Search results for: speeded up robust features

Authors: Agata Twardoch

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The processes of commodification of real estate and changes in housing markets have led to a situation where the prices of free market housing in European capitals are significantly higher than the purchasing value of average wages. This phenomenon has many negative social and spatial consequences. At the same time, the attractiveness of real estate as an asset makes these processes progress. Out of concern for sustainable social development, city authorities apply solutions to balance the burdensome effects of codification of housing. One of them is a social provision for housing investments. The article presents a comparative study of solutions applied in selected European capitals, on the example of Warsaw, Paris, London, Berlin, Copenhagen, and Vienna. The study was conducted along with works on expert report for the master plan for Warsaw. The forms of commissions applied in Local Planning Acts were compared, with particular reference to spatial solutions. The results of the analysis made it possible to determine common features of the solutions applied and to establish recommendations for further practice. Major findings of the study indicate that requirement of social provision is achievable in spatial planning documents. Study shows that application of social provision in private housing investments is a useful tool in housing policy against commodification.

Keywords: affordable housing, housing provision, spatial planning, sustainable social development

Procedia PDF Downloads 177

Authors: Didem Aslantaş

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In this study, the reforms carried out by the Justice and Development Party, which came to power in 2002, and how the foreign policy understanding it transformed reflected on the relations with Syria will be analyzed from the social constructivist theory. Contrary to the increasing security concerns of the states after the September 11 attacks, the main problem of the research is how the relations between Syria and Turkey developed and how they progressed in non-security dimensions. In order to find an answer to this question, the basic assumptions of the constructivist theory will be used. Since there is a limited number of studies in the literature, a comparative analysis of the Adana Consensus and the Cooperation Agreement between the Republic of Turkey and the Syrian Arab Republic, and the Joint Cooperation Agreement Against Terrorism and Terrorist Organizations will be included. In order to answer the main problem of the research and to support the arguments, document and archive scanning methods from qualitative research methods will be used. In the first part of the study, what the social constructivist theory is and its basic assumptions are explained, while in the second part, Turkey-Syria relations between 2002-2011 are included. In the third and last part, the relations between the two countries will be tried to be read through social constructivism by referring to the foreign policy features of the Ak Party period.

Keywords: Social Constructivist Theory, foreign policy analysis, Justice and Development Party, Syria

Procedia PDF Downloads 81

Authors: Leah Wright, Trevor Townsend

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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

Procedia PDF Downloads 386

Authors: Changyong Liu, Junda Tong, Feng Xu, Ninggui Huang

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In the injection molding process, the performance of cooling channels is crucial to the part quality. Through the application of conformal cooling channels fabricated via metal additive manufacturing, part distortion, warpage can be greatly reduced and cycle time can be greatly shortened. However, the properties of additively manufactured conformal cooling channels are quite different from conventional drilling processes such as the poorer dimensional accuracy and larger surface roughness. These features have significant influences on its cooling performance. In this study, test molds with the cooling channel diameters of φ2 mm, φ3 mm and φ4 mm were fabricated via selective laser melting and conventional drilling process respectively. A test system was designed and manufactured to measure the pressure difference between the channel inlet and outlet, the coolant flow rate and the temperature variation during the heating process. It was found that the cooling performance of SLM-fabricated channels was poorer than drilled cooling channels due to the smaller sectional area of cooling channels resulted from the low dimensional accuracy and the unmolten particles adhered to the channel surface. Theoretical models were established to determine the friction factor and heat transfer coefficient of SLM-fabricated cooling channels. These findings may provide guidance to the design of conformal cooling channels.

