Search results for: machine vision applications

Authors: Jaouadi Imen, Ben Djemaa Raoudha, Ben Abdallah Hanene

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The access to relevant information that is adapted to users’ needs, preferences and environment is a challenge in many applications running. That causes an appearance of context-aware systems. To facilitate the development of this class of applications, it is necessary that these applications share a common context meta-model. In this article, we will present our context meta-model that is defined using the OMG Meta Object facility (MOF). This meta-model is based on the analysis and synthesis of context concepts proposed in literature.

Keywords: context, meta-model, MOF, awareness system

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Anderson Rocha, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar

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Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.

Keywords: agricultural mobile robot, image processing, path recognition, hough transform

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 139

Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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Authors: Nouredine Benouzza, Ahmed Hamida Boudinar, Azeddine Bendiabdellah

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An algorithm for Eccentricity Fault Detection (EFD) applied to a squirrel cage induction machine is proposed in this paper. This algorithm employs the behavior of the stator current spectral analysis and the localization of the Rotor Slot Harmonic (RSH) frequency to detect eccentricity faults in three phase induction machine. The RHS frequency once obtained is used as a key parameter into a simple developed expression to directly compute the eccentricity fault frequencies in the induction machine. Experimental tests performed for both a healthy motor and a faulty motor with different eccentricity fault severities illustrate the effectiveness and merits of the proposed EFD algorithm.

Keywords: squirrel cage motor, diagnosis, eccentricity faults, current spectral analysis, rotor slot harmonic

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Authors: Rasheed Gbenga Jimoh, Akinbowale Nathaniel Babatunde

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The use of Automated Teller Machine (ATM) has become an important tool among commercial banks, customers of banks have come to depend on and trust the ATM conveniently meet their banking needs. Although the overwhelming advantages of ATM cannot be over-emphasized, its alarming fraud rate has become a bottleneck in it’s full adoption in Nigeria. This study examined the menace of ATM in the society another cost of running ATM services by banks in the country. The researcher developed a prototype of an enhanced Automated Teller Machine Authentication using Short Message Service (SMS) Verification. The developed prototype was tested by Ten (10) respondents who are users of ATM cards in the country and the data collected was analyzed using Statistical Package for Social Science (SPSS). Based on the results of the analysis, it is being envisaged that the developed prototype will go a long way in reducing the alarming rate of ATM fraud in Nigeria.

Keywords: ATM, ATM fraud, e-banking, prototyping

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Authors: Thomas, Daniel Tajo, Kish

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Sight is primarily associated with the function of gathering and processing near and extended spatial information which is largely used to support self-determined interaction with the environment through self-directed movement and navigation. By contrast, hearing is primarily associated with the function of gathering and processing sequential information which may typically be used to support self-determined communication through the self-directed use of music and language. Blindness or the lack of vision is traditionally characterized by a lack of capacity to access spatial information which, in turn, is presumed to result in a lack of capacity for self-determined interaction with the environment due to limitations in self-directed movement and navigation. However, through a specific protocol of FlashSonar education developed by World Access for the Blind, the function of hearing can be expanded in blind people to carry out some of the functions normally associated with sight, that is to access and process near and extended spatial information to construct three-dimensional acoustic images of the environment. This perceptual education protocol results in a significant restoration in blind people of self-determined environmental interaction, movement, and navigational capacities normally attributed to vision - a new way to see. Thus, by expanding the function of hearing to process spatial information to restore self-determined movement, we are not only changing the meaning of blindness, and what it means to be blind, but we are also recasting the meaning of vision and what it is to see.

