Search results for: Machine Translation

Authors: Kanthida Kusonmano, Michael Netzer, Bernhard Pfeifer, Christian Baumgartner, Klaus R. Liedl, Armin Graber

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Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.

Keywords: Classification, High dimensional data, Machine learning

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Authors: Nyeng P. Gyang

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

Keywords: Cloud computing systems, multicore systems, parallel delaunay triangulation, parallel surface modeling and generation.

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

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

Keywords: Environmental industry, Separator, CFD, Fine aggregate.

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Authors: Farshideh Einsele, Hassan Foroosh

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In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using well-known geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: Camera-based OCR, Feature extraction, Document and image processing.

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Authors: Junichiro Kawaguchi

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Space debris problems have emerged and threatened the use of low earth orbit around the Earth owing to a large number of spacecrafts.. The robots should be sophisticated enough to access automatically the debris articulating the attitude and the translation motion with respect to the debris. This paper presents the idea of using the torpedo-like third unsophisticated and disposable body, in addition to the first body of the servicing robot and the second body of the target debris. The third body is launched from the first body from a distance further than the size of the second body. This paper presents the method and the system, so that the third body is launched from the first body. The third body carries both a net and an inflatable or extendible drag deceleration device and is built small and light. This method enables even micro to nano satellites to perform contactless and multiple debris removal even via a single flight.

Keywords: Ballute, Debris Removal, Echo satellite, Gossamer, Gun-Net, Inflatable Space Structure, Small Satellite, Un-cooperated Target.

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Authors: Gurjeet Singh, M. K. Pradhan, Ajay Verma

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The plastic industry plays very important role in the economy of any country. It is generally among the leading share of the economy of the country. Since metals and their alloys are very rarely available on the earth. Therefore, to produce plastic products and components, which finds application in many industrial as well as household consumer products is beneficial. Since 50% plastic products are manufactured by injection moulding process. For production of better quality product, we have to control quality characteristics and performance of the product. The process parameters plays a significant role in production of plastic, hence the control of process parameter is essential. In this paper the effect of the parameters selection on injection moulding process has been described. It is to define suitable parameters in producing plastic product. Selecting the process parameter by trial and error is neither desirable nor acceptable, as it is often tends to increase the cost and time. Hence, optimization of processing parameter of injection moulding process is essential. The experiments were designed with Taguchi’s orthogonal array to achieve the result with least number of experiments. Plastic material polypropylene is studied. Tensile strength test of material is done on universal testing machine, which is produced by injection moulding machine. By using Taguchi technique with the help of MiniTab-14 software the best value of injection pressure, melt temperature, packing pressure and packing time is obtained. We found that process parameter packing pressure contribute more in production of good tensile plastic product.

Keywords: Injection moulding, tensile strength, Taguchi method, poly-propylene.

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Authors: Saeed Khorasanizadeh

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Adhesion strength of exterior or interior coating of steel pipes is too important. Increasing of coating adhesion on surfaces can increase the life time of coating, safety factor of transmitting line pipe and decreasing the rate of corrosion and costs. Preparation of steel pipe surfaces before doing the coating process is done by shot and grit blasting. This is a mechanical way to do it. Some effective parameters on that process, are particle size of abrasives, distance to surface, rate of abrasive flow, abrasive physical properties, shapes, selection of abrasive, kind of machine and its power, standard of surface cleanness degree, roughness, time of blasting and weather humidity. This search intended to find some better conditions which improve the surface preparation, adhesion strength and corrosion resistance of coating. So, this paper has studied the effect of varying abrasive flow rate, changing the abrasive particle size, time of surface blasting on steel surface roughness and over blasting on it by using the centrifugal blasting machine. After preparation of numbers of steel samples (according to API 5L X52) and applying epoxy powder coating on them, to compare strength adhesion of coating by Pull-Off test. The results have shown that, increasing the abrasive particles size and flow rate, can increase the steel surface roughness and coating adhesion strength but increasing the blasting time can do surface over blasting and increasing surface temperature and hardness too, change, decreasing steel surface roughness and coating adhesion strength.

Keywords: surface preparation, abrasive particles, adhesionstrength

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling. The research proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling. The paper concludes the challenges and improvement directions for Deep Reinforcement Learning-based resource scheduling algorithms.

Keywords: Resource scheduling, deep reinforcement learning, distributed system, artificial intelligence.

