Search results for: machine readable format

Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Shaina Nagpal, Amit Gupta

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In this work, all optical Stimulated Brillouin Scattering (SBS) generated single sideband with suppressed carrier is presented to provide better efficiency. The generation of single sideband and enhanced carrier power signal using the SBS technique is further used to strengthen the low shifted sideband and to suppress the upshifted sideband. These generated single sideband signals are able to work at high frequency ranges. Also, generated single sideband is validated over 90 km transmission using single mode fiber with acceptable bit error rate. The results for an equivalent are then compared so that the acceptable technique is chosen and also the required quality for the optimum performance of the system is reported.

Keywords: stimulated Brillouin scattering, radio over fiber, upper side band, quality factor

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Authors: Adnan Mazari, Engin Akcagun, Antonin Havelka, Funda Buyuk Mazari, Pavel Kejzlar

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This article experimentally investigates various physical properties of special fire retardant sewing threads under different sewing speeds. The aramid threads are common for sewing the fire-fighter clothing due to high strength and high melting temperature. 3 types of aramid threads with different linear densities are used for sewing at different speed of 2000 to 4000 r/min. The needle temperature is measured at different speeds of sewing and tensile properties of threads are measured before and after the sewing process respectively. The results shows that the friction and abrasion during the sewing process causes a significant loss to the tensile properties of the threads and needle temperature rises to nearly 300^oC at 4000 r/min of machine speed. The Scanning electron microscope images are taken before and after the sewing process and shows no melting spots but significant damage to the yarn. It is also found that machine speed of 2000r/min is ideal for sewing firefighter clothing for higher tensile properties and production.

Keywords: Kevlar, needle temperautre, nomex, sewing

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Authors: Gaoguang Yang, Shan Fu

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Human-machine interface (HMI) intermediate system information to human operators to facilitate human ability to manage and control the system. Well-designed HMI would enhance human ability. An evaluation must be performed to confirm that the designed HMI would enhance but not degrade human ability. However, the prevalent HMI evaluation techniques have difficulties in more thoroughly and accurately evaluating the suitability and fitness of a given HMI for the wide variety of uncertainty contained in both the existing HMI evaluation techniques and the large number of task scenarios. The first limitation should be attributed to the subjective and qualitative analysis characteristics of these evaluation methods, and the second one should be attributed to the cost balance. This study aims to explore the connection between objective HMI characters and ergonomic safety and step forward toward solving these limitations with objective, characterized HMI parameters. A simulation experiment was performed with the time needed for human operators to recognize the HMI information as characterized HMI parameter, and the result showed a strong correlation between the parameter and ergonomic safety level.

Keywords: Human-Machine Interface (HMI), evaluation, objective, characterization, simulation

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Authors: N. Hatraf, L. Merabti, Z. Neffah, W. Taane

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The excessive consumption of fossil energies (electrical energy) during summer caused by the technological development involves more and more climate warming. In order to reduce the worst impact of gas emissions produced from classical air conditioning, heat driven solar absorption chiller is pretty promising; it consists on using solar as motive energy which is clean and environmentally friendly to provide cold. Solar absorption machine is composed by four components using Lithium Bromide /water as a refrigerating couple. LiBr- water is the most promising in chiller applications due to high safety, high volatility ratio, high affinity, high stability and its high latent heat. The lithium bromide solution is constitute by the salt lithium bromide which absorbs water under certain conditions of pressure and temperature however if the concentration of the solution is high in the absorption chillers; which exceed 70%, the solution will crystallize. The main aim of this article is to study the phenomena of the crystallization and to evaluate how the dependence between the electric conductivity and the concentration which should be controlled.

