Search results for: features of structural

Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

Abstract:

Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Hussin K. Ragb, Vijayan K. Asari

Abstract:

In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

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Authors: Natalya G. Filimonova, Mariya G. Ozerova, Irina N. Ermakova

Abstract:

The long-term strategy of the economic development of Russia up to 2030 is based on the concept of sustainable growth. The determining factor of such development is complex changes in the economic system which may be achieved by making progressive changes in its structure. The structural changes determine the character and the direction of economic development, as well as they include all elements of this system without exception, and their regulated character ensures the most rapid aim achievement. This article has discussed the industrial structure of the agriculture in Russia. With the use of the system of indexes, the article has determined the directions, intensity, and speed of structural shifts. The influence of structural changes on agricultural production development has been found out. It is noticed that the changes in the industrial structure are synchronized with the changes in the organisation and economic structure. Efficiency assessment of structural changes allowed to trace the efficiency of structural changes and elaborate the main directions for agricultural policy improvement.

Keywords: Russian agricultural sectors, sectoral structure, organizational and economic structure, structural changes

Procedia PDF Downloads 146

Authors: Shi-Yu Xu

Abstract:

The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

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Authors: Saja M. Nabat Al-Ajrash, Charles Browning, Rose Eckerle, Li Cao

Abstract:

A process that utilizes a combination of additive manufacturing (AM), a preceramic polymer, and a chopped carbon fiber precursorto fabricate Silicon Carbon/ Carbon fibers (SiC/C) composites have been developed. The study has shown a promising, cost-effective, and efficient route to fabricate complex SiC/C composites using additive manufacturing. A key part of this effort was the mapping of the material’s microstructure through the thickness of the composite. Microstructural features in the pyrolyzed composites through the successive AM layers, such as defects, crystal size and their distribution, interatomic spacing, chemical bonds, were investigated using high-resolution scanning and transmission electron microscopy. As a result, the microstructure developed in SiC/C composites after printing, cure, and pyrolysis has been successfully mapped through the thickness of the derived composites. Dense and nearly defect-free parts after polymer to ceramic conversion were observed. The ceramic matrix composite displayed three coexisting phases, including silicon carbide, silicon oxycarbide, and turbostratic carbon. Lattice fringes imaging and X-Ray Diffraction analysis showed well-defined SiC and turbostratic carbon features. The cross-sectional mapping of the printed-then-pyrolyzed structures has confirmed consistent structural and chemical features within the internal layers of the AM parts. Noteworthy, however, is that a crust-like area with high crystallinity has been observed in the first and last external layers. Not only do these crust-like regions have structural characteristics distinct from the internal layers, but they also have elemental distributions different than the internal layers.

Keywords: SiC, preceramic polymer, additive manufacturing, ceramic

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

Abstract:

Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Keywords: discriminative LMA features, features reduction, human motion recognition, random forest

Procedia PDF Downloads 171

Authors: Z. Masoumi, B. Moaveni

Abstract:

This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied.

Keywords: Kalman filter (KF), least square estimation (LSE), structural health monitoring (SHM), structural system identification

Procedia PDF Downloads 290

Authors: Khadra Ahmed, Khaled Kheireldin

Abstract:

In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: planktons, diversity indices, water quality index, water ways

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Authors: Rims Janeliukstis, Deniss Mironovs, Andrejs Kovalovs

Abstract:

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

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Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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Authors: N. Benkhettoua, Y. Barkata

Abstract:

We present first-principles studies of structural and thermodynamic properties of MnNi According to the calculated total energies, by using an all-electron full-potential linear muffin–tin orbital method (FP-LMTO) within LDA and the quasi-harmonic Debye model implemented in the Gibbs program is used for the temperature effect on structural and calorific properties.

Keywords: magnetic materials, structural properties, thermodynamic properties, metallurgical and materials engineering

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Authors: Selda Uzal Seyfi

Abstract:

Goat milk is a quite important in human nutrition. In order to meet the demand to the goat and sheep milk occurring in the recent years, an increase is seen in the demand to housing projects, which will enable animals to be sheltered in the suitable environments. This study was carried out in between 2012 and 2013, in order to identify the existing cases of sheep and goat housings in the province Konya and their possibilities to be developed. In the study, in the province Konya, 25 pieces of sheep and goat farms and 46 pieces of sheep and goat housings (14 sheep housings, 3 goat housings, and 29 housings, in which both sheep and goat are bred ) that are present in the farm were investigated as material. In the study, examining the general features of the farms that are present in the region and structural features of housings that are present in the farms, it is studied whether or not they are suitable for animal breeding. As a result of the study, the barns were evaluated as insufficient in terms of barn design, although 48% of they were built after 2000. In 63% of housings examined, stocking density of resting area was below the value of 1 m2/animal and in 59% of the housings, stocking density of courtyard area was below the 2 m2/animal. Feeding length, in 57% of housings has a value of 0.30 m and below. In the region, it will be possible to obtain the desired productivity level by building new barn designs, developed in accordance with the animal behaviors and welfare. Carrying out the necessary works is an important issue in terms of country and regional economy.