Keywords: conformal cooling channels, selective laser melting, cooling performance, injection molding

Procedia PDF Downloads 148

Authors: Mitakshara Sharma, Sonal Sharma

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Vesiculobullous diseases are heterogeneous group of dermatological disorders with protean manifestations. The most important technique for the patients with vesiculobullous diseases is conventional histopathology and confirmatory tests like direct immunofluorescence (DIF) and indirect immunofluorescence (IIF). DIF has been used for decades to investigate pathophysiology and in the diagnosis. It detects molecules such as immunoglobulins and complement components. It is done on the perilesional skin. Diagnosis of DIF test depends on features like primary site of the immune deposits, class of immunoglobulin, number of immune deposits and deposition at other sites. The aim of the study is to correlate DIF with clinical and histopathological findings and to analyze the utility of DIF in the diagnosis of these disorders. It is a retrospective descriptive study conducted for 2 years from 2015 to 2017 in Department of Pathology, GTB Hospital on perilesional punch biopsies of vesiculobullous lesions. Biopsies were sent in Michael’s medium. The specimens were washed, frozen and incubated with fluorescein isothiocyanate (FITC) tagged antihuman antibodies IgA, IgG, IgM, C3 & F and were viewed under fluorescent microscope. Out of 401 skin biopsies submitted for DIF, 285 were vesiculobullous diseases, in which the most common was Pemphigus vulgaris (34%) followed by Bullous pemphigoid (21.5%), Dermatitis herpetiformis (16%), Pemphigus foliaceus (11.9%), Linear IgA disease (11.9%), Epidermolysisbullosa (2.39%) and Pemphigus herpetiformis (1.7%). We will be presenting the DIF findings in the all these vesiculobullous diseases. DIF in conjugation with histopathology gives the best diagnostic yield in these lesions. It also helps in the diagnosis whenever there is a clinical and histopathological overlap.

Keywords: antibodies, direct immunofluorescence, pemphigus, vesiculobullous

Procedia PDF Downloads 360

Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

Procedia PDF Downloads 74

Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

Procedia PDF Downloads 48

Authors: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

Procedia PDF Downloads 116

Authors: Amr AbdelAzim Ali, G. A. Elsheikh, Moutaz M. Hegazy

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Reaction wheels (RWs) are generally used as main actuator in the attitude control system (ACS) of spacecraft (SC) for fast orientation and high pointing accuracy. In order to achieve the required accuracy for the RWs model, the main characteristics of the RWs that necessitate analysis during the ACS design phase include: technical features, sequence of operating and RW control logic are included in function (behavior) model. A mathematical model is developed including the various errors source. The errors in control torque including relative, absolute, and error due to time delay. While the errors in angular velocity due to differences between average and real speed, resolution error, loose in installation of angular sensor, and synchronization errors. The friction torque is presented in the model include the different feature of friction phenomena: steady velocity friction, static friction and break-away torque, and frictional lag. The model response is compared with the experimental torque and frequency-response characteristics of tested RWs. Based on the created RW model, some criteria of optimization based control torque allocation problem can be recommended like: avoiding the zero speed crossing, bias angular velocity, or preventing wheel from running on the same angular velocity.

Keywords: friction torque, reaction wheels modeling, software in the loop, spacecraft attitude control

Procedia PDF Downloads 265

Authors: Zeinab Yazdanfar, Dilan Robert, Daniel Lester, S. Setunge

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Scour has been identified as the most common threat to bridge stability worldwide. Traditionally, scour around bridge piers is calculated using the empirical approaches that have considerable limitations and are difficult to generalize. The multi-physic nature of scouring which involves turbulent flow, soil mechanics and solid-fluid interactions cannot be captured by simple empirical equations developed based on limited laboratory data. These limitations can be overcome by direct numerical modeling of coupled hydro-mechanical scour process that provides a robust prediction of bridge scour and valuable insights into the scour process. Several numerical models have been proposed in the literature for bridge scour estimation including Eulerian flow models and coupled Euler-Lagrange models incorporating an empirical sediment transport description. However, the contact forces between particles and the flow-particle interaction haven’t been taken into consideration. Incorporating collisional and frictional forces between soil particles as well as the effect of flow-driven forces on particles will facilitate accurate modeling of the complex nature of scour. In this study, a coupled Computational Fluid Dynamics and Discrete Element Model (CFD-DEM) has been developed to simulate the scour process that directly models the hydro-mechanical interactions between the sediment particles and the flowing water. This approach obviates the need for an empirical description as the fundamental fluid-particle, and particle-particle interactions are fully resolved. The sediment bed is simulated as a dense pack of particles and the frictional and collisional forces between particles are calculated, whilst the turbulent fluid flow is modeled using a Reynolds Averaged Navier Stocks (RANS) approach. The CFD-DEM model is validated against experimental data in order to assess the reliability of the CFD-DEM model. The modeling results reveal the criticality of particle impact on the assessment of scour depth which, to the authors’ best knowledge, hasn’t been considered in previous studies. The results of this study open new perspectives to the scour depth and time assessment which is the key to manage the failure risk of bridge infrastructures.