Keywords: absorption, crystallization, experimental results, lithium bromide solution

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Authors: S. M. Mahdi Niktabara, K. Seshagiri Raob, Amit Kumar Shrivastavac, Jiří Ščučkaa

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The presence of the discontinuity in the form of joints is one of the most significant factors causing instability in the rock mass. On the other hand, dynamic loads, including earthquake and blasting induce cyclic shear loads along the joints in rock masses; therefore, failure of rock mass exacerbates along the joints due to changing shear resistance. Joints are under constant normal load (CNL) and constant normal stiffness (CNS) conditions. Normal stiffness increases on the joints with increasing depth, and it can affect shear resistance. For correct assessment of joint shear resistance under varying normal stiffness and number of cycles, advanced laboratory shear machine is essential for the shear test. Conventional direct shear equipment has limitations such as boundary conditions, working under monotonic movements only, or cyclic shear loads with constant frequency and amplitude of shear loads. Hence, a large-scale servo-controlled direct shear testing machine was designed and fabricated to perform shear test under the both CNL and CNS conditions with varying normal stiffness at different frequencies and amplitudes of shear loads. In this study, laboratory cyclic shear tests were conducted on non-planar joints under varying normal stiffness. In addition, the effects of different frequencies and amplitudes of shear loads were investigated. The test results indicate that shear resistance increases with increasing normal stiffness at the first cycle, but the influence of normal stiffness significantly decreases with an increase in the number of shear cycles. The frequency of shear load influences on shear resistance, i.e. shear resistance increases with increasing frequency. However, at low shear amplitude the number of cycles does not affect shear resistance on the joints, but it decreases with higher amplitude.

Keywords: cyclic shear load, frequency of load, amplitude of displacement, normal stiffness

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Authors: Pantaleon Lutta, Mohamed Sedky, Mohamed Hassan

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The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data are stored on the devices. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality).

Keywords: cloud forensics, data protection Laws, GDPR, IoT forensics, machine Learning

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Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: M. Barański

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This paper presents a several diagnostic methods designed to electrical machines especially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives. Those methods are preferred by the author in periodic diagnostic of electrical machines. The special attention should be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methods were created in Institute of Electrical Drives and Machines Komel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.

Keywords: electrical vehicle, generator, main insulation, permanent magnet, thermography, turn-to-traction drive, turn insulation, vibrations

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Radoslaw Nagay

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The Paulina Project aims to address the challenges faced by immobilized individuals, such as those with multiple sclerosis, muscle dystrophy, or spinal cord injuries, by developing a flexible hardware and software solution. This paper presents the research and development efforts of our team, which commenced in 2019 and is now in its final stage. Recognizing the diverse needs and limitations of individuals with limited mobility, we conducted in-depth testing with a group of 30 participants. The insights gained from these tests led to the complete redesign of the system. Our presentation covers the initial project ideas, observations from in-situ tests, and the newly developed system that is currently under construction. Moreover, in response to the financial constraints faced by many disabled individuals, we propose an affordable business model for the future commercialization of our invention. Through the Paulina Project, we strive to empower immobilized individuals, providing them with greater independence and improved quality of life.

Keywords: UI, human-machine interface, social inclusion, multiple sclerosis, muscular dystrophy, spinal cord injury, quadriplegic

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Wanqing You, Kai Qian, Ying Qian

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The concepts of Cloud Computing provide users with infrastructure, platform and software as service, which make those services more accessible for people via Internet. To better analysis the performance of Cloud Computing provisioning policies as well as resources allocation strategies, a toolkit named CloudSim proposed. With CloudSim, the Cloud Computing environment can be easily constructed by modelling and simulating cloud computing components, such as datacenter, host, and virtual machine. A good scheduling strategy is the key to achieve the load balancing among different machines as well as to improve the utilization of basic resources. Recently, the existing scheduling algorithms may work well in some presumptive cases in a single machine; however they are unable to make the best decision for the unforeseen future. In real world scenario, there would be numbers of tasks as well as several virtual machines working in parallel. Based on the concepts of multi-queue, this paper presents a new scheduling algorithm to schedule tasks with CloudSim by taking into account several parameters, the machines’ capacity, the priority of tasks and the history log.

