Search results for: industrial machine

Authors: M. Amir Abas, M. Dahlui

Abstract:

This paper presents the investigation results of UV measurement at different level of altitudes and the development of a new portable instrument for measuring UV. The rapid growth of industrial sectors in developing countries including Malaysia, brings not only income to the nation, but also causes pollution in various forms. Air pollution is one of the significant contributors to global warming by depleting the Ozone layer, which would reduce the filtration of UV rays. Long duration of exposure to high to UV rays has many devastating health effects to mankind directly or indirectly through destruction of the natural resources. This study aimed to show correlation between UV and altitudes which indirectly can help predict Ozone depletion. An instrument had been designed to measure and monitors the level of UV. The instrument comprises of two main blocks namely data logger and Graphic User Interface (GUI). Three sensors were used in the data logger to detect changes in the temperature, humidity and ultraviolet. The system has undergone experimental measurement to capture data at two different conditions; industrial area and high attitude area. The performance of the instrument showed consistency in the data captured and the results of the experiment drew a significantly high reading of UV at high altitudes.

Keywords: Ozone Layer, Monitoring, Global Warming, Measurement, Ultraviolet

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Authors: Muhamad Nugraha

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Economic growth always correlate positively with the magnitude of the unemployment rate. This is caused by labor which one of important variable to keep growth in the real sector of the region. Meanwhile, the economic structure in districts of Jember showed an increase of economic activity began to shift towards the industrial sector and some other economic sectors, so they have an affects to considerations for policy makers to increase economic growth in Jember as an autonomous region in East Java Province. At the fact, SMEs is among the factors driving economic growth in the region. This is shown by the high amount of SMEs. However, employment in the sector grew slightly slowed. It is caused by a lack of productivity in SMEs. Through the analysis of the transformation of economic structure theory, and the theory of Triple Helix using descriptive analytical method Location Quotient and Shift - Share, found that the results of the economic structure in Jember slowly shifting from the agricultural sector to the industrial sector, because it is dominated by trade sector, hotel and restaurant sector. In addition, SMEs is the potential sector of economic growth in Jember. While to maximizing role and functions of the institution's Research and Development Center of SMEs, there are three points to be known, that are Business Landscape, Business Architecture and Value Added.

Keywords: Economic Growth, SMEs, Labor, Research and Development Center of SMEs.

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Authors: D. Kurt, Z. U. Yümün, I. F. Barut, E. Kam

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Since the development of the industrial revolution, industrial plants and settlements have spread widely along coastlines. This concentration of development brings environmental pollution to the seas. This study focuses on the Gulf of Izmir, a natural gulf of the Eastern Aegean Sea, located west of Turkey. Investigating marine current sediment is extremely important to detect pollution. This study considered natural radioactivity pollution of the marine environment. Ground drilling cores (the depth of each sediment is different) were taken from four different locations in the Gulf of izmir, Karşıyaka (12.5-13.5 m), Inciralti (6.5-7.5 m), Cesmealti (4.5-5 m) and Bayrakli (10-12 m). These sediment cores were put in preserving bags with weight around 1 kg, and were dried at room temperature to remove moisture. The samples were then sieved into fine powder (100 mesh), and these samples were relocated to 1000 mL polyethylene Marinelli beakers. The prepared sediments were stored for 40 days to reach radioactive equilibrium between uranium and thorium. Gamma spectrometry measurement of each sample was made using an HPGe (High-Purity Germanium) semiconductor detector. In this study, the results display that the average concentrations of the activity values are 8.4 ± 0.23 Bq kg^-1, 19.6 ± 0.51 Bq kg^-1, 8 ± 0.96 Bq kg^-1, 1.93 ± 0.3 Bq kg^-1, and 77.4 ± 0.96 Bq kg^-1, respectively.

Keywords: Gamma, Gulf of Izmir, Eastern Aegean Sea, Turkey, natural radionuclides, pollution.

