after applying green roofing techniques. The paper also attempts to provide a thermal analysis after the application of green roofs. A villa in Dubai was chosen as a case study. With the aid of energy simulation software, namely Design Builder, as well as manual recording and calculations, the energy savings after applying the green roofing were detected. To that extent, the paper draws some recommendations with regard to the types of green roofing that should be used in these particular climatic conditions based on this real experiment that took place over a one year period.

Keywords: residential buildings, Dubai, energy saving, green roofing, CFD, thermal comfort

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Authors: Vimala Rani P, Narasimha Malikarjunan, Mercy Shalinie S

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Data Networking was brought forth as an instantiation of information-centric networking. The attackers can send a colossal number of spoofs to take hold of the Pending Interest Table (PIT) named an Interest Flooding attack (IFA) since the in- interests are recorded in the PITs of the intermediate routers until they receive corresponding Data Packets are go beyond the time limit. These attacks can be detrimental to network performance. PIT expiration rate or the Interest satisfaction rate, which cannot differentiate the IFA from attacks, is the criterion Traditional IFA detection techniques are concerned with. Threshold values can casually affect Threshold-based traditional methods. This article proposes an accurate IFA detection mechanism based on a Multiple Feature-based Extreme Learning Machine (MF-ELM). Accuracy of the attack detection can be increased by presenting the entropy of Internet names, Interest satisfaction rate and PIT usage as features extracted in the MF-ELM classifier. Furthermore, we deploy a queue-based hostile Interest prefix mitigation mechanism. The inference of this real-time test bed is that the mechanism can help the network to resist IFA with higher accuracy and efficiency.

Keywords: information-centric network, pending interest table, interest flooding attack, MF-ELM classifier, queue-based mitigation strategy

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Authors: Levi Corallo

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Typed language made by humans on computers and cell phones has made a significant distinction from previous modes of written language exchanges. While acronyms remain one of the most predominant markings of typed language, another and perhaps more recent revolution in the way humans communicate has been with the use of symbols or glyphs, primarily Emojis—globally introduced on the iPhone keyboard by Apple in 2008. This paper seeks to analyze the use of symbols in typed communication from both a linguistic and machine learning perspective. The Unicode system will be explored and methods of encoding will be juxtaposed with the current machine and human perception. Topics in how typed symbol usage exists in conversation will be explored as well as topics across current research methods dealing with Emojis like sentiment analysis, predictive text models, and so on. This study proposes that sequential analysis is a significant feature for analyzing unicode characters in a corpus with machine learning. Current models that are trying to learn or translate the meaning of Emojis should be starting to learn using bi- and tri-grams of Emoji, as well as observing the relationship between combinations of different Emoji in tandem. The sociolinguistics of an entire new vernacular of language referred to here as ‘typed language’ will also be delineated across my analysis with unicode glyphs from both a semantic and technical perspective.

Keywords: unicode, text symbols, emojis, glyphs, communication

Procedia PDF Downloads 198

Authors: Mohammad Darbani, Jafar Masoud Sinaki, Armaghan Abedzadeh Neyshaburi

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An experiment was conducted as split plot factorial design using randomized complete block design in Damghan in 2012-2013 in order to investigate the effects of irrigation cut off (based on the Phenological stages of plants) on physiological properties of forage millet cultivars. The treatments included three irrigation levels (control with full irrigation, irrigation cut off when flowering started, and irrigation cut off when flowering ended) in the main plots, and applying nitroxin biofertilizer (+), not applying nitroxin biofertilizer (control), and Iranian forage millet cultivars (Bastan, Pishahang, and Isfahan) in the subplots. The highest rate of ashes and water-soluble carbohydrates content were observed in the cultivar Bastan (8.22 and 8.91%, respectively), the highest content of fiber and water (74.17 and 48.83%, respectively) in the treatment of irrigation cut off when flowering started, and the largest proline concentration (μmol/gfw-1) was seen in the treatment of irrigation cut off when flowering started. very rapid growth of millet, its short growing season, drought tolerance, its unique feature regarding harvest time, and its response to nitroxin biofertilizer can help expanding its cultivation in arid and semi-arid regions of Iran.