Keywords: hierarchical queue, load balancing, CloudSim, information technology

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Authors: Jawad Ul Haq, Evan Mazur, Ahmed Qureshi, Mohamed Al-Hussein

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The uses of wood in furniture, building, bridges and aluminum in transportation and construction, make aluminum and forest economy a prominent matter in North America. Machines available to date to cut the aforementioned materials are mostly industry oriented with complex structure and operations which require special training and skill. Furthermore, requirements such as pneumatics, 3-phase supply are associated with cost, maintenance, and safety hazards. Power saws are very useful tools used to cut and shape materials; however, they can cause serious hand injuries. Operator’s hands in table saw are vulnerable as they are used to guide pieces into the saw. Apart from hands, saw operator is also prone to material being kicked back out of the saw or sustain eye or respiratory injuries due to rapidly flying sawdust and other debris. In this paper, design of an automatic saw cutting machine has been proposed to ensure safety, portability, usage at domestic level and capability to cut both aluminum and wood. This paper demonstrates detailed Mechanical design in SOLIDWORKS and Control Systems using Programmable Logic Controller (PLC), based on the aforementioned design objectives.

Keywords: programmable logic controller, saw cutting, control, automation

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: Srinidhi. A, Naveed Ahmed, Twinkle Hareendran, Vriksha Prakash

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This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models.

Keywords: image processing, machine learning applications, template matching, emotion detection

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Stefano Zappacosta, Cristiano Bove, Maria Carmela Marinelli, Paola di Lauro, Katarina Spasenovic, Lorenzo Ostano, Giuseppe Aiello, Marco Pietrosanto

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This paper proposes a novel neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The proposed hybrid model can be used to classify four different increasing levels of hazard. The classification capability was compared with the flood hazard mapping River Basin Plans (PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Ali Asghar Memon

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The simulation modeling of switched reluctance (SR) machine often relies and uses the three data tables identified as static torque characteristics that include flux linkage characteristics, co energy characteristics and static torque characteristics separately. It has been noticed from the literature that the data of static torque used in the simulation model is often calculated so far the literature is concerned. This paper presents the simulation model that include the data of measured and calculated static torque separately to see its effect on instantaneous torque profile of the machine. This is probably for the first time so far the literature review is concerned that static torque from co energy information, and measured static torque directly from experiments are separately used in the model. This research is helpful for accurate modeling of switched reluctance drive.

Keywords: static characteristics, current chopping, flux linkage characteristics, switched reluctance motor

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Authors: Onur Ozveri, Korkut Karabag, Cagri Keles

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It is an important issue that the occurring of production downtime and repair costs when machines fail in the machine intensive production industries. In the case of failure of more than one machine at the same time, which machines will have the priority to repair, how to determine the optimal repair time should be allotted for this machines and how to plan the resources needed to repair are the key issues. In recent years, Business Process Management (BPM) technique, bring effective solutions to different problems in business. The main feature of this technique is that it can improve the way the job done by examining in detail the works of interest. In the industries, maintenance and repair works are operating as a process and when a breakdown occurs, it is known that the repair work is carried out in a series of process. Maintenance main-process and repair sub-process are evaluated with process management technique, so it is thought that structure could bring a solution. For this reason, in an international manufacturing company, this issue discussed and has tried to develop a proposal for a solution. The purpose of this study is the implementation of maintenance and repair works which is integrated with process management technique and at the end of implementation, analyzing the maintenance related parameters like quality, cost, time, safety and spare part. The international firm that carried out the application operates in a free region in Turkey and its core business area is producing original equipment technologies, vehicle electrical construction, electronics, safety and thermal systems for the world's leading light and heavy vehicle manufacturers. In the firm primarily, a project team has been established. The team dealt with the current maintenance process again, and it has been revised again by the process management techniques. Repair process which is sub-process of maintenance process has been discussed again. In the improved processes, the ABC equipment classification technique was used to decide which machine or machines will be given priority in case of failure. This technique is a prioritization method of malfunctioned machine based on the effect of the production, product quality, maintenance costs and job security. Improved maintenance and repair processes have been implemented in the company for three months, and the obtained data were compared with the previous year data. In conclusion, breakdown maintenance was found to occur in a shorter time, with lower cost and lower spare parts inventory.

