Search results for: Permanent magnet synchronous machine (PMSM)

Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

Procedia PDF Downloads 568

Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

Procedia PDF Downloads 125

Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

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Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

Procedia PDF Downloads 512

Authors: M. K. Yusuf, W. O. Hamman, U. E. Umana, S. B. Oladele

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Background: Dilatation of the inferior vena cava (IVC) is used as the ultrasonic diagnostic feature in patients suspected of congestive heart failure. The IVC diameter has been reported to vary among the various body mass indexes (BMI) and body shape indexes (ABSI). Knowledge of these variations is useful in precision diagnoses of CHF by imaging scientists. Aim: The study aimed to establish an equation for predicting the ultrasonic mean diameter of the IVC among the various BMI/ABSI of inhabitants of Azare, Bauchi State-Nigeria. Methodology: Two hundred physically healthy adult subjects of both sexes were classified into under, normal, over, and obese weights using their BMIs after selection using a structured questionnaire following their informed consent for an abdominal ultrasound scan. The probe was placed on the midline of the body, halfway between the xiphoid process and the umbilicus, with the marker on the probe directed towards the patient's head to obtain a longitudinal view of the IVC. The maximum IVC diameter was measured from the subcostal view using the electronic caliper of the scan machine. The mean value of each group was obtained, and the results were analysed. Results: A novel equation {(IVC Diameter = 1.04 +0.01(X) where X= BMI} has been generated for determining the IVC diameter among the populace. Conclusion: An equation for predicting the IVC diameter from individual BMI values in apparently healthy subjects has been established.

Keywords: equation, ultrasonic, IVC diameter, body adiposities

Procedia PDF Downloads 64

Authors: Ashok Kalagura

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This paper presents a data-mining model for fault-zone identification of flexible AC transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides an effective decision on the fault-zone identification. Half-cycle post-fault current and voltage samples from the fault inception are used as an input vector against target output ‘1’ for the fault after TCSC/UPFC and ‘1’ for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate the reliable identification of the fault zone in FACTS-based transmission lines.

Keywords: distance relaying, fault-zone identification, random forests, RFs, support vector machine, SVM, thyristor-controlled series compensator, TCSC, unified power-flow controller, UPFC

Procedia PDF Downloads 420

Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

Procedia PDF Downloads 155

Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

Procedia PDF Downloads 153

Authors: Thiago Viana

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Eventually train accidents occur on railways and for some specific cases it is necessary to use a train rescue with a crane positioned under a platform wagon. These tumbled machines are collected and sent to the machine shop or scrap yard. In one of these cranes that were being used to rescue a wagon, occurred a fall of hoist due to fracture of two large pins. The two pins were collected and sent for failure analysis. This work investigates the main cause and the secondary causes for the initiation of the fatigue crack. All standard failure analysis procedures were applied, with careful evaluation of the characteristics of the material, fractured surfaces and, mainly, metallographic tests using an optical microscope to compare the geometry of the peaks and valleys of the thread of the pins and their respective seats. By metallographic analysis, it was concluded that the fatigue cracks were started from a notch (stress concentration) in the valley of the threads of the pin applied to the right side of the crane (pin 1). In this, it was verified that the peaks of the threads of the pin seat did not have proper geometry, with sharp edges being present that caused such notches. The visual analysis showed that fracture of the pin on the left side of the crane (pin 2) was brittle type, being a consequence of the fracture of the first one. Recommendations for this and other railway cranes have been made, such as nondestructive testing, stress calculation, design review, quality control and suitability of the mechanical forming process of the seat threads and pin threads.