Keywords: irrigation cut off, forage millet, Nitroxin fertilizer, physiological properties

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

Procedia PDF Downloads 139

Authors: Tolga Aydin, M. Fatih Alaeddinoğlu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: apriori algorithm, association rules, data mining, spatio-temporal data

Procedia PDF Downloads 377

Authors: Hanbey Hazar, Hakan Gul, Ugur Ozturk

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In order to decrease the hazardous emissions of the internal combustion engines and to improve the combustion and thermal efficiency, thermal barrier coatings are applied. In this experimental study, cylinder, piston, exhaust, and inlet valves which are combustion chamber components have been coated with a ceramic material, and this earned the engine LHR feature. Cylinder, exhaust and inlet valves of the diesel engine used in the tests were coated with ekabor-2 commercial powder, which is a ceramic material, to a thickness of 50 µm, by using the boriding method. The piston of a diesel engine was coated in 300 µm thickness with bor-based powder by using plasma coating method. Pumpkin seeds oil methyl ester (PSME) was produced by the transesterification method. In addition, dimethoxymethane additive materials were used to improve the properties of diesel fuel, pumpkin seeds oil methyl ester (PSME) and its mixture. Dimethoxymethane was blended with test fuels, which was used as a pilot fuel, at the volumetric ratios of 4% and 8%. Due to thermal barrier coating, the diesel engine's CO, HC, and smoke density values decreased; but, NOx and exhaust gas temperature (EGT) increased.

Keywords: boriding, diesel engine, exhaust emission, thermal barrier coating

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Authors: Jyoti Agrawal

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Introduction: Renal diseases in children and young adult can be difficult to diagnose early as it may present only with few symptoms, tends to have different course than adult and respond variously to different treatment. The pattern of renal disease in children is different from developing countries as compared to developed countries. Methods: This study was a hospital based prospective observational study carried from March, 2014 to February 2015 at BP Koirala institute of health sciences. Patients with renal disease, both inpatient and outpatient from birth to 14 years of age were enrolled in the study. The diagnosis of renal disease was be made on clinical and laboratory criteria. Results: Total of 120 patients were enrolled in our study which contributed to 3.74% % of total admission. The commonest feature of presentation was edema (75%), followed by fever (65%), hypertension (60%), decreased urine output (45%) and hematuria (25%). Most common diagnosis was acute glomerulonephritis (40%) followed by Nephrotic syndrome (25%) and urinary tract infection (25%). Renal biopsy was done for 10% of cases and most of them were steroid dependent nephrotic syndrome. 5% of our cases expired because of multiorgan dysfunction syndrome, sepsis and acute kidney injury. Conclusion: Renal disease contributes to a large part of hospital pediatric admission as well as mortality and morbidity to the children.

Keywords: glomerulonephritis, nephrotic syndrome, renal disease, urinary tract infection

Procedia PDF Downloads 428

Authors: Shinoy Vengaramkode Bhaskaran, Kaushik Sathupadi, Sandesh Achar

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Human locomotion recognition is important in a variety of sectors, such as robotics, security, healthcare, fitness tracking and cloud computing. With the increasing pervasiveness of peripheral devices, particularly Inertial Measurement Units (IMUs) sensors, researchers have attempted to exploit these advancements in order to precisely and efficiently identify and categorize human activities. This research paper introduces a state-of-the-art methodology for the recognition of human locomotion patterns in a cloud environment. The methodology is based on a publicly available benchmark dataset. The investigation implements a denoising and windowing strategy to deal with the unprocessed data. Next, feature extraction is adopted to abstract the main cues from the data. The SelectKBest strategy is used to abstract optimal features from the data. Furthermore, state-of-the-art ML classifiers are used to evaluate the performance of the system, including logistic regression, random forest, gradient boosting and SVM have been investigated to accomplish precise locomotion classification. Finally, a detailed comparative analysis of results is presented to reveal the performance of recognition models.

