Search results for: rapid compression machine

Authors: Martin Alexander Eder, Sergei Semenov

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Many glass-epoxy composite structures, such as large utility wind turbine rotor blades (WTBs), comprise of adhesive joints with typically thick bond lines used to connect the different components during assembly. Performance optimization of rotor blades to increase power output by simultaneously maintaining high stiffness-to-low-mass ratios entails intricate geometries in conjunction with complex anisotropic material behavior. Consequently, adhesive joints in WTBs are subject to multiaxial stress states with significant stress gradients depending on the local joint geometry. Moreover, the dynamic aero-elastic interaction of the WTB with the airflow generates non-proportional, variable amplitude stress histories in the material. Empiricism shows that a prominent failure type in WTBs is high cycle fatigue failure of adhesive bond line interfaces, which in fact over time developed into a design driver as WTB sizes increase rapidly. Structural optimization employed at an early design stage, therefore, sets high demands on computationally efficient interface fatigue models capable of predicting the critical locations prone for interface failure. The numerical stress-based interface fatigue model presented in this work uses the Drucker-Prager criterion to compute three different damage indices corresponding to the two interface shear tractions and the outward normal traction. The two-parameter Drucker-Prager model was chosen because of its ability to consider shear strength enhancement under compression and shear strength reduction under tension. The governing interface damage index is taken as the maximum of the triple. The damage indices are computed through the well-known linear Palmgren-Miner rule after separate rain flow-counting of the equivalent shear stress history and the equivalent pure normal stress history. The equivalent stress signals are obtained by self-similar scaling of the Drucker-Prager surface whose shape is defined by the uniaxial tensile strength and the shear strength such that it intersects with the stress point at every time step. This approach implicitly assumes that the damage caused by the prevailing multiaxial stress state is the same as the damage caused by an amplified equivalent uniaxial stress state in the three interface directions. The model was implemented as Python plug-in for the commercially available finite element code Abaqus for its use with solid elements. The model was used to predict the interface damage of an adhesively bonded, tapered glass-epoxy composite cantilever I-beam tested by LM Wind Power under constant amplitude compression-compression tip load in the high cycle fatigue regime. Results show that the model was able to predict the location of debonding in the adhesive interface between the webfoot and the cap. Moreover, with a set of two different constant life diagrams namely in shear and tension, it was possible to predict both the fatigue lifetime and the failure mode of the sub-component with reasonable accuracy. It can be concluded that the fidelity, robustness and computational efficiency of the proposed model make it especially suitable for rapid fatigue damage screening of large 3D finite element models subject to complex dynamic load histories.

Keywords: adhesive, fatigue, interface, multiaxial stress

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Authors: Minho Kwon, Jeonghee Lim, Yeongseok Jeong, Jongyoon Moon, Donghoon Shin, Kiyoung Kim

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In the past, the criteria and procedures for the design of concrete structures were mainly based on the stresses allowed for structural components. However, although the frequency of earthquakes has increased and the risk has increased recently, it has been difficult to determine the safety factor for earthquakes in the safety assessment of structures based on allowable stresses. Recently, limit state design method has been introduced for reinforced concrete structures, and limit state-based approach has been recognized as a more effective technique for seismic design. Therefore, in this study, the limit state of the bridge, which is a structure requiring higher stability against earthquakes, was evaluated. The finite element program LS-DYNA and twenty ground motion were used for time history analysis. The fracture caused by tensile and compression of the pier were set to the limit state. In the concrete tensile fracture, the limit state arrival rate was 100% at peak ground acceleration 0.4g. In the concrete compression fracture, the limit state arrival rate was 100% at peak ground acceleration 0.2g.

