Search results for: automatic tool changer.

Authors: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammadhossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy CMeans (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic CMeans (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: Facial image, segmentation, PCM, FCM, skin error, facial surgery.

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Authors: Nuawi M. Z., Lamin F., Ismail A. R., Abdullah S., Wahid Z.

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A filter is used to remove undesirable frequency information from a dynamic signal. This paper shows that the Znotch filter filtering technique can be applied to remove the noise nuisance from a machining signal. In machining, the noise components were identified from the sound produced by the operation of machine components itself such as hydraulic system, motor, machine environment and etc. By correlating the noise components with the measured machining signal, the interested components of the measured machining signal which was less interfered by the noise, can be extracted. Thus, the filtered signal is more reliable to be analysed in terms of noise content compared to the unfiltered signal. Significantly, the I-kaz method i.e. comprises of three dimensional graphical representation and I-kaz coefficient, Z∞ could differentiate between the filtered and the unfiltered signal. The bigger space of scattering and the higher value of Z∞ demonstrated that the signal was highly interrupted by noise. This method can be utilised as a proactive tool in evaluating the noise content in a signal. The evaluation of noise content is very important as well as the elimination especially for machining operation fault diagnosis purpose. The Z-notch filtering technique was reliable in extracting noise component from the measured machining signal with high efficiency. Even though the measured signal was exposed to high noise disruption, the signal generated from the interaction between cutting tool and work piece still can be acquired. Therefore, the interruption of noise that could change the original signal feature and consequently can deteriorate the useful sensory information can be eliminated.

Keywords: Digital signal filtering, I-kaz method, Machiningmonitoring, Noise Cancelling, Sound

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Authors: Y. A. Adla, R. Soubra, M. Kasab, M. O. Diab, A. Chkeir

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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals out of which 11 were chosen based on their Intraclass Correlation Coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, five features were introduced to the Linear Discriminant Analysis classifier and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90% respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: Robert Niese, Ayoub Al-Hamadi, Bernd Michaelis

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In modern human computer interaction systems (HCI), emotion recognition is becoming an imperative characteristic. The quest for effective and reliable emotion recognition in HCI has resulted in a need for better face detection, feature extraction and classification. In this paper we present results of feature space analysis after briefly explaining our fully automatic vision based emotion recognition method. We demonstrate the compactness of the feature space and show how the 2d/3d based method achieves superior features for the purpose of emotion classification. Also it is exposed that through feature normalization a widely person independent feature space is created. As a consequence, the classifier architecture has only a minor influence on the classification result. This is particularly elucidated with the help of confusion matrices. For this purpose advanced classification algorithms, such as Support Vector Machines and Artificial Neural Networks are employed, as well as the simple k- Nearest Neighbor classifier.

Keywords: Facial expression analysis, Feature extraction, Image processing, Pattern Recognition, Application.

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Authors: Karen Armstrong

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Recent concerns about the value of teaching cursive handwriting in the classroom are based on the belief that cursive handwriting or penmanship is an outdated and unnecessary skill in today’s online world. The discussion of this issue begins with a description of current initiatives to eliminate handwriting instruction in schools. This is followed by a brief history of cursive writing through the ages. Next considered is a description of its benefits as a preliminary process for younger children as compared with immediate instruction in keyboarding, particularly in the areas of vision, cognition, motor skills and automatic fluency. Also considered, is cursive’s companion, paper itself, and the impact of a paperless, “screen and keyboard” environment. The discussion concludes with a consideration of the unique contributions of cursive and keyboarding as written forms of communication, along with their respective surfaces, paper and screen. Finally, an assessment of the practical utility of each skill is followed by an informal assessment of what is lost and what remains as we move from a predominantly paper and pen world of handwriting to texting and keyboarding in an environment of screens.

Keywords: Asemic writing, cursive, handwriting, keyboarding, paper.

