Search results for: field regulated reluctance machine

Authors: S. Meguenni, A. Djahbar, K. Tounsi

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Electric railway traction systems are complex; they have electrical couplings, magnetic and solid mechanics. These couplings impose several constraints that complicate the modeling and analysis of these systems. An example of drive systems, which combine the advantages of the use of multiphase machines, power electronics and computing means, is mono convert isseur multi-machine system which can control a fully decoupled so many machines whose electric windings are connected in series. In this approach, our attention especially on modeling and independent control of two five phase synchronous machine with permanent magnet connected in series and fed by a matrix converter application to the rail traction (bogie of a locomotive BB 36000).

Keywords: synchronous machine, vector control Multi-machine/ Multi-inverter, matrix inverter, Railway traction

Procedia PDF Downloads 344

Authors: Md. Minhazul Islam, Shah Ashisul Abed Nipun, Majharul Islam, Md. Abdur Rakib Rahat, Jonayet Miah, Salsavil Kayyum, Anwar Shadaab, Faiz Al Faisal

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The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

Keywords: Machine Learning, K-means, Hierarchical Clustering, Myocardial Infarction, Heart Disease

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

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Authors: Bing Gu

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Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system.

Keywords: DBN, face recognition, light field, Lytro

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Authors: Song Zhiyong, Zhao Bo, Du Li, Wang Wei

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Aircraft structures generally have complex surface. Because of constantly switching postures of motion axis, five-axis CNC machine’s composite stiffness changes during CNC machining. It gives rise to different amplitude of vibration of processing system, which further leads to the different effects on surface waviness. In order to provide a solution for this problem, we take the “S” shape test specimen’s CNC machining for the object, through calculate the five axis CNC machine’s composite stiffness and establish vibration model, we analysis of the influence mechanism between vibration amplitude and surface waviness. Through carry out the surface quality measurement experiments, verify the validity and accuracy of the theoretical analysis. This paper’s research results provide a theoretical basis for surface waviness control.

Keywords: five axis CNC machine, “S” shape test specimen, composite stiffness, surface waviness

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Authors: Chothmal, Basant Agarwal

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Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: feature selection methods, machine learning, NB, one-class SVM, sentiment analysis, support vector machine

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Authors: Gurpreet Kaur, Manoj Kumar, Vandana Bhalla

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Strong acceptor-weak acceptor system FN-TPy has been designed and synthesized which undergoes solvent dependent self-assembly in mixed aqueous media to generate through space intermolecular charge transfer assemblies. The as prepared entropically favoured assemblies of FN-TPy exhibit excellent photostability and photosensitizing properties in the assembled state to activate aerial oxygen for efficient generation of reactive oxygen species (ROS) through Type-I and Type-II pathways. The FN-TPy assemblies exhibit excellent potential for regulated oxidation of alcohols and aldehydes under mild reaction conditions (visible light irradiation, aqueous media, room temperature) using aerial oxygen as the ‘oxidant’. The present study demonstrates the potential of FN-TPy assemblies to catalyze controlled oxidation of benzyl alcohol to benzaldehyde and to corresponding benzoic acid.

Keywords: oxidations, photosensitizer, reactive oxygen species, supramolecular assemblies, through space charge transfer.

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

Procedia PDF Downloads 180

Authors: Ali Moharrek, Hosein Mobli, Ali Jafari, Ahmad Tabataee Far

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Biomass is a material generally produced by plants living on soil or water and their derivatives. The remains of agricultural and forest products contain biomass which is changeable into fuel. Besides, you can obtain biogas and ethanol from the charcoal produced from biomass through specific actions. this technology was designed for as a useful Native Fuel and Technology in Energy disasters Management Due to the sudden interruption of the flow of heat energy One of the problems confronted by mankind in the future is the limitations of fossil energy which necessitates production of new energies such as biomass. In order to produce biomass from the remains of the plants, different methods shall be applied considering factors like cost of production, production technology, area of requirement, speed of work easy utilization, ect. In this article we are focusing on designing a biomass briquetting portable machine. The speed of installation of the machine on a tractor is estimated as 80 MF 258. Screw press is used in designing this machine. The needed power for running this machine which is estimated as 17.4 kW is provided by the power axis of tractor. The pressing speed of the machine is considered to be 375 RPM Finally the physical and mechanical properties of the product were compared with utilized material which resulted in appropriate outcomes. This machine is designed for Gathering Raw materials of the ground by Head Section. During delivering the raw materials to Briquetting section, they Crushed, Milled & Pre Heated in Transmission section. This machine is a Combine Portable Tractor unit machine and can use all type of Agriculture, Forest & Livestock Animals Resides as Raw material to make Bio fuel. The Briquetting Section was manufactured and it successfully made bio fuel of Sawdust. Also this machine made a biofuel with Ethanol of sugarcane Wastes. This Machine is using P.T.O power source for Briquetting and Hydraulic Power Source for Pre Processing of Row Materials.

