Search results for: ground truth for supervised learning

Authors: Eleonora Di Mario

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This paper describes the application of ground improvement to replace a typical piled foundation scheme in a power plant in Singapore. Several buildings within the plant were founded on vibro-compacted sand, including a turbine unit which had extremely stringent requirements on the allowable settlement. The achieved savings in terms of cost and schedule are presented. The monitoring data collected during the operation of the turbine are compared to the design predictions to validate the design approach, and the quality of the ground improvement works. In addition, the calculated carbon footprint of the ground improvement works are compared to the piled solution, showing that the vibro-compaction has a significantly lower carbon footprint.

Keywords: ground improvement, vibro-compaction, case study, sustainability, carbon footprint

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Authors: Edward Schwalb, Matthias Dehmer, Michael Schlenkrich, Farzaneh Taslimi, Ketron Mitchell-Wynne, Horen Kuecuekyan

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We present a generalized feature extraction approach for supporting Machine Learning (ML) algorithms which perform tasks similar to Finite-Element Analysis (FEA). We report results for estimating the Head Injury Categorization (HIC) of vehicle engine compartments across various impact scenarios. Our experiments demonstrate that models learned using features derived with a simple discretization approach provide a reasonable approximation of a full simulation. We observe that Decision Trees could be as effective as Neural Networks for the HIC task. The simplicity and performance of the learned Decision Trees could offer a trade-off of a multiple order of magnitude increase in speed and cost improvement over full simulation for a reasonable approximation. When used as a complement to full simulation, the approach enables rapid approximate feedback to engineering teams before submission for full analysis. The approach produces mesh independent features and is further agnostic of the assembly structure.

Keywords: mechanical design validation, FEA, supervised decision tree, convolutional neural network.

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Authors: Mehar Fatima

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This study intends to make analysis of Emotional Intelligence (EI) in the process of pedagogy and look into its repercussions in different educational institutions including school, college, and university in the capital state of India, Delhi in 2015. Field of education is a complex area with challenging issues in a modern society. Education is the breeding ground for nurturing human souls, and personalities. Since antiquity, man has been in search of truth, wisdom, contentment, peace. His efforts have brought him to acquire these through hardship, evidently through the process of teaching and learning. Computer aids and artificial intelligence have made life easy but complex. Efficient pedagogy involves direct human intervention despite the flux of technological advancements. Time and again, pedagogical practices demand sincere human efforts to understand and improve upon life’s many pragmatic concerns. Apart from the intense academic scientific approaches, EI in academia plays a vital role in the growth of education, positively achieving national progression; ‘pedagogy of pragmatic purpose.’ Use of literature is found to be one of the valuable pragmatic tools of Emotional Intelligence. This research examines the way literature provides useful influence in building better practices in teaching-learning process. The present project also scrutinizes various pieces of world literature and translation, incorporating efforts of intellectuals in promoting comprehensive amity. The importance of EI in educational arena with its pragmatic uses was established by the study of interviews, and questionnaire collected from teachers and students. In summary the analysis of obtained empirical data makes it possible to accomplish that the use Emotional Intelligence in academic scenario yields multisided positive pragmatic outcomes; positive attitude, constructive aptitude, value-added learning, enthusiastic participation, creative thinking, lower apprehension, diminished fear, leading to individual as well as collective advancement, progress, and growth of pedagogical agents.

Keywords: emotional intelligence, human efforts, pedagogy, pragmatic concerns

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Authors: Mikheil Kalmakhelidze

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Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

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Authors: Maha Wiss, Wael Khreich

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Animals, objects, foods, plants, and other non-human terms are commonly used as a source of metaphors to describe females in formal and slang language. Comparing women to non-human items not only reflects cultural views that might conceptualize women as subordinates or in a lower position than humans, yet it conveys this degradation to the listeners. Moreover, the dehumanizing representation of females in the language normalizes the derogation and even encourages sexism and aggressiveness against women. Although dehumanization has been a popular research topic for decades, according to our knowledge, no studies have linked women's dehumanizing language to the machine learning field. Therefore, we introduce our research work as one of the first attempts to create a tool for the automated detection of the dehumanizing depiction of females in English texts. We also present the first labeled dataset on the charted topic, which is used for training supervised machine learning algorithms to build an accurate classification model. The importance of this work is that it accomplishes the first step toward mitigating dehumanizing language against females.