Keywords: barn design, goat housing, sheep housing, structural analysis

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Authors: Yeseul Park

Abstract:

Biopolymers produced by organisms highly contribute to the production of metal sulfides, both in extracellular and intracellular biomineralization. We discovered a new type of intracellular biomineral composed of copper sulfide in the periplasm of a sulfate-reducing bacterium. We suggest that the structural features of biomineral composed of 1-2 nm subgrains are based on biopolymer-based capping agents and an organic compartment. We further compare with other types of metal sulfide biominerals.

Keywords: biomineralization, copper sulfide, metal sulfide, biopolymer, capping agent

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Authors: Mahsa Falahatpour

Abstract:

Introduction: This study aims to explore how inter-observer variability in manual tumor segmentation impacts the reliability of radiomic features in non–small cell lung cancer (NSCLC). Methods: The study included twenty-three NSCLC tumors. Each patient had three tumor segmentations (VOL1, VOL2, VOL3) contoured on PET/CT scans by three radiation oncologists. Dice coefficients (DCS) were used to measure the segmentation variability. Radiomic features were extracted with 3D-slicer software, consisting of 66 features: first-order (n=15), second-order (GLCM, GLDM, GLRLM, and GLSZM) (n=33). The inter-observer variability of radiomic features was assessed using the intraclass correlation coefficient (ICC). An ICC > 0.8 indicates good stability. Results: The mean DSC of VOL1, VOL2, and VOL3 was 0.80 ± 0.04, 0.85 ± 0.03, and 0.76 ± 0.06, respectively. 92% of all extracted radiomic features were found to be stable (ICC > 0.8). The GLCM texture features had the highest stability (96%), followed by GLRLM features (90%) and GLSZM features (87%). The DSC was found to be highly correlated with the stability of radiomic features. Conclusion: The variability in inter-observer segmentation significantly impacts radiomics analysis, leading to a reduction in the number of appropriate radiomic features.

Keywords: PET/CT, radiomics, radiotherapy, segmentation, NSCLC

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Authors: Ruichong Zhang, Fadi Sawaged, Lotfi Gargab

Abstract:

This paper introduces a wave-based approach for system identification of high-rise building structures with a pair of seismic recordings, which can be used to evaluate structural integrity and detect damage in post-earthquake structural condition assessment. The fundamental of the approach is based on wave features of generalized impulse and frequency response functions (GIRF and GFRF), i.e., wave responses at one structural location to an impulsive motion at another reference location in time and frequency domains respectively. With a pair of seismic recordings at the two locations, GFRF is obtainable as Fourier spectral ratio of the two recordings, and GIRF is then found with the inverse Fourier transformation of GFRF. With an appropriate continuous model for the structure, a closed-form solution of GFRF, and subsequent GIRF, can also be found in terms of wave transmission and reflection coefficients, which are related to structural physical properties above the impulse location. Matching the two sets of GFRF and/or GIRF from recordings and the model helps identify structural parameters such as wave velocity or shear modulus. For illustration, this study examines ten-story Millikan Library in Pasadena, California with recordings of Yorba Linda earthquake of September 3, 2002. The building is modelled as piecewise continuous layers, with which GFRF is derived as function of such building parameters as impedance, cross-sectional area, and damping. GIRF can then be found in closed form for some special cases and numerically in general. Not only does this study reveal the influential factors of building parameters in wave features of GIRF and GRFR, it also shows some system-identification results, which are consistent with other vibration- and wave-based results. Finally, this paper discusses the effectiveness of the proposed model in system identification.

Keywords: wave-based approach, seismic responses of buildings, wave propagation in structures, construction

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Gianni Blasi, Daniele Perrone, Maria Antonietta Aiello

Abstract:

The seismic vulnerability of non-structural components is nowadays recognized to be a key issue in performance-based earthquake engineering. Recent loss estimation studies, as well as the damage observed during past earthquakes, evidenced how non-structural damage represents the highest rate of economic loss in a building and can be in many cases crucial in a life-safety view during the post-earthquake emergency. The procedures developed to evaluate the seismic demand on non-structural components have been constantly improved and recent studies demonstrated how the existing formulations provided by main Standards generally ignore features which have a sensible influence on the definition of the seismic acceleration/displacements subjecting non-structural components. Since the influence of the infills on the dynamic behaviour of RC structures has already been evidenced by many authors, it is worth to be noted that the evaluation of the seismic demand on non-structural components should consider the presence of the infills as well as their mechanical properties. This study focuses on the evaluation of time-history floor acceleration in RC buildings; which is a useful mean to perform seismic vulnerability analyses of non-structural components through the well-known cascade method. Dynamic analyses are performed on an 8-storey RC frame, taking into account the presence of the infills; the influence of the elastic modulus of the panel on the results is investigated as well as the presence of openings. Floor accelerations obtained from the analyses are used to evaluate the floor response spectra, in order to define the demand on non-structural components depending on the properties of the infills. Finally, the results are compared with formulations provided by main International Standards, in order to assess the accuracy and eventually define the improvements required according to the results of the present research work.