Keywords: artificial intelligence, cloud computing, IoT, human locomotion, gradient boosting, random forest, neural networks, body-worn sensors

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Authors: Atul Kumar, Veeranjaneyulu Gannedi, Qazi Naveed Ahmed

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Oligosaccharides, which are essential to all cellular organisms, play vital roles in cell recognition, signaling, and are involved in a broad range of biological processes. The chemical synthesis of carbohydrates represents a powerful tool to provide homogeneous glycans. In carbohydrate synthesis, the major concern is the orthogonal protection of hydroxyl groups that can be unmasked independently. Classical protecting groups include benzyl ethers (Bn), which are normally cleaved through hydrogenolysis or by means of metal reduction, and acetate (Ac), benzoate (Bz) or pivaloate esters, which are removed using base promoted hydrolysis. In present work a series of α-Oxoester (COBz) protected saccharides, with divergent base sensitivity profiles against benzoyl (Bz) and acetyl (Ac), were designed and KHSO₅/CH₃COCl in methanol was identified as an easy, mild, selective and efficient deprotecting reagent for their removal in the perspective of carbohydrate synthesis. Timely monitoring of later reagent was advantageous in establishing both sequential as well as simultaneous deprotecting of COBz, Bz, and Ac. The salient feature of our work is its ease to generate different acceptors using designed monosaccharides. In summary, we demonstrated α-Oxoester (COBz) as a new protecting group for carbohydrates and the application of this group for the synthesis of Glycosylphosphatidylinositol (GPI) anchor are in progress.

Keywords: α-Oxoester, oligosaccharides, new protecting group, acceptor synthesis, glycosylation

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Authors: Sonam Kumari

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Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, possesses a remarkable feature of entering into and emerging from a persistent state. The mechanism by which Mtb switches from the dormant state to the replicative form is still poorly characterized. Proteome studies have given us an insight into the role of certain proteins in giving stupendous virulence to Mtb, but numerous dotsremain unconnected and unaccounted. The WhiB family of proteins is one such protein that is associated with developmental processes in actinomycetes.Mtb has seven such proteins (WhiB1 to WhiB7).WhiB proteins are transcriptional regulators; their conserved C-terminal HTH motif is involved in DNA binding. They regulate various essential genes of Mtbby binding to their promoter DNA. Biophysical Analysis of the effect of DNA binding on WhiB proteins has not yet been appropriately characterized. Interaction with DNA induces conformational changes in the WhiB proteins, confirmed by steady-state fluorescence and circular dichroism spectroscopy. ITC has deduced thermodynamic parameters and the binding affinity of the interaction. Since these transcription factors are highly unstable in vitro, their stability and solubility were enhanced by the co-expression of molecular chaperones. The present study findings help determine the conditions under which the WhiB proteins interact with their interacting partner and the factors that influence their binding affinity. This is crucial in understanding their role in regulating gene expression in Mtbandin targeting WhiB proteins as a drug target to cure TB.

Keywords: tuberculosis, WhiB proteins, mycobacterium tuberculosis, nucleic acid binding

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Authors: Akrem Sellami, Imed Riadh Farah, Basel Solaiman

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With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI.

Keywords: band selection, dimensionality reduction, feature extraction, hyperspectral imagery, semantic interpretation

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Authors: Misagh Irandoost Shahrestani, Mehdi Ashjaee, Shahrokh Zandieh Vakili

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In this paper, partially premixed laminar methane/air co-flow flame is studied experimentally. Methane-air flame was established on an axisymmetric coannular burner. The fuel-air jet flows from the central tube while the secondary air flows from the region between the inner and the outer tube. The aim is to investigate the flame features and to develop a nonintrusive method for temperature measurement of methane/air partially premixed flame using Mach-Zehnder interferometry method. Different equivalence ratios and Reynolds numbers are considered. Flame generic visible appearance was also investigated and its various structures were studied. Three distinguished flame regimes were seen based on its appearance. A double flame structure can be seen for the equivalence ratio in the range of 1<Φ<2.1. By adding air to the mixture up to Φ=4 the flame has the characteristics of both premixed and non-premixed flames. Finally for 4<Φ<∞ the flame mainly becomes non-premixed like and the luminous sooting region on its tip is the obvious feature of this type of flames. The Mach-Zehnder method is used to obtain temperature field of a transparent fluid by means of index of refraction. Temperature obtained from optical techniques was compared with that of obtained from thermocouples in order to validate the results. Good agreement was observed for these two methods.