Keywords: biomass, briquette, screw press, sawdust, animal wastes, portable, tractors

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Authors: N. Q. Bau, N. V. Nghia

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The acoustomagnetoelectric (AME) field in a rectangular quantum wire with an infinite potential (RQWIP) is calculated in the presence of an external magnetic field (EMF) by using the quantum kinetic equation for the distribution function of electrons system interacting with external phonons and electrons scattering with internal acoustic phonon in a RQWIP. We obtained ananalytic expression for the AME field in the RQWIP in the presence of the EMF. The dependence of AME field on the frequency of external acoustic wave, the temperature T of system, the cyclotron frequency of the EMF and the intensity of the EMF is obtained. Theoretical results for the AME field are numerically evaluated, plotted and discussed for a specific RQWIP GaAs/GaAsAl. This result has shown that the dependence of the AME field on intensity of the EMF is nonlinearly and it is many distinct maxima in the quantized magnetic region. We also compared received fields with those for normal bulk semiconductors, quantum well and quantum wire to show the difference. The influence of an EMF on AME field in a RQWIP is newly developed.

Keywords: rectangular quantum wire, acoustomagnetoelectric field, electron-phonon interaction, kinetic equation method

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Authors: Sholeh Motaghian, Pekka Toivanen, Keiji Haataja

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The swift expansion of the transportation industry and its associated emissions have captured the focus of policymakers who are dedicated to upholding ecological sustainability. As a result, understanding the key contributors to transportation emissions is of utmost significance. Amidst the escalating transportation emissions, the significance of electric vehicles cannot be overstated. Electric vehicles play a critical role in steering us towards a low-carbon economy and a sustainable ecological setting. The effective integration of electric vehicles hinges on the development of energy consumption models capable of accurately and efficiently predicting energy usage. Enhancing the energy efficiency of electric vehicles will play a pivotal role in reducing driver concerns and establishing a vital framework for the efficient operation, planning, and management of charging infrastructure. In this article, the works done in this field are reviewed, and the advantages and disadvantages of each are stated.

Keywords: deep learning, electrical vehicle, energy consumption, machine learning, smart grid

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Selina Schwaabe

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Community pharmacies deliver high-quality health care and are responsible for medication safety. During the pandemic, accessibility to the nearest pharmacy became more essential to get vaccinated against Covid-19 and to get medical aid. The government's goal is to ensure nationwide, reachable, and affordable medical health care services by pharmacies. Therefore, the density of community pharmacies matters. Overall, the density of community pharmacies is fluctuating, with slightly decreasing tendencies in some countries. So far, the literature has shown that changes in the system affect prices and density. However, a European overview of the development of the density of community pharmacies and its triggers is still missing. This research is essential to counteract against decreasing density consulting in a lack of professional health care through pharmacies. The analysis focuses on liberal versus regulated market structures, mail-order prescription drug regulation, and third-party ownership consequences. In a panel analysis, the relative influence of the measures is examined across 27 European countries over the last 21 years. In addition, the paper examines seven selected countries in depth, selected for the substantial variance in their pharmacy system: Germany, Austria, Portugal, Denmark, Sweden, Finland and Poland. Overall, the results show that regulated pharmacy markets have over 10.75 pharmacies/100.000 inhabitants more than liberal markets. Further, mail-order prescription drugs decrease the density by -17.98 pharmacies/100.000 inhabitants. Countries allowing third-party ownership have 7.67 pharmacies/100.000 inhabitants more. The results are statistically significant at a 0.001 level. The output of this analysis recommends regulated pharmacy markets, with a ban on mail-order prescription drugs allowing third-party ownership to support nationwide medical health care through community pharmacies.