Keywords: crane, fracture, pin, railway

Procedia PDF Downloads 101

Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

Procedia PDF Downloads 183

Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

Procedia PDF Downloads 390

Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

Procedia PDF Downloads 314

Authors: Fereshteh Zarei, Mahdi Kohandel

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The aim of this study was investigate and compare plyometric and vibration exercises on muscle explosive power in non-athlete female students. For this purpose, 45 female students from non-athletes selected target then divided in to the three groups, two experimental and one control groups. From all groups were getting pre-tested. Experimental A did whole-body vibration exercises involved standing on one of machine vibration with frequency 30 Hz, amplitude 10 mm and in 5 different postures. Training for each position was 40 seconds with 60 seconds rest between it, and each season 5 seconds was added to duration of each body condition, until time up to 2 minutes for each postures. Exercises were done three times a week for 2 month. Experimental group B did plyometric exercises that include jumping, such as horizontal, vertical, and skipping .They included 10 times repeat for 5 set in each season. Intensity with increasing repetitions and sets were added. At this time, asked from control group that keep a daily activity and avoided strength training, explosive power and. after do exercises by groups we measured factors again. One-way analysis of variance and paired t statistical methods were used to analyze the data. There was significant difference in the amount of explosive power between the control and vibration groups (p=0/048) there was significant difference between the control and plyometric groups (019/0 = p). But between vibration and plyometric groups didn't observe significant difference in the amount of explosive power.

Keywords: vibration, plyometric, exercises, explosive power, non-athlete

Procedia PDF Downloads 446

Authors: N. Slepickova Kasalkova, P. Slepicka, L. Bacakova, V. Svorcik

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Tissue engineering includes combination of materials and techniques used for the improvement, repair or replacement of the tissue. Scaffolds, permanent or temporally material, are used as support for the creation of the "new cell structures". For this important component (scaffold), a variety of materials can be used. The advantage of some polymeric materials is their cytocompatibility and possibility of biodegradation. Poly(L-lactic acid) (PLLA) is a biodegradable,  semi-crystalline thermoplastic polymer. PLLA can be fully degraded into H₂O and CO₂. In this experiment, the effect of the surface modification of biodegradable polymer (performed by plasma treatment) on the various cell types was studied. The surface parameters and changes of the physicochemical properties of modified PLLA substrates were studied by different methods. Surface wettability was determined by goniometry, surface morphology and roughness study were performed with atomic force microscopy and chemical composition was determined using photoelectron spectroscopy. The physicochemical properties were studied in relation to cytocompatibility of human osteoblast (MG 63 cells), rat vascular smooth muscle cells (VSMC), and human stem cells (ASC) of the adipose tissue in vitro. A fluorescence microscopy was chosen to study and compare cell-material interaction. Important parameters of the cytocompatibility like adhesion, proliferation, viability, shape, spreading of the cells were evaluated. It was found that the modification leads to the change of the surface wettability depending on the time of modification. Short time of exposition (10-120 s) can reduce the wettability of the aged samples, exposition longer than 150 s causes to increase of contact angle of the aged PLLA. The surface morphology is significantly influenced by duration of modification, too. The plasma treatment involves the formation of the crystallites, whose number increases with increasing time of modification. On the basis of physicochemical properties evaluation, the cells were cultivated on the selected samples. Cell-material interactions are strongly affected by material chemical structure and surface morphology. It was proved that the plasma treatment of PLLA has a positive effect on the adhesion, spreading, homogeneity of distribution and viability of all cultivated cells. This effect was even more apparent for the VSMCs and ASCs which homogeneously covered almost the whole surface of the substrate after 7 days of cultivation. The viability of these cells was high (more than 98% for VSMCs, 89-96% for ASCs). This experiment is one part of the basic research, which aims to easily create scaffolds for tissue engineering with subsequent use of stem cells and their subsequent "reorientation" towards the bone cells or smooth muscle cells.

Keywords: poly(L-lactic acid), plasma treatment, surface characterization, cytocompatibility, human osteoblast, rat vascular smooth muscle cells, human stem cells