Keywords: flame structure, Mach-Zehnder interferometry, partially premixed flame, temperature field

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Authors: Nursel Koyuncu, Nihal Ata Tutkun

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Frailty model is a survival model that takes into account the unobserved heterogeneity for exploring the relationship between the survival of an individual and several covariates. In the recent years, proposed survival models become more complex and this feature causes convergence problems especially in large data sets. Therefore selection of sample from these big data sets is very important for estimation of parameters. In sampling literature, some authors have defined new sampling schemes to predict the parameters correctly. For this aim, we try to see the effect of sampling design in semi-parametric frailty model. We conducted a simulation study in R programme to estimate the parameters of semi-parametric frailty model for different sample sizes, censoring rates under classical simple random sampling and ranked set sampling schemes. In the simulation study, we used data set recording 17260 male Civil Servants aged 40–64 years with complete 10-year follow-up as population. Time to death from coronary heart disease is treated as a survival-time and age, systolic blood pressure are used as covariates. We select the 1000 samples from population using different sampling schemes and estimate the parameters. From the simulation study, we concluded that ranked set sampling design performs better than simple random sampling for each scenario.

Keywords: frailty model, ranked set sampling, efficiency, simple random sampling

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Authors: R. Noorani, K. Peerless, J. Mandrell, A. Lopez, R. Dalberto, M. Alzebaq

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Rapid prototyping (RP) is an additive manufacturing technology used in industry that works by systematically depositing layers of working material to construct larger, computer-modeled parts. A key challenge associated with this technology is that RP parts often feature undesirable levels of surface roughness for certain applications. To combat this phenomenon, an experimental technique called Design of Experiments (DOE) can be employed during the growth procedure to statistically analyze which RP growth parameters are most influential to part surface roughness. Utilizing DOE to identify such factors is important because it is a technique that can be used to optimize a manufacturing process, which saves time, money, and increases product quality. In this study, a four-factor/two level DOE experiment was performed to investigate the effect of temperature, layer thickness, infill percentage, and infill speed on the surface roughness of RP prototypes. Samples were grown using the sixteen different possible growth combinations associated with a four-factor/two level study, and then the surface roughness data was gathered for each set of factors. After applying DOE statistical analysis to these data, it was determined that layer thickness played the most significant role in the prototype surface roughness.

Keywords: rapid prototyping, surface roughness, design of experiments, statistical analysis, factors and levels

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Authors: Aoqi Zhang, Changgil Lee Lee, Seunghee Park

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A non-contact nondestructive technique using laser-induced ultrasonic wave generation method was applied to visualize corrosion damage at aluminum alloy plate structures. The ultrasonic waves were generated by a Nd:YAG pulse laser, and a galvanometer-based laser scanner was used to scan specific area at a target structure. At the same time, wave responses were measured at a piezoelectric sensor which was attached on the target structure. The visualization of structural damage was achieved by calculating logarithmic values of root mean square (RMS). Damage-sensitive feature was defined as the scattering characteristics of the waves that encounter corrosion damage. The corroded damage was artificially formed by hydrochloric acid. To observe the effect of the location where the corrosion was formed, the both sides of the plate were scanned with same scanning area. Also, the effect on the depth of the corrosion was considered as well as the effect on the size of the corrosion. The results indicated that the damages were successfully visualized for almost cases, whether the damages were formed at the front or back side. However, the damage could not be clearly detected because the depth of the corrosion was shallow. In the future works, it needs to develop signal processing algorithm to more clearly visualize the damage by improving signal-to-noise ratio.

Keywords: non-destructive testing, corrosion, pulsed laser scanning, ultrasonic waves, plate structure

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Authors: Wenhao Lan, Ning Li, Qiang Tong

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To improve the registration accuracy of a source point cloud and template point cloud when the initial relative deflection angle is too large, a PointNetLK algorithm combined with an oriented bounding box (PointNetLK-OBB) is proposed. In this algorithm, the OBB of a 3D point cloud is used to represent the macro feature of source and template point clouds. Under the guidance of the iterative closest point algorithm, the OBB of the source and template point clouds is aligned, and a mirror symmetry effect is produced between them. According to the fitting degree of the source and template point clouds, the mirror symmetry plane is detected, and the optimal rotation and translation of the source point cloud is obtained to complete the 3D point cloud registration task. To verify the effectiveness of the proposed algorithm, a comparative experiment was performed using the publicly available ModelNet40 dataset. The experimental results demonstrate that, compared with PointNetLK, PointNetLK-OBB improves the registration accuracy of the source and template point clouds when the initial relative deflection angle is too large, and the sensitivity of the initial relative position between the source point cloud and template point cloud is reduced. The primary contribution of this paper is the use of PointNetLK to avoid the non-convex problem of traditional point cloud registration and leveraging the regularity of the OBB to avoid the local optimization problem in the PointNetLK context.