Keywords: community pharmacy, market conditions, pharmacy, pharmacy market, pharmacy lobby, prescription, e-prescription, ownership structures

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Authors: Jin Uk Han, Hye Won Han, Hyo Lim Kang, Tae An Kim, Seung Ho Han

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Direct-drive generators for large-scale wind turbine, which are divided into AFPM(Axial Flux Permanent Magnet) and RFPM(Radial Flux Permanent Magnet) type machine, have attracted interest because of a higher energy density in comparison with gear train type generators. Each type of the machines provides distinguishable geometrical features such as narrow width with a large diameter for the AFPM-type machine and wide width with a certain diameter for the RFPM-type machine. When the AFPM-type machine is applied, an increase of electric power production through a multi-stage arrangement in axial direction is easily achieved. On the other hand, the RFPM-type machine can be applied by using its geometric feature of wide width. In this study, a hybrid two-stage direct-drive generator for 6.2MW class wind turbine was proposed, in which the two-stage AFPM-type machine for 5 MW was composed of two models arranged in axial direction with a hollow shape topology of the rotor with annular disc, the stator and the main shaft mounted on coupled slew bearings. In addition, the RFPM-type machine for 1.2MW was installed at the empty space of the rotor. Analytic results obtained from an electro-magnetic and structural interaction analysis showed that the structural weight of the proposed hybrid two-stage direct-drive generator can be achieved as 155tonf in a condition satisfying the requirements of structural behaviors such as allowable air-gap clearance and strength. Therefore, it was sure that the 6.2MW hybrid two-stage direct-drive generator is competitive than conventional generators. (NRF grant funded by the Korea government MEST, No. 2017R1A2B4005405).

Keywords: AFPM-type machine, direct-drive generator, electro-magnetic analysis, large-scale wind turbine, RFPM-type machine

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Authors: Joel Weymouth, Ramesh K. Karne, Alexander L. Wijesinha

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Bare Machine Internet is part of the Bare Machine Computing (BMC) paradigm. It is used in programming application ns to run directly on a device. It is software that runs directly against the hardware using CPU, Memory, and I/O. The software application runs without an Operating System and resident mass storage. An important part of the BMC paradigm is the Bare Machine Internet. It utilizes an Application Development model software that interfaces directly with the hardware on a network server and file server. Because it is “bare,” it is a powerful teaching and research tool that can readily display the internals of the network protocols, software, and hardware of the applications running on the Bare Server. It was also demonstrated that the bare server was accessible by laptop and by smartphone/android. The purpose was to show the further practicality of Bare Internet in Computer Engineering and Computer Science Education and Research. It was also to show that an undergraduate student could take advantage of a bare server with any device and any browser at any release version connected to the internet. This paper presents the Bare Web Server as an educational tool. We will discuss possible applications of this paradigm.

Keywords: bare machine computing, online research, network technology, visualizing network internals

Procedia PDF Downloads 139

Authors: M. Marjaneh, M. Y. Narazah, H. Shahrul

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Array-based gene expression analysis is a powerful tool to profile expression of genes and to generate information on therapeutic effects of new anti-cancer compounds. Anti-apoptotic effect of thymoquinone was studied in MCF7 breast cancer cell line using gene expression profiling with cDNA micro array. The purity and yield of RNA samples were determined using RNeasyPlus Mini kit. The Agilent RNA 6000 Nano LabChip kit evaluated the quantity of the RNA samples. AffinityScript RT oligo-dT promoter primer was used to generate cDNA strands. T7 RNA polymerase was used to convert cDNA to cRNA. The cRNA samples and human universal reference RNA were labelled with Cy-3-CTP and Cy-5-CTP, respectively. Feature Extraction and GeneSpring software analysed the data. The single experiment analysis revealed involvement of 64 pathways with up-regulated genes and 78 pathways with down-regulated genes. The MAPK and p38-MAPK pathways were inhibited due to the up-regulation of PTPRR gene. The inhibition of p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of p38 - MAPK caused up-regulation of TP53 and down-regulation of Bcl2 genes indicating involvement of intrinsic apoptotic pathway. Down-regulation of CARD16 gene as an adaptor molecule regulated CASP1 and suggested necrosis-like programmed cell death and involvement of caspase in apoptosis. Furthermore, down-regulation of GPCR, EGF-EGFR signalling pathways suggested reduction of ER. Involvement of AhR pathway which control cytochrome P450 and glucuronidation pathways showed metabolism of Thymoquinone. The findings showed differential expression of several genes in apoptosis pathways with thymoquinone treatment in estrogen receptor-positive breast cancer cells.

