Search results for: reluctance machines

Authors: Khalid Abdelghany

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Additive Manufacturing (AM) is the manufacturing technology that is used to fabricate fast products direct from CAD models in very short time and with minimum operation steps. Jointly with the advancement in medical computer modeling, AM proved to be a very efficient tool to help physicians, orthopedic surgeons and dentists design and fabricate patient-tailored surgical guides, templates and customized implants from the patient’s CT / MRI images. AM jointly with computer-assisted designing/computer-assisted manufacturing (CAD/CAM) technology have enabled medical practitioners to tailor physical models in a patient-and purpose-specific fashion and helped to design and manufacture of templates, appliances and devices with a high range of accuracy using biocompatible materials. In developing countries, there are some technical and financial limitations of implementing such advanced tools as an essential portion of medical applications. CMRDI institute in Egypt has been working in the field of Medical Additive Manufacturing since 2003 and has assisted in the recovery of hundreds of poor patients using these advanced tools. This paper focuses on the surgical and dental use of 3D printing technology in Egypt as a developing country. The presented case studies have been designed and processed using the software tools and additive manufacturing machines in CMRDI through cooperative engineering and medical works. Results showed that the implementation of the additive manufacturing tools in developed countries is successful and could be economical comparing to long treatment plans.

Keywords: additive manufacturing, dental and orthopeadic stents, patient specific surgical tools, titanium implants

Procedia PDF Downloads 315

Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

Procedia PDF Downloads 205

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

Procedia PDF Downloads 409

Authors: Nyeng P. Gyang

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Even though past, current and future trends suggest that multicore and cloud computing systems are increasingly prevalent/ubiquitous, this class of parallel systems is nonetheless underutilized, in general, and barely used for research on employing parallel Delaunay triangulation for parallel surface modeling and generation, in particular. The performances, of actual/physical and virtual/cloud multicore systems/machines, at executing various algorithms, which implement various parallelization strategies of the incremental insertion technique of the Delaunay triangulation algorithm, were evaluated. T-tests were run on the data collected, in order to determine whether various performance metrics differences (including execution time, speedup and efficiency) were statistically significant. Results show that the actual machine is approximately twice faster than the virtual machine at executing the same programs for the various parallelization strategies. Results, which furnish the scalability behaviors of the various parallelization strategies, also show that some of the differences between the performances of these systems, during different runs of the algorithms on the systems, were statistically significant. A few pseudo superlinear speedup results, which were computed from the raw data collected, are not true superlinear speedup values. These pseudo superlinear speedup values, which arise as a result of one way of computing speedups, disappear and give way to asymmetric speedups, which are the accurate kind of speedups that occur in the experiments performed.

Keywords: cloud computing systems, multicore systems, parallel Delaunay triangulation, parallel surface modeling and generation

Procedia PDF Downloads 206

Authors: Khlood F. Salman, Richard Zoucha, Hani Nawafleh

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Introduction: Jordan ranks the fourth highest breast cancer prevalence after Lebanon, Bahrain, and Kuwait. Considerable evidence showed that cultural, ethnic, and economic differences influence a woman’s practice to early detection and prevention of breast cancer. Objectives: To understand women’s health beliefs and values in relation to early detection of breast cancer; and to explore the impact of these beliefs on their decisions regarding reluctance or acceptance of early detection measures such as mammogram screening. Design: A qualitative focused ethnography was used to collect data for this study. Settings: The study was conducted in the second largest city surrounded by a large rural area in Ma’an- Jordan. Participants: A total of twenty seven women, with no history of breast cancer, between the ages of 18 and older, who had prior health experience with health providers, and were willing to share elements of personal health beliefs related to breast health within the larger cultural context. The participants were recruited using the snowball method and words of mouth. Data collection and analysis: A short questionnaire was designed to collect data related to socio demographic status (SDQ) from all participants. A Semi-structured interviews guide was used to elicit data through interviews with the informants. Nvivo10 a data manager was utilized to assist with data analysis. Leininger’s four phases of qualitative data analysis was used as a guide for the data analysis. The phases used to analyze the data included: 1) Collecting and documenting raw data, 2) Identifying of descriptors and categories according to the domains of inquiry and research questions. Emic and etic data is coded for similarities and differences, 3) Identifying patterns and contextual analysis, discover saturation of ideas and recurrent patterns, and 4) Identifying themes and theoretical formulations and recommendations. Findings: Three major themes were emerged within the cultural and religious context; 1. Fear, denial, embarrassment and lack of knowledge were common perceptions of Ma’anis’ women regarding breast health and screening mammography, 2. Health care professionals in Jordan were not quick to offer information and education about breast cancer and screening, and 3. Willingness to learn about breast health and cancer prevention. Conclusion: The study indicated the disparities between the infrastructure and resourcing in rural and urban areas of Jordan, knowledge deficit related to breast cancer, and lack of education about breast health may impact women’s decision to go for a mammogram screening. Cultural beliefs, fear, embarrassments as well as providers lack of focus on breast health were significant contributors against practicing breast health. Health providers and policy makers should provide resources for the establishment health education programs regarding breast cancer early detection and mammography screening. Nurses should play a major role in delivering health education about breast health in general and breast cancer in particular. A culturally appropriate health awareness messages can be used in creating educational programs which can be employed at the national levels.