Keywords: floor spectra, infilled RC frames, non-structural components, seismic demand

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Authors: S. Perera, T. R. Walsh, M. Solvang

Abstract:

The structure-property relationships of calcium aluminosilicate (CAS) glass surfaces are of scientific and technological interest regarding dissolution phenomena. Molecular dynamics (MD) simulations can provide atomic-scale insights into the structure and properties of the CAS interfaces in vacuo as the first step to conducting computational dissolution studies on CAS surfaces. However, one limitation to date is that although the bulk properties of CAS glasses have been well studied by MD simulation, corresponding efforts on CAS surface properties are relatively few in number (both theoretical and experimental). Here, a systematic computational protocol to create CAS surfaces in vacuo is developed by evaluating the sensitivity of the resultant surface structure with respect to different factors. Factors such as the relative thickness of the surface layer, the relative thickness of the bulk region, the cooling rate, and the annealing schedule (time and temperature) are explored. Structural features such as ring size distribution, defect concentrations (five-coordinated aluminium (AlV), non-bridging oxygen (NBO), and tri-cluster oxygen (TBO)), and linkage distribution are identified as significant features in dissolution studies.

Keywords: MD simulation, CAS glasses, surface structure, structure-property, CAS interface

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Authors: Baohua Yu

Abstract:

Despite widely reported 'Mainland-Hong Kong conflicts', recent years have witnessed progressive growth in the numbers of Mainland Chinese students in Hong Kong’s universities. This research investigated Mainland Chinese students’ intercultural communication in relation to cross-cultural adaptation in a major university in Hong Kong. The features of intercultural communication examined in this study were competence in the second language (L2) communication and L2 Willingness to Communicate (WTC), while the features of cross-cultural adaptation examined were socio-cultural, psychological and academic adaptation. Based on a questionnaire, structural equation modelling was conducted among a sample of 196 Mainland Chinese students. Results showed that the competence in L2 communication played a significant role in L2 WTC, which had an influential effect on academic adaptation, which was itself identified as a mediator between the psychological adaptation and socio-cultural adaptation. Implications for curriculum design for courses and instructional practice on international students are discussed.

Keywords: L2 willingness to communicate, competence in L2 communication, psychological adaptation, socio-cultural adaptation, academic adaptation, structural equation modelling

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Authors: Skiker Kaoutar, Mounir Maouene

Abstract:

Neuroimaging studies indicate that a large fronto-parieto-temporal network support nouns and their features, with some areas store semantic knowledge (visual, auditory, olfactory, gustatory,…), other areas store lexical representation and other areas are implicated in general semantic processing. However, it is not well understood how this fronto-parieto-temporal network can be modulated by different semantic tasks and different semantic relations between nouns. In this study, we combine a behavioral semantic network, functional MRI studies involving object’s related nouns and brain network studies to explain how different semantic tasks and different semantic relations between nouns can modulate the activity within the brain network of nouns and their features. We first describe how nouns and their features form a large scale brain network. For this end, we examine the connectivities between areas recruited during the processing of nouns to know which configurations of interaction areas are possible. We can thus identify if, for example, brain areas that store semantic knowledge communicate via functional/structural links with areas that store lexical representations. Second, we examine how this network is modulated by different semantic tasks involving nouns and finally, we examine how category specific activation may result from the semantic relations among nouns. The results indicate that brain network of nouns and their features is highly modulated and flexible by different semantic tasks and semantic relations. At the end, this study can be used as a guide to help neurosientifics to interpret the pattern of fMRI activations detected in the semantic processing of nouns. Specifically; this study can help to interpret the category specific activations observed extensively in a large number of neuroimaging studies and clinical studies.

Keywords: nouns, features, network, category specificity

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Authors: Meenal Badki

Abstract:

We address the issue of active feature determination, where the objective is to determine the set of examples on which additional data (such as lab tests) needs to be gathered, given a large number of examples with some features (such as demographics) and some examples with all the features (such as the complete Electronic Health Record). We note that certain features may be more costly, unique, or laborious to gather. Our proposal is a general active learning approach that is independent of classifiers and similarity metrics. It allows us to identify examples that differ from the full data set and obtain all the features for the examples that match. Our comprehensive evaluation shows the efficacy of this approach, which is driven by four authentic clinical tasks.