Keywords: bridge scour, discrete element method, CFD-DEM model, multi-phase model

Procedia PDF Downloads 130

Authors: Ahmed Tafraoui, Gilles Escadeillas, Thierry Vidal

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The southern Algeria is known for its huge sand dunes that cover part of its territory (Sahara). This sand has features that allow a glimpse of a recovery in the construction field in the form of Ultra High Performance Concrete (UHPC). This type of concrete using a large amount of silica fume, ultra fine addition that gives very high performance but is also relatively rare and expensive. Replacing it with another addition to equivalent properties, such as metakaolin, can also be considered. The objective of this study is to both enhance the sand dunes of Erg south west western Algeria but also reduce manufacturing costs of Ultra High Performance Concrete to incorporating metakaolin to instead of silica fume. Performances to determine mechanical performance are instantaneous, compression and bending. Initially, we characterized the Algerian sand dune. Then, we have to find a formulation of UHPC, adequate in terms of implementation and to replace silica fume by metakaolin. Finally, we studied the actual value of the sand dune. Concrete obtained have very high mechanical performance, up to a compressive strength of 250 MPa, a tensile strength of 45 MPa by bending with the method of heat treatment. This study shows that the enhancement of dune sand studied is quite possible in UHPC, and in particular UHPC bundles and the replacement of silica fume by metakaolin do not alter the properties of these concretes.

Keywords: Ultra High Performance Concrete, sand dune, formulations, silica fume, metakaolin, strength

Procedia PDF Downloads 468

Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

Procedia PDF Downloads 198

Authors: Luc Conti, Dimitry Dumont-Fillon, Harald van Lintel, Eric Chappel

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Flow control valves comprise a silicon flexible membrane that deflects against a substrate, usually made of glass, containing pillars, an outlet hole, and anti-stiction features. However, there is a strong interest in using silicon instead of glass as substrate material, as it would simplify the process flow by allowing the use of well controlled anisotropic etching. Moreover, specific devices demanding a bending of the substrate would also benefit from the inherent outstanding mechanical strength of monocrystalline silicon. Unfortunately, direct Si-Si bonding is not easily achieved with highly structured wafers since residual stress may prevent the good adhesion between wafers. Using a thermoplastic polymer, such as parylene, as intermediate layer is not well adapted to this design as the wafer-to-wafer alignment is critical. An alternative anodic bonding method using an intermediate borosilicate layer has been successfully tested. This layer has been deposited onto the silicon substrate. The bonding recipe has been adapted to account for the presence of the SOI buried oxide and intermediate glass layer in order not to exceed the breakdown voltage. Flow control valves dedicated to infusion of viscous fluids at very high pressure have been made and characterized. The results are compared to previous data obtained using the standard anodic bonding method.

Keywords: anodic bonding, evaporated glass, flow control valve, drug delivery

Procedia PDF Downloads 199

Authors: Ashish Trivedi, Amol Singh

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In the recent years, the magnitude and frequency of disasters have increased at an alarming rate. Every year, more than 400 natural disasters affect global population. A large-scale disaster leads to destruction or damage to houses, thereby rendering a notable number of residents homeless. Since humanitarian response and recovery process takes considerable time, temporary establishments are arranged in order to provide shelter to affected population. These shelter areas are vital for an effective humanitarian relief; therefore, they must be strategically planned. Choosing the locations of temporary shelter areas for accommodating homeless people is critical to the quality of humanitarian assistance provided after a large-scale emergency. There has been extensive research on the facility location problem both in theory and in application. In order to deliver sufficient relief aid within a relatively short timeframe, humanitarian relief organisations pre-position warehouses at strategic locations. However, such approaches have received limited attention from the perspective of providing shelters to disaster-affected people. In present research work, this aspect of humanitarian logistics is considered. The present work proposes a hybrid decision support model to determine relative preference of potential shelter locations by assessing them based on key subjective criteria. Initially, the factors that are kept in mind while locating potential areas for establishing temporary shelters are identified by reviewing extant literature and through consultation from a panel of disaster management experts. In order to determine relative importance of individual criteria by taking into account subjectivity of judgements, a hybrid approach of fuzzy sets and Analytic Hierarchy Process (AHP) was adopted. Further, Technique for order preference by similarity to ideal solution (TOPSIS) was applied on an illustrative data set to evaluate potential locations for establishing temporary shelter areas for homeless people in a disaster scenario. The contribution of this work is to propose a range of possible shelter locations for a humanitarian relief organization, using a robust multi criteria decision support framework.