Procedia PDF Downloads 227

Authors: A. Luft, S. Bremen, N. Balc

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The task of flexibility planning and design, just like factory planning, for example, is to create the long-term systemic framework that constitutes the restriction for short-term operational management. This is a strategic challenge since, due to the decision defect character of the underlying flexibility problem, multiple types of flexibility need to be considered over the course of various scenarios, production programs, and production system configurations. In this context, an evaluation model has been developed that integrates both conventional and additive resources on a basic task level and allows the quantification of flexibility enhancement in terms of mix and volume flexibility, complexity reduction, and machine capacity. The model helps companies to decide in early decision-making processes about the potential gains of implementing additive manufacturing technologies on a strategic level. For companies, it is essential to consider both additive and conventional manufacturing beyond pure unit costs. It is necessary to achieve an integrative view of manufacturing that incorporates both additive and conventional manufacturing resources and quantifies their potential with regard to flexibility and manufacturing complexity. This also requires a structured process for the strategic production systems design that spans the design of various scenarios and allows for multi-dimensional and comparative analysis. A respective guideline for the planning of additive resources on a strategic level is being laid out in this paper.

Keywords: additive manufacturing, production system design, flexibility enhancement, strategic guideline

Procedia PDF Downloads 118

Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

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The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

Procedia PDF Downloads 45

Authors: Nicolò Vaiana, Mariacristina Spizzuoco, Giorgio Serino

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In this paper, the results of experimental tests performed on a Helical Wire Rope Isolator (HWRI) are presented in order to describe the dynamic and static behavior of the selected metal device in three different displacements ranges, namely small, relatively large, and large displacements ranges, without and under the effect of a vertical load. A testing machine, allowing to apply horizontal displacement or load histories to the tested bearing with a constant vertical load, has been adopted to perform the dynamic and static tests. According to the experimental results, the dynamic behavior of the tested device depends on the applied displacement amplitude. Indeed, the HWRI displays a softening and a hardening stiffness at small and relatively large displacements, respectively, and a stronger nonlinear stiffening behavior at large displacements. Furthermore, the experimental tests reveal that the application of a vertical load allows to have a more flexible device with higher damping properties and that the applied vertical load affects much less the dynamic response of the metal device at large displacements. Finally, a decrease in the static to dynamic effective stiffness ratio with increasing displacement amplitude has been observed.

Keywords: base isolation, earthquake engineering, experimental hysteresis loops, wire rope isolators

Procedia PDF Downloads 426

Authors: Idris Idris Sunusi, U.S. Muhammed, N.A. Sale, I.B. Dalha, N.A. Adam

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Threshing efficiency and mechanical grain damages are among the important parameters used in rating the performance of agricultural threshers. To be acceptable to farmers, threshers should have high threshing efficiency and low grain. The objective of the research is to evaluate the performances of the thresher using sorghum and millet, the performances parameters considered are; threshing efficiency and mechanical grain damage. For millet, four drum speed levels; 700, 800, 900 and 1000 rpm were considered while for sorghum; 600, 700, 800 and 900 rpm were considered. The feed rate levels were 3, 4, 5 and 6 kg/min for both sorghum and millet; the levels of moisture content were 8.93 and 10.38% for sorghum and 9.21 and 10.81% for millet. For millet the test result showed a maximum of 98.37 threshing efficiencies and a minimum of 0.24% mechanical grain damage while for sorghum the test result indicated a maximum of 99.38 threshing efficiencies, and a minimum of 0.75% mechanical grain damage. In comparison to the previous thresher, the threshing efficiency and mechanical grain damage of the modified machine has improved by 2.01% and 330.56% for millet and 5.31%, 287.64% for sorghum. Also analysis of variance (ANOVA) showed that, the effect of drum speed, feed rate and moisture content were significant on the performance parameters.

Keywords: Threshing Efficiency, Mechanical Grain Damages, Sorghum and Millet, Multi Crop Thresher