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Authors: Chaiwat Tantarangsee

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The purposes of this study are 1) to study the frequent English writing errors of students registering the course: Reading and Writing English for Academic Purposes II, and 2) to find out the results of writing error correction by using coded indirect corrective feedback and writing error treatments. Samples include 28 2nd year English Major students, Faculty of Education, Suan Sunandha Rajabhat University. Tool for experimental study includes the lesson plan of the course; Reading and Writing English for Academic Purposes II, and tool for data collection includes 4 writing tests of short texts. The research findings disclose that frequent English writing errors found in this course comprise 7 types of grammatical errors, namely Fragment sentence, Subject-verb agreement, Wrong form of verb tense, Singular or plural noun endings, Run-ons sentence, Wrong form of verb pattern and Lack of parallel structure. Moreover, it is found that the results of writing error correction by using coded indirect corrective feedback and error treatment reveal the overall reduction of the frequent English writing errors and the increase of students’ achievement in the writing of short texts with the significance at .05.

Keywords: Coded indirect corrective feedback, error correction, and error treatment.

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Authors: Ágoston Winkler, Sándor Juhász

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The localization of software products is essential for reaching the users of the international market. An important task for this is the translation of the user interface into local national languages. As graphical interfaces are usually optimized for the size of the texts in the original language, after the translation certain user controls (e.g. text labels and buttons in dialogs) may grow in such a manner that they slip above each other. This not only causes an unpleasant appearance but also makes the use of the program more difficult (or even impossible) which implies that the arrangement of the controls must be corrected subsequently. The correction should preserve the original structure of the interface (e.g. the relation of logically coherent controls), furthermore, it is important to keep the nicely proportioned design: the formation of large empty areas should be avoided. This paper describes an algorithm that automatically rearranges the controls of a graphical user interface based on the principles above. The algorithm has been implemented and integrated into a translation support system and reached results pleasant for the human eye in most test cases.

Keywords: Graphical user interface, GUI, natural languages, software localization, translation support systems.

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Authors: José M. Brotons, Manuel E. Sansalvador

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The relationship between Corporate Social Responsibility (CSR) and financial performance (FP) is a subject of great interest that has not yet been resolved. In this work, we have developed a new and original tool to measure this relation. The tool quantifies the value contributed to companies that are committed to CSR. The theoretical model used is the fuzzy discounted cash flow method. Two assumptions have been considered, the first, the company has implemented the IQNet SR10 certification, and the second, the company has not implemented that certification. For the first one, the growth rate used for the time horizon is the rate maintained by the company after obtaining the IQNet SR10 certificate. For the second one, both, the growth rates company prior to the implementation of the certification, and the evolution of the sector will be taken into account. By using triangular fuzzy numbers, it is possible to deal adequately with each company’s forecasts as well as the information corresponding to the sector. Once the annual growth rate of the sales is obtained, the profit and loss accounts are generated from the annual estimate sales. For the remaining elements of this account, their regression with the nets sales has been considered. The difference between these two valuations, made in a fuzzy environment, allows obtaining the value of the IQNet SR10 certification. Although this study presents an innovative methodology to quantify the relation between CSR and FP, the authors are aware that only one company has been analyzed. This is precisely the main limitation of this study which in turn opens up an interesting line for future research: to broaden the sample of companies.

Keywords: Corporate social responsibility, case study, financial performance, company valuation.

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: Baby care system, internet of things, deep learning, machine vision.

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Authors: Alessandro Bevilacqua, Alessandro Gherardi, Ludovico Carozza, Filippo Piccinini

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The last years have seen an increasing use of image analysis techniques in the field of biomedical imaging, in particular in microscopic imaging. The basic step for most of the image analysis techniques relies on a background image free of objects of interest, whether they are cells or histological samples, to perform further analysis, such as segmentation or mosaicing. Commonly, this image consists of an empty field acquired in advance. However, many times achieving an empty field could not be feasible. Or else, this could be different from the background region of the sample really being studied, because of the interaction with the organic matter. At last, it could be expensive, for instance in case of live cell analyses. We propose a non parametric and general purpose approach where the background is built automatically stemming from a sequence of images containing even objects of interest. The amount of area, in each image, free of objects just affects the overall speed to obtain the background. Experiments with different kinds of microscopic images prove the effectiveness of our approach.

Keywords: Microscopy, flat field correction, background estimation, image segmentation.