Keywords: AHP, disaster preparedness, fuzzy set theory, humanitarian logistics, TOPSIS, temporary shelters

Procedia PDF Downloads 201

Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

Procedia PDF Downloads 300

Authors: Marissa Dallara, Amalia Ardeljan, Lexi Frankel, Nadia Obaed, Naureen Rashid, Omar Rashid

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Human Cytomegalovirus (HCMV) is a ubiquitous virus that remains latent in approximately 60% of individuals in developed countries. Viral load is kept at a minimum due to a robust immune response that is produced in most individuals who remain asymptomatic. HCMV has been recently implicated in cancer research because it may impose oncomodulatory effects on tumor cells of which it infects, which could have an impact on the progression of cancer. HCMV has been implicated in increased pathogenicity of certain cancers such as gliomas, but in contrast, it can also exhibit anti-tumor activity. HCMV seropositivity has been recorded in tumor cells, but this may also have implications in decreased pathogenesis of certain forms of cancer such as leukemia as well as increased pathogenesis in others. This study aimed to investigate the correlation between cytomegalovirus and the incidence of breast cancer. Methods The data used in this project was extracted from a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to analyze the patients infected versus patients not infection with cytomegalovirus using ICD-10, ICD-9 codes. Permission to utilize the database was given by Holy Cross Health, Fort Lauderdale, for the purpose of academic research. Data analysis was conducted using standard statistical methods. Results The query was analyzed for dates ranging from January 2010 to December 2019, which resulted in 14,309 patients in both the infected and control groups, respectively. The two groups were matched by age range and CCI score. The incidence of breast cancer was 1.642% and 235 patients in the cytomegalovirus group compared to 4.752% and 680 patients in the control group. The difference was statistically significant by a p-value of less than 2.2x 10^-16 with an odds ratio of 0.43 (0.4 to 0.48) with a 95% confidence interval. Investigation into the effects of HCMV treatment modalities, including Valganciclovir, Cidofovir, and Foscarnet, on breast cancer in both groups was conducted, but the numbers were insufficient to yield any statistically significant correlations. Conclusion This study demonstrates a statistically significant correlation between cytomegalovirus and a reduced incidence of breast cancer. If HCMV can exert anti-tumor effects on breast cancer and inhibit growth, it could potentially be used to formulate immunotherapy that targets various types of breast cancer. Further evaluation is warranted to assess the implications of cytomegalovirus in reducing the incidence of breast cancer.

Keywords: human cytomegalovirus, breast cancer, immunotherapy, anti-tumor

Procedia PDF Downloads 206

Authors: Rims Janeliukstis, Deniss Mironovs, Andrejs Kovalovs

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Operational Modal Analysis (OMA) is widely applied as a method for Structural Health Monitoring for structural damage identification and assessment by tracking the changes of the identified modal parameters over time. Unfortunately, modal parameters also depend on such external factors as temperature and loads. Any structural condition assessment using modal parameters should be done taking into consideration those external factors, otherwise there is a high chance of false positives. A method of structural reliability assessment based on anomaly detection technique called Machalanobis Squared Distance (MSD) is proposed. It requires a set of reference conditions to learn healthy state of a structure, which all future parameters are compared to. In this study, structural modal parameters (natural frequency and mode shape), as well as ambient temperature and loads acting on the structure are used as features. Numerical tests were performed on a finite element model of a carbon fibre reinforced polymer composite beam with delamination damage at various locations and of various severities. The advantages of the demonstrated approach include relatively few computational steps, ability to distinguish between healthy and damaged conditions and discriminate between different damage severities. It is anticipated to be promising in reliability assessment of massively produced structural parts.