Procedia PDF Downloads 344

Authors: Chuanqiao Zheng, Wei Zeng, Haozhen Lin

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As of March 2020, the number of Chinese netizens has reached 904 million. The proportion of Internet users accessing the Internet through mobile phones is as high as 99.3%. Under the background of 'Internet +', tourists have a stronger sense of independence in the choice of tourism destinations and tourism products. Tourists are more inclined to learn about the relevant information on tourism destinations and other tourists' evaluations of tourist products through the Internet. The search engine, as an integrated platform that contains a wealth of information, is highly valuable to the analysis of the characteristics of the Internet attention given to various tourism destinations, through big data mining and analysis. This article uses the Baidu Index as the data source, which is one of the products of Baidu Search. The Baidu Index is based on big data, which collects and shares the search results of a large number of Internet users on the Baidu search engine. The big data used in this article includes search index, demand map, population profile, etc. The main research methods used are: (1) based on the search index, analyzing the Internet attention given to the tourism in five cities in Northeast Sichuan at different times, so as to obtain the overall trend and individual characteristics of tourism development in the region; (2) based on the demand map and the population profile, analyzing the demographic characteristics and market positioning of the tourist groups in these cities to understand the characteristics and needs of the target groups; (3) correlating the Internet attention data with the permanent population of each province in China in the corresponding to construct the Boston matrix of the Internet attention rate of the Northeast Sichuan tourism, obtain the tourism target markets, and then propose development strategies for different markets. The study has found that: a) the Internet attention given to the tourism in the region can be categorized into tourist off-season and peak season; the Internet attention given to tourism in different cities is quite different. b) tourists look for information including tour guide information, ticket information, traffic information, weather information, and information on the competing tourism cities; with regard to the population profile, the main group of potential tourists searching for the keywords of tourism in the five prefecture-level cities in Northeast Sichuan are youth. The male to female ratio is about 6 to 4, with males being predominant. c) through the construction of the Boston matrix, it is concluded that the star market for tourism in the Northeast Sichuan Economic Zone includes Sichuan and Shaanxi; the cash cows market includes Hainan and Ningxia; the question market includes Jiangsu and Shanghai; the dog market includes Hubei and Jiangxi. The study concludes with the following planning strategies and recommendations: i) creating a diversified business format that integrates cultural and tourism; ii) creating a brand image of niche tourism; iii) focusing on the development of tourism products; iv) innovating composite three-dimensional marketing channels.

Keywords: Baidu Index, big data, internet attention, tourism

Procedia PDF Downloads 117

Authors: James Taylor

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This paper considers the political, geo-philosophical musings of Deleuze and Guattari on spatialisation, place and movement in relation to the religious nomad (wandering ascetics and reclusive forest monks) inhabiting the borderlands of Thailand. A nomadic science involves improvised ascetic practices between the molar lines striated by modern state apparatuses. The wandering ascetics, inhabiting a frontier political ecology, stand in contrast to the appropriating, sedentary metaphysics and sanctifying arborescence of statism and its corollary place-making, embedded in rootedness and territorialisation. It is argued that the religious nomads, residing on the endo-exteriorities of the state, came to represent a rhizomatic and politico-ontological threat to centre-nation and its apparatus of capture. The paper also theorises transitions and movement at the borderlands in the context of the state’s monastic reforms. These reforms, and its pervasive royal science, problematised the interstitial zones of the early ascetic wanderers in their radical cross-cutting networks and lines, moving within and across demarcated frontiers. Indeed, the ascetic wanderers and their allegorical war machine were seen as a source of wild, free-floating charisma and mystical power, eventually appropriated by the centre-nation in it’s becoming unitary and fixed.

Keywords: Deleuze and Guattari, religious nomad, centre-nation, borderlands, Buddhism