Keywords: feature determination, classification, active learning, sample-efficiency

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

Abstract:

Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Man Singh Meena

Abstract:

Structural engineers are facing different kind of challenges due to innovative & bold ideas of architects who are trying to design every structure with uniqueness. In RCC frame structures different geometry of columns can be used in design and rectangular columns can be placed with different type orientation. The analysis is design of structures can also be carried out by different type of software available i.e., STAAD Pro, ETABS and TEKLA. In recent times high-rise building modeling & analysis is done by ETABS due to its certain features which are superior to other software. The case study in this paper mainly emphasizes on structural behavior of high rise building for different column shape configurations like Circular, Square, Rectangular and Rectangular with 90-degree Rotation and rectangular shape plan. In all these column shapes the areas of columns are kept same to study the effect on design of concrete area is same. Modelling of 20-storeys R.C.C. framed building is done on the ETABS software for analysis. Post analysis of the structure, maximum bending moments, shear forces and maximum longitudinal reinforcement are computed and compared for three different story structures to identify the effectiveness of geometry of column.

Keywords: high-rise building, column geometry, building modelling, ETABS analysis, building design, structural analysis, structural optimization

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Authors: Danilo Habermann, Aleksander Medella, Carla Cremon, Yusef Caceres

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This paper aims to analyze the ability of 2d point clouds features to classify different models of helicopters using radars. This method does not need to estimate the blade length, the number of blades of helicopters, and the period of their micro-Doppler signatures. It is also not necessary to generate spectrograms (or any other image based on time and frequency domain). This work transforms a radar return signal into a 2D point cloud and extracts features of it. Three classifiers are used to distinguish 9 different helicopter models in order to analyze the performance of the features used in this work. The high accuracy obtained with each of the classifiers demonstrates that the 2D point clouds features are very useful for classifying helicopters from radar signal.

Keywords: helicopter classification, point clouds features, radar, supervised classifiers

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Authors: T. Hari Prasath, P. Ithaya Rani

Abstract:

In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods.

Keywords: detecting face, Gabor filter, multi-class AdaBoost classifier, Z-score normalization

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Authors: Michael Zimba

Abstract:

A novel set of features for copy-move image forgery, CMIF, detection method is proposed. The proposed set presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilised to extract robust features. The extracted features are shown to be invariant to additive noise, JPEG compression, and affine transformation. The proposed features can also be used in general object matching.

Keywords: virtual electrostatic field, features, affine transformation, copy-move image forgery

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Authors: Sireen Irsheid, Stephanie Keeney Parks, Michael A. Lindsey

Abstract:

The medical and mental health field have been organized as reactive systems to respond to symptoms of mental health problems and disability. This becomes problematic particularly for those harmed by structural violence and racism, typically pushing us in the direction of alleviating symptoms and personalizing structural problems. The current paper examines how we assess, diagnose, and treat mental health and disability challenges in clinical spaces. We provide the readers with some context to think about the problem of racism and mental health/disability, ways to deconstruct the problem through the lens of structural violence, and recommendations to critically engage in clinical assessments, diagnosis, and treatment for young people impacted by structural violence and racism.

Keywords: mental health, disability, race and ethnicity, structural violence, structural racism, young people

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Authors: Roman Kalvin, Anam Nadeem, Saba Arif

Abstract:

Turbocharger is a device that is driven by the turbine and increases efficiency and power output of the engine by forcing external air into the combustion chamber. This study focused on the distribution of stress on the turbine blades and total deformation that may occur during its working along with turbocharger to carry out its static structural analysis of turbine blades. Structural steel was selected as the material for turbocharger. Assembly of turbocharger and turbine blades was designed on PRO ENGINEER. Furthermore, the structural analysis is performed by using ANSYS. This research concluded that by using structural steel, the efficiency of engine is improved and by increasing number of turbine blades, more waste heat from combustion chamber is emitted.

Keywords: turbocharger, turbine blades, structural steel, ANSYS

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

Abstract:

This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Amjad Naveed

Abstract:

This study analysed the role of structural change in the process of labour productivity convergence at country and regional levels. Many forms of structural changes occurred within the European Union (EU) countries i.e. variation in sectoral employment share, changes in demand for products, variations in trade patterns and advancement in technology which may have an influence on the process of convergence. Earlier studies on convergence have neglected the role of structural changes which can have resulted in different conclusion on the nature of convergence. The contribution of this study is to examine the role of structural change in testing labour productivity convergence at various levels. For the empirical purpose, the data of 19 EU countries, 259 regions and 6 industries is used for the period of 1991-2009. The results indicate that convergence varies across regional and country levels for different industries when considered the role of structural change.

Keywords: labor produvitivty, convergence, structural change, panel unit root

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