Keywords: gender bias, machine learning, NLP, women dehumanization

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Authors: Tonguc Basaran

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History is a record of a people’s past based on a critical examination of documents and other facts. The essentials of this historical method are not beyond the grasp of even young children. Concrete examples, such as the story of the Rosetta stone, which enabled Champollion to establish the first principles of the deciphering of Egyptian hieroglyphics, vividly illustrate the fundamental processes involved. This search for the facts can be used to illustrate one side of the search for historic truth. The other side is the truth of historic interpretation. The facts cannot be changed, but the interpretation of them can and does change.

Keywords: history, primary education, teaching methods, social studies

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Authors: Konstantinos Sofianos, Michael Stefanidakis

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Nowadays, advances in information and communication technologies offer a range of opportunities for new approaches, methods, and tools in the field of education and training. Teacher-centered learning has changed to student-centered learning. E-learning has now matured and enables the design and construction of intelligent learning systems. A smart learning system fully adapts to a student's needs and provides them with an education based on their preferences, learning styles, and learning backgrounds. It is a wise friend and available at any time, in any place, and with any digital device. In this paper, we propose an intelligent learning system, which includes an ontology with all elements of the learning process (learning objects, learning activities) and a massive open online course (MOOC) system. This intelligent learning system can be used in music education.

Keywords: intelligent learning systems, e-learning, music education, ontology, semantic web

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: O. Giorgishvili

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For calculation of foundations one of the important characteristics is the module of deformation (E0). As we all know, the main goal of calculation of the foundations of buildings on deformation is to arrange the base settling and difference in settlings in such limits that do not cause origination of cracks and changes in design levels that will be dangerous to standard operation in the buildings and their individual structures. As is known from the literature and the practical application, the modulus of deformation is determined by two basic methods: laboratory method, soil test on compression (without the side widening) and soil test in field conditions. As we know, the deformation modulus of soil determined by field method is closer to the actual modulus deformation of soil, but the complexity of the tests to be carried out and the financial concerns did not allow determination of ground deformation modulus by field method. Therefore, we determine the ground modulus of deformation by compression method without side widening. Concerning this, we introduce a new way for determination of ground modulus of deformation by laboratory order that occurs by side widening and more accurately reflects the ground modulus of deformation and more accurately reflects the actual modulus of deformation and closer to the modulus of deformation determined by the field method. In this regard, we bring a new approach on the ground deformation detection laboratory module, which is done by widening sides. The tests and the results showed that the proposed method of ground deformation modulus is closer to the results that are obtained in the field, which reflects the foundation's work in real terms more accurately than the compression of the ground deformation module.

Keywords: build, deformation modulus, foundations, ground, laboratory research

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Authors: O. Abusaeeda, J. P. O. Evans, D. Downes

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We demonstrate the synthesis of intermediary views within a sequence of X-ray images that exhibit depth from motion or kinetic depth effect in a visual display. Each synthetic image replaces the requirement for a linear X-ray detector array during the image acquisition process. Scale invariant feature transform, SIFT, in combination with epipolar morphing is employed to produce synthetic imagery. Comparison between synthetic and ground truth images is reported to quantify the performance of the approach. Our work is a key aspect in the development of a 3D imaging modality for the screening of luggage at airport checkpoints. This programme of research is in collaboration with the UK Home Office and the US Dept. of Homeland Security.

Keywords: X-ray, kinetic depth, KDE, view synthesis

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

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Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Rohit Mittal, Bright Keswani, Amit Mithal

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This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

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Authors: Meera Rajeev Kumar

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An overall social, emotional, and cultural development in a child is what a parent expects. There is no point in comparing the generations of 70’s or 80’s with that of the children of today as the trends are changing drastically. Technology has enabled them to become smart as well as over smart in one way or the other. Children today are quite ignorant of today’s values or ethics and are imbibing different cultures around them and ultimately confused on what to follow. The researcher has gained experience in transmitting or imparting the traditional culture through performing arts. It is understood that the children undergo a transformation from what they knew to what the truth is, through learning and experience. Through performing arts, the child develops an emotional, quick learning, abundant creativity, and ultimately self-realisation on what is right and wrong. The child also gains good organising skills, good decision making skills, therefore summing up to a holistic development. The sample study is 50, and a random sampling technique is adopted to differentiate between a normal child and a child learning an art. The study is conducted in Tamil Nadu, in India.