Keywords: breast health, beliefs, cultural context, ethnography, mammogram screening

Procedia PDF Downloads 298

Authors: H. T. Le Luong, C. Hénaux, F. Messine, G. Bueno-Mariani, S. Mollov, N. Voyer

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This paper presents a comparative study of different modular brushless wound rotor synchronous machine (MB-WRSM). The goal of the study is to highlight the structure which offers the best fault tolerant capability and the highest output performances. The fundamental winding factor is calculated by using the method based on EMF phasors as a significant criterion to select the preferred number of phases, stator slots, and poles. With the limited number of poles for a small machine (3.67kW/7000rpm), 15 different machines for preferred phase/slot/pole combinations are analyzed using two-dimensional (2-D) finite element method and compared according to three criteria: torque density, torque ripple and efficiency. The 7phase/7slot/6pole machine is chosen with the best compromise of high torque density, small torque ripple (3.89%) and high nominal efficiency (95%). This machine is then compared with a reference design surface permanent magnet synchronous machine (SPMSM). In conclusion, this paper provides an electromagnetic analysis of a new brushless wound-rotor synchronous machine using multiphase non-overlapping fractional slot double layer winding. The simulation results are discussed and demonstrate that the MB-WRSM presents interesting performance features, with overall performance closely matching that of an equivalent SPMSM.

Keywords: finite element method (FEM), machine performance, modular wound rotor synchronous machine, non-overlapping concentrated winding

Procedia PDF Downloads 290

Authors: G. K. Awari, Vishwajeet V. Ambade, S. W. Rajurkar

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The field of additive manufacturing is growing, and discoveries are being made. 3D printing machines are also being developed to accommodate a wider range of 3D printing materials, including plastics, metals (metal AM powders), composites, filaments, and other materials. There are numerous printing materials available for industrial additive manufacturing. Such materials have their unique characteristics, advantages, and disadvantages. In order to avoid errors in additive manufacturing, key elements such as 3D printing material type, texture, cost, printing technique and procedure, and so on must be examined. It can be complex to select the best material for a particular job. Polylactic acid (PLA) is made from sugar cane or cornstarch, both of which are renewable resources. "Black plastic" is another name for it. Because it is safe to use and print, it is frequently used in primary and secondary schools. This is also how FDM screen printing is done. PLA is simple to print because of its low warping impact. It's also possible to print it on a cold surface. When opposed to ABS, it allows for sharper edges and features to be printed. This material comes in a wide range of colours. Polylactic acid (PLA) is the most common material used in fused deposition modelling (FDM). PLA can be used to print a wide range of components, including medical implants, household items, and mechanical parts. The mechanical behaviour of the printed item is affected by variations in infill patterns that are subjected to compressive tests in the current investigation to examine their behaviour under compressive stresses.

Keywords: fused deposition modelling, polylactic acid, infill density, infill pattern, compressive strength

Procedia PDF Downloads 73

Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Sung Uk Park, Young Su Kang, Sangmo Kang, Young Kweon Suh

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Mineral product, waste concrete (fine aggregates), waste in the optical field, industry, and construction employ separators to separate solids and classify them according to their size. Various sorting machines are used in the industrial field such as those operating under electrical properties, centrifugal force, wind power, vibration, and magnetic force. Study on separators has been carried out to contribute to the environmental industry. In this study, we perform CFD analysis for understanding the basic mechanism of the separation of waste concrete (fine aggregate) particles from air with a machine built with a rotor with blades. In CFD, we first performed two-dimensional particle tracking for various particle sizes for the model with 1 degree, 1.5 degree, and 2 degree angle between each blade to verify the boundary conditions and the method of rotating domain method to be used in 3D. Then we developed 3D numerical model with ANSYS CFX to calculate the air flow and track the particles. We judged the capability of particle separation for given size by counting the number of particles escaping from the domain toward the exit among 10 particles issued at the inlet. We confirm that particles experience stagnant behavior near the exit of the rotating blades where the centrifugal force acting on the particles is in balance with the air drag force. It was also found that the minimum particle size that can be separated by the machine with the rotor is determined by its capability to stay at the outlet of the rotor channels.