Keywords: cDNA microarray, thymoquinone, CARD16, PTPRR, CASP10

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Authors: Orestis Κ. Efthymiou, Stavros T. Ponis

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In the last decade, a new industrial revolution seems to be emerging, supported -once again- by the rapid advancements of Information Technology in the areas of Machine-to-Machine (M2M) communication permitting large numbers of intelligent devices, e.g. sensors to communicate with each other and take decisions without any or minimum indirect human intervention. The advent of these technologies have triggered the emergence of a new category of hybrid (cyber-physical) manufacturing systems, combining advanced manufacturing techniques with innovative M2M applications based on the Internet of Things (IoT), under the umbrella term Industry 4.0. Even though the topic of Industry 4.0 has attracted much attention during the last few years, the attempts of providing a systematic literature review of the subject are scarce. In this paper, we present the authors’ initial study of the field with a special focus on the use and applications of Industry 4.0 principles in material handling automations and in-house logistics. Research shows that despite the vivid discussion and attractiveness of the subject, there are still many challenges and issues that have to be addressed before Industry 4.0 becomes standardized and widely applicable.

Keywords: Industry 4.0, internet of things, manufacturing systems, material handling, logistics

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Authors: Paula Paulino, Alzira Matias, Ana Margarida Veiga Simão

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Self-regulated learning (SRL) directs students in analyzing proposed tasks, setting goals and designing plans to achieve those goals. The literature has suggested a metacognitive strategy for goal attainment known as Mental Contrasting with Implementation Intentions (MCII). This strategy involves Mental Contrasting (MC), in which a significant goal and an obstacle are identified, and Implementation Intentions (II), in which an "if... then…" plan is conceived and operationalized to overcome that obstacle. The present study proposes to assess the MCII process and whether it promotes students’ commitment towards learning goals during school tasks in sciences subjects. In this investigation, we intended to study the MCII strategy in a systemic context of the classroom. Fifty-six students from middle school and secondary education attending a public school in Lisbon (Portugal) participated in the study. The MCII strategy was explicitly taught in a procedure that included metacognitive modeling, guided practice and autonomous practice of strategy. A mental contrast between a goal they wanted to achieve and a possible obstacle to achieving that desire was instructed, and then the formulation of plans in order to overcome the obstacle identified previously. The preliminary results suggest that the MCII metacognitive strategy, applied to the school context, leads to more sophisticated reflections, the promotion of learning goals and the elaboration of more complex and specific self-regulated plans. Further, students achieve better results on school tests and worksheets after strategy practice. This study presents important implications since the MCII has been related to improved outcomes and increased attendance. Additionally, MCII seems to be an innovative process that captures students’ efforts to learn and enhances self-efficacy beliefs during learning tasks.

Keywords: implementation intentions, learning goals, mental contrasting, metacognitive strategy, self-regulated learning

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Authors: Florin Leon, Silvia Curteanu

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Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: batch bulk methyl methacrylate polymerization, adaptive sampling, machine learning, large margin nearest neighbor regression

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Authors: Jehnyne Lalaine Bautista, Marquise Baldemor, Ciara Mendoza

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This study is an attempt to investigate the extent to which pre-teens engage in social media, the effects of social media use on the different facets of their identity development such as physical, social, cognitive, aspirations, and personality, as well as the effects of parental regulation on their identity formation. Twelve Filipino children, ages from 9-12 years old and are either regulated, semi-regulated, or unregulated on social media use, participated in this study along with their parent or guardian. The data were gathered through in-depth interviews with the participants and were analyzed through the use of thematic analysis. Results show that despite accessing similar social media applications, the effects of these on children from different types of parental regulation vary since they have different levels of exposure to social media content. Those who have parents with high parental regulation on the use of social media tend to perform better in school, to find time for extracurricular activities, and to develop positive identity formation. The results of this study suggest that parental regulation on social media use has the positive influence on the identity development of children while there are dangers to unregulated use of social media.