Keywords: operational modal analysis, reliability assessment, anomaly detection, damage, mahalanobis squared distance

Procedia PDF Downloads 113

Authors: Muhammad Fiaz

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The article in hand is the study of complex features like Zero Hopf Bifurcation, Chaos and Synchronization of integer and fractional order version of a new 3D finance system. Trusted tools of averaging theory and active control method are utilized for investigation of Zero Hopf bifurcation and synchronization for both versions respectively. Inventiveness of the paper is to find the answer of a question that is it possible to find a chaotic system which can be synchronized with any other of the same dimension? Based on different examples we categorically develop a theory that if a couple of master and slave chaotic dynamical system is synchronized by selecting a suitable gain matrix with special conditions then the master system is synchronized with any chaotic dynamical system of the same dimension. With the help of this study we developed generalized theorems for synchronization of n-dimension dynamical systems for integer as well as fractional versions. it proposed that this investigation will contribute a lot to control dynamical systems and only a suitable gain matrix with special conditions is enough to synchronize the system under consideration with any other chaotic system of the same dimension. Chaotic properties of fractional version of the new finance system are also analyzed at fractional order q=0.87. Simulations results, where required, also provided for authenticity of analytical study.

Keywords: complex analysis, chaos, generalized synchronization, control dynamics, fractional order analysis

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Authors: Harish Rajak, Swati Singh

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A series of novel pyrimidine based semicarbazone were designed and synthesized on the basis of semicarbazone based pharmacophoric model to satisfy the structural prerequisite crucial for antiepileptic activity. The semicarbazones based pharmacophoric model consists of following four essential binding sites: (i) An aryl hydrophobic binding site with halo substituent; (ii) A hydrogen bonding domain; (iii) An electron donor group and (iv) Another hydrophobic-hydrophilic site controlling the pharmacokinetic features of the anticonvulsant. The aryl semicarbazones has been recognized as a structurally novel class of compounds with remarkable anticonvulsant activity. In the present study, all the test semicarbazones were subjected to molecular docking using Glide v5.8. Some of the compounds were found to interact with ARG192, GLU270 and THR353 residues of 1OHV protein, present in GABA-AT receptor. The chemical structures of the synthesized molecules were characterized by elemental and spectral (IR, 1H NMR, 13C NMR and MS) analysis. The anticonvulsant activities of the compounds were investigated using maximal electroshock seizure (MES) and subcutaneous pentylenetrtrazole (scPTZ) models. The neurotoxicity was evaluated in mice by the rotorod test. The attempts were also made to establish structure-activity relationships among synthesized compounds. The results of the present study confirmed that the pharmacophore model with four binding sites is essential for antiepileptic activity.

Keywords: pyrimidine, semicarbazones, anticonvulsant activity, neurotoxicity

Procedia PDF Downloads 252

Authors: N. P. Patidar, J. Earnest, Laxmikant Nagar, Akshay Sharma

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This paper describes the diligence of a new input signal based fuzzy power system stabilizer in multi-machine power system. Instead of conventional input pairs like speed deviation (∆ω) and derivative of speed deviation i.e. acceleration (∆ω ̇) or speed deviation and accelerating power deviation of each machine, in this paper, deviation of active power through the tie line colligating two areas is used as one of the inputs to the fuzzy logic controller in concurrence with the speed deviation. Fuzzy Logic has the features of simple concept, easy effectuation, and computationally efficient. The advantage of this input is that, the same signal can be fed to each of the fuzzy logic controller connected with each machine. The simulated system comprises of two fully symmetrical areas coupled together by two 230 kV lines. Each area is equipped with two superposable generators rated 20 kV/900MVA and area-1 is exporting 413 MW to area-2. The effectiveness of the proposed control scheme has been assessed by performing small signal stability assessment and transient stability assessment. The proposed control scheme has been compared with a conventional PSS. Digital simulation is used to demonstrate the performance of fuzzy logic controller.