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Authors: Chul Kwak, Oh-Wook Kwon

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In this paper, an extreme learning machine with an automatic segmentation algorithm is applied to heart disorder classification by heart sound signals. From continuous heart sound signals, the starting points of the first (S1) and the second heart pulses (S2) are extracted and corrected by utilizing an inter-pulse histogram. From the corrected pulse positions, a single period of heart sound signals is extracted and converted to a feature vector including the mel-scaled filter bank energy coefficients and the envelope coefficients of uniform-sized sub-segments. An extreme learning machine is used to classify the feature vector. In our cardiac disorder classification and detection experiments with 9 cardiac disorder categories, the proposed method shows significantly better performance than multi-layer perceptron, support vector machine, and hidden Markov model; it achieves the classification accuracy of 81.6% and the detection accuracy of 96.9%.

Keywords: Heart sound classification, extreme learning machine

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Authors: Xinyue Zhao, Yutaka Satoh, Hidenori Takauji, Shun'ichi Kaneko

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A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera. The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental results show the good performance of the proposed method.

Keywords: Hybrid feature, adaptive Particle Filter, robust Object Tracking, Grayscale Arranging Pairs (GAP) feature.

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Authors: Panagiotis Karadimos, Leonidas Anthopoulos

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Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: Actual cost and duration, attribute selection, bridge projects, neural networks, predicting models, FANN TOOL, WEKA.

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT.

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Authors: Y. Duvarci, S. Mizokami

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Today, the preferences and participation of the TD groups such as the elderly and disabled is still lacking in decision-making of transportation planning, and their reactions to certain type of policies are not well known. Thus, a clear methodology is needed. This study aimed to develop a method to extract the preferences of the disabled to be used in the policy-making stage that can also guide to future estimations. The method utilizes the combination of cluster analysis and data filtering using the data of the Arao city (Japan). The method is a process that follows: defining the TD group by the cluster analysis tool, their travel preferences in tabular form from the household surveys by policy variableimpact pairs, zones, and by trip purposes, and the final outcome is the preference probabilities of the disabled. The preferences vary by trip purpose; for the work trips, accessibility and transit system quality policies with the accompanying impacts of modal shifts towards public mode use as well as the decreasing travel costs, and the trip rate increase; for the social trips, the same accessibility and transit system policies leading to the same mode shift impact, together with the travel quality policy area leading to trip rate increase. These results explain the policies to focus and can be used in scenario generation in models, or any other planning purpose as decision support tool.

Keywords: Transportation Disadvantaged, Disabled, Mixed Preference, Stated Preference Data.

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Authors: Perminder JitKaur, Santosh Satya, K. K. Pant, S. N. Naik

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Bamboo is extensively used in construction industry. Low durability of bamboo due to fungus infestation and termites attack under storage puts certain constrains for it usage as modern structural material. Looking at many chemical formulations for bamboo treatment leading to severe harmful environment effects, research on eco-friendly preservatives for bamboo treatment has been initiated world-over. In the present studies, eco-friendly preservative for bamboo treatment has been developed. To validate its application for structural purposes, investigation of effect of treatment on compressive strength has been investigated. Neemoil (25%) integrated with copper naphthenate (0.3%) on dilution with kerosene oil impregnated into bamboo culm at 2 bar pressure, has shown weight loss of only 3.15% in soil block analysis method. The results from compressive strength analysis using HEICO Automatic Compression Testing Machine reveal that preservative treatment has not altered the structural properties of bamboo culms. Compressive strength of control (11.72 N/mm2) and above treated samples (11.71 N/mm2) was found to be comparable.

Keywords: Compressive strength, D. strictus bamboo, Ecofriendly treatment, neem oil.

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Authors: Ion-Cosmin Dita, Vasile Gui, Franz Quint, Marius Otesteanu

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In automatic manufacturing and assembling of mechanical, electrical and electronic parts one needs to reliably identify the position of components and to extract the information of these components. Data Matrix Codes (DMC) are established by these days in many areas of industrial manufacturing thanks to their concentration of information on small spaces. In today’s usually order-related industry, where increased tracing requirements prevail, they offer further advantages over other identification systems. This underlines in an impressive way the necessity of a robust code reading system for detecting DMC on the components in factories. This paper compares two methods for estimating the angle of orientation of Data Matrix Codes: one method based on the Hough Transform and the other based on the Mean Shift Algorithm. We concentrate on Data Matrix Codes in industrial environment, punched, milled, lasered or etched on different materials in arbitrary orientation.