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Authors: Imane El Karaoui, Mohammed Maaroufi, Hamid Chaikhy

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Doubly Fed Induction Generator (DFIG) is one of the most reliable wind generator. Major problem in wind power generation is to generate Sinusoidal signal with very low THD on variable speed caused by inverter two levels used. This paper presents a multi-level inverter whose objective is to reduce the THD and the dimensions of the output filter. This work proposes a three-level NPC-type inverter, the results simulation are presented demonstrating the efficiency of the proposed inverter.

Keywords: DFIG, multilevel inverter, NPC inverter , THD, Induction machine.

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Authors: O. S. Ebrahim, K. O. Shawky, M. A. Badr, P. K. Jain

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This paper proposes an intelligent, supervisory, hysteresis liquid-level control with three-state energy saving mode (ESM) for induction motor (IM) in fluid transportation system (FTS) including storage tank. The IM pump drive comprises a modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to the computer’s ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. Considering the motor’s thermal capacity used (TCU) and grid-compatible tariff structure, a logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction. Fuzzy-logic (FL) based availability assessment is designed and deployed on cloud, in order to provide mobilized service for the star/delta switch and highly reliable contactors. Moreover, an artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and computer simulations are performed to demonstrate the validity and effectiveness of the proposed control system in terms of reliability, power quality and operational cost reduction with a motivation of power factor correction.

Keywords: Artificial Neural Network, ANN, Contactor Health Assessment, Energy Saving Mode, Induction Machine, IM, Supervisory Control, Fluid Transportation, Fuzzy Logic, FL, cloud computing, pumped storage.

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: Subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing.

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Authors: Si-Hwan Kim, Gum-Sung Ryu, Kyung-Taek Koh, Jang-Hwa Lee

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Despite of the preponderant role played by cement among the construction materials, it is today considered as a material destructing the environment due to the large quantities of carbon dioxide exhausted during its manufacture. Besides, global warming is now recognized worldwide as the new threat to the humankind against which advanced countries are investigating measures to reduce the current amount of exhausted gases to the half by 2050. Accordingly, efforts to reduce green gases are exerted in all industrial fields. Especially, the cement industry strives to reduce the consumption of cement through the development of alkali-activated geopolymer mortars using industrial byproducts like bottom ash. This study intends to gather basic data on the flowability and strength development characteristics of alkali-activated geopolymer mortar by examining its FT-IT features with respect to the effects and strength of the alkali-activator in order to develop bottom ash-based alkali-activated geopolymer mortar. The results show that the 35:65 mass ratio of sodium hydroxide to sodium silicate is appropriate and that a molarity of 9M for sodium hydroxide is advantageous. The ratio of the alkali-activators to bottom ash is seen to have poor effect on the strength. Moreover, the FT-IR analysis reveals that larger improvement of the strength shifts the peak from 1060 cm–1 (T-O, T=Si or Al) toward shorter wavenumber.

Keywords: Bottom Ash, Geopolymer mortar, Flowability, Strength Properties.

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Authors: Mochammad Dewo, Sumarsono Sudarto

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The Exponential Triple Smoothing Algorithm approach nowadays, which is used to anticipate the export value of Indonesia's two major commodities, coal and palm oil, has a Mean Percentage Absolute Error (MAPE) value of 30-50%, which may be considered as a "reasonable" forecasting mistake. Forecasting errors of more than 30% shall have a domino effect on industrial output, as extra production adds to raw material, manufacturing and storage expenses. Whereas, reaching an "excellent" classification with an error value of less than 10% will provide new investors and exporters with confidence in the commercial development of related sectors. Industrial growth will bring out a positive impact on economic development. It can be applied for other commodities if the forecast error is less than 10%. The purpose of this project is to create a forecasting technique that can produce precise forecasting results with an error of less than 10%. This research analyzes forecasting methods such as ARIMA (Autoregressive Integrated Moving Average), ANN (Artificial Neural Network) and LSTM (Long-Short Term Memory). By providing a MAPE of 1%, this study reveals that ANN is the most successful strategy for forecasting coal and palm oil commodities in Indonesia.

Keywords: ANN, Artificial Neural Network, ARIMA, Autoregressive Integrated Moving Average, export value, forecast, LSTM, Long Short Term Memory.