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Authors: J. G. Batista, L. J. de Bessa Neto, M. A. F. B. Lima, J. R. Leite, J. I. de Andrade Nunes

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The robot manipulator is an equipment that stands out for two reasons: Firstly because of its characteristics of movement and reprogramming, resembling the arm; secondly, by adding several areas of knowledge of science and engineering. The present work shows the development of the prototype of a robotic manipulator driven by a Programmable Logic Controller (PLC), having two degrees of freedom, which allows the movement and displacement of mechanical parts, tools, and objects in general of small size, through an electronic system. The aim is to study direct and inverse kinematics of the robotic manipulator to describe the translation and rotation between two adjacent links of the robot through the Denavit-Hartenberg parameters. Currently, due to the many resources that microcomputer systems offer us, robotics is going through a period of continuous growth that will allow, in a short time, the development of intelligent robots with the capacity to perform operations that require flexibility, speed and precision.

Keywords: Direct and inverse kinematics, Denavit-Hartenberg, microcontrollers, robotic manipulator.

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Authors: Oke Oktavianty, Tadayuki Kyoutani, Shigeyuki Haruyama, Junji Kaneko, Ken Kaminishi

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Modelica has many advantages and it is very useful in modeling and simulation especially for the multi-domain with a complex technical system. However, the big obstacle for a beginner is to understand the basic concept and to build a new system model for a real system. In order to understand how to solve the simple circuit model by hand translation and to get a better understanding of how modelica works, we provide a detailed explanation about solver ordering system in horizontal and vertical sorting and make some proposals for improvement. In this study, some difficulties in using modelica software with the original concept and the comparison with Finite Element Method (FEM) approach is discussed. We also present our textual modeling approach using FEM concept for acausal and causal model construction. Furthermore, simulation results are provided that demonstrate the comparison between using textual modeling with original coding in modelica and FEM concept.

Keywords: FEM, acausal model, modelica, horizontal and vertical sorting.

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme Gradient Boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impairment, multiclass classification, ADNI, support vector machine, random forest.

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Authors: Khairul Akram Zainol Ariffin, Ahmad Kamil Mahmood, Jafreezal Jaafar, Solahuddin Shamsuddin

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Random Access Memory (RAM) is an important device in computer system. It can represent the snapshot on how the computer has been used by the user. With the growth of its importance, the computer memory has been an issue that has been discussed in digital forensics. A number of tools have been developed to retrieve the information from the memory. However, most of the tools have their limitation in the ability of retrieving the important information from the computer memory. Hence, this paper is aimed to discuss the limitation and the setback for two main techniques such as process signature search and process enumeration. Then, a new hybrid approach will be presented to minimize the setback in both individual techniques. This new approach combines both techniques with the purpose to retrieve the information from the process block and other objects in the computer memory. Nevertheless, the basic theory in address translation for x86 platforms will be demonstrated in this paper.

Keywords: Algorithms, Digital Forensics, Memory Analysis, Signature Search.

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Authors: Amir Khalid, B. Manshoor

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The mixture formation prior to the ignition process plays as a key element in the diesel combustion. Parametric studies of mixture formation and ignition process in various injection parameter has received considerable attention in potential for reducing emissions. Purpose of this study is to clarify the effects of injection pressure on mixture formation and ignition especially during ignition delay period, which have to be significantly influences throughout the combustion process and exhaust emissions. This study investigated the effects of injection pressure on diesel combustion fundamentally using rapid compression machine. The detail behavior of mixture formation during ignition delay period was investigated using the schlieren photography system with a high speed camera. This method can capture spray evaporation, spray interference, mixture formation and flame development clearly with real images. Ignition process and flame development were investigated by direct photography method using a light sensitive high-speed color digital video camera. The injection pressure and air motion are important variable that strongly affect to the fuel evaporation, endothermic and prolysis process during ignition delay. An increased injection pressure makes spray tip penetration longer and promotes a greater amount of fuel-air mixing occurs during ignition delay. A greater quantity of fuel prepared during ignition delay period thus predominantly promotes more rapid heat release.

Keywords: Mixture Formation, Diesel Combustion, Ignition Process, Spray, Rapid Compression Machine.

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Authors: R.Anita, V.Ganga Bharani, N.Nityanandam, Pradeep Kumar Sahoo

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The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based approach for extracting data from the deep web. Deep iCrawl splits the process into two phases. The first phase includes Query analysis and Query translation and the second covers vision-based extraction of data from the dynamically created deep web pages. There are several established approaches for the extraction of deep web pages but the proposed method aims at overcoming the inherent limitations of the former. This paper also aims at comparing the data items and presenting them in the required order.