Keywords: environmental industry, separator, CFD, fine aggregate

Procedia PDF Downloads 595

Authors: Jing Wu, Wei Lv, Yibing Li, Yuanfan You

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The separation of speech signals has become a research hotspot in the field of signal processing in recent years. It has many applications and influences in teleconferencing, hearing aids, speech recognition of machines and so on. The sounds received are usually noisy. The issue of identifying the sounds of interest and obtaining clear sounds in such an environment becomes a problem worth exploring, that is, the problem of blind source separation. This paper focuses on the under-determined blind source separation (UBSS). Sparse component analysis is generally used for the problem of under-determined blind source separation. The method is mainly divided into two parts. Firstly, the clustering algorithm is used to estimate the mixing matrix according to the observed signals. Then the signal is separated based on the known mixing matrix. In this paper, the problem of mixing matrix estimation is studied. This paper proposes an improved algorithm to estimate the mixing matrix for speech signals in the UBSS model. The traditional potential algorithm is not accurate for the mixing matrix estimation, especially for low signal-to noise ratio (SNR).In response to this problem, this paper considers the idea of an improved potential function method to estimate the mixing matrix. The algorithm not only avoids the inuence of insufficient prior information in traditional clustering algorithm, but also improves the estimation accuracy of mixing matrix. This paper takes the mixing of four speech signals into two channels as an example. The results of simulations show that the approach in this paper not only improves the accuracy of estimation, but also applies to any mixing matrix.

Keywords: DBSCAN, potential function, speech signal, the UBSS model

Procedia PDF Downloads 135

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

Procedia PDF Downloads 480

Authors: Jocelyne Uwibambe, Ange Thaina Ndizeye, Dinah Ishimwe, Emmanuel Mugabo Byakagaba

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Background: In low- and middle-income countries (LMICs), the use of modern contraceptive methods among women, including adolescents, is still low despite the desire to avoid pregnancy. In addition, countries have set a minimum age for marriage, which is 21 years for most countries, including Rwanda. The Rwandan culture, to a certain extent, and religion, to a greater extent, however, limit the freedom of young women to use contraceptive services because it is wrongly perceived as an encouragement for premarital sexual intercourse. In the end, what doesn’t change is that denying access to contraceptives to either male or female adolescents does not translate into preventing them from sexual activities, hence leading to an ever-increasing number of unwanted pregnancies, possible STIs, HIV, Human Papilloma Virus, and subsequent unsafe abortion followed by avoidable expensive complications. The purpose of this study is to evaluate the perception of healthcare providers regarding contraceptive use among adolescents. Methodology: This was a qualitative study. Interviews were done with different healthcare providers, including doctors, nurses, midwives, and pharmacists, through focused group discussions and in-depth interviews, then the audio was transcribed, translated and thematic coding was done. Results: This study explored the perceptions of healthcare workers regarding the provision of modern contraception to adolescents in Rwanda. The findings revealed that while healthcare providers had a good understanding of family planning and contraception, they were hesitant to provide contraception to adolescents. Sociocultural beliefs played a significant role in shaping their attitudes, as many healthcare workers believed that providing contraception to adolescents would encourage promiscuous behavior and go against cultural norms. Religious beliefs also influenced their reluctance, with some healthcare providers considering premarital sex and contraception as sinful. Lack of knowledge among parents and adolescents themselves was identified as a contributing factor to unwanted pregnancies, as inaccurate information from peers and social media influenced risky sexual behavior. Conditional policies, such as the requirement for parental consent, further hindered adolescents' access to contraception. The study suggested several solutions, including comprehensive sexual and reproductive health education, involving multiple stakeholders, ensuring easy access to contraception, and involving adolescents in policymaking. Overall, this research highlights the need for addressing sociocultural beliefs, improving healthcare providers' knowledge, and revisiting policies to ensure adolescents' reproductive health rights are met in Rwanda. Conclusion: The study highlights the importance of enhancing healthcare provider training, expanding access to modern contraception, implementing community-based interventions, and strengthening policy and programmatic support for adolescent contraception. Addressing these challenges is crucial for improving the provision of family planning services to adolescents in Rwanda and achieving the Sustainable Development Goals related to sexual and reproductive health. Collaborative efforts involving various stakeholders and organizations can contribute to overcoming these barriers and promoting the well-being of adolescents in Rwanda.