Keywords: creativity, cultural, emotional, empower

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Authors: Romina Beqiri

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In the context of the prosecution of those responsible for the commission of the most hideous crimes and the fight against impunity, a fundamental role is played by witnesses of the crimes who contribute to ascertaining the ‘procedural truth’. This article examines recent decisions and legislation of the Hague-based International Criminal Court in terms of the endangerment of the integrity of the criminal proceedings in consequence of witness tampering. The analysis focuses on the new developments in the courtroom and the academia, in particular, on the first-ever sentence confirming the charges of corruptly influencing witnesses, interpretation of presenting false evidence and giving false testimony when under an obligation to tell the truth. Confronted with recent tampering with witnesses and their credibility at stake in the ongoing cases, the research explores different Court’s decisions and scholars’ legal disputes concerning the deterrence approach to punish the authors of offences against the administration of justice when committed intentionally. Therefore, the analysis concludes that the Court cannot tolerate any witness false testimony and should enhance consistency and severity of sanctions for the sake of fair trial and end impunity.

Keywords: International Criminal Court, administration of justice, credibility of witness, fair trial, false testimony, witness tampering

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Authors: Sandro Samir Nasief, Hussein Hamed Ghouz, Mohamed Fathy

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A compact ultra-wide band micro-strip MIMO antenna is introduced. The antenna consists of two elements each of size 24X24 mm2 (square millimetre) while the total MIMO size is 58X24 mm2 after the spacing between MIMO elements and adding a decouple circuit. The first one covers from 3.29 to 6.9 GHZ using digital ground and the second antenna covers from 8.76 to 13.27 GHZ using defective ground. This type of antenna is used for 3G and 4G applications. The introduction for the antenna structure and the parametric study (reflection coefficients, gain, coupling and decoupling) will be introduced.

Keywords: micro-strip antenna, MIMO, digital ground, defective ground, decouple circuit, bandwidth

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Lihong Wu, Raymond Young

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The ability to learn is one of the most critical skills in the information age. However, many students do not have a clear understanding of what learning is, what they are learning, and why they are learning. Many students study simply to pass rather than to learn something useful for their career and their life. They have a misconception about learning and a wrong attitude towards learning. This research explores student attitudes to study in management education and explores how to intercede to lead students from shallow to deeper modes of learning.

Keywords: knowledge, surface learning, deep learning, education

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Authors: Patrick Duffley

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Hanks (2021) identifies three Frege-inspired commitments concerning propositions that are widely shared across the philosophy of language: (1) propositions are the primary, inherent bearers of representational properties and truth-conditions; (2) propositions are neutral representations possessing a ‘content’ that is devoid of ‘force; (3) propositions can be entertained or expressed without being asserted. Hanks then argues that the postulate of neutral content must be abandoned, and the primary bearers of truth-evaluable representation must be identified as the token acts of assertoric predication that people perform when they are thinking or speaking about the world. Propositions are ‘types of acts of predication, which derive their representational features from their tokens.’ Their role is that of ‘classificatory devices that we use for the purposes of identifying and individuating mental states and speech acts,’ so that ‘to say that Russell believes that Mont Blanc is over 4000 meters high is to classify Russell’s mental state under a certain type, and thereby distinguish that mental state from others that Russell might possess.’ It is argued in this paper that there is no need to classify an utterance of 'Russell believes that Mont Blanc is over 4000 meters high' as a token of some higher-order utterance-type in order to identify what Russell believes; the meanings of the words themselves and the syntactico-semantic relations between them are sufficient. In our view what Hanks has accomplished in effect is to build a convincing argument for dispensing with propositions completely in the philosophy of language. By divesting propositions of the role of being the primary bearers of representational properties and truth-conditions and fittingly transferring this role to the token acts of predication that people perform when they are thinking or speaking about the world, he has situated truth in its proper place and obviated any need for abstractions like propositions to explain how language can express things that are true. This leaves propositions with the extremely modest role of classifying mental states and speech acts for the purposes of identifying and individuating them. It is demonstrated here however that there is no need whatsoever to posit such abstract entities to explain how people identify and individuate such states/acts. We therefore make the modest proposal that the term ‘proposition’ be stricken from the vocabulary of philosophers of language.