Keywords: mirror symmetry, oriented bounding box, point cloud registration, PointNetLK-OBB

Procedia PDF Downloads 155

Authors: Jyh-Cherng Gu, Ming-Ta Yang, Chao-Fong Yan, Hsin-Yung Chung, Yung-Ruei Chang, Yih-Der Lee, Chen-Min Chan, Chia-Hao Hsu

Abstract:

There are a number of distributed generations (DGs) installed in microgrid, which may have diverse path and direction of power flow or fault current. The overcurrent protection scheme for the traditional radial type distribution system will no longer meet the needs of microgrid protection. Integrating the intelligent electronic device (IED) and a supervisory control and data acquisition (SCADA) with IEC 61850 communication protocol, the paper proposes a microgrid protection management system (MPMS) to protect power system from the fault. In the proposed method, the MPMS performs logic programming of each IED to coordinate their tripping sequence. The GOOSE message defined in IEC 61850 is used as the transmission information medium among IEDs. Moreover, to cope with the difference in fault current of microgrid between grid-connected mode and islanded mode, the proposed MPMS applies the group setting feature of IED to protect system and robust adaptability. Once the microgrid topology varies, the MPMS will recalculate the fault current and update the group setting of IED. Provided there is a fault, IEDs will isolate the fault at once. Finally, the Matlab/Simulink and Elipse Power Studio software are used to simulate and demonstrate the feasibility of the proposed method.

Keywords: IEC 61850, IED, group Setting, microgrid

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Authors: Mojtaba Heydarizad

Abstract:

Andarokh basin is one of the main karstic regions in Khorasan Razavi province NE Iran. This basin is part of Kopeh-Dagh mega zone extending from Caspian Sea in the east to northern Afghanistan in the west. This basin is covered by Mozdooran Formation, Ngr evaporative formation and quaternary alluvium deposits in descending order of age. Mozdooran carbonate formation is notably karstified. The main surface karstic features in Mozdooran formation are Groove karren, Cleft karren, Rain pit, Rill karren, Tritt karren, Kamintza, Domes, and Table karren. In addition to surface features, deep karstic feature Andarokh Cave also exists in the region. Studying Ca, Mg, Mn, Sr, Fe concentration and Sr/Mn ratio in Mozdooran formation samples with distance to main faults and joints system using PCA analyses demonstrates intense meteoric digenesis role in controlling carbonate rock geochemistry. The karst evaluation in Andarokh basin varies from early stages 'deep seated karst' in Mesozoic to mature karstic system 'Exhumed karst' in quaternary period. Andarokh cave (the main cave in Andarokh basin) is rudimentary branch work consists of three passages of A, B and C and two entrances Andarokh and Sky.

Keywords: Andarokh basin, Andarokh cave, geochemical analyses, karst evaluation

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Authors: Bipasha Sen, Aditya Agarwal

Abstract:

Multilingual automatic speech recognition (ASR) system is a single entity capable of transcribing multiple languages sharing a common phone space. Performance of such a system is highly dependent on the compatibility of the languages. State of the art speech recognition systems are built using sequential architectures based on recurrent neural networks (RNN) limiting the computational parallelization in training. This poses a significant challenge in terms of time taken to bootstrap and validate the compatibility of multiple languages for building a robust multilingual system. Complex architectural choices based on self-attention networks are made to improve the parallelization thereby reducing the training time. In this work, we propose Reed, a simple system based on 1D convolutions which uses very short context to improve the training time. To improve the performance of our system, we use raw time-domain speech signals directly as input. This enables the convolutional layers to learn feature representations rather than relying on handcrafted features such as MFCC. We report improvement on training and inference times by atleast a factor of 4x and 7.4x respectively with comparable WERs against standard RNN based baseline systems on SpeechOcean's multilingual low resource dataset.