Keywords: Crawler, Deep web, Web Database

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Authors: Oi Mean Foong, Tan Jung Low, Satrio Wibowo

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Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems have been developed by manufacturers around the globe but they are neither flexible nor cost-effective for the end users. This paper presents a system prototype that is able to automatically recognize sign language to help normal people to communicate more effectively with the hearing or speech impaired people. The Sign to Voice system prototype, S2V, was developed using Feed Forward Neural Network for two-sequence signs detection. Different sets of universal hand gestures were captured from video camera and utilized to train the neural network for classification purpose. The experimental results have shown that neural network has achieved satisfactory result for sign-to-voice translation.

Keywords: Hand gesture detection, neural network, signlanguage, sequence detection.

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Authors: Saher Ali Al-Sabbah, See Ching Mey, Ong Saw Lan

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This article addresses the procedures to validate the Arabic version of Multiple Intelligence Development Assessment Scale (MIDAS). The content validity was examined based on the experts- judgments on the MIDAS-s items in the Arabic version. The content of eleven items in the Arabic version of MIDAS was modified to match the Arabic context. Then a translation from original English version of MIDAS into Arabic language was performed. The reliability of the Arabic MIDAS was calculated based on test and retest method and found to be 0.85 for the overall MIDAS and for the different subscales ranging between 0.78 - 0.87. The examination of construct validity for the overall Arabic MIDAS and its subscales was established by using Winsteps program version 6 based on Rasch model in order to fit the items into the Arabic context. The findings indicated that, the eight subscales in Arabic version of MIDAS scale have a unidimensionality, and the total number of kept items in the overall scale is 108 items.

Keywords: Rasch-Model, validation, multiple intelligence, and MIDAS scale.

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Authors: Mohammad Yasrebi, Somayeh Khosravi

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Machine-understandable data when strongly interlinked constitutes the basis for the SemanticWeb. Annotating web documents is one of the major techniques for creating metadata on the Web. Annotating websitexs defines the containing data in a form which is suitable for interpretation by machines. In this paper, we present a better and improved approach than previous [1] to annotate the texts of the websites depends on the knowledge base.

Keywords: Knowledge base, ontology, semantic annotation, XML.

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Authors: S. Fore, L. Zuze

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Frequent machine breakdowns, low plant availability and increased overtime are a great threat to a manufacturing plant as they increase operating costs of an industry. The main aim of this study was to improve Overall Equipment Effectiveness (OEE) at a manufacturing company through the implementation of innovative maintenance strategies. A case study approach was used. The paper focuses on improving the maintenance in a manufacturing set up using an innovative maintenance regime mix to improve overall equipment effectiveness. Interviews, reviewing documentation and historical records, direct and participatory observation were used as data collection methods during the research. Usually production is based on the total kilowatt of motors produced per day. The target kilowatt at 91% availability is 75 Kilowatts a day. Reduced demand and lack of raw materials particularly imported items are adversely affecting the manufacturing operations. The company had to reset its targets from the usual figure of 250 Kilowatt per day to mere 75 per day due to lower availability of machines as result of breakdowns as well as lack of raw materials. The price reductions and uncertainties as well as general machine breakdowns further lowered production. Some recommendations were given. For instance, employee empowerment in the company will enhance responsibility and authority to improve and totally eliminate the six big losses. If the maintenance department is to realise its proper function in a progressive, innovative industrial society, then its personnel must be continuously trained to meet current needs as well as future requirements. To make the maintenance planning system effective, it is essential to keep track of all the corrective maintenance jobs and preventive maintenance inspections. For large processing plants these cannot be handled manually. It was therefore recommended that the company implement (Computerised Maintenance Management System) CMMS.