Keywords: echolocation, changing, sensory, function

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Authors: Samad Sajjadi

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Machine translation is a new subject increasingly being used by academic writers, especially students and researchers whose native language is not English. There are numerous studies conducted on machine translation, but few investigations have assessed the accuracy of machine translation from Persian to English at lexical, semantic, and syntactic levels. Using Groves and Mundt’s (2015) Model of error taxonomy, the current study evaluated Persian-to-English translations produced by two famous online translators, Google Translate and Bing Translator. A total of 240 texts were randomly selected from different academic fields (law, literature, medicine, and mass media), and 60 texts were considered for each domain. All texts were rendered by the two translation systems and then by four human translators. All statistical analyses were applied using SPSS. The results indicated that Google translations were more accurate than the translations produced by the Bing Translator, especially in the domains of medicine (lexis: 186 vs. 225; semantic: 44 vs. 48; syntactic: 148 vs. 264 errors) and mass media (lexis: 118 vs. 149; semantic: 25 vs. 32; syntactic: 110 vs. 220 errors), respectively. Nonetheless, both machines are reasonably accurate in Persian-to-English translation of lexicons and syntactic structures, particularly from mass media and medical texts.

Keywords: machine translations, accuracy, human translation, efficiency

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Authors: Ali Ganoun, Wesam Algablawi, Wasim BenAnaif

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Video based traffic surveillance based on License Plate Recognition (LPR) system is an essential part for any intelligent traffic management system. The LPR system utilizes computer vision and pattern recognition technologies to obtain traffic and road information by detecting and recognizing vehicles based on their license plates. Generally, the video based LPR system is a challenging area of research due to the variety of environmental conditions. The LPR systems used in a wide range of commercial applications such as collision warning systems, finding stolen cars, controlling access to car parks and automatic congestion charge systems. This paper presents an automatic LPR system of Libyan license plate. The performance of the proposed system is evaluated with three video sequences.

Keywords: license plate recognition, localization, segmentation, recognition

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Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

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Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Seung-Hwan Ju, Yo-Han Choi, Hee-Suk Seo, Tae-Kyung Kim

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This study investigates the permissions requested by Android applications, and the possibility of identifying suspicious applications based only on information presented to the user before an application is downloaded. The pattern analysis is based on a smaller data set consisting of confirmed malicious applications. The method is evaluated based on its ability to recognize malicious potential in the analyzed applications. In this study, we develop a system to monitor that mobile application permission at application update. This study is a service-based malware analysis. It will be based on the mobile security study.

Keywords: malware patterns, application permission, application analysis, security

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Authors: N. Lakshmipathy, Ch. S. N. Murthy, M. Aruna

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The design of lighting systems for surface mines is not an easy task because of the unique environment and work procedures encountered in the mines. The primary objective of this paper is to identify the major problems encountered in mine lighting application and to provide guidance in the solution of these problems. In the surface mining reflectance of surrounding surfaces is one of the important factors, which improve the vision, in the night hours. But due to typical working nature in the mines it is very difficult to fulfill these requirements, and also the orientation of the light at work site is a challenging task. Due to this reason machine operator and other workers in a mine need to be able to orient themselves in a difficult visual environment. The haul roads always keep on changing to tune with the mining activity. Other critical area such as dumpyards, stackyards etc. also change their phase with time, and it is difficult to illuminate such areas. Mining is a hazardous occupation, with workers exposed to adverse conditions; apart from the need for hard physical labor, there is exposure to stress and environmental pollutants like dust, noise, heat, vibration, poor illumination, radiation, etc. Visibility is restricted when operating load haul dumper and Heavy Earth Moving Machinery (HEMM) vehicles resulting in a number of serious accidents. one of the leading causes of these accidents is the inability of the equipment operator to see clearly people, objects or hazards around the machine. Results indicate blind spots are caused primarily by posts, the back of the operator's cab, and by lights and light brackets. The careful designed and implemented, lighting systems provide mine workers improved visibility and contribute to improved safety, productivity and morale. Properly designed lighting systems can improve visibility and safety during working in the opencast mines.

Keywords: contrast, efficacy, illuminance, illumination, light, luminaire, luminance, reflectance, visibility

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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: Barenten Suciu, Akio Miyamura

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In this paper, following the study-case of an inclined plane gravitational machine, efficiency of a double-cone gravitational motor and generator is evaluated. Two types of efficiency ratios, called translational efficiency and rotational efficiency, are defined relative to the intended duty of the gravitational machine, which can be either the production of translational kinetic energy, or rotational kinetic energy. One proved that, for pure rolling movement of the double- cone, in the absence of rolling friction, the total mechanical energy is conserved. In such circumstances, as the motion of the double-cone progresses along rails, the translational efficiency decreases and the rotational efficiency increases, in such way that sum of the rotational and translational efficiencies remains unchanged and equal to 1. Results obtained allow a comparison of the gravitational machine with other types of motor-generators, in terms of the achievable efficiency.