Procedia PDF Downloads 78

Authors: Tamir Michal

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The subject of Bedouin dispersion has accompanied the State of Israel from the day of its establishment. From a legal point of view, this subject has offered a launchpad for creative judicial decisions. Thus, for example, the first court decision in Israel to recognize affirmative action (Avitan), dealt with a petition submitted by a Jew appealing the refusal of the State to recognize the Petitioner’s entitlement to the long-term lease of a plot designated for Bedouins. The Supreme Court dismissed the petition, holding that there existed a public interest in assisting Bedouin to establish permanent urban settlements, an interest which justifies giving them preference by selling them plots at subsidized prices. In another case (The Forum for Coexistence in the Negev) the Supreme Court extended equitable relief for the purpose of constructing a bridge, even though the construction infringed the Law, in order to allow the children of dispersed Bedouin to reach school. Against this background, the recent verdict, delivered during the Protective Edge military campaign, which dismissed a petition aimed at forcing the State to spread out Protective Structures in Bedouin villages in the Negev against the risk of being hit from missiles launched from Gaza (Abu Afash) is disappointing. Even if, in arguendo, no selective discrimination was involved in the State’s decision not to provide such protection, the decision, and its affirmation by the Court, is problematic when examined through the prism of the Theory of Recognition. The article analyses the issue by tools of theory of Recognition, according to which people develop their identities through mutual relations of recognition in different fields. In the social context, the path to recognition is cognitive respect, which is provided by means of legal rights. By seeing other participants in Society as bearers of rights and obligations, the individual develops an understanding of his legal condition as reflected in the attitude to others. Consequently, even if the Court’s decision may be justified on strict legal grounds, the fact that Jewish settlements were protected during the military operation, whereas Bedouin villages were not, is a setback in the struggle to make the Bedouin citizens with equal rights in Israeli society. As the Court held, ‘Beyond their protective function, the Migunit [Protective Structures] may make a moral and psychological contribution that should not be undervalued’. This contribution is one that the Bedouin did not receive in the Abu Afash verdict. The basic thesis is that the Court’s verdict analyzed above clearly demonstrates that the reliance on classical liberal instruments (e.g., equality) cannot secure full appreciation of all aspects of Bedouin life, and hence it can in fact prejudice them. Therefore, elements of the recognition theory should be added, in order to find the channel for cognitive dignity, thereby advancing the Bedouins’ ability to perceive themselves as equal human beings in the Israeli society.

Keywords: bedouin dispersion, cognitive respect, recognition theory, sustainable development

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

Procedia PDF Downloads 213

Authors: Jacob Wahl, Jane Zhang

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This paper describes the development and characterization of a prototype IoT network for use with agricultural imaging and monitoring applications. The sensor and gateway nodes are designed using the ESP32 SoC with integrated Bluetooth Low Energy 4.2 and Wi-Fi. A development board, the Arducam IoTai ESP32, is used for prototyping, testing, and power measurements. Google’s Firebase is used as the cloud storage site for image data collected by the sensor. The sensor node captures images using the OV2640 2MP camera module and transmits the image data to the gateway via Bluetooth Low Energy. The gateway then uploads the collected images to Firebase via a known nearby Wi-Fi network connection. This image data can then be processed and analyzed by computer vision and machine learning pipelines to assess crop growth or other needs. The sensor node achieves a wireless transmission data throughput of 220kbps while consuming 150mA of current; the sensor sleeps at 162µA. The sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day based on the development board power measurements. This network can be utilized by any application that requires high data rates, low power consumption, short-range communication, and large amounts of data to be transmitted at low-frequency intervals.

Keywords: Bluetooth low energy, ESP32, firebase cloud, IoT, smart farming

Procedia PDF Downloads 134

Authors: Linet Melisa Daubanes, Nhleko Monique Chiloane

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The shovel-truck system is the most prevalent material handling system used in surface mining operations. Material handling entails the loading and hauling of material from production areas to dumping areas. The material handling process has operational delays that have a negative impact on the productivity of the load and haul fleet. Factors that may contribute to operational delays include shovel-truck mismatch, haul routes, machine breakdowns, extreme weather conditions, etc. The aim of this paper is to investigate factors that contribute to operational delays affecting the productivity of the load and haul fleet at the mine. Productivity is the measure of the effectiveness of producing products from a given quantity of units, the ratio of output to inputs. Productivity can be improved by producing more outputs with the same or fewer units and/or introducing better working methods etc. Several key performance indicators (KPI) for the evaluation of productivity will be discussed in this study. These KPIs include but are not limited to hauling conditions, bucket fill factor, cycle time, and utilization. The research methodology of this study is a combination of on-site time studies and observations. Productivity can be optimized by managing the factors that affect the operational performance of the haulage fleet.