Keywords: convolutional neural networks, language compatibility, low resource languages, multilingual automatic speech recognition

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Authors: Nasrin Hosseinzad

Abstract:

Troxerutin, a trihydroxyethylated derived of rutin, is a flavonoid existing in tea, coffee, cereal grains, various fruits and vegetables have been conveyed to display radioprotective, antithrombotic, nephron-protective and hepato-protective possessions. Troxerutin, has been well-proved to utilize hepatoprotective assets. Troxerutin could upturn the resistance of hippocampal neurons alongside apoptosis by lessening the action of AChE and oxidative stress. Consequently, troxerutin may have advantageous properties in the administration of Alzheimer's disease and cancer. Troxerutin has been testified to have several welfares and medicinal stuffs. It could shelter the mouse kidney against d-gal-induced damage by refining renal utility, decreasing histopathologic changes, dropping ROS construction, reintroducing the activities of antioxidant enzymes and reducing DNA oxidative destruction. The DNA cleavage study clarifies that troxerutin showed DNA protection against hydroxyl radical persuaded DNA mutilation. Troxerutin uses anti-cancer effect in HuH-7 hepatocarcinoma cells conceivably through synchronized regulation of the molecular signalling pathways, Nrf2 and NF-κB. DNA binding at slight channel by troxerutin may have donated to feature breaks leading to improved radiation brought cell death. Furthermore, the mechanism principal the observed variance in the antioxidant activities of troxerutin and its esters was qualified to equally their free radical scavenging capabilities and dissemination on the cell membrane outward.

Keywords: troxerutin, DNA, oxidative stress, antioxidant, free radical

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Authors: Pravallika Chithuloori, Jin-Man Kim

Abstract:

Soil liquefaction, triggered by increased porewater pressure, poses a significant threat to infrastructure stability in seismically active regions, and its forecasting remains challenging due to intricate nonlinear interactions. This study uses a dataset of 540 samples that includes seismic parameters and standard penetration test (SPT) results to evaluate liquefaction prediction. SPT N60 values, soil fine content (FC), ground water table (GWT), effective stress of overburden (ESO), peak ground acceleration (PGA), and earthquake magnitude (Mw) are key inputs. A gradient boost classifier (GBC) machine learning (ML) model was utilized to classify liquefaction events. The model’s performance was evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrix analysis, sensitivity analysis, feature importance ranking, and Shapley Additive Explanations (SHAP). According to these evaluations, the most significant variables in predicting liquefaction were PGA, SPT-N60, and GWT. The robustness of the GBC model was further validated through precision-recall curves and k-fold cross-validation, and it achieved an impressive 99.38% prediction accuracy. These results highlight the potential of the GBC technique to advance the reliability of liquefaction forecasting.

Keywords: liquefaction, standard penetration test, gradient boost, machine learning, SHAP

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Authors: Jay Ananth

Abstract:

The drug discovery process currently requires numerous years of clinical testing as well as money just for a single drug to earn FDA approval. For drugs that even make it this far in the process, there is a very slim chance of receiving FDA approval, resulting in detrimental hurdles to drug accessibility. To minimize these inefficiencies, numerous studies have implemented computational methods, although few computational investigations have focused on a crucial feature of drugs: lipophilicity. Lipophilicity is a physical attribute of a compound that measures its solubility in lipids and is a determinant of drug efficacy. This project leverages Artificial Intelligence to predict the impact of a drug’s lipophilicity on its performance by accounting for factors such as binding affinity and toxicity. The model predicted lipophilicity and binding affinity in the validation set with very high R² scores of 0.921 and 0.788, respectively, while also being applicable to a variety of target receptors. The results expressed a strong positive correlation between lipophilicity and both binding affinity and toxicity. The model helps in both drug development and discovery, providing every pharmaceutical company with recommended lipophilicity levels for drug candidates as well as a rapid assessment of early-stage drugs prior to any testing, eliminating significant amounts of time and resources currently restricting drug accessibility.

Keywords: drug discovery, lipophilicity, ligand-receptor interactions, machine learning, drug development

Procedia PDF Downloads 114