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Authors: Haytham Al-Feel, M.A.Koutb, Hoda Suoror

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There are many problems associated with the World Wide Web: getting lost in the hyperspace; the web content is still accessible only to humans and difficulties of web administration. The solution to these problems is the Semantic Web which is considered to be the extension for the current web presents information in both human readable and machine processable form. The aim of this study is to reach new generic foundation architecture for the Semantic Web because there is no clear architecture for it, there are four versions, but still up to now there is no agreement for one of these versions nor is there a clear picture for the relation between different layers and technologies inside this architecture. This can be done depending on the idea of previous versions as well as Gerber-s evaluation method as a step toward an agreement for one Semantic Web architecture.

Keywords: Semantic Web Architecture, XML, RDF and Ontology.

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Authors: Itgel Bayarsaikhan, Jimin Lee, Sejong Oh

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Classification is one of the primary themes in computational biology. The accuracy of classification strongly depends on quality of a dataset, and we need some method to evaluate this quality. In this paper, we propose a new graphical analysis method using 'Membership-Deviation Graph (MDG)' for analyzing quality of a dataset. MDG represents degree of membership and deviations for instances of a class in the dataset. The result of MDG analysis is used for understanding specific feature and for selecting best feature for classification.

Keywords: feature, classification, machine learning algorithm.

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: Hekmatpanah, M.

Abstract:

Since the beginning of human history, human activities have caused many changes in the environment. Today, a particular attention should be paid to gaining knowledge about water quality of wetlands which are pristine natural environments rich in genetic reserves. If qualitative conditions of industrial areas (in terms of both physicochemical and biological conditions) are not addressed properly, they could cause disruption in natural ecosystems, especially in rivers. With regards to the quality of water resources, determination of pollutant sources plays a pivotal role in engineering projects as well as designing water quality control systems. Thus, using different methods such as flow duration curves, dischargepollution load model and frequency analysis by HYFA software package, risk of various industrial pollutants in international and ecologically important Gavkhoni wetland is analyzed. In this study, a station located at Varzaneh City is used as the last station on Zayanderud River, from where the river water is discharged into the wetland. Results showed that elements- concentrations often exceeded the allowed level and river water can endanger regional ecosystem. In addition, if the river discharge is managed on Q25 basis, this basis can lower concentrations of elements, keeping them within the normal level.

Keywords: Pollutants Risk, Industry, Flow Discharge, Management, Gavkhoni Wetland

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Authors: M. Rahou, F. Sebaa, A. Cheikh

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Manufacturing tolerancing is intended to determine the intermediate geometrical and dimensional states of the part during its manufacturing process. These manufacturing dimensions also serve to satisfy not only the functional requirements given in the definition drawing, but also the manufacturing constraints, for example geometrical defects of the machine, vibration and the wear of the cutting tool. The choice of positioning has an important influence on the cost and quality of manufacture. To avoid this problem, a two-step approach has been developed. The first step is dedicated to the determination of the optimum position. As for the second step, a study was carried out for the tightening effect on the tolerance interval.

Keywords: Dispersion, tolerance, manufacturing, position.

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Authors: Osama M. Darwesh, Sahar H. Hassan, Abd El-Raheem R. El-Shanshoury, Shawky Z. Sabae

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Nanotechnology as multidisciplinary technology is growing rapidly with important applications in several sectors. Also, nanobiotechnology is known for the use of microorganisms for the synthesis of targeted nanoparticles. The present study deals with the green synthesis of silver nanoparticles using aquatic bacteria and the development of a biogenic nanocomposite for environmental applications. 20 morphologically different colonies were isolated from the collected water samples from eight different locations at the Rosetta branch of the Nile Delta, Egypt. The obtained results illustrated that the most effective bacterial isolate (produced the higher amount of AgNPs after 24 h of incubation time) is isolate R3. Bacillus tequilensis was the strongest extracellular bio-manufactory of AgNPs. Biosynthesized nanoparticles had a spherical shape with a mean diameter of 2.74 to 28.4 nm. The antimicrobial activity of silver nanoparticles against many pathogenic microbes indicated that the produced AgNPs had high activity against all tested multi-antibiotic resistant pathogens. Also, the stabilized prepared AgNPs-SA nanocomposite has greater catalytic activity for the decolourization of some dyes like Methylene blue (MB) and Crystal violet. Such results represent a promising stage for producing eco-friendly, cost-effective, and easy-to-handle devices for the bioremediation of contaminated industrial wastewater.