Keywords: identity formation, parental regulation, pre-teens, social media

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Authors: Asma Ameur, Dhafer Malouche

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Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

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Authors: Nacera Yassa, Abdelmalek Saidoune, Ghania Ouadfel, Hamza Houassine

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The rapid advancement in electrical technologies has underscored the increasing importance of multiphase machines across various industrial sectors. These machines offer significant advantages in terms of efficiency, compactness, and reliability compared to their single-phase counterparts. However, early detection and diagnosis of electrical faults remain critical challenges to ensure the durability and safety of these complex systems. This paper presents an advanced analytical model for multiphase machines, with a particular focus on dual stator asynchronous machines. The primary objective is to develop a robust diagnostic tool capable of effectively detecting and locating electrical faults in these machines, including short circuits, winding faults, and voltage imbalances. The proposed methodology relies on an analytical approach combining electrical machine theory, modeling of magnetic and electrical circuits, and advanced signal analysis techniques. By employing detailed analytical equations, the developed model accurately simulates the behavior of multiphase machines in the presence of electrical faults. The effectiveness of the proposed model is demonstrated through a series of case studies and numerical simulations. In particular, special attention is given to analyzing the dynamic behavior of machines under different types of faults, as well as optimizing diagnostic and recovery strategies. The obtained results pave the way for new advancements in the field of multiphase machine diagnostics, with potential applications in various sectors such as automotive, aerospace, and renewable energies. By providing precise and reliable tools for early fault detection, this research contributes to improving the reliability and durability of complex electrical systems while reducing maintenance and operation costs.

Keywords: faults, diagnosis, modelling, multiphase machine

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Authors: Nima Mahmoudi, Hamzeh Khazaei

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Serving machine learning inference workloads on the cloud is still a challenging task at the production level. The optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system’s efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Keywords: serverless computing, machine learning, inference serving, Knative, google cloud run, optimization

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

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

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

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Authors: S. Apprich, F. Wulle, A. Lechler, A. Pott, A. Verl

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Large-scale machine tools for the manufacturing of large work pieces, e.g. blades, casings or gears for wind turbines, feature pose-dependent dynamic behavior. Small structural damping coefficients lead to long decay times for structural vibrations that have negative impacts on the production process. Typically, these vibrations are handled by increasing the stiffness of the structure by adding mass. That is counterproductive to the needs of sustainable manufacturing as it leads to higher resource consumption both in material and in energy. Recent research activities have led to higher resource efficiency by radical mass reduction that rely on control-integrated active vibration avoidance and damping methods. These control methods depend on information describing the dynamic behavior of the controlled machine tools in order to tune the avoidance or reduction method parameters according to the current state of the machine. The paper presents the appearance, consequences and challenges of the pose-dependent dynamic behavior of lightweight large-scale machine tool structures in production. The paper starts with the theoretical introduction of the challenges of lightweight machine tool structures resulting from reduced stiffness. The statement of the pose-dependent dynamic behavior is corroborated by the results of the experimental modal analysis of a lightweight test structure. Afterwards, the consequences of the pose-dependent dynamic behavior of lightweight machine tool structures for the use of active control and vibration reduction methods are explained. Based on the state of the art on pose-dependent dynamic machine tool models and the modal investigation of an FE-model of the lightweight test structure, the criteria for a pose-dependent model for use in vibration reduction are derived. The description of the approach for a general pose-dependent model of the dynamic behavior of large lightweight machine tools that provides the necessary input to the aforementioned vibration avoidance and reduction methods to properly tackle machine vibrations is the outlook of the paper.

Keywords: dynamic behavior, lightweight, machine tool, pose-dependency

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Authors: Rahisham Abd Rahman

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In this paper, a nonlinear pollution model has been proposed to compute electric field distribution over the polymeric insulator surface under wet contaminated conditions. A 2D axial-symmetric insulator geometry, energized with 11kV was developed and analysed using Finite Element Method (FEM). A field-dependent conductivity with simplified assumptions was established to characterize the electrical properties of the pollution layer. Comparative field studies showed that simulation of dynamic pollution model results in a more realistic field profile, offering better understanding on how the electric field behaves under wet polluted conditions.