Keywords: Power System Stabilizer (PSS), small signal stability, inter-area oscillation, fuzzy logic controller, membership function, rule base

Procedia PDF Downloads 530

Authors: E. Satoh, R. Kasahara, T. Aoki, K. Fukatsu, D. Venkata Ramanarao, H. Harayama, T. Murata, A. Suwa

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Rice plant hoppers (Hemiptera: Delphacidae) have been causing extensive economic damage in rice and are considered as serious threat in rice producing countries of Asia. They have developed resistance to major groups of chemical insecticide, and severe outbreaks occur commonly throughout Asia. To control these nuisance pests, Nihon Nohyaku Co., Ltd., recently discovered an insecticide, benzpyrimoxan (proposed ISO name), which is under development as NNI-1501 (development code). Benzpyrimoxan has a unique chemical structure which contains benzyloxy and cyclic acetal groups on pyrimidine moiety (5-(1,3-dioxan-2-yl)-4-[4- (trifluoromethyl)benzyloxy]pyrimidine). In order to clarify the biological properties of benzpyrimoxan, we conducted several experiments and found the following results. Benzpyrimoxan has high activity against nymphal stages of rice plant hoppers without any adulticidal activity. It provides excellent and long lasting control against rice plant hoppers, including populations that have developed resistance to several other chemical groups of insecticide. The study on its mode of action is undergoing. These features highlight the versatility of this insecticide as an effective and valuable tool from the viewpoints of insecticide resistance management and integrated pest management program. With the use of benzpyrimoxan, farmers shall be able to lead the best yield potential by keeping the population density of rice plant hoppers and associated virus diseases under control.

Keywords: acetal, benzpyrimoxan, insecticide, NNI-1501, pyrimidine, rice plant hoppers

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Authors: Hooshag Asadi Haroni, Maryam Veiskarami, Yongjun Lu

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The Dalli gold-rich porphyry deposit (17 Mt @ 0.5% Cu and 0.65 g/t Au) is located in the Urumieh-Dokhtar Magmatic Arc (UDMA), a small segment of the Tethyan metallogenic belt, hosting several porphyry Cu (Mo-Au) systems in Iran. This research characterizes the Dalli deposit to define exploration criteria in advanced exploration such as the drilling of possible blind porphyry centers. Geological map, trench/drill hole geochemical and ground magnetic data, and age dating and isotope trace element analyses, carried out at the John De Laeter Research Center of Curtin University, were used to characterize the Delli deposit. Mineralization at Dalli is hosted by NE-trending quartz-diorite porphyry stocks (~ 200m in diameter) intruded by a wall-rock andesite porphyry. Disseminated and stockwork Cu-Au mineralization is related to potassic alteration, comprising magnetite, late K-feldspar and biotite, and quartz-sericite-specularite overprint, surrounded by extensive barren argillic and propylitic alterations. In the peripheries of the porphyry centers, there are N-trending vuggy quartz veins, hosting epithermal Au-Ag-As-Sb mineralization. Geochemical analyses of drill core samples showed that the core of the porphyry stocks is low-grade, whereas the high-grade disseminated and stockwork mineralization (~ 1% Cu and ~ 1.2 g/t Au) occurred at the contact of the porphyry stocks and andesite porphyry. Geochemical studies of the drill hole and trench samples showed a strong correlation between Cu and Au and both show a second-order correlation with Fe and As. Magnetic survey revealed two significant magnetic anomalies, associated with intensive potassic alteration, in the reduced-to-the-pole magnetic map of the area. A relatively weaker magnetic anomaly, showing no surface porphyry expressions, is located on a lithocap, consisting of advanced argillic alteration, vuggy quartz veins, and surface expressions of epithermal geochemical signatures. The association of the lithocap and the weak magnetic anomaly could be indicative of a hidden mineralized porphyry center. Litho-geochemical analyses of the least altered Dalli intrusions and volcanic rocks indicated high Sr/Y (49-61) and Eu/Eu* (0.89-0.92), features typical of Cu porphyries. The U-Pb dating of zircons of the mineralized quartz diorite and andesite porphyry, carried out by laser ablation inductively coupled plasma mass spectrometry, yielded magmatic crystallization ages of 15.4-16.0 Ma (Middle Miocene). The zircon trace element concentrations of Dalli are characterized by high Eu/Eu* (0.3-0.8), (Ce/Nd)/Y (0.01-0.3), and 10000*(Eu/Eu*)/Y (2-15) ratios, similar to fertile porphyry suites such as the giant Sar-Cheshmeh and Qulong porphyry Cu deposits along the Tethyan belt. This suggests that the Middle Miocene Dalli intrusions are fertile and require extensive deep drillings to define their potential. Chondrite-normalized rare earth element (REE) patterns show no significant Eu anomalies, and are characterized by light-REE enrichments (La/Sm)n = 2.57–6.40). In normalized multi-element diagrams, analyzed rocks are characterized by enrichments in large ion lithophile elements (LILE) and depletions in high field strength elements (HFSE), and display typical features of subduction-related calc-alkaline magmas. The characteristics of the Dalli deposit provided several recognition criteria for detailed exploration of Cu-Au porphyry deposits and highlighted the importance of the UDMA as a potentially significant, economically important, but relatively underexplored porphyry province.