Keywords: Bioremediation, AgNPs, AgNPs-SA nanocomposite, Bacillus tequilensis, nanobiotechnology.

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Authors: Carlos Sanchís-Pedregosa, José A. D. M achuca, María del Mar González-Zamora

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The growing outsourcing of logistics services resulting from the ongoing current in firms of costs reduction/increased efficiency means that it is becoming more and more important for the companies doing the outsourcing to carry out a proper evaluation. The multiple definitions and measures of logistics service performance found in research on the topic create a certain degree of confusion and do not clear the way towards the proper measurement of their performance. Do a model and a specific set of indicators exist that can be considered appropriate for measuring the performance of logistics services outsourcing in industrial environments? Are said indicators in keeping with the objectives pursued by outsourcing? We aim to answer these and other research questions in the study we have initiated in the field within the framework of the international High Performance Manufacturing (HPM) project of which this paper forms part. As the first stage of this research, this paper reviews articles dealing with the topic published in the last 15 years with the aim of detecting the models most used to make this measurement and determining which performance indicators are proposed as part of said models and which are most used. The first steps are also taken in determining whether these indicators, financial and operational, cover the aims that are being pursued when outsourcing logistics services. The findings show there is a wide variety of both models and indicators used. This would seem to testify to the need to continue with our research in order to try to propose a model and a set of indicators for measuring the performance of logistics services outsourcing in industrial environments.

Keywords: Logistics, objectives, outsourcing, performancemeasurement systems

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Authors: Ling-fei Xu, Qi Zhao, Yan-ru Chen, Mu-chun Zhou, Meng Zhang, Shi-xue Xu

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SVM ( Support Vector Machine ) is a new method in the artificial neural network ( ANN ). In the steel making, how to use computer to predict the end point of BOF accuracy is a great problem. A lot of method and theory have been claimed, but most of the results is not satisfied. Now the hot topic in the BOF end point predicting is to use optical way the predict the end point in the BOF. And we found that there exist some regular in the characteristic curve of the flame from the mouse of pudding. And we can use SVM to predict end point of the BOF, just single spectrum intensity should be required as the input parameter. Moreover, its compatibility for the input space is better than the BP network.

Keywords: SVM, predict, BOF, single spectrum intensity.

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Authors: Allan Tubaileh, Ibrahim Hammad, Loay Al Kafafi

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A new approach to determine the machine layout in flexible manufacturing cell, and to find the feasible robot configuration of the robot to achieve minimum cycle time is presented in this paper. The location of the input/output location and the optimal robot configuration is obtained for all sequences of work tasks of the robot within a specified period of time. A more realistic approach has been presented to model the problem using the robot joint space. The problem is formulated as a nonlinear optimization problem and solved using Sequential Quadratic Programming algorithm.

Keywords: Robotics, Layout.

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Authors: K. Beheshti Maal, R. Shafiee