Keywords: electric field distributions, pollution layer, dynamic model, polymeric outdoor insulators, finite element method (FEM)

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Authors: Todd Zhou, Mikhail Yurochkin

Abstract:

Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Hamidreza Akbari

Abstract:

In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: induction machine, fault, DWT, electric

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Authors: S. Schröder, P. Ennen, T. Langer, S. Müller, M. Shehadeh, M. Haberstroh, F. Hees

Abstract:

Since a bit over a year ago, Festo AG and Co. KG, Festo Didactic SE, robomotion GmbH, the researchers of the Cybernetics-Lab IMA/ZLW and IfU, as well as the Human-Computer Interaction Center at the RWTH Aachen University, have been working together in the focal point of assembly competences to realize different scenarios in the field of human-machine interaction (HMI). In the framework of project ARIZ, questions concerning the future of production within the fourth industrial revolution are dealt with. There are many perspectives of human-robot collaboration that consist Industry 4.0 on an individual, organization and enterprise level, and these will be addressed in ARIZ. The aim of the ARIZ projects is to link AI-Approaches to assembly problems and to implement them as prototypes in demonstrators. To do so, island and flow based production scenarios will be simulated and realized as prototypes. These prototypes will serve as applications of flexible robotics as well as AI-based planning and control of production process. Using the demonstrators, human interaction strategies will be examined with an information system on one hand, and a robotic system on the other. During the tests, prototypes of workspaces that illustrate prospective production work forms will be represented. The human being will remain a central element in future productions and will increasingly be in charge of managerial tasks. Questions thus arise within the overall perspective, primarily concerning the role of humans within these technological revolutions, as well as their ability to act and design respectively to the acceptance of such systems. Roles, such as the 'Trainer' of intelligent systems may become a possibility in such assembly scenarios.

Keywords: human-machine interaction, information technology, island based production, assembly competences

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Authors: Priyanka Jain, Ashish K. Sharma, Esha Shukla, K. J. Mukherjee

Abstract:

We propose a paradigm shift in our approach to design improved platforms for recombinant protein production, by addressing system level issues rather than the individual steps associated with recombinant protein synthesis like transcription, translation, etc. We demonstrate that by controlling and modulating the cellular stress response (CSR), which is responsible for feedback control of protein synthesis, we can generate hyper-producing strains. We did transcriptomic profiling of post-induction cultures, expressing different types of protein, to analyze the nature of this cellular stress response. We found significant down-regulation of substrate utilization, translation, and energy metabolism genes due to generation CSR inside the host cell. However, transcription profiling has also shown that many genes are up-regulated post induction and their role in modulating the CSR is unclear. We hypothesized that these up-regulated genes trigger signaling pathways, generating the CSR and concomitantly reduce the recombinant protein yield. To test this hypothesis, we knocked out the up-regulated genes, which did not have any downstream regulatees, and analyzed their impact on cellular health and recombinant protein expression. Two model proteins i.e., GFP and L-Asparaginase were chosen for this analysis. We observed a significant improvement in expression levels, with some knock-outs showing more than 7-fold higher expression compared to control. The 10 best single knock-outs were chosen to make 45 combinations of all possible double knock-outs. A further increase in expression was observed in some of these double knock- outs with GFP levels being highest in a double knock-out ΔyhbC + ΔelaA. However, for L-Asparaginase which is a secretory protein, the best results were obtained using a combination of ΔelaA+ΔcysW knock-outs. We then tested all the knock outs for their ability to enhance the expression of a 'difficult-to-express' protein. The Rubella virus E1 protein was chosen and tagged with sfGFP at the C-terminal using a linker peptide for easy online monitoring of expression of this fusion protein. Interestingly, the highest increase in Rubella-sGFP levels was obtained in the same double knock-out ΔelaA + ΔcysW (5.6 fold increase in expression yield compared to the control) which gave the highest expression for L-Asparaginase. However, for sfGFP alone, the ΔyhbC+ΔmarR knock-out gave the highest level of expression. These results indicate that there is a fair degree of commonality in the nature of the CSR generated by the induction of different proteins. Transcriptomic profiling of the double knock out showed that many genes associated with the translational machinery and energy biosynthesis did not get down-regulated post induction, unlike the control where these genes were significantly down-regulated. This confirmed our hypothesis of these genes playing an important role in the generation of the CSR and allowed us to design a strategy for making better expression hosts by simply knocking out key genes. This strategy is radically superior to the previous approach of individually up-regulating critical genes since it blocks the mounting of the CSR thus preventing the down-regulation of a very large number of genes responsible for sustaining the flux through the recombinant protein production pathway.

Keywords: cellular stress response, GFP, knock-outs, up-regulated genes

Procedia PDF Downloads 201