Keywords: propositions, truth-conditions, predication, Frege, truth-bearers

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Authors: Xia Chen, Hua-Quan Yang, Shi-Hua Zhou

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Alkali-active sandstone aggregate was ground by vertical and ball mill into particles with residue over 45 μm less than 12%, and investigations have been launched on particles distribution and characterization of ground sandstone powder, fluidity, heat of hydration, strength as well as hydration products morphology of pastes with incorporation of ground sandstone powder. Results indicated that ground alkali-active sandstone powder with residue over 45 μm less than 8% was easily obtainable, and specific surface area was more sensitive to characterize its fineness with extension of grinding length. Incorporation of sandstone powder resulted in higher water demand and lower strength, advanced hydration of C₃A and C₂S within 3days and refined pore structure. Based on its manufacturing, characteristics and influence on properties of pastes, it was concluded that sandstone powder was a good selection for use in concrete as mineral admixture.

Keywords: concrete, mineral admixture, hydration, structure

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Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

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Authors: P. S. Thein, S. Pramumijoyo, K. S. Brotopuspito, J. Kiyono, W. Wilopo, A. Furukawa, A. Setianto

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In this study, we estimated the seismic ground motion parameters based on microtremor measurements at Palu City. Several earthquakes have struck along the Palu-Koro Fault during recent years. The USGS epicenter, magnitude Mw 6.3 event that occurred on January 23, 2005 caused several casualties. We conducted a microtremor survey to estimate the strong ground motion distribution during the earthquake. From this survey we produced a map of the peak ground acceleration, velocity, seismic vulnerability index and ground shear strain maps in Palu City. We performed single observations of microtremor at 151 sites in Palu City. We also conducted 8-site microtremors array investigation to gain a representative determination of the soil condition of subsurface structures in Palu City. From the array observations, Palu City corresponds to relatively soil condition with Vs ≤ 300 m/s, the predominant periods due to horizontal vertical ratios (HVSRs) are in the range of 0.4 to 1.8 s and the frequency are in the range of 0.7 to 3.3 Hz. Strong ground motions of the Palu area were predicted based on the empirical stochastic green’s function method. Peak ground acceleration and velocity becomes more than 400 gal and 30 kine in some areas, which causes severe damage for buildings in high probability. Microtremor survey results showed that in hilly areas had low seismic vulnerability index and ground shear strain, whereas in coastal alluvium was composed of material having a high seismic vulnerability and ground shear strain indication.

Keywords: Palu-Koro fault, microtremor, peak ground acceleration, peak ground velocity, seismic vulnerability index

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Mohamed Elassaly

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The damage status of RC buildings is greatly influenced by the characteristics of the imposed ground motion. Peak Ground Acceleration and frequency contents are considered the main two factors that affect ground motion characteristics; hence, affecting the seismic response of RC structures and consequently their damage state. A detailed investigation on the combined effects of these two factors on damage assessment of RC buildings, is carried out. Twenty one earthquake records are analyzed and arranged into three groups, according to their frequency contents. These records are used in an investigation to define the expected damage state that would be attained by RC buildings, if subjected to varying ground motion characteristics. The damage assessment is conducted through examining drift ratios and damage indices of the overall structure and the significant structural components of RC building. Base and story shear of RC building model, are also investigated, for cases when the model is subjected to the chosen twenty one earthquake records. Nonlinear dynamic analyses are performed on a 2-dimensional model of a 12-story R.C. building.

Keywords: damage, frequency content, ground motion, PGA, RC building, seismic

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Authors: Farhad Sedaghati, Shahram Pezeshk

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Regional variations in strong ground motions for the Iranian Plateau have been investigated by using a simple statistical method called Analysis of Variance (ANOVA). In this respect, a large database consisting of 1157 records occurring within the Iranian Plateau with moment magnitudes of greater than or equal to 5 and Joyner-Boore distances up to 200 km has been considered. Geometric averages of horizontal peak ground accelerations (PGA) as well as 5% damped linear elastic response spectral accelerations (SA) at periods of 0.2, 0.5, 1.0, and 2.0 sec are used as strong motion parameters. The initial database is divided into two different datasets, for Northern Iran (NI) and Central and Southern Iran (CSI). The comparison between strong ground motions of these two regions reveals that there is no evidence for significant differences; therefore, data from these two regions may be combined to estimate the unknown coefficients of attenuation relationships.