Keywords: cycle time, fleet performance, load and haul, surface mining

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: M. Hisham, S. Cabemaiwai, S. Prasad, T. Dauvakatini, R. Ananthanarayanan

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A solenoid valve is an important part of many fluid systems. Its purpose is to regulate fluid flow in a machine. Due to the crucial role of the solenoid valve and its design intricacy, it is quite expensive to obtain in Fiji and is not manufactured locally. A concern raised by the local health industry is that the housing of the solenoid valve gets damaged when machines are continuously being used and this part of the valve is very costly to replace due to the lack of availability in Fiji and many other South Pacific region countries. This study explores the agile manufacturing of a solenoid coil housing using the Fused Deposition Modeling (FDM) process. An emergy study was carried out to analyze the feasibility and sustainability of producing the part locally after estimating a Unit Emergy Value (or emergy transformity) of 1.27E+05 sej/j for the electricity in Fiji. The total emergy of the process was calculated to be 3.05E+12 sej, of which a majority was sourced from imported services and materials. Renewable emergy sources contributed to just 16.04% of the total emergy. Therefore, the part is suitable to be manufactured in Fiji with a reasonable quality and a cost of $FJ 2.85. However, the loading on the local environment is found to be significant and therefore, alternative raw materials for the filament like recycled PET should be explored or alternative manufacturing processes may be analyzed before committing to fabricating the part using FDM in its analyzed state.

Keywords: emergy analysis, fused deposition modeling, solenoid valve housing, sustainable production

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing

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Authors: Waleed Basuliman

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Anthropometric dimensions are considered one of the important factors when designing human-machine systems. In this study, the estimation of anthropometric dimensions has been improved by using Artificial Neural Network (ANN) model that is able to predict the anthropometric measurements of Saudi males in Riyadh City. A total of 1427 Saudi males aged 6 to 60 years participated in measuring 20 anthropometric dimensions. These anthropometric measurements are considered important for designing the work and life applications in Saudi Arabia. The data were collected during eight months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining 15 dimensions were set to be the measured variables (Model’s outcomes). The hidden layers varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was able to estimate the body dimensions of Saudi male population in Riyadh City. The network's mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found to be 0.0348 and 3.225, respectively. These results were found less, and then better, than the errors found in the literature. Finally, the accuracy of the developed neural network was evaluated by comparing the predicted outcomes with regression model. The ANN model showed higher coefficient of determination (R2) between the predicted and actual dimensions than the regression model.

Keywords: artificial neural network, anthropometric measurements, back-propagation

Procedia PDF Downloads 482

Authors: Giulia Bonaldo, Alberto Vaccheri, Ottavio D'Annibali, Domenico Motola

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The Human Papilloma Virus (HPV) was shown to be the cause of different types of carcinomas, first of all of the cervical intraepithelial neoplasia. Since the early 80s to today, thanks first to the preventive screening campaigns (pap-test) and following to the introduction of HPV vaccines on the market; the number of new cases of cervical cancer has decreased significantly. The HPV vaccines currently approved are three: Cervarix® (HPV2 - virus type: 16 and 18), Gardasil® (HPV4 - 6, 11, 16, 18) and Gardasil 9® (HPV9 - 6, 11, 16, 18, 31, 33, 45, 52, 58), which all protect against the two high-risk HPVs (6, 11) that are mainly involved in cervical cancers. Despite the remarkable effectiveness of these vaccines has been demonstrated, in the recent years, there have been many complaints about their risk-benefit profile due to Adverse Events Following Immunization (AEFI). The purpose of this study is to provide a support about the ongoing discussion on the safety profile of HPV vaccines based on real life data deriving from spontaneous reports of suspected AEFIs collected in the Vaccine Adverse Events Reporting System (VAERS). VAERS is a freely-available national vaccine safety surveillance database of AEFI, co-administered by the Centers for Disease Control and Prevention (CDC) and Food and Drug Administration (FDA). We collected all the reports between January 2007 to December 2017 related to the HPV vaccines with a brand name (HPV2, HPV4, HPV9) or without (HPVX). A disproportionality analysis using Reporting Odds Ratio (ROR) with 95% confidence interval and p value ≤ 0.05 was performed. Over the 10-year period, 54889 reports of AEFI related to HPV vaccines reported in VAERS, corresponding to 224863 vaccine-event pairs, were retrieved. The highest number of reports was related to Gardasil (n = 42244), followed by Gardasil 9 (7212) and Cervarix (3904). The brand name of the HPV vaccine was not reported in 1529 cases. The two events more frequently reported and statistically significant for each vaccine were: dizziness (n = 5053) ROR = 1.28 (CI95% 1.24 – 1.31) and syncope (4808) ROR = 1.21 (1.17 – 1.25) for Gardasil. For Gardasil 9, injection site pain (305) ROR = 1.40 (1.25 – 1.57) and injection site erythema (297) ROR = 1.88 (1.67 – 2.10) and for Cervarix, headache (672) ROR = 1.14 (1.06 – 1.23) and loss of consciousness (528) ROR = 1.71 (1.57 – 1.87). In total, we collected 406 reports of death and 2461 cases of permanent disability in the ten-year period. The events consisting of incorrect vaccine storage or incorrect administration were not considered. The AEFI analysis showed that the most frequently reported events are non-serious and listed in the corresponding SmPCs. In addition to these, potential safety signals arose regarding less frequent and severe AEFIs that would deserve further investigation. This already happened with the referral of the European Medicines Agency (EMA) for the adverse events POTS (Postural Orthostatic Tachycardia Syndrome) and CRPS (Complex Regional Pain Syndrome) associated with anti-papillomavirus vaccines.