Keywords: efficiency, friction, gravitational motor and generator, rolling and sliding, truncated double-cone

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Authors: Kemal Polat

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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: Ali Albadri

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The logistics equation has never been used or studied in scientific fields outside the field of ecology. It has never been used to understand the behavior of a dynamic system of mechanical machines, like an escalator. We have studied the compatibility of the logistic map against real measurements from an escalator. This study has proven that there is good compatibility between the logistics equation and the experimental measurements. It has discovered the potential of a relationship between the fractal dimension and the non-linearity parameter, R, in the logistics equation. The fractal dimension increases as the R parameter (non-linear parameter) increases. It implies that the fractal dimension increases as the phase of the life span of the machine move from the steady/stable phase to the periodic double phase to a chaotic phase. The fractal dimension and the parameter R can be used as a tool to verify and check the health of machines. We have come up with a theory that there are three areas of behaviors, which they can be classified during the life span of a machine, a steady/stable stage, a periodic double stage, and a chaotic stage. The level of attention to the machine differs depending on the stage that the machine is in. The rate of faults in a machine increases as the machine moves through these three stages. During the double period and the chaotic stages, the number of faults starts to increase and become less predictable. The rate of predictability improves as our monitoring of the changes in the fractal dimension and the parameter R improves. The principles and foundations of our theory in this work have and will have a profound impact on the design of systems, on the way of operation of systems, and on the maintenance schedules of the systems. The systems can be mechanical, electrical, or electronic. The discussed methodology in this paper will give businesses the chance to be more careful at the design stage and planning for maintenance to control costs. The findings in this paper can be implied and used to correlate the three stages of a mechanical system to more in-depth mechanical parameters like wear and fatigue life.

Keywords: logistcs map, bifurcation map, fractal dimension, logistics equation

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Authors: Khoshnaw Khalid Hama Saleh, Ergun Ercelebi

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Increasingly complex modern power systems require stability, especially for transient and small disturbances. Transient stability plays a major role in stability during fault and large disturbance. This paper compares a power system stabilizer (PSS) and static Var compensator (SVC) to improve damping oscillation and enhance transient stability. The effectiveness of a PSS connected to the exciter and/or governor in damping electromechanical oscillations of isolated synchronous generator was tested. The SVC device is a member of the shunt FACTS (flexible alternating current transmission system) family, utilized in power transmission systems. The designed model was tested with a multi-machine system consisting of four machines six bus, using MATLAB/SIMULINK software. The results obtained indicate that SVC solutions are better than PSS.

Keywords: FACTS, MATLAB/SIMULINK, multi-machine system, PSS, SVC, transient stability

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Authors: Elham Sharifineyestani, Mohammad Farshchin

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Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

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Authors: Hyeon Sop Baek

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This paper will briefly investigate Jeong Do-jeon’s political philosophy. Jeong Do-jeon was a Korean Confucian philosopher and politician during the turbulent 14th Century who revolted against the old order, founded Joseon Dynasty, and significantly impacted the development of Korean culture. Jeong’s vision of an ideal state involved a polity that has its roots in the people -that is, an ideal government prioritizes caring for the welfare of the people, respecting and attending to the diverse opinions and concerns of the people, and relies on the genuine, voluntary support of the people. With the neo-Confucian worldview in mind -that every human being has the equal potential to become a moral person- Jeong sought to create a world suitable for everybody to contribute to the decision-making procedure and be able to realize their potential fully. This paper will first examine his works and present a quick overview of his vision of the ideal government. Then, it will examine the Confucian virtues of ren (仁) and yi (義) and how they formulate the basis of his philosophy, and then discuss the central features of his vision of government: popular mandate, equity of wealth, promoting freedom of expression and political participation, and elevating caring disposition as the paramount quality of the political leaders. Furthermore, this paper aims to analyze the element of care inherent within his political philosophy, namely his view on the dynamics of power, nurturing the people, and noncoercive justice. Finally, a discussion on why his philosophy is still relevant in the contemporary context will be provided. Jeong’s view aimed at building a sustainable model of government, by proposing that the people should be the foundation of a state and that they need to be carefully nurtured so they can realize their inborn potential and continue to contribute to the sustenance of the world, is the focal point of Jeong’s philosophy. Just as he sought to rebuild his world following the turmoils of the 14th Century, his philosophy still has a substantial implication on how we should strive to rebuild our society today.

