Search results for: Fully automatic machine

Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity and aflatoxinogenic capacity of the strains, topography, soil and climate parameters of the fig orchards are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high-performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques i.e., dimensionality reduction on the original dataset (Principal Component Analysis), metric learning (Mahalanobis Metric for Clustering) and K-nearest Neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson Correlation Coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: Machine learning, Imbalanced data, Data mining, Big data.

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Authors: Nerijus Remeikis, Ignas Skucas, Vida Melninkaite

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Text categorization - the assignment of natural language documents to one or more predefined categories based on their semantic content - is an important component in many information organization and management tasks. Performance of neural networks learning is known to be sensitive to the initial weights and architecture. This paper discusses the use multilayer neural network initialization with decision tree classifier for improving text categorization accuracy. An adaptation of the algorithm is proposed in which a decision tree from root node until a final leave is used for initialization of multilayer neural network. The experimental evaluation demonstrates this approach provides better classification accuracy with Reuters-21578 corpus, one of the standard benchmarks for text categorization tasks. We present results comparing the accuracy of this approach with multilayer neural network initialized with traditional random method and decision tree classifiers.

Keywords: Text categorization, decision trees, neural networks, machine learning.

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Authors: Randy Gomez, Keisuke Nakamura, Kazuhiro Nakadai

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Distant-talking voice-based HCI system suffers from performance degradation due to mismatch between the acoustic speech (runtime) and the acoustic model (training). Mismatch is caused by the change in the power of the speech signal as observed at the microphones. This change is greatly influenced by the change in distance, affecting speech dynamics inside the room before reaching the microphones. Moreover, as the speech signal is reflected, its acoustical characteristic is also altered by the room properties. In general, power mismatch due to distance is a complex problem. This paper presents a novel approach in dealing with distance-induced mismatch by intelligently sensing instantaneous voice power variation and compensating model parameters. First, the distant-talking speech signal is processed through microphone array processing, and the corresponding distance information is extracted. Distance-sensitive Gaussian Mixture Models (GMMs), pre-trained to capture both speech power and room property are used to predict the optimal distance of the speech source. Consequently, pre-computed statistic priors corresponding to the optimal distance is selected to correct the statistics of the generic model which was frozen during training. Thus, model combinatorics are post-conditioned to match the power of instantaneous speech acoustics at runtime. This results to an improved likelihood in predicting the correct speech command at farther distances. We experiment using real data recorded inside two rooms. Experimental evaluation shows voice recognition performance using our method is more robust to the change in distance compared to the conventional approach. In our experiment, under the most acoustically challenging environment (i.e., Room 2: 2.5 meters), our method achieved 24.2% improvement in recognition performance against the best-performing conventional method.

Keywords: Human Machine Interaction, Human Computer Interaction, Voice Recognition, Acoustic Model Compensation, Acoustic Speech Enhancement.

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Authors: Mostafa.A. Fellani, Dawo.E. Abaid

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This paper presents a controller design technique for Synchronous Reluctance Motor to improve its dynamic performance with fast response and high accuracy. The sliding mode control is the most attractive and suitable method to use for this purpose, since it is simple in design and for its insensitivity to parameter variations or external disturbances. When this method implemented it yields fast dynamic response without overshoot and a zero steady-state error. The current loop control with decentralized sliding mode is presented in this paper. The mathematical model for the synchronous machine, the inverter and the controller is developed. The stability of the sliding mode controller is analyzed. Simulation of synchronous reluctance motor and the controller with PWM-inverter has been curried out, using the SIMULINK software package of MATLAB. Simulation results are presented to show the effectiveness of the approach.

Keywords: Dynamic Simulation, MATLAB, PWM-inverter, Reluctance Machine, Sliding-mode.

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Authors: M. Jändel

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A new conceptual architecture for low-level neural pattern recognition is presented. The key ideas are that the brain implements support vector machines and that support vectors are represented as memory patterns in competitive queuing memories. A binary classifier is built from two competitive queuing memories holding positive and negative valence training examples respectively. The support vector machine classification function is calculated in synchronized evaluation cycles. The kernel is computed by bisymmetric feed-forward networks feed by sensory input and by competitive queuing memories traversing the complete sequence of support vectors. Temporary summation generates the output classification. It is speculated that perception apparatus in the brain reuses structures that have evolved for enabling fluent execution of prepared action sequences so that pattern recognition is built on internalized motor programmes.

Keywords: Competitive queuing model, Olfactory system, Pattern recognition, Support vector machine, Thalamus

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Authors: Hamzah Jusoh, Azmizam Abdul Rashid

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Malaysia has successfully applied economic planning to guide the development of the country from an economy of agriculture and mining to a largely industrialised one. Now, with its sights set on attaining the economic level of a fully developed nation by 2020, the planning system must be made even more efficient and focused. It must ensure that every investment made in the country, contribute towards creating the desirable objective of a strong, modern, internationally competitive, technologically advanced, post-industrial economy. Cities in Malaysia must also be fully aware of the enormous competition it faces in a region with rapidly expanding and modernising economies, all contending for the same pool of potential international investments. Efficiency of urban governance is also fundamental issue in development characterized by sustainability, subsidiarity, equity, transparency and accountability, civic engagement and citizenship, and security. As described above, city competitiveness is harnessed through 'city marketing and city management'. High technology and high skilled industries, together with finance, transportation, tourism, business, information and professional services shopping and other commercial activities, are the principal components of the nation-s economy, which must be developed to a level well beyond where it is now. In this respect, Kuala Lumpur being the premier city must play the leading role.

Keywords: Economic planning, sustainability, efficiency, urban governance and city competitiveness.

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Authors: U. Ramesh Babu, V. Vijay Kumar Reddy, S. Tara Kalyani

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The growth in the demand of electrical energy is leading to load on the Power system which increases the occurrence of frequent oscillations in the system. The reason for the oscillations is due to the lack of damping torque which is required to dominate the disturbances of Power system. By using FACT devices, such as Unified Power Flow Controller (UPFC) can control power flow, reduce sub-synchronous resonances and increase transient stability. Hence, UPFC is used