Keywords: adolescent, health care providers, contraception, reproductive health

Procedia PDF Downloads 50

Authors: N. J. M. Yusof, T. Sabir, J. McLoughlin

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Garment manufacturing industry involves sequential processes that are subjected to uncontrollable variations. The industry depends on the skill of labour in handling the varieties of fabrics and accessories, machines, and also a complicated sewing operation. Due to these reasons, garment manufacturers created systems to monitor and control the product’s quality regularly by conducting quality approaches to minimize variation. The aims of this research were to ascertain the quality approaches deployed by Malaysian garment manufacturers in three key areas-quality systems and tools; quality control and types of inspection; sampling procedures chosen for garment inspection. The focus of this research also aimed to distinguish quality approaches used by companies that supplied the finished garments to both domestic and international markets. The feedback from each of company’s representatives was obtained using the online survey, which comprised of five sections and 44 questions on the organizational profile and quality approaches used in the garment industry. The results revealed that almost all companies had established their own mechanism of process control by conducting a series of quality inspection for daily production either it was formally been set up or vice versa. Quality inspection was the predominant quality control activity in the garment manufacturing and the level of complexity of these activities was substantially dictated by the customers. AQL-based sampling was utilized by companies dealing with the export market, whilst almost all the companies that only concentrated on the domestic market were comfortable using their own sampling procedures for garment inspection. This research provides an insight into the implementation of quality approaches that were perceived as important and useful in the garment manufacturing sector, which is truly labour-intensive.

Keywords: garment manufacturing, quality approaches, quality control, inspection, Acceptance Quality Limit (AQL), sampling

Procedia PDF Downloads 442

Authors: Song Zhang, Hui Wang, Johannes Henrich Schleifenbaum

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Due to the increasing demand for quality assurance and reliability for additive manufacturing, the development of an advanced in-situ monitoring system is required to monitor the process anomalies as input for further process control. Optical-based monitoring systems, such as CMOS cameras and NIR cameras, are proved as effective ways to monitor the geometrical distortion and exceptional thermal distribution. Therefore, many studies and applications are focusing on the availability of the optical-based monitoring system for detecting varied types of defects. However, the capability of the monitoring setup is not quantified. In this study, a quantification model to evaluate the capability of the monitoring setups for the LPBF machine based on acquired monitoring data of a designed test artifact is presented, while the design of the relevant test artifacts is discussed. The monitoring setup is evaluated based on its hardware properties, location of the integration, and light condition. Methodology of data processing to quantify the capacity for each aspect is discussed. The minimal capability of the detectable size of the monitoring set up in the application is estimated by quantifying its resolution and accuracy. The quantification model is validated using a CCD camera-based monitoring system for LPBF machines in the laboratory with different setups. The result shows the model to quantify the monitoring system's performance, which makes the evaluation of monitoring systems with the same concept but different setups possible for the LPBF process and provides the direction to improve the setups.

Keywords: data processing, in-situ monitoring, LPBF process, optical system, quantization model, test artifact