Keywords: Korea, Confucianism, Jeong Do-jeon, Joseon, Korean philosophy, political philosophy

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Authors: Mohd Javaid, Shahbaz Khan, Abid Haleem

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Purpose: Large numbers of 3D printing technologies are now available for sophisticated applications in different fields. Additive manufacturing has established its dominance in design, development, and customisation of the product. In the era of developing technologies, there is a need to identify the appropriate technology for different application. In order to fulfil this need, two widely used printing technologies such as Fused Deposition Modeling (FDM), and Binding-Jet 3D Printing are compared for effective utilisation in the current scenario for different applications. Methodology: Systematic literature review conducted for both technologies with applications and associated factors enabling for the same. Appropriate MCDM tool is used to compare critical factors for both the technologies. Findings: Both technologies have their potential and capabilities to provide better direction to the industry. Additionally, this paper is helpful to develop a decision support system for the proper selection of technologies according to their continuum of applications and associated research and development capability. The vital issue is raw materials, and research-based material development is key to the sustainability of the developed technologies. FDM is a low-cost technology which provides high strength product as compared to binding jet technology. Researcher and companies can take benefits of this study to achieve the required applications in lesser resources. Limitations: Study has undertaken the comparison with the opinion of experts, which may not always be free from bias, and some own limitations of each technology. Originality: Comparison between these technologies will help to identify best-suited technology as per the customer requirements. It also provides development in this different field as per their extensive capability where these technologies can be successfully adopted. Conclusion: FDM and binding jet technology play an active role in industrial development. These help to assist the customisation and production of personalised parts cost-effectively. So, there is a need to understand how these technologies can provide these developments rapidly. These technologies help in easy changes or in making revised versions of the product, which is not easily possible in the conventional manufacturing system. High machine cost, the requirement of skilled human resources, low surface finish, and mechanical strength of product and material changing option is the main limitation of this technology. However, these limitations vary from technology to technology. In the future, these technologies are to be commercially viable for efficient usage in direct manufacturing of varied parts.

Keywords: 3D printing, comparison, fused deposition modeling, FDM, binding jet technology

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Authors: Seyed Abdol Mohammad Rezavand, Mohammad Nikbakhsh

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Withdrawal resistance of screws driven into HDPE/Sugarcane Bagasse injection molded parts was investigated. After chemical treatment and drying, SCB was pre-mixed with HDPE using twin extruder. The resulting granules are used in producing samples in injection molding machine. SCB with the quantity of %10, %20, and %30 was used. By using a suitable fixture, screw heads can take with tensile test machine grips. Parts with screws in the center and edge were fasten together. Then, withdrawal resistance was measured with tensile test machine. Injection gate is at the one edge of the part. The results show that by increasing SCB content in composite, the withdrawal resistance is decreased. Furthermore, the withdrawal resistance at the edges (near injection gate and the end of the filling path of mold cavity) is more than that of the center.

Keywords: polyethylene, sugarcane bagasse, wood plastic, screw, withdrawal resistance

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Authors: Hassan Almoslim

Abstract:

Background: The machine resistance training is an exercise that uses the equipment as loads to strengthen and condition the musculoskeletal system and improving muscle tone. The machine resistance training is easy to use, allow the individual to train with heavier weights without assistance, useful for beginners and elderly populations and specific muscle groups. Purpose: The purpose of this study was to examine the impact of nine weeks of machine resistance training on maximum strength among lean and normal weight male college students. Method: Thirty-six male college students aged between 19 and 21 years from King Fahd University of petroleum & minerals participated in the study. The subjects were divided into two an equal groups called Lean Group (LG, n = 18) and Normal Weight Group (NWG, n = 18). The subjects whose body mass index (BMI) is less than 18.5 kg / m2 is considered lean and who is between 18.5 to 24.9 kg / m2 is normal weight. Both groups performed machine resistance training nine weeks, twice per week for 40 min per training session. The strength measurements, chest press, leg press and abdomen exercises were performed before and after the training period. 1RM test was used to determine the maximum strength of all subjects. The training program consisted of several resistance machines such as leg press, abdomen, chest press, pulldown, seated row, calf raises, leg extension, leg curls and back extension. The data were analyzed using independent t-test (to compare mean differences) and paired t-test. The level of significance was set at 0.05. Results: No change was (P ˃ 0.05) observed in all body composition variables between groups after training. In chest press, the NWG recorded a significantly greater mean different value than the LG (19.33 ± 7.78 vs. 13.88 ± 5.77 kg, respectively, P ˂ 0.023). In leg press and abdomen exercises, both groups revealed similar mean different values (P ˃ 0.05). When the post-test was compared with the pre-test, the NWG showed significant increases in the chest press by 47% (from 41.16 ± 12.41 to 60.49 ± 11.58 kg, P ˂ 001), abdomen by 34% (from 45.46 ± 6.97 to 61.06 ± 6.45 kg, P ˂ 0.001) and leg press by 23.6% (from 85.27 ± 15.94 to 105.48 ± 21.59 kg, P ˂ 0.001). The LG also illustrated significant increases by 42.6% in the chest press (from 32.58 ± 7.36 to 46.47 ± 8.93 kg, P ˂ 0.001), the abdomen by 28.5% (from 38.50 ± 7.84 to 49.50 ± 7.88 kg, P ˂ 0.001) and the leg press by 30.8% (from 70.2 ± 20.57 to 92.01 ± 22.83 kg, P ˂ 0.001). Conclusion: It was concluded that the lean and the normal weight male college students can benefit from the machine resistance-training program remarkably.

Keywords: body composition, lean, machine resistance training, normal weight

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Authors: Milind Patil

Abstract:

Present paper is a discussion on Hindi-Marathi Morphological Analysis and generating rules for Machine Translation on the basis of Machine Readable Dictionary (MRD). This used Transformative Generative Grammar (TGG) rules to design the MRD. As per TGG rules, the suffix of a particular root word is based on its Tense, Aspect, Modality and Voice. That's why the suffix is very important for the word meanings (or root meanings). The Hindi and Marathi Language both have relation with Indo-Aryan language family. Both have been derived from Sanskrit language and their script is 'Devnagari'. But there are lots of differences in terms of semantics and grammatical level too. In Marathi, there are three genders, but in Hindi only two (Masculine and Feminine), the Natural gender is absent in Hindi. Likewise other grammatical categories also differ in their level of use. For MRD the suffixes (or Morpheme) are of particular root word for GNP (Gender, Number and Person) are based on its natural phenomena. A particular Suffix and Morphine change as per the need of person, number and gender. The design of MRD also based on this format. In first, Person, Number, Gender and Tense are key points than root words and suffix of particular Person, Number Gender (PNG). After that the inferences are drawn on the basis of rules that is (V.stem) (Pre.T/Past.T) (x) + (Aux-Pre.T) (x) → (V.Stem.) + (SP.TM) (X).

Keywords: MRD, TGG, stem, morph, morpheme, suffix, PNG, TAM&V, root

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Authors: Saradha Priya

Abstract:

Developing virtual human is very important to meet the challenges occurred in many applications where human find difficult or risky to perform the task. A robot is a machine that can perform a task automatically or with guidance. Robotics is generally a combination of artificial intelligence and physical machines (motors). Computational intelligence involves the programmed instructions. This project proposes a robotic vehicle that has a camera, PIR sensor and text command based movement. It is specially designed to perform surveillance and other few tasks in the most efficient way. Serial communication has been occurred between a remote Base Station, GUI Application, and PC.

Keywords: Zigbee, camera, pirsensor, wireless transmission, DC motor

Procedia PDF Downloads 342

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

Procedia PDF Downloads 251