Keywords: porphyry, gold, geochronology, magnetic, exploration

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Yoonjung An, Yongtae Park

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Offering a Product-Service System (PSS) is a well-accepted strategy that companies may adopt to provide a set of systemic solutions to customers. PSSs were initially provided in a simple form but now take diversified and complex forms involving multiple services, products and technologies. With the growing interest in the PSS, frameworks for the PSS development have been introduced by many researchers. However, most of the existing frameworks fail to examine various relations existing in a complex PSS. Since designing a complex PSS involves full integration of multiple products and services, it is essential to identify not only product-service relations but also product-product/ service-service relations. It is also equally important to specify how they are related for better understanding of the system. Moreover, as customers tend to view their purchase from a more holistic perspective, a PSS should be developed based on the whole system’s requirements, rather than focusing only on the product requirements or service requirements. Thus, we propose a framework to develop a complex PSS that is coordinated fully with the requirements of both worlds. Specifically, our approach adopts a multi-domain matrix (MDM). A MDM identifies not only inter-domain relations but also intra-domain relations so that it helps to design a PSS that includes highly desired and closely related core functions/ features. Also, various dependency types and rating schemes proposed in our approach would help the integration process.

Keywords: inter-domain relations, intra-domain relations, multi-domain matrix, product-service system design

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Authors: Abu Bakr Ahmed Shater

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Hypocenters of 250 earthquakes recorded by more than 5 stations from the Egyptian seismic network around the Gulf of Suez were relocated and the seismic stations correction for the P-wave is estimated, using the modified joint hypocenter method determination. Five stations TR1, SHR, GRB, ZAF and ZET have minus signs in the station P-wave travel time corrections and their values are -0.235, -0.366, -0.288, -0.366 and -0.058, respectively. It is possible to assume that, the underground model in this area has a particular characteristic of high velocity structure in which the other stations TR2, RDS, SUZ, HRG and ZNM have positive signs and their values are 0.024, 0.187, 0.314, 0.645 and 0.145, respectively. It is possible to assume that, the underground model in this area has particular characteristic of low velocity structure. The hypocenteral location determined by the Modified joint hypocenter method is more precise than those determined by the other routine work program. This method simultaneously solves the earthquake locations and station corrections. The station corrections reflect, not only the different crustal conditions in the vicinity of the stations, but also the difference between the actual and modeled seismic velocities along each of the earthquake - station ray paths. The stations correction obtained is correlated with the major surface geological features in the study area. As a result of the relocation, the low velocity area appears in the northeastern and southwestern sides of the Gulf of Suez, while the southeastern and northwestern parts are of high velocity area.