Keywords: adverse drug reactions, pharmacovigilance, safety, vaccines

Procedia PDF Downloads 157

Authors: Christiana Ebobo

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Cybercrime consists of numerous crimes that are perpetrated on the internet on daily basis. The forms include but not limited to Identity theft, Pretentious dating, Desktop counterfeiting, Internet chat room, Cyber harassment, Fraudulent electronic mails, Automated Teller Machine Spoofing, Pornography, Piracy, Hacking, Credit card frauds, Phishing and Spamming. The general term used among the youths for this type of crime in Nigeria is ‘Yahoo Yahoo’. Cybercrime is on the increase among the youths at all levels as such this study aims at examining the impact of cybercrime on youth development in Nigeria. The study examines the impact of cybercrime on youths’ academic performance, integrity, employment and religious practices. The study is a survey which made use of questionnaire and focus group discussion among 150 randomly selected youths in Gwagwalada LCDA, Federal Capital Territory, Nigeria. The study adopts the systems theory as its theoretical framework. The study also adopts the simple frequency table and percentage for its data analysis. The study reveals that cybercrime has eaten deep into the minds of some youths and some of them are practicing diabolic means to succeed in it. It is also reveals that majority (68%) of the respondents believe that cybercrime impacts negatively on youths’ academic performance in Nigeria. The major recommendation of this study is that cybercrime offenders should be treated like armed robbers in order to discourage other youths from getting involved in it.

Keywords: armed robber, cybercrime, integrity, youth

Procedia PDF Downloads 515

Authors: A. Soualem

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The design of multi stage deep drawing processes requires the evaluation of many process parameters such as the intermediate die geometry, the blank shape, the sheet thickness, the blank holder force, friction, lubrication etc..These process parameters have to be determined for the optimum forming conditions before the process design. In general sheet metal forming may involve stretching drawing or various combinations of these basic modes of deformation. It is important to determine the influence of the process variables in the design of sheet metal working process. Especially, the punch and die corner for deep drawing will affect the formability. At the same time the prediction of sheet metals springback after deep drawing is an important issue to solve for the control of manufacturing processes. Nowadays, the importance of this problem increases because of the use of steel sheeting with high stress and also aluminum alloys. The aim of this paper is to give a better understanding of the springback and its effect in various sheet metals forming process such as expansion and restraint deep drawing in the cup drawing process, by varying radius die, lubricant for two commercially available materials e.g. galvanized steel and Aluminum sheet. To achieve these goals experiments were carried out and compared with other results. The original of our purpose consist on tests which are ensured by adapting a U-type stretching-bending device on a tensile testing machine, where we studied and quantified the variation of the springback.

Keywords: springback, deep drawing, expansion, restricted deep drawing

Procedia PDF Downloads 450