Procedia PDF Downloads 197

Authors: Daniela Stephanie Kuenstle

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Complex industries such as banking or insurance experience slow growth in mobile sales. While today’s mobile applications are sophisticated and enable location based and personalized services, consumers prefer online or even face-to-face services to complete complex transactions. A possible reason for this reluctance is that the provided service within transactional mobile applications (apps) does not adequately correspond to users’ needs. Therefore, this paper examines the impact of the user context on mobile service (m-service) in m-commerce. Motivated by the potential which context-sensitive m-services hold for the future, the impact of temporal variations as a dimension of user context, on m-service design is examined. In particular, the research question asks: Does consumer urgency function as a determinant of m-service composition in transactional apps by moderating the relation between m-service type and m-service success? Thus, the aim is to explore the moderating influence of urgency on m-service types, which includes Technology Mediated Service and Technology Generated Service. While mobile applications generally comprise features of both service types, this thesis discusses whether unexpected urgency changes customer preferences for m-service types and how this consequently impacts the overall m-service success, represented by purchase intention, loyalty intention and service quality. An online experiment with a random sample of N=1311 participants was conducted. Participants were divided into four treatment groups varying in m-service types and urgency level. They were exposed to two different urgency scenarios (high/ low) and two different app versions conveying either technology mediated or technology generated service. Subsequently, participants completed a questionnaire to measure the effectiveness of the manipulation as well as the dependent variables. The research model was tested for direct and moderating effects of m-service type and urgency on m-service success. Three two-way analyses of variance confirmed the significance of main effects, but demonstrated no significant moderation of urgency on m-service types. The analysis of the gathered data did not confirm a moderating effect of urgency between m-service type and service success. Yet, the findings propose an additive effects model with the highest purchase and loyalty intention for Technology Generated Service and high urgency, while Technology Mediated Service and low urgency demonstrate the strongest effect for service quality. The results also indicate an antagonistic relation between service quality and purchase intention depending on the level of urgency. Although a confirmation of the significance of this finding is required, it suggests that only service convenience, as one dimension of mobile service quality, delivers conditional value under high urgency. This suggests a curvilinear pattern of service quality in e-commerce. Overall, the paper illustrates the complex interplay of technology, user variables, and service design. With this, it contributes to a finer-grained understanding of the relation between m-service design and situation dependency. Moreover, the importance of delivering situational value with apps depending on user context is emphasized. Finally, the present study raises the demand to continue researching the impact of situational variables on m-service design in order to develop more sophisticated m-services.

Keywords: mobile consumer behavior, mobile service design, mobile service success, self-service technology, situation dependency, user-context sensitivity

Procedia PDF Downloads 268

Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

Procedia PDF Downloads 326

Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

Procedia PDF Downloads 72

Authors: Michael Zenker, Henrik Prinzhorn, Christian Böning, Tom Strating

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Large-scale products are often assembled according to the job-site principle, meaning that during the assembly the product is located at a fixed position, while the area requirements are constantly changing. On one hand, the product itself is growing with each assembly step, whereas varying areas for storage, machines or working areas are temporarily required. This is an important factor when arranging products to be assembled within the factory. Currently, it is common to reserve a fixed area for each product to avoid overlaps or collisions with the other assemblies. Intending to be large enough to include the product and all adjacent areas, this reserved area corresponds to the superposition of the maximum extents of all required areas of the product. In this procedure, the reserved area is usually poorly utilized over the course of the entire assembly process; instead a large part of it remains unused. If the available area is a limited resource, a systematic arrangement of the products, which complies with the dynamic area requirements, will lead to an increased area utilization and productivity. This paper presents the results of a study on the arrangement of assembly objects assuming dynamic, competing area requirements. First, the problem situation is extensively explained, and existing research on associated topics is described and evaluated on the possibility of an adaptation. Then, a newly developed mathematical optimization model is introduced. This model allows an optimal arrangement of dynamic areas, considering logical and practical constraints. Finally, in order to quantify the potential of the developed method, some test series results are presented, showing the possible increase in area utilization.

Keywords: dynamic area requirements, facility layout problem, optimization model, product assembly

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Authors: Emil F. Khisamutdinov

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Development of functional materials undergoing structural transformations in response to an external stimulus such as environmental changes (pH, temperature, etc.), the presence of particular proteins, or short oligonucleotides are of great interest for a variety of applications ranging from medicine to electronics. The dynamic operations of most nucleic acid (NA) devices, including circuits, nano-machines, and biosensors, rely on networks of NA strand displacement processes in which an external or stimulus strand displaces a target strand from a DNA or RNA duplex. The rate of strand displacement can be greatly increased by the use of “toeholds,” single-stranded regions of the target complex to which the invading strand can bind to initiate the reaction, forming additional base pairs that provide a thermodynamic driving force for transformation. Herein, we developed a highly robust nanoparticle shape transition, sequentially transforming DNA polygons from one shape to another using the toehold-mediated DNA strand displacement technique. The shape transformation was confirmed by agarose gel electrophoresis and atomic force microscopy. Furthermore, we demonstrate that our approach is applicable for RNA shape transformation from triangle to square, which can be detected by fluorescence emission from malachite green binding RNA aptamer. Using gel-shift and fluorescence assays, we demonstrated efficient transformation occurs at isothermal conditions (37°C) that can be implemented within living cells as reporter molecules. This work is intended to provide a simple, cost-effective, and straightforward model for the development of biosensors and regulatory devices in nucleic acid nanotechnology.