Keywords: ANOVA, attenuation relationships, Iranian Plateau, PGA, regional variation, SA, strong ground motion

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Authors: Masoud Mahdavi

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The use of sloping lands is increasing due to the reduction of suitable lands for the construction of buildings. In the design and construction of buildings on sloping lands, the foundation has special loading conditions that require the designer and executor to use the slopped foundation. The creation of underground cavities, including urban and subway tunnels, sewers, urban facilities, etc., inside the ground, causes the behavior of the foundation to be unknown. In the present study, using Abacus software, a 45-degree stepped foundation on the ground is designed. The foundations are placed on the ground in a cohesive (no-hole) manner with circular cavities that show the effect of increasing the cross-sectional area of ​​the underground cavities on the foundation's performance. The Kobe earthquake struck the foundation and ground for two seconds. The underground cavities have a circular cross-sectional area with a radius of 5 m, which is located at a depth of 22.54 m above the ground. The results showed that as the number of underground cavities increased, von Mises stress (in the vertical direction) increased. With the increase in the number of underground cavities, the plastic strain on the ground has increased. Also, with the increase in the number of underground cavities, the change in location and speed in the foundation has increased.

Keywords: stepped foundation, sloping ground, Kobe earthquake, Abaqus software, underground excavations

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Lee Bih Ni

Abstract:

This paper discusses that good learning involves all academic groups in the school. Blended learning is learning outside the classroom. Google Classroom is a free service learning app for schools, non-profit organizations and anyone with a personal Google account. Facilities accessed through computers and mobile phones are very useful for school teachers and students. Blended learning classrooms using both traditional and technology-based methods for teaching have become the norm for many educators. Using Google Classroom gives students access to online learning. Even if the teacher is not in the classroom, the teacher can provide learning. This is the supervision of the form of the teacher when the student is outside the school.

Keywords: blended learning, learning app, google classroom, schools

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Authors: Andre Wittenborn, Jarek Krajewski

Abstract:

Trauma-related sadness in speech can alter the voice in several ways. The generation of non-linear aerodynamic phenomena within the vocal tract is crucial when analyzing trauma-influenced speech production. They include non-laminar flow and formation of jets rather than well-behaved laminar flow aspects. Especially state-space reconstruction methods based on chaotic dynamics and fractal theory have been suggested to describe these aerodynamic turbulence-related phenomena of the speech production system. To extract the non-linear properties of the speech signal, we used the time delay embedding method to reconstruct from a scalar time series (reconstructed phase space, RPS). This approach results in the extraction of 7238 Features per .wav file (N= 47, 32 m, 15 f). The speech material was prompted by telling about autobiographical related sadness-inducing experiences (sampling rate 16 kHz, 8-bit resolution). After combining these features in a support vector machine based machine learning approach (leave-one-sample out validation), we achieved a correlation of r = .41 with the well-established, self-report ground truth measure (RATS) of post-traumatic stress disorder (PTSD).

Keywords: non-linear dynamics features, post traumatic stress disorder, reconstructed phase space, support vector machine

Procedia PDF Downloads 79

Authors: Entfellner Manuel, Wannenmacher Helmut, Reisenbauer Josef, Schubert Wulf

Abstract:

When tunnelling in overstressed weak rocks ("squeezing ground"), two basic design approaches are available: the resistance principle, and the yielding principle. The resistance principle relies on rigid support systems to withstand the ground pressure. Alternatively, the yielding principle prioritizes controlled deformation, allowing the ground to deform without compromising tunnel integrity. This paper highlights the beneficial factors of the yielding principle for conventionally excavated tunnels in overstressed weak rocks. Especially the application of a ductile shotcrete lining with yielding elements is analysed in detail. Construction costs, safety, short- and long-term stabilities are discussed.

Keywords: squeezing ground, yielding principle, yielding element, conventional tunneling

Procedia PDF Downloads 39