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Vinegar is a precious food additive and complement as well as effective preservative against food spoilage. Recently traditional vinegar production has been improved using various natural substrates and fruits such as grape, palm, cherry, coconut, date, sugarcane, rice and balsam. These neoclassical fermentations resulted in several vinegar types with different tastes, fragrances and nutritional values because of applying various acetic acid bacteria as starters. Acetic acid bacteria include genera Acetobacter, Gluconacetobacter and Gluconobacter according to latest edition of Bergy-s Manual of Systematic Bacteriology that classifies genera on the basis of their 16s RNA differences. Acetobacter spp as the main vinegar starters belong to family Acetobacteraceae that are gram negative obligate aerobes, chemoorganotrophic bacilli that are oxidase negative and oxidize ethanol to acetic acid. In this research we isolated and identified a native Acetobacter strain with high acetic acid productivity and tolerance against high ethanol concentrations from Iranian peach as a summer delicious fruit that is very susceptible to food spoilage and decay. We used selective and specific laboratorial culture media such as Standard GYC, Frateur and Carr medium. Also we used a new industrial culture medium and a miniature fermentor with a new aeration system innovated by Pars Yeema Biotechnologists Co., Isfahan Science and Technology Town (ISTT), Isfahan, Iran. The isolated strain was successfully cultivated in modified Carr media with 2.5% and 5% ethanol simultaneously in high temperatures, 34 - 40º C after 96 hours of incubation period. We showed that the increase of ethanol concentration resulted in rising of strain sensitivity to high temperature. In conclusion we isolated and characterized a new Acetobacter strain from Iranian peach that could be considered as a potential strain for production of a new vinegar type, peach vinegar, with a delicious taste and advantageous nutritional value in food biotechnology and industrial microbiology.

Keywords: Acetobacter, Acetic Acid Bacteria, Vinegar, Peach, Food Biotechnology, Industrial Microbiology, Fermentation

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Authors: Arnaud Nougues

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This paper describes a two-stage methodology derived from IMC (Internal Model Control) for tuning a PID (Proportional-Integral-Derivative) controller for levels or other integrating processes in an industrial environment. Focus is ease of use and implementation speed which are critical for an industrial application. Tuning can be done with minimum effort and without the need of time-consuming open-loop step tests on the plant. The first stage of the method applies to levels only: the vessel residence time is calculated from equipment dimensions and used to derive a set of preliminary PI (Proportional-Integral) settings with IMC. The second stage, re-tuning in closed-loop, applies to levels as well as other integrating processes: a tuning correction mechanism has been developed based on a series of closed-loop simulations with model errors. The tuning correction is done from a simple closed-loop step test and application of a generic correlation between observed overshoot and integral time correction. A spin-off of the method is that an estimate of the vessel residence time (levels) or open-loop process gain (other integrating process) is obtained from the closed-loop data.

Keywords: closed-loop model identification, IMC-PID tuning method, integrating process control, on-line PID tuning adaptation

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Authors: Khalil el Hindi

Abstract:

This paper presents a simple and effective method for approximate indexing of instances for instance based learning. The method uses an interval tree to determine a good starting search point for the nearest neighbor. The search stops when an early stopping criterion is met. The method proved to be very effective especially when only the first nearest neighbor is required.

Keywords: Instance based learning, interval trees, the knn algorithm, machine learning.

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Authors: Nebi Gedik, Ayten Atasoy

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This paper investigates successful sub-bands of wave atom transform via classification of mammograms, when the coefficients of sub-bands are used as features. A computer-aided diagnosis system is constructed by using wave atom transform, support vector machine and k-nearest neighbor classifiers. Two-class classification is studied in detail using two data sets, separately. The successful sub-bands are determined according to the accuracy rates, coefficient numbers, and sensitivity rates.

Keywords: Breast cancer, wave atom transform, SVM, k-NN.

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Authors: K. Beheshti Maal, R. Shafiee

Abstract:

According to FDA (Food and Drug Administration of the United States), vinegar is definedas a sour liquid containing at least 4 grams acetic acid in 100 cubic centimeter (4% solution of acetic acid) of solution that is produced from sugary materials by alcoholic fermentation. In the base of microbial starters, vinegars could be contained of more than 50 types of volatile and aromatic substances that responsible for their sweet taste and smelling. Recently the vinegar industry has a great proportion in agriculture, food and microbial biotechnology. The acetic acid bacteria are from the family Acetobacteraceae. Regarding to the latest version of Bergy-s Mannual of Systematic Bacteriology that has categorized bacteria in the base of their 16s RNA differences, the most important acetic acid genera are included Acetobacter (genus I), Gluconacetobacter (genus VIII) and Gluconobacter (genus IX). The genus Acetobacter that is primarily used in vinegar manufacturing plants is a gram negative, obligate aerobe coccus or rod shaped bacterium with the size 0.6 - 0.8 X 1.0 - 4.0 μm, nonmotile or motile with peritrichous flagella and catalase positive – oxidase negative biochemically. Some strains are overoxidizer that could convert acetic acid to carbon dioxide and water.In this research one Acetobacter native strain with high acetic acid productivity was isolated from Iranian white – red cherry. We used two specific culture media include Carr medium [yeast extract, 3%; ethanol, 2% (v/v); bromocresol green, 0.002%; agar, 2% and distilled water, 1000 ml], Frateur medium [yeast extract, 10 g/l; CaCO3, 20 g/l; ethanol, 20 g/l; agar, 20 g/l and distilled water, 1000 ml] and an industrial culture medium. In addition to high acetic acid production and high growth rate, this strain had a good tolerance against ethanol concentration that was examined using modified Carr media with 5%, 7% and 9% ethanol concentrations. While the industrial strains of acetic acid bacteria grow in the thermal range of 28 – 30 °C, this strain was adapted for growth in 34 – 36 °C after 96 hours incubation period. These dramatic characteristics suggest a potential biotechnological strain in production of cherry vinegar with a sweet smell and different nutritional properties in comparison to recent vinegar types. The lack of growth after 24, 48 and 72 hours incubation at 34 – 36 °C and the growth after 96 hours indicates a good and fast thermal flexibility of this strain as a significant characteristic of biotechnological and industrial strains.

Keywords: Acetobacte, acetic acid bacteria, white – red cherry, food and agriculture biotechnology, industrial fermentation, vinegar

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Authors: Zeynep Orhan, Zeynep Altan

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This paper summarizes the results of some experiments for finding the effective features for disambiguation of Turkish verbs. Word sense disambiguation is a current area of investigation in which verbs have the dominant role. Generally verbs have more senses than the other types of words in the average and detecting these features for verbs may lead to some improvements for other word types. In this paper we have considered only the syntactical features that can be obtained from the corpus and tested by using some famous machine learning algorithms.

Keywords: Word sense disambiguation, feature selection.

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Authors: Hind Bouami, Patrick Millot

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Medication dispensing system is a life-critical system whose failure may result in preventable adverse events leading to longer patient stays in hospitals or patient death. Automation has led to great improvements in life-critical systems as it increased safety, efficiency, and comfort. However, critical risks related to medical organization complexity and automated solutions integration can threaten drug dispensing security and performance. Knowledge about the system’s complexity aspects and human machine parameters to control for automated equipment’s security and performance will help operators to secure their automation process and to optimize their system’s reliability. In this context, this study aims to document the operator’s situation awareness about automation risks and parameters involved in automation security and performance. Our risk management approach has been deployed in the North Luxembourg hospital center’s pharmacy, which is equipped with automated drug dispensing systems since 2009. With more than 4 million euros of gains generated, North Luxembourg hospital center’s success story was enabled by the management commitment, pharmacy’s involvement in the implementation and improvement of the automation project, and the close collaboration between the pharmacy and Sinteco’s firm to implement the necessary innovation and organizational actions for automated solutions integration security and performance. An analysis of the actions implemented by the hospital and the parameters involved in automated equipment’s integration security and performance has been made. The parameters to control for automated equipment’s integration security and performance are human aspects (6.25%), technical aspects (50%), and human-machine interaction (43.75%). The implementation of an anthropocentric analysis system before automation would have prevented and optimized the control of risks related to automation.

Keywords: Automated drug delivery systems, hospitals, human-centered automated system, risk management.