Keywords: RNA nanotechnology, bionanotechnology, toehold mediated DNA switch, RNA split fluorogenic aptamers

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Authors: Lucian Capitanu, Virgil Florescu

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Often for the laboratory studies, modeling of specific tribological processes raises special problems. One such problem is the modeling of some temperatures and extremely high contact pressures, allowing modeling of temperatures and pressures at which the injection or extrusion processing of thermoplastic materials takes place. Tribological problems occur mainly in thermoplastics materials reinforced with glass fibers. They produce an advanced wear to the barrels and screws of processing machines, in short time. Obtaining temperatures around 210 °C and higher, as well as pressures around 100 MPa is very difficult in the laboratory. This paper reports a simple and convenient solution to get these conditions, using friction sliding couples with linear contact, cylindrical liner plastic filled with glass fibers on plate steel samples, polished and super-finished. C120 steel, which is a steel for moulds and Rp3 steel, high speed steel for tools, were used. Obtaining the pressure was achieved by continuous request of the liner in rotational movement up to its elasticity limits, when the dry friction coefficient reaches or exceeds the hardness value of 0.5 HB. By dissipation of the power lost by friction on flat steel sample, are reached contact temperatures at the metal surface that reach and exceed 230 °C, being placed in the range temperature values of the injection. Contact pressures (in load and materials conditions used) ranging from 16.3-36.4 MPa were obtained depending on the plastic material used and the glass fibers content.

Keywords: plastics with glass fibers, dry friction, linear contact, contact temperature, contact pressure, experimental simulation

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Authors: Shao Bo Cheng, Yong-Jin Han, Se Young Park, Seong-Bae Park

Abstract:

Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce.

Keywords: spam keyword, e-commerce, keyword features, spam filtering

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Authors: R. Pravin Kumar, L. Roopa

Abstract:

Enzymes are molecular machines used in various industries such as pharmaceuticals, cosmetics, food and animal feed, paper and leather processing, biofuel, and etc. Nevertheless, this has been possible only by the breath-taking efforts of the chemists and biologists to evolve/engineer these mysterious biomolecules to work the needful. Main agenda of this enzyme engineering project is to derive screening and selection tools to obtain focused libraries of enzyme variants with desired qualities. The methodologies for this research include the well-established directed evolution, rational redesign and relatively less established yet much faster and accurate insilico methods. This concept was initiated as a Receptor Rependent-4Dimensional Quantitative Structure Activity Relationship (RD-4D-QSAR) to predict kinetic properties of enzymes and extended here to study transaminase by a 7D QSAR approach. Induced-fit scenarios were explored using Quantum Mechanics/Molecular Mechanics (QM/MM) simulations which were then placed in a grid that stores interactions energies derived from QM parameters (QMgrid). In this study, the mutated enzymes were immersed completely inside the QMgrid and this was combined with solvation models to predict descriptors. After statistical screening of descriptors, QSAR models showed > 90% specificity and > 85% sensitivity towards the experimental activity. Mapping descriptors on the enzyme structure revealed hotspots important to enhance the enantioselectivity of the enzyme.

Keywords: QMgrid, QM/MM simulations, RD-4D-QSAR, transaminase

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: M. Mutemwa

Abstract:

A Cybersecurity Operation Centre (SOC) is a centralized hub for network event monitoring and incident response. SOCs are critical when determining an organization’s cybersecurity posture because they can be used to detect, analyze and report on various malicious activities. For most organizations, a SOC is not part of the initial design and implementation of the Information Technology (IT) environment but rather an afterthought. As a result, it is not natively a plug and play component; therefore, there are integration challenges when a SOC is introduced into an organization. A SOC is an independent hub that needs to be integrated with existing procedures, policies and IT systems of an organization such as the service desk, ticket logging system, reporting, etc. This paper discussed the challenges of integrating a newly developed SOC to an organization’s existing IT environment. Firstly, the paper begins by looking at what data sources should be incorporated into the Security Information and Event Management (SIEM) such as which host machines, servers, network end points, software, applications, web servers, etc. for security posture monitoring. That is which systems need to be monitored first and the order by which the rest of the systems follow. Secondly, the paper also describes how to integrate the organization’s ticket logging system with the SOC SIEM. That is how the cybersecurity related incidents should be logged by both analysts and non-technical employees of an organization. Also the priority matrix for incident types and notifications of incidents. Thirdly, the paper looks at how to communicate awareness campaigns from the SOC and also how to report on incidents that are found inside the SOC. Lastly, the paper looks at how to show value for the large investments that are poured into designing, building and running a SOC.