Keywords: Maintenance, Manufacturing, Overall Equipment Effectiveness

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Authors: Wei Zhang, Su-Yan Tang, Yi-Fan Zhu, Wei-Ping Wang

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Support vector regression (SVR) has been regarded as a state-of-the-art method for approximation and regression. The importance of kernel function, which is so-called admissible support vector kernel (SV kernel) in SVR, has motivated many studies on its composition. The Gaussian kernel (RBF) is regarded as a “best" choice of SV kernel used by non-expert in SVR, whereas there is no evidence, except for its superior performance on some practical applications, to prove the statement. Its well-known that reproducing kernel (R.K) is also a SV kernel which possesses many important properties, e.g. positive definiteness, reproducing property and composing complex R.K by simpler ones. However, there are a limited number of R.Ks with explicit forms and consequently few quantitative comparison studies in practice. In this paper, two R.Ks, i.e. SV kernels, composed by the sum and product of a translation invariant kernel in a Sobolev space are proposed. An exploratory study on the performance of SVR based general R.K is presented through a systematic comparison to that of RBF using multiple criteria and synthetic problems. The results show that the R.K is an equivalent or even better SV kernel than RBF for the problems with more input variables (more than 5, especially more than 10) and higher nonlinearity.

Keywords: admissible support vector kernel, reproducing kernel, reproducing kernel Hilbert space, support vector regression.

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Authors: Oliver Ohneiser, Francesca De Crescenzio, Gianluca Di Flumeri, Jan Kraemer, Bruno Berberian, Sara Bagassi, Nicolina Sciaraffa, Pietro Aricò, Gianluca Borghini, Fabio Babiloni

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An increasing degree of automation in air traffic will also change the role of the air traffic controller (ATCO). ATCOs will fulfill significantly more monitoring tasks compared to today. However, this rather passive role may lead to Out-Of-The-Loop (OOTL) effects comprising vigilance decrement and less situation awareness. The project MINIMA (Mitigating Negative Impacts of Monitoring high levels of Automation) has conceived a system to control and mitigate such OOTL phenomena. In order to demonstrate the MINIMA concept, an experimental simulation set-up has been designed. This set-up consists of two parts: 1) a Task Environment (TE) comprising a Terminal Maneuvering Area (TMA) simulator as well as 2) a Vigilance and Attention Controller (VAC) based on neurophysiological data recording such as electroencephalography (EEG) and eye-tracking devices. The current vigilance level and the attention focus of the controller are measured during the ATCO’s active work in front of the human machine interface (HMI). The derived vigilance level and attention trigger adaptive automation functionalities in the TE to avoid OOTL effects. This paper describes the full-scale experimental set-up and the component development work towards it. Hence, it encompasses a pre-test whose results influenced the development of the VAC as well as the functionalities of the final TE and the two VAC’s sub-components.

Keywords: Automation, human factors, air traffic controller, MINIMA, OOTL, Out-Of-The-Loop, EEG, electroencephalography, HMI, human machine interface.

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Authors: Mohd. Khairil Sailan, Rosilah Hassan, Zuhaizal Zulkifli

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Internet Protocol version 4 (IPv4) address is decreasing and a rapid transition method to the next generation IP address (IPv6) should be established. This study aims to evaluate and select the best performance of the IPv6 address network transitionmechanisms, such as IPv4/IPv6 dual stack, transport Relay Translation (TRT) and Reverse Proxy with additional features. It is also aim to prove that faster access can be done while ensuring optimal usage of available resources used during the test and actual implementation. This study used two test methods such asInternet Control Message Protocol (ICMP)ping and ApacheBenchmark (AB) methodsto evaluate the performance.Performance metrics for this study include aspects ofaverageaccessin one second,time takenfor singleaccess,thedata transfer speed and the costof additional requirements.Reverse Proxy with Caching featureis the most efficientmechanism because of it simpler configurationandthe best performerfrom the test conducted.

Keywords: IPv4, IPv6, network transition, apache benchmark andreverse proxy

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Authors: S. Chee Choy, Pauline Swee Choo Goh, Yow Lin Liew

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The purpose of this study is to evaluate the English version and a Malay translation of the 21-item Learner Awareness Questionnaire for its application to assess student learning in higher education. The Learner Awareness Questionnaire, originally written in English, is a quantitative measure of how and why students learn. The questionnaire gives an indication of the process and motives to learn using four scales: survival, establishing stability, approval and loving to learn. Data in the present study came from 680 university students enrolled in various programmes in Malaysia. The Malay version of the questionnaire supported a similar four factor structure and internal consistency to the English version. The four factors of the Malay version also showed moderate to strong correlations with those of the English versions. The results suggest that the Malay version of the questionnaire is similar to the English version. However, further refinement to the questions is needed to strengthen the correlations between the two questionnaires.

Keywords: Student learning, learner awareness, instrument validation.

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: Human machine interface, industrial internet of things, internet of things, optical character recognition, video analytic.