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Authors: Lida Haghnegahdar, Ezzatollah Asgharizadeh

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Value engineering is an efficacious contraption for administrators to make up their minds. Value perusals proffer the gaffers a suitable instrument to decrease the expenditures of the life span, quality amelioration, structural improvement, curtailment of the construction schedule, longevity prolongation or a merging of the aforementioned cases. Subjecting organizers to pressures on one hand and their accountability towards their pertinent fields together with inherent risks and ambiguities of other options on the other hand set some comptrollers in a dilemma utilization of risk management and the value engineering in projects manipulation with regard to complexities of implementing projects can be wielded as a contraption to identify and efface each item which wreaks unnecessary expenses and time squandering sans inflicting any damages upon the essential project applications. Of course It should be noted that implementation of risk management and value engineering with regard to the betterment of efficiency and functions may lead to the project implementation timing elongation. Here time revamping does not refer to time diminishing in the whole cases. his article deals with risk and value engineering conceptualizations at first. The germane reverberations effectuated due to its execution in Iran Khodro Corporation are regarded together with the joint features and amalgamation of the aforesaid entia; hence the proposed blueprint is submitted to be taken advantage of in engineering and industrial projects including Iran Khodro Corporation.

Keywords: Management, risk engineering, value engineering, project manipulation, Iran Khodro.

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Authors: Amira Kheriji Abbes, Faouzi Bouani, Mekki Ksouri

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Model Predictive Control (MPC) is an established control technique in a wide range of process industries. The reason for this success is its ability to handle multivariable systems and systems having input, output or state constraints. Neverthless comparing to PID controller, the implementation of the MPC in miniaturized devices like Field Programmable Gate Arrays (FPGA) and microcontrollers has historically been very small scale due to its complexity in implementation and its computation time requirement. At the same time, such embedded technologies have become an enabler for future manufacturing enterprisers as well as a transformer of organizations and markets. In this work, we take advantage of these recent advances in this area in the deployment of one of the most studied and applied control technique in the industrial engineering. In this paper, we propose an efficient firmware for the implementation of constrained MPC in the performed STM32 microcontroller using interior point method. Indeed, performances study shows good execution speed and low computational burden. These results encourage to develop predictive control algorithms to be programmed in industrial standard processes. The PID anti windup controller was also implemented in the STM32 in order to make a performance comparison with the MPC. The main features of the proposed constrained MPC framework are illustrated through two examples.

Keywords: Embedded software, microcontroller, constrainedModel Predictive Control, interior point method, PID antiwindup, Keil tool, C/Cµ language.

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

Abstract:

This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: Semantic links, data mining, linked data, SKOS.

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Authors: Surinder Deswal, Vineet Verma

Abstract:

Air Quality Index (AQI) is used as a tool to indicate the level of severity and disseminate the information on air pollution to enable the public to understand the health and environmental impacts of air pollutant concentration levels. The annual and seasonal variation of criteria air pollutants concentration based on the National Ambient Air Quality Monitoring Programme has been conducted for a period of nine years (2006-2014) using the AQI system. AQI was calculated using IND-AQI methodology and Maximum Operator Concept is applied. An attempt has been made to quantify the variations in AQI on an annual and seasonal basis over a period of nine years. Further, year-wise frequency of occurrence of AQI in each category for all the five stations is analysed, which presents in depth analysis of trends over the period of study. The best air quality was observed in the Noida residential area, followed by Noida industrial area during the study period; whereas, Bulandshahar industrial area and Faridabad residential area were observed to have the worst air quality. A shift in the worst air quality from winter to summer season has also been observed during the study period. Further, the level of Respirable Suspended Particulate Matter was found to be above permissible limit at all the stations. The present study helps in enhancing public awareness and calls for the need of immediate measures to be taken to counter-effect the cause of the increasing level of air pollution.

Keywords: Air quality index, annual trends, criteria pollutants, seasonal variation.

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Authors: César Estébanez, Ricardo Aler, José M. Valls

Abstract:

In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.

Keywords: Classification, genetic programming, projections.

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Authors: Xuan Qi, Changzhi Zhao

Abstract:

In this paper, a uniform calculus-based approach for synthesizing monitors checking correctness properties specified by a large variety of logics at runtime is provided, including future and past time logics, interval logics, state machine and parameterized temporal logics. We present a calculus mechanism to synthesize monitors from the logical specification for the incremental analysis of execution traces during test and real run. The monitor detects both good and bad prefix of a particular kind, namely those that are informative for the property under investigation. We elaborate the procedure of calculus as monitors.

Keywords: calculus, eagle logic, monitor synthesis, runtime verification

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