Keywords: ABC equipment classification, business process management (BPM), maintenance, repair performance

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Authors: Abdul-Rahman Al-Ali

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As the number of mobile devices is growing exponentially, it is estimated to connect about 50 million devices to the Internet by the year 2020. At the end of this decade, it is expected that an average of eight connected devices per person worldwide. The 50 billion devices are not mobile phones and data browsing gadgets only, but machine-to-machine and man-to-machine devices. With such growing numbers of devices the Internet of Things (I.o.T) concept is one of the emerging technologies as of recently. Within the smart grid technologies, smart home appliances, Intelligent Electronic Devices (IED) and Distributed Energy Resources (DER) are major I.o.T objects that can be addressable using the IPV6. These objects are called the smart grid internet of things (SG-I.o.T). The SG-I.o.T generates big data that requires high-speed computing infrastructure, widespread computer networks, big data storage, software, and platforms services. A company’s utility control and data centers cannot handle such a large number of devices, high-speed processing, and massive data storage. Building large data center’s infrastructure takes a long time, it also requires widespread communication networks and huge capital investment. To maintain and upgrade control and data centers’ infrastructure and communication networks as well as updating and renewing software licenses which collectively, requires additional cost. This can be overcome by utilizing the emerging computing paradigms such as cloud computing. This can be used as a smart grid enabler to replace the legacy of utilities data centers. The talk will highlight the role of I.o.T, cloud computing services and their development models within the smart grid technologies.

Keywords: intelligent electronic devices (IED), distributed energy resources (DER), internet, smart home appliances

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Authors: Yassa Nacera, Badji Abderrezak, Saidoune Abdelmalek, Houassine Hamza

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Several kinds of faults can occur in a permanent magnet synchronous machine (PMSM) systems: bearing faults, electrically short/open faults, eccentricity faults, and demagnetization faults. Demagnetization fault means that the strengths of permanent magnets (PM) in PMSM decrease, and it causes low output torque, which is undesirable for EVs. The fault is caused by physical damage, high-temperature stress, inverse magnetic field, and aging. Motor current signature analysis (MCSA) is a conventional motor fault detection method based on the extraction of signal features from stator current. a simulation model of the PMSM under partial demagnetization and uniform demagnetization fault was established, and different degrees of demagnetization fault were simulated. The harmonic analyses using the Fast Fourier Transform (FFT) show that the fault diagnosis method based on the harmonic wave analysis is only suitable for partial demagnetization fault of the PMSM and does not apply to uniform demagnetization fault of the PMSM.

Keywords: permanent magnet, diagnosis, demagnetization, modelling

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Authors: Minh Huynh, Aaron Heuser, Luke Patterson, Chris Zhang, Mason Miller, Daniel Wang, Sandeep Shetty, Mike Trinh, Abigail Miller, Adaeze Enekwechi, Tenille Daniels, Lu Huynh

Abstract:

Machine learning and text analytics have been used to analyze child abuse, cyberbullying, domestic abuse and domestic violence, and hate speech. However, to the authors’ knowledge, no research to date has used these methods to study elder abuse in nursing homes or skilled nursing facilities from field inspection reports. We used machine learning and text analytics methods to analyze 356,000 inspection reports, which have been extracted from CMS Form-2567 field inspections of US nursing homes and skilled nursing facilities between 2016 and 2021. Our algorithm detected occurrences of the various types of abuse, including physical abuse, psychological abuse, verbal abuse, sexual abuse, and passive and active neglect. For example, to detect physical abuse, our algorithms search for combinations or phrases and words suggesting willful infliction of damage (hitting, pinching or burning, tethering, tying), or consciously ignoring an emergency. To detect occurrences of elder neglect, our algorithm looks for combinations or phrases and words suggesting both passive neglect (neglecting vital needs, allowing malnutrition and dehydration, allowing decubiti, deprivation of information, limitation of freedom, negligence toward safety precautions) and active neglect (intimidation and name-calling, tying the victim up to prevent falls without consent, consciously ignoring an emergency, not calling a physician in spite of indication, stopping important treatments, failure to provide essential care, deprivation of nourishment, leaving a person alone for an inappropriate amount of time, excessive demands in a situation of care). We further compare the prevalence of abuse before and after Covid-19 related restrictions on nursing home visits. We also identified the facilities with the most number of cases of abuse with no abuse facilities within a 25-mile radius as most likely candidates for additional inspections. We also built an interactive display to visualize the location of these facilities.

Keywords: machine learning, text analytics, elder abuse, elder neglect, nursing home abuse

Procedia PDF Downloads 148

Authors: Susan Diamond

Abstract:

Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

Procedia PDF Downloads 209

Authors: Saumya Srivastava, Rina Maiti

Abstract:

In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

Procedia PDF Downloads 124

Authors: Li Li, Li-Ru Xia, He-Ye Wang, Xiao-Dong Bi

Abstract:

In this paper, we presented a highly sensitive immune-affinity monolithic array for detection of α-fetoprotein (AFP) and carcinoembryonic antigen (CEA). Firstly, the epoxy functionalized monolith arrays were fabricated using UV initiated copolymerization method. Scanning electron microscopy (SEM) image showed that the poly(BABEA-co-GMA) monolith exhibited a well-controlled skeletal and well-distributed porous structure. Then, AFP and CEA immune-affinity monolithic arrays were prepared by immobilization of AFP and CEA antibodies on epoxy functionalized monolith arrays. With a non-competitive immune response format, the presented AFP and CEA immune-affinity arrays were demonstrated as an inexpensive, flexible, homogeneous and stable array for detection of AFP and CEA.

Keywords: chemiluminescent detection, immune-affinity, monolithic copolymer array, UV-initiated copolymerization

Procedia PDF Downloads 342

Authors: Mina Malak Zakaria Henin

Abstract:

The finite detail evaluation of magnetic fields in electromagnetic devices shows that the machine cores revel in extraordinary flux patterns consisting of alternating and rotating fields. The rotating fields are generated in different configurations variety, among circular and elliptical, with distinctive ratios between the fundamental and minor axes of the flux locus. Experimental measurements on electrical metal uncovered one-of-a-kind flux patterns that divulge distinctive magnetic losses in the samples below the test. Therefore, electric machines require unique interest throughout the core loss calculation technique to bear in mind the flux styles. In this look, a circular rotational unmarried sheet tester is employed to measure the middle losses in the electric-powered metallic pattern of M36G29. The sample becomes exposed to alternating fields, circular areas, and elliptical fields with axis ratios of zero.2, zero. Four, 0.6 and 0.8. The measured statistics changed into applied on 6-4 switched reluctance motors at 3 distinctive frequencies of interest to the industry 60 Hz, 400 Hz, and 1 kHz. The effects reveal an excessive margin of error, which can arise at some point in the loss calculations if the flux pattern difficulty is overlooked. The mistake in exceptional components of the gadget associated with considering the flux styles may be around 50%, 10%, and a couple of at 60Hz, 400Hz, and 1 kHz, respectively. The future paintings will focus on the optimization of gadget geometrical shape, which has a primary effect on the flux sample on the way to decrease the magnetic losses in system cores.

Keywords: converters, electric machines, MEA (more electric aircraft), PES (power electronics systems) synchronous machine, vector control Multi-machine/ Multi-inverter, matrix inverter, Railway tractionalternating core losses, finite element analysis, rotational core losses

Procedia PDF Downloads 31