Keywords: allowable stress, limit state, safety factor, peak ground acceleration

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Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

Procedia PDF Downloads 136

Authors: George Mariano Soriano

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In this study, a Rapid Earthquake Risk Ranking Criteria was formulated by integrating various existing maps and databases by the Department of Public Works and Highways (DPWH) and Philippine Institute of Volcanology and Seismology (PHIVOLCS). Utilizing Geographic Information System (GIS) software, the above-mentioned maps and databases were used in extracting seismic hazard parameters and bridge vulnerability characteristics in order to rank the seismic damage risk rating of bridges in the National Capital Region.

Keywords: bridge, earthquake, GIS, hazard, risk, vulnerability

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Authors: Haibo Zhao, Thomas Andre, Katherine Avery, Alper Kiziltas, Deborah Mielewski

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Silica aerogels were fabricated through a modified rapid supercritical extraction (RSCE) process. The silica aerogels were made using a tetramethyl orthosilicate precursor and then placed in a hot press and brought to the supercritical point of the solvent, ethanol. In order to control the pressure release without a pressure controller, a needle valve was used. The resulting aerogels were then characterized for their physical and chemical properties and compared to silica aerogels created using similar methods. The aerogels fabricated using this modified RSCE method were found to have similar properties to those in other papers using the unmodified RSCE method. Silica aerogel infused glass blanket composite, graphene reinforced silica aerogel composite were also successfully fabricated by this new method. The modified RSCE process and system is a prototype for better gas outflow control with a lower cost of equipment setup. Potentially, this process could be evolved to a continuous low-cost high-volume production process to meet automotive requirements.

Keywords: aerogel, automotive, rapid supercritical extraction process, low cost production

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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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: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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Authors: Carlos Theran, Yohn Parra Bautista, Victor Adankai, Richard Alo, Jimwi Liu, Clement G. Yedjou

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Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. AD has gained relevant attention in the last decade. An estimated 24 million people worldwide suffered from this disease by 2011. In 2016 an estimated 40 million were diagnosed with AD, and for 2050 is expected to reach 131 million people affected by AD. Therefore, detecting and confirming AD at its different stages is a priority for medical practices to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to study AD's stages handling missing values in multiclass, focusing on the delineation of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and normal cognitive (CN). But, to our best knowledge, robust performance information of these models and the missing data analysis has not been presented in the literature. In this paper, we propose studying the performance of five different machine learning models for AD's stages multiclass prediction in terms of accuracy, precision, and F1-score. Also, the analysis of three imputation methods to handle the missing value problem is presented. A framework that integrates ML model for AD's stages multiclass prediction is proposed, performing an average accuracy of 84%.

Keywords: alzheimer's disease, missing value, machine learning, performance evaluation

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Authors: Artiya Sopharak, Tosaphol Ratniyomchai, Thanatchai Kulworawanichpong

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This paper presents an energy-saving study of Mass Rapid Transit (MRT) using an optimal train coasting operation. For the dynamic train movement with four modes of operation, including accelerating mode, constant speed or cruising mode, coasting mode, and braking mode are considered in this study. The acceleration rate, the deceleration rate, and the starting coasting point are taken into account the optimal train speed profile during coasting mode with considering the energy saving and acceptable travel time comparison to the based case with no coasting operation. In this study, the mathematical method as a Quadratic Search Method (QDS) is conducted to carry out the optimization problem. A single train of MRT services between two stations with a distance of 2 km and a maximum speed of 80 km/h is taken to be the case study. Regarding the coasting mode operation, the results show that the longer distance of costing mode, the less energy consumption in cruising mode and the less braking energy. On the other hand, the shorter distance of coasting mode, the more energy consumption in cruising mode and the more braking energy.