Keywords: Industrial data matrix code, Hough transform, mean shift.

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Authors: Nevine M. Labib, Michael N. Malek

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Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily comprehensible to humans. One of the useful applications in Egypt is the Cancer management, especially the management of Acute Lymphoblastic Leukemia or ALL, which is the most common type of cancer in children. This paper discusses the process of designing a prototype that can help in the management of childhood ALL, which has a great significance in the health care field. Besides, it has a social impact on decreasing the rate of infection in children in Egypt. It also provides valubale information about the distribution and segmentation of ALL in Egypt, which may be linked to the possible risk factors. Undirected Knowledge Discovery is used since, in the case of this research project, there is no target field as the data provided is mainly subjective. This is done in order to quantify the subjective variables. Therefore, the computer will be asked to identify significant patterns in the provided medical data about ALL. This may be achieved through collecting the data necessary for the system, determimng the data mining technique to be used for the system, and choosing the most suitable implementation tool for the domain. The research makes use of a data mining tool, Clementine, so as to apply Decision Trees technique. We feed it with data extracted from real-life cases taken from specialized Cancer Institutes. Relevant medical cases details such as patient medical history and diagnosis are analyzed, classified, and clustered in order to improve the disease management.

Keywords: Data Mining, Decision Trees, Knowledge Discovery, Leukemia.

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Authors: José A. Menezes, Giordano Cabral, Bruno T. Gomes

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Choosing good features is an essential part of machine learning. Recent techniques aim to automate this process. For instance, feature learning intends to learn the transformation of raw data into a useful representation to machine learning tasks. In automatic audio classification tasks, this is interesting since the audio, usually complex information, needs to be transformed into a computationally convenient input to process. Another technique tries to generate features by searching a feature space. Genetic algorithms, for instance, have being used to generate audio features by combining or modifying them. We find this approach particularly interesting and, despite the undeniable advances of feature learning approaches, we wanted to take a step forward in the use of genetic algorithms to find audio features, combining them with more conventional methods, like PCA, and inserting search control mechanisms, such as constraints over a confusion matrix. This work presents the results obtained on particular audio classification problems.

Keywords: Feature generation, feature learning, genetic algorithm, music information retrieval.

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Authors: Y.C. Yeh, Y.S. Tsay, C.M. Chiang

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A good green building design project, designers should consider not only energy consumption, but also healthy and comfortable needs of inhabitants. In recent years, the Taiwan government paid attentions on both carbon reduction and indoor air quality issues, which be presented in the legislation of Building Codes and other regulations. Taiwan located in hot and humid climates, dampness in buildings leads to significant microbial pollution and building damage. This means that the high temperature and humidity present a serious indoor air quality issue. The interactions between vapor transfers and energy fluxes are essential for the whole building Heat Air and Moisture (HAM) response. However, a simulation tool with short calculation time, property accuracy and interface is needed for practical building design processes. In this research, we consider the vapor transfer phenomenon of building materials as well as temperature and humidity and energy consumption in a building space. The simulation bases on the EMPD method, which was performed by EnergyPlus, a simulation tool developed by DOE, to simulate the indoor moisture variation in a one-zone residential unit based on the Effective Moisture Penetration Depth Method, which is more suitable for practical building design processes.

Keywords: Effective Moisture Penetration Depth Method, Moisture Buffering Effect, Interior Material, Green Material, EnergyPlus

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Authors: Freeman Ntuli

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An overview of the important aspects of managing and controlling industrial effluent discharges to public sewers namely sampling, characterization, quantification and legislative controls has been presented. The findings have been validated by means of a case study covering three industrial sectors namely, tanning, textile finishing and food processing industries. Industrial effluents discharges were found to be best monitored by systematic and automatic sampling and quantified using water meter readings corrected for evaporative and consumptive losses. Based on the treatment processes employed in the public owned treatment works and the chemical oxygen demand and biochemical oxygen demand levels obtained, the effluent from all the three industrial sectors studied were found to lie in the toxic zone. Thus, physico-chemical treatment of these effluents is required to bring them into the biodegradable zone. KL values (quoted to base e) were greater than 0.50 day-1 compared to 0.39 day-1 for typical municipality wastewater.