Keywords: cybersecurity operation centre, incident response, priority matrix, procedures and policies

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Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: S. Ziaran, M. Musil, M. Cekan, O. Chlebo

Abstract:

The paper analyzes the response of buildings and industrially structures on seismic waves (low frequency mechanical vibration) generated by blasting operations. The principles of seismic analysis can be applied for different kinds of excitation such as: earthquakes, wind, explosions, random excitation from local transportation, periodic excitation from large rotating and/or machines with reciprocating motion, metal forming processes such as forging, shearing and stamping, chemical reactions, construction and earth moving work, and other strong deterministic and random energy sources caused by human activities. The article deals with the response of seismic, low frequency, mechanical vibrations generated by nearby blasting operations on a residential home. The goal was to determine the fundamental natural frequencies of the measured structure; therefore it is important to determine the resonant frequencies to design a suitable modal damping. The article also analyzes the package of seismic waves generated by blasting (Primary waves – P-waves and Secondary waves S-waves) and investigated the transfer regions. For the detection of seismic waves resulting from an explosion, the Fast Fourier Transform (FFT) and modal analysis, in the frequency domain, is used and the signal was acquired and analyzed also in the time domain. In the conclusions the measured results of seismic waves caused by blasting in a nearby quarry and its effect on a nearby structure (house) is analyzed. The response on the house, including the fundamental natural frequency and possible fatigue damage is also assessed.

Keywords: building structure, seismic waves, spectral analysis, structural response

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Authors: Philipp Zopf, Franz Haas

Abstract:

Manufacturing of very hard and refractory materials like ceramics, glass or carbide poses particular challenges on tools and machines. The company Sauer GmbH developed especially for this application area ultrasonic tool holders working in a frequency range from 15 to 60 kHz and superimpose the common tool movement in the vertical axis. This technique causes a structural weakening in the contact area and facilitates the machining. The possibility of the force reduction for these special materials especially in drilling of carbide with diamond tools up to 30 percent made the authors try to expand the application range of this method. To make the results evaluable, the authors decide to start with existing processes in which the positive influence of the ultrasonic assistance is proven to understand the mechanism. The comparison of a grinding process the Institute use to machine materials mentioned in the beginning and steel could not be more different. In the first case, the authors use tools with geometrically undefined edges. In the second case, the edges are geometrically defined. To get valid results of the tests, the authors decide to investigate two manufacturing methods, drilling and milling. The main target of the investigation is to reduce the cutting force measured with a force measurement platform underneath the workpiece. Concerning to the direction of the ultrasonic assistance, the authors expect lower cutting forces and longer endurance of the tool in the drilling process. To verify the frequencies and the amplitudes an FFT-analysis is performed. It shows the increasing damping depending on the infeed rate of the tool. The reducing of amplitude of the cutting force comes along.

Keywords: drilling, machining, milling, ultrasonic

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Authors: Anja Gräff, Stefan Holzer, Manfred Höld, Jörn Stumpenhausen, Heinz Bernhardt

Abstract:

In view of the increasing quantity of 'green energy' from renewable raw materials and photovoltaic facilities, it is quite conceivable that power supply variations may occur, so that constantly working machines like automatic milking systems (AMS) may break down temporarily. The usage of farm-made energy is steadily increasing in order to keep energy costs as low as possible. As a result, power cuts are likely to happen more frequently. Current work in the framework of the project 'stable 4.0' focuses on possible stress reactions by simulating power cuts up to four hours in dairy farms. Based on heart activity it should be found out whether stress on dairy cows increases under these circumstances. In order to simulate a power cut, 12 random cows out of 2 herds were not admitted to the AMS for at least two hours on three consecutive days. The heart rates of the cows were measured and the collected data evaluated with HRV Program Kubios Version 2.1 on the basis of eight parameters (HR, RMSSD, pNN50, SD1, SD2, LF, HF and LF/HF). Furthermore, stress reactions were examined closely via video analysis, milk yield, ruminant activity, pedometer and measurements of cortisol metabolites. Concluding it turned out, that during the test only some animals were suffering from minor stress symptoms, when they tried to get into the AMS at their regular milking time, but couldn´t be milked because the system was manipulated. However, the stress level during a regular “time-dependent milking rejection” was just as high. So the study comes to the conclusion, that the low psychological stress level in the case of a 2-4 hours failure of an AMS does not have any impact on animal welfare and health.