Keywords: energy saving, coasting mode, mass rapid transit, quadratic search method

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Pablo Chico De Guzman, Cesar Sanchez

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Microservices are a software development methodology in which applications are built by composing a set of independently deploy-able, small, modular services. Each service runs a unique process and it gets instantiated and deployed in one or more machines (we assume that different microservices are deployed into different machines). Microservices are becoming the de facto standard for developing distributed cloud applications due to their reduced release cycles. In principle, the responsibility of a microservice can be as simple as implementing a single function, which can lead to the following issues: - Resource fragmentation due to the virtual machine boundary. - Poor communication performance between microservices. Two composition techniques can be used to optimize resource fragmentation and communication performance: aggregation and embedding of microservices. Aggregation allows the deployment of a set of microservices on the same machine using a proxy server. Aggregation helps to reduce resource fragmentation, and is particularly useful when the aggregated services have a similar scalability behavior. Embedding deals with communication performance by deploying on the same virtual machine those microservices that require a communication channel (localhost bandwidth is reported to be about 40 times faster than cloud vendor local networks and it offers better reliability). Embedding can also reduce dependencies on load balancer services since the communication takes place on a single virtual machine. For example, assume that microservice A has two instances, a1 and a2, and it communicates with microservice B, which also has two instances, b1 and b2. One embedding can deploy a1 and b1 on machine m1, and a2 and b2 are deployed on a different machine m2. This deployment configuration allows each pair (a1-b1), (a2-b2) to communicate using the localhost interface without the need of a load balancer between microservices A and B. Aggregation and embedding techniques are complex since different microservices might have incompatible runtime dependencies which forbid them from being installed on the same machine. There is also a security concern since the attack surface between microservices can be larger. Luckily, container technology allows to run several processes on the same machine in an isolated manner, solving the incompatibility of running dependencies and the previous security concern, thus greatly simplifying aggregation/embedding implementations by just deploying a microservice container on the same machine as the aggregated/embedded microservice container. Therefore, a wide variety of deployment configurations can be described by combining aggregation and embedding to create an efficient and robust microservice architecture. This paper presents a formal method that receives a declarative definition of a microservice architecture and proposes different optimized deployment configurations by aggregating/embedding microservices. The first prototype is based on i2kit, a deployment tool also submitted to ICWS 2018. The proposed prototype optimizes the following parameters: network/system performance, resource usage, resource costs and failure tolerance.

Keywords: aggregation, deployment, embedding, resource allocation

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Authors: Wenlong Feng, Zhenchun Du, Jianguo Yang

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To decrease the grating scale thermal expansion error, a novel method which based on multiple temperature detections is proposed. Several temperature sensors are installed on the grating scale and the temperatures of these sensors are recorded. The temperatures of every point on the grating scale are calculated by interpolating between adjacent sensors. According to the thermal expansion principle, the grating scale thermal expansion error model can be established by doing the integral for the variations of position and temperature. A novel compensation method is proposed in this paper. By applying the established error model, the grating scale thermal expansion error is decreased by 90% compared with no compensation. The residual positioning error of the grating scale is less than 15um/10m and the accuracy of the machine tool is significant improved.

Keywords: thermal expansion error of grating scale, error compensation, machine tools, integral method

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Mengying Du, Xiang Chen

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‘villages-in-city’ is the unique product of rapid urbanization in China which embodies the contradiction between historical context and urbanization. This article mainly analyzes the corresponding strategy to the common problems such as urban texture, historical context, community structure, and industry pattern during the regeneration of ‘villages-in-city’ of Luojiazhuang. Taking government investment, community demands, the trend of urban renewal and transformation models of the ‘villages-in-city’ into consideration, the author propose a mechanism to balance those factors, and to achieve mutual confirmation with the instance of Luojiazhuang.

Keywords: community demands, historical context, villages-in-city, urbanization

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Authors: S. Sarumathi, C. Deepadharani, Garimella Archana, S. Dakshayani, D. Logeshwaran, D. Jayakumar, Vijayarangan Natarajan

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The need of the hour for the customers who use a dial-up or a low broadband connection for their internet services is to access HD video data. This can be achieved by developing a new video format using H. 265. This is the latest video codec standard developed by ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) on April 2013. This new standard for video compression has the potential to deliver higher performance than the earlier standards such as H. 264/AVC. In comparison with H. 264, HEVC offers a clearer, higher quality image at half the original bitrate. At this lower bitrate, it is possible to transmit high definition videos using low bandwidth. It doubles the data compression ratio supporting 8K Ultra HD and resolutions up to 8192×4320. In the proposed model, we design a new video format which supports this H. 265 standard. The major areas of applications in the coming future would lead to enhancements in the performance level of digital television like Tata Sky and Sun Direct, BluRay Discs, Mobile Video, Video Conferencing and Internet and Live Video streaming.