Keywords: biodegradability, industrial effluent, pollution control, public sewers

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Authors: A. Lauvray, F. Poulhaon, P. Michaud, P. Joyot, E. Duc

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Additive Friction Stir Manufacturing, or AFSM, is a new industrial process that follows the emergence of friction-based processes. The AFSM process is a solid-state additive process using the energy produced by the friction at the interface between a rotating non-consumable tool and a substrate. Friction depends on various parameters like axial force, rotation speed or friction coefficient. The feeder material is a metallic rod that flows through a hole in the tool. There is still a lack in understanding of the physical phenomena taking place during the process. This research aims at a better AFSM process understanding and implementation, thanks to numerical simulation and experimental validation performed on a prototype effector. Such an approach is considered a promising way for studying the influence of the process parameters and to finally identify a process window that seems relevant. The deposition of material through the AFSM process takes place in several phases. In chronological order these phases are the docking phase, the dwell time phase, the deposition phase, and the removal phase. The present work focuses on the dwell time phase that enables the temperature rise of the system due to pure friction. An analytic modeling of heat generation based on friction considers as main parameters the rotational speed and the contact pressure. Another parameter considered influential is the friction coefficient assumed to be variable, due to the self-lubrication of the system with the rise in temperature or the materials in contact roughness smoothing over time. This study proposes through a numerical modeling followed by an experimental validation to question the influence of the various input parameters on the dwell time phase. Rotation speed, temperature, spindle torque and axial force are the main monitored parameters during experimentations and serve as reference data for the calibration of the numerical model. This research shows that the geometry of the tool as well as fluctuations of the input parameters like axial force and rotational speed are very influential on the temperature reached and/or the time required to reach the targeted temperature. The main outcome is the prediction of a process window which is a key result for a more efficient process implementation.

Keywords: numerical model, additive manufacturing, frictional heat generation, process

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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: Mayank Kumar, D B Mahesh Kumar, Ashwin S Kumar, N K Srinath

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In this era of technology, fueled by the pervasive usage of the internet, security is a prime concern. The number of new attacks by the so-called “bots", which are automated programs, is increasing at an alarming rate. They are most likely to attack online registration systems. Technology, called “CAPTCHA" (Completely Automated Public Turing test to tell Computers and Humans Apart) do exist, which can differentiate between automated programs and humans and prevent replay attacks. Traditionally CAPTCHA-s have been implemented with the challenge involved in recognizing textual images and reproducing the same. We propose an approach where the visual challenge has to be read out from which randomly selected keywords are used to verify the correctness of spoken text and in turn detect the presence of human. This is supplemented with a speaker recognition system which can identify the speaker also. Thus, this framework fulfills both the objectives – it can determine whether the user is a human or not and if it is a human, it can verify its identity.

Keywords: CAPTCHA, automatic speech recognition, keyword spotting.

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Authors: Helen S. Ghali, Engy Serag, A. Samer Ezeldin

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In many countries, the construction industry relies heavily on outsourcing models in executing their projects and expanding their businesses to fit in the diverse market. Such extensive integration of subcontractors is becoming an influential factor in contractor’s cash flow management. Accordingly, subcontractors’ financial terms are important phenomena and pivotal components for the well-being of the contractor’s cash flow. The aim of this research is to study the contractor’s cash flow with respect to the owner and subcontractor’s payment management plans, considering variable advance payment, payment frequency, and lag and retention policies. The model is developed to provide contractors with a decision support tool that can assist in selecting the optimum subcontracting plan to minimize the contractor’s financing limits and optimize the profit values. The model is built using Microsoft Excel VBA coding, and the genetic algorithm is utilized as the optimization tool. Three objective functions are investigated, which are minimizing the highest negative overdraft value, minimizing the net present worth of overdraft, and maximizing the project net profit. The model is validated on a full-scale project which includes both self-performed and subcontracted work packages. The results show potential outputs in optimizing the contractor’s negative cash flow values and, in the meantime, assisting contractors in selecting suitable subcontractors to achieve the objective function.