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Authors: Omer Rashid, Ayoub Al-Hamadi, Axel Panning, Bernd Michaelis

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It is hard to percept the interaction process with machines when visual information is not available. In this paper, we have addressed this issue to provide interaction through visual techniques. Posture recognition is done for American Sign Language to recognize static alphabets and numbers. 3D information is exploited to obtain segmentation of hands and face using normal Gaussian distribution and depth information. Features for posture recognition are computed using statistical and geometrical properties which are translation, rotation and scale invariant. Hu-Moment as statistical features and; circularity and rectangularity as geometrical features are incorporated to build the feature vectors. These feature vectors are used to train SVM for classification that recognizes static alphabets and numbers. For the alphabets, curvature analysis is carried out to reduce the misclassifications. The experimental results show that proposed system recognizes posture symbols by achieving recognition rate of 98.65% and 98.6% for ASL alphabets and numbers respectively.

Keywords: Feature Extraction, Posture Recognition, Pattern Recognition, Application.

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Authors: Ventzas Dimitrios, Petropoulos George

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The paper presents a compressor anti-surge control system, that results in maximizing compressor throughput with pressure standard deviation reduction, increased safety margin between design point and surge limit line and avoiding possible machine surge. Alternative control strategies are presented.

Keywords: Anti-surge, control, compressor, PID control, safety, fault tolerance, start-up, ESD.

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Authors: Shiraz Ahmad, Zhe-Ming Lu

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An effective visual error concealment method has been presented by employing a robust rotation, scale, and translation (RST) invariant partial patch matching model (RSTI-PPMM) and exemplar-based inpainting. While the proposed robust and inherently feature-enhanced texture synthesis approach ensures the generation of excellent and perceptually plausible visual error concealment results, the outlier pruning property guarantees the significant quality improvements, both quantitatively and qualitatively. No intermediate user-interaction is required for the pre-segmented media and the presented method follows a bootstrapping approach for an automatic visual loss recovery and the image and video error concealment.

Keywords: Exemplar-based image and video inpainting, outlierpruning, RST-invariant partial patch matching model (RSTI-PPMM), visual error concealment.

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Authors: Miika Toivanen, Jouko Lampinen

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This paper presents an online method that learns the corresponding points of an object from un-annotated grayscale images containing instances of the object. In the first image being processed, an ensemble of node points is automatically selected which is matched in the subsequent images. A Bayesian posterior distribution for the locations of the nodes in the images is formed. The likelihood is formed from Gabor responses and the prior assumes the mean shape of the node ensemble to be similar in a translation and scale free space. An association model is applied for separating the object nodes and background nodes. The posterior distribution is sampled with Sequential Monte Carlo method. The matched object nodes are inferred to be the corresponding points of the object instances. The results show that our system matches the object nodes as accurately as other methods that train the model with annotated training images.

Keywords: Bayesian modeling, Gabor filters, Online learning, Sequential Monte Carlo.

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Authors: Mehdi Ghayoumi

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We have applied new accelerated algorithm for linear discriminate analysis (LDA) in face recognition with support vector machine. The new algorithm has the advantage of optimal selection of the step size. The gradient descent method and new algorithm has been implemented in software and evaluated on the Yale face database B. The eigenfaces of these approaches have been used to training a KNN. Recognition rate with new algorithm is compared with gradient.

Keywords: lda, adaptive, svm, face recognition.

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

Abstract:

Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: Diesel engine, machine learning, NOx emission, semi-empirical.

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Authors: R. A. Salam, M.A. Rodrigues

Abstract:

The number of features required to represent an image can be very huge. Using all available features to recognize objects can suffer from curse dimensionality. Feature selection and extraction is the pre-processing step of image mining. Main issues in analyzing images is the effective identification of features and another one is extracting them. The mining problem that has been focused is the grouping of features for different shapes. Experiments have been conducted by using shape outline as the features. Shape outline readings are put through normalization and dimensionality reduction process using an eigenvector based method to produce a new set of readings. After this pre-processing step data will be grouped through their shapes. Through statistical analysis, these readings together with peak measures a robust classification and recognition process is achieved. Tests showed that the suggested methods are able to automatically recognize objects through their shapes. Finally, experiments also demonstrate the system invariance to rotation, translation, scale, reflection and to a small degree of distortion.

Keywords: Image mining, feature selection, shape recognition, peak measures.

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