Keywords: access HD video, H. 265 video standard, high performance, high quality image, low bandwidth, new video format, video streaming applications

Procedia PDF Downloads 351

Authors: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: Saeedeh Bakhtiari, Johannes Depessemier, Stijn Hertelé, Wim De Waele

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High cycle fatigue comprising up to 10⁷ load cycles has been the subject of many studies, and the behavior of many materials was recorded adequately in this regime. However, many applications involve larger numbers of load cycles during the lifetime of machine components. In this ultra-high cycle regime, other failure mechanisms play, and the concept of a fatigue endurance limit (assumed for materials such as steel) is often an oversimplification of reality. When machine component design demands a high geometrical complexity, cast iron grades become interesting candidate materials. Grey cast iron is known for its low cost, high compressive strength, and good damping properties. However, the ultra-high cycle fatigue behavior of cast iron is poorly documented. The current work focuses on the ultra-high cycle fatigue behavior of EN-GJL-250 (GG25) grey cast iron by developing an ultrasonic (20 kHz) fatigue testing system. Moreover, the testing machine is instrumented to measure the temperature and the displacement of  the specimen, and to control the temperature. The high resonance frequency allowed to assess the  behavior of the cast iron of interest within a matter of days for ultra-high numbers of cycles, and repeat the tests to quantify the natural scatter in fatigue resistance.

Keywords: GG25, cast iron, ultra-high cycle fatigue, ultrasonic test

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Authors: Cristian Crespo

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Conditional cash transfers (CCTs) have been targeted towards the poor. Thus, their targeting assessments check whether these schemes have been allocated to low-income households or individuals. However, CCTs have more than one goal and target group. An additional goal of CCTs is to increase school enrolment. Hence, students at risk of dropping out of school also are a target group. This paper analyses whether one of the most common targeting mechanisms of CCTs, a proxy means test (PMT), is suitable to identify the poor and future school dropouts. The PMT is compared with alternative approaches that use the outputs of a predictive model of school dropout. This model was built using machine learning algorithms and rich administrative datasets from Chile. The paper shows that using machine learning outputs in conjunction with the PMT increases targeting effectiveness by identifying more students who are either poor or future dropouts. This joint targeting approach increases effectiveness in different scenarios except when the social valuation of the two target groups largely differs. In these cases, the most likely optimal approach is to solely adopt the targeting mechanism designed to find the highly valued group.

Keywords: conditional cash transfers, machine learning, poverty, proxy means tests, school dropout prediction, targeting

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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

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Lee Rui, Rajeev Ramachandran