Keywords: Cash flow optimization, payment plan, procurement management, subcontracting plan.

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Authors: Katsunori Miura, Kiyoshi Akama, Hiroshi Mabuchi

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In the Equivalent Transformation (ET) computation model, a program is constructed by the successive accumulation of ET rules. A method by meta-computation by which a correct ET rule is generated has been proposed. Although the method covers a broad range in the generation of ET rules, all important ET rules are not necessarily generated. Generation of more ET rules can be achieved by supplementing generation methods which are specialized for important ET rules. A Specialization-by-Equation (Speq) rule is one of those important rules. A Speq rule describes a procedure in which two variables included in an atom conjunction are equalized due to predicate constraints. In this paper, we propose an algorithm that systematically and recursively generate Speq rules and discuss its effectiveness in the synthesis of ET programs. A Speq rule is generated based on proof of a logical formula consisting of given atom set and dis-equality. The proof is carried out by utilizing some ET rules and the ultimately obtained rules in generating Speq rules.

Keywords: Equivalent transformation, ET rule, Equation of two variables, Rule generation, Specialization-by-Equation rule

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Authors: Jung-Hyun Kim, Jihyung Kim, Kwangjae Lim, Dong Seung Kwon

Abstract:

Network coding has recently attracted attention as an efficient technique in multicast/broadcast services. The problem of finding the optimal network coding mechanism maximizing the bandwidth efficiency is hard to solve and hard to approximate. Lots of network coding-based schemes have been suggested in the literature to improve the bandwidth efficiency, especially network coding-based automatic repeat request (NCARQ) schemes. However, existing schemes have several limitations which cause the performance degradation in resource limited systems. To improve the performance in resource limited systems, we propose NCARQ with overlapping selection (OS-NCARQ) scheme. The advantages of OS-NCARQ scheme over the traditional ARQ scheme and existing NCARQ schemes are shown through the analysis and simulations.

Keywords: ARQ, Network coding, Multicast/Broadcast services, Packet-based systems.

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Authors: Çiğdem Bakır, Mecit Yüzkat

Abstract:

With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software used in the study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: Preprocessing of the data used, feature detection and classification. We tried to determine the success of our study with different accuracy metrics and the results were presented comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: Decision tree, water quality, water pollution, machine learning.

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Authors: Ali Dehghani, Mojtaba Hakimzadeh, Amir Habibi, Navid Mehdizadeh Afroozi

Abstract:

In this paper an isolated wind-diesel hybrid power system has been considered for reactive power control study having an induction generator for wind power conversion and synchronous alternator with automatic voltage regulator (AVR) for diesel unit is presented. The dynamic voltage stability evaluation is dependent on small signal analysis considering a Static VAR Compensator (SVC) and IEEE type -I excitation system. It's shown that the variable reactive power source like SVC is crucial to meet the varying demand of reactive power by induction generator and load and to acquire an excellent voltage regulation of the system with minimum fluctuations. Integral square error (ISE) criterion can be used to evaluate the optimum setting of gain parameters. Finally the dynamic responses of the power systems considered with optimum gain setting will also be presented.

Keywords: SVC, Small Signal Stability, Reactive Power, Control, Hybrid System.

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Authors: M. Polanský

Abstract:

This paper introduces a new method called ARPDC (Advanced Robust Parallel Distributed Compensation) for automatic control of nonlinear systems. This method improves a quality of robust control by interpolating of robust and optimal controller. The weight of each controller is determined by an original criteria function for model validity and disturbance appreciation. ARPDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy systems and Parallel Distributed Compensation (PDC) control scheme. The relaxed stability conditions of ARPDC control of nominal system have been derived. The advantages of presented method are demonstrated on the inverse pendulum benchmark problem. From comparison between three different controllers (robust, optimal and ARPDC) follows, that ARPDC control is almost optimal with the robustness close to the robust controller. The results indicate that ARPDC algorithm can be a good alternative not only for a robust control, but in some cases also to an adaptive control of nonlinear systems.

Keywords: Robust control, optimal control, Takagi–Sugeno (TS) fuzzy models, linear matrix inequality (LMI), observer, Advanced Robust Parallel Distributed Compensation (ARPDC).

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