Keywords: dairy cow, heart activity, power cut, stable 4.0

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Authors: Mehrnoosh Abouzari, Shahrokh Shahraei

Abstract:

With the development of artificial intelligence in the present age, intelligent machines and systems have proven their actual and potential capabilities and are mindful of increasing their presence in various fields of human life in the fields of industry, financial transactions, marketing, manufacturing, service affairs, politics, economics and various branches of the humanities .Therefore, despite the conservatism and prudence of law enforcement, the traces of artificial intelligence can be seen in various areas of law. Including judicial robotics capability estimation, intelligent judicial decision making system, intelligent defender and attorney strategy adjustment, dissemination and regulation of different and scattered laws in each case to achieve judicial coherence and reduce opinion, reduce prolonged hearing and discontent compared to the current legal system with designing rule-based systems, case-based, knowledge-based systems, etc. are efforts to apply AI in law. In this article, we will identify the ways in which AI is applied in its laws and regulations, identify the dominant concerns in this area and outline the relationship between these two areas in order to answer the question of how artificial intelligence can be used in different areas of law and what the implications of this application will be. The authors believe that the use of artificial intelligence in the three areas of legislative, judiciary and executive power can be very effective in governments' decisions and smart governance, and helping to reach smart communities across human and geographical boundaries that humanity's long-held dream of achieving is a global village free of violence and personalization and human error. Therefore, in this article, we are going to analyze the dimensions of how to use artificial intelligence in the three legislative, judicial and executive branches of government in order to realize its application.

Keywords: artificial intelligence, law, intelligent system, judge

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Authors: Yu-Hsin Tsai

Abstract:

A new, mandated county regional plan system has been initiated since 2010 nationwide in Taiwan, with its role situated in-between the policy-led cross-county regional plan and the blueprint-led city plan. This new regional plan contain both urban and rural areas in one single plan, which provides a more complete planning territory, i.e., city region within the county’s jurisdiction, and to be executed and managed effectively by the county government. However, the full picture of its functions and characteristics seems still not totally clear, compared with other levels of plans; either are planning goals and issues that can be most appropriately dealt with at this spatial scale. In addition, the extent to which the inclusion of sustainability ideal and measures to cope with climate change are unclear. Based on the above issues, this study aims to clarify the roles of county regional plan, to analyze the extent to which the measures cope with sustainability, climate change, and forecasted declining population, and the success factors and issues faced in the planning process. The methodology applied includes literature review, plan quality evaluation, and interview with officials of the central and local governments and urban planners involved for all the 23 counties in Taiwan. The preliminary research results show, first, growth management related policies have been widely implemented and expected to have effective impact, including incorporating resources capacity to determine maximum population for the city region as a whole, developing overall vision of urban growth boundary for all the whole city region, prioritizing infill development, and use of architectural land within urbanized area over rural area to cope with urban growth. Secondly, planning-oriented zoning is adopted in urban areas, while demand-oriented planning permission is applied in the rural areas with designated plans. Then, public participation has been evolved to the next level to oversee all of government’s planning and review processes due to the decreasing trust in the government, and development of public forum on the internet etc. Next, fertile agricultural land is preserved to maintain food self-supplied goal for national security concern. More adoption-based methods than mitigation-based methods have been applied to cope with global climate change. Finally, better land use and transportation planning in terms of avoiding developing rail transit stations and corridor in rural area is promoted. Even though many promising, prompt measures have been adopted, however, challenges exist to surround: first, overall urban density, likely affecting success of UGB, or use of rural agricultural land, has not been incorporated, possibly due to implementation difficulties. Second, land-use related measures to mitigating climate change seem less clear and hence less employed. Smart decline has not drawn enough attention to cope with predicted population decrease in the next decade. Then, some reluctance from county’s government to implement county regional plan can be observed vaguely possibly since limits have be set on further development on agricultural land and sensitive areas. Finally, resolving issue on existing illegal factories on agricultural land remains the most challenging dilemma.

Keywords: city region plan, sustainability, global climate change, growth management

Procedia PDF Downloads 349