Keywords: gulf of Suez, seismicity, relocation of hypocenter, joint hypocenter determination

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

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Authors: Vitalii Yu. Kapitan, Aleksandr V. Perzhu, Konstantin V. Nefedev

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The necessity of studying magnetic multilayer structures is explained by the prospects of their practical application as a technological base for creating new storages medium. Magnetic multilayer films have many unique features that contribute to increasing the density of information recording and the speed of storage devices. Multilayer structures are structures of alternating magnetic and nonmagnetic layers. In frame of the classical Heisenberg model, lattice spin systems with direct short- and long-range exchange interactions were investigated by Monte Carlo methods. The thermodynamic characteristics of multilayer structures, such as the temperature behavior of magnetization, energy, and heat capacity, were investigated. The processes of magnetization reversal of multilayer structures in external magnetic fields were investigated. The developed software is based on the new, promising programming language Rust. Rust is a new experimental programming language developed by Mozilla. The language is positioned as an alternative to C and C++. For the Monte Carlo simulation, the Metropolis algorithm and its parallel implementation using MPI and the Wang-Landau algorithm were used. We are planning to study of magnetic multilayer films with asymmetric Dzyaloshinskii–Moriya (DM) interaction, interfacing effects and skyrmions textures. This work was supported by the state task of the Ministry of Education and Science of the Russia # 3.7383.2017/8.9

Keywords: The Monte Carlo methods, Heisenberg model, multilayer structures, magnetic skyrmion

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Authors: Oshin Anand, Atanu Rakshit

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The vast increment in the e-commerce ventures makes this area a prominent research stream. Besides several quantified parameters, the textual content of reviews is a storehouse of many information that can educate companies and help them earn profit. This study is an attempt in this direction. The article attempts to categorize data based on a computed metric that quantifies the influencing capacity of reviews rendering two categories of high and low influential reviews. Further, each of these document is studied to conclude several product feature categories. Each of these categories along with the computed metric is converted to linguistic identifiers and are used in an association mining model. The article makes a novel attempt to combine feature attraction with quantified metric to categorize review text and finally provide frequent patterns that depict customer preferences. Frequent mentions in a highly influential score depict customer likes or preferred features in the product whereas prominent pattern in low influencing reviews highlights what is not important for customers. This is achieved using a hybrid approach of text mining for feature and term extraction, sentiment analysis, multicriteria decision-making technique and association mining model.

Keywords: association mining, customer preference, frequent pattern, online reviews, text mining

Procedia PDF Downloads 387

Authors: Gaurav Gupta, Jitendra Mahakud

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This study focuses on the significance of internal financing constraints on the determination of corporate fixed investments in the case of Indian manufacturing companies. Financing constraints companies which have less internal fund or retained earnings face more transaction and borrowing costs due to imperfections in the capital market. The period of study is 1999-2000 to 2013-2014 and we consider 618 manufacturing companies for which the continuous data is available throughout the study period. The data is collected from PROWESS data base maintained by Centre for Monitoring Indian Economy Pvt. Ltd. Panel data methods like fixed effect and random effect methods are used for the analysis. The Likelihood Ratio test, Lagrange Multiplier test, and Hausman test results conclude the suitability of the fixed effect model for the estimation. The cash flow and liquidity of the company have been used as the proxies for the internal financial constraints. In accordance with various theories of corporate investments, we consider other firm specific variable like firm age, firm size, profitability, sales and leverage as the control variables in the model. From the econometric analysis, we find internal cash flow and liquidity have the significant and positive impact on the corporate investments. The variables like cost of capital, sales growth and growth opportunities are found to be significantly determining the corporate investments in India, which is consistent with the neoclassical, accelerator and Tobin’s q theory of corporate investment. To check the robustness of results, we divided the sample on the basis of cash flow and liquidity. Firms having cash flow greater than zero are put under one group, and firms with cash flow less than zero are put under another group. Also, the firms are divided on the basis of liquidity following the same approach. We find that the results are robust to both types of companies having positive and negative cash flow and liquidity. The results for other variables are also in the same line as we find for the whole sample. These findings confirm that internal financing constraints play a significant role for determination of corporate investment in India. The findings of this study have the implications for the corporate managers to focus on the projects having higher expected cash inflows to avoid the financing constraints. Apart from that, they should also maintain adequate liquidity to minimize the external financing costs.

Keywords: cash flow, corporate investment, financing constraints, panel data method

Procedia PDF Downloads 241