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The approach to recurrent fevers in the paediatric or adolescent age group is not a straightforward one. Causes range from infectious diseases to rheumatological conditions to endocrinopathies, and are usually accompanied by weight loss rather than weight gain. We present an interesting case of a 16-year-old girl brought by her mother to the General Pediatrics Clinic for concerns of recurrent fever paired with significant weight gain over 1.5 years, with no identifiable cause found despite extensive work-up by specialists ranging from Rheumatologists to Oncologists. This case provides a learning opportunity on the approach to weight gain paired with persistent fevers in a paediatric population, one which is not commonly encountered and prompts further evaluation and consideration of less common diagnoses. In a span of 2 years, the girl’s weight had increased from 55 kg at 13 years old (75th centile) to 73.9 kg at 16 years old (>97th centile). About 1 year into her rapid weight gain, she started developing recurrent fevers of documented temperatures > 37.5 – 38.6 every 2-3 days, resulting in school absenteeism when she was sent home after temperature-taking in school found her to be febrile. The rapid onset of weight gain paired with unexplained fevers prompted the treating physician to consider the diagnosis of ROHHAD syndrome. Rapid onset obesity with hypoventilation, hypothalamic dysfunction and autonomic dysregulation (ROHHAD) syndrome is a rare disorder first described in 2007. It is characterized by dysfunction of the autonomic and endocrine system, characterized by hyperphagia and rapid-onset weight gain. This rapid weight gain is classically followed by hypothalamic manifestations with neuroendocrine deficiencies, hypo-ventilatory breathing abnormalities, and autonomic dysregulation. ROHHAD is challenging to diagnose with and diagnosis is made based mostly on clinical judgement. However if truly diagnosed, the condition is characterized by high morbidity and mortality rates. Early recognition of sleep disorders breathing and targeted therapeutic interventions helps limit morbidity and mortality associated with ROHHAD syndrome. This case poses an interesting diagnostic challenge and a diagnosis of ROHHAD has to be considered, given the serious complications that can come with disease progression while conditions such as Munchausen’s or drug fever remain as diagnoses of exclusion until we have exhausted all other possible conditions.

Keywords: pediatrics, endocrine, weight gain, recurrent fever, adolescent

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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: Sun Ailu, Zhao Wanmin

Abstract:

Since the reform and opening, the urbanization process of China has entered a rapid development period. In recent years, the authors participated in some projects of urban master planning in China and found a phenomenon that the rapid urbanization area of China is experiencing frequent adjustment process of urban master planning. This phenomenon is not the natural process of urbanization development. It may be caused by different government roles from different levels. Through the methods of investigation, data comparison and case study, this paper aims to explore the reason why the rapid urbanization area is experiencing frequent adjustment of master planning and give some solution strategies. Firstly, taking Changshou district of Chongqing city as an example, this paper wants to introduce the phenomenon about frequent adjustment process in China. And then, discuss distinct roles in the process between national government, provincial government and local government of China. At last, put forward preliminary solutions strategies for this area in China from the aspects of land use, intergovernmental cooperation and so on.

Keywords: urban master planning, frequent adjustment, urbanization development, problems and strategies, China

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Authors: J. I. Aguwa, S. Sadiku, M. Abdullahi

Abstract:

This study examined the structural reliability of the Nigerian grown Opepe timber as bridge beam material. The strength of a particular specie of timber depends so much on some factors such as soil and environment in which it is grown. The steps involved are collection of the Opepe timber samples, seasoning/preparation of the test specimens, determination of the strength properties/statistical analysis, development of a computer programme in FORTRAN language and finally structural reliability analysis using FORM 5 software. The result revealed that the Nigerian grown Opepe is a reliable and durable structural bridge beam material for span of 5000mm, depth of 400mm, breadth of 250mm and end bearing length of 150mm. The probabilities of failure in bending parallel to the grain, compression perpendicular to the grain, shear parallel to the grain and deflection are 1.61 x 10^-7, 1.43 x 10^-8, 1.93 x 10^-4 and 1.51 x 10^-15 respectively. The paper recommends establishment of Opepe plantation in various Local Government Areas in Nigeria for structural applications such as in bridges, railway sleepers, generation of income to the nation as well as creating employment for the numerous unemployed youths.

Keywords: bending and deflection, bridge beam, compression, Nigerian Opepe, shear, structural reliability

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Authors: Stefan Papastefanou

Abstract:

Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

Abstract:

Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: C. Slim

Abstract:

This paper presents an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to class countries according to their efficiency and performance. This model takes into account aspects of multi-dimensional indicators, decision-making hierarchy and relativity of measurement. Starting from a set of indicators of performance as exhaustive as possible, a process of successive aggregations has been developed to attain an overall evaluation of a country’s competitiveness.

Keywords: Artificial Neural Networks (ANN), Support vector machine (SVM), Data Envelopment Analysis (DEA), Aggregations, indicators of performance

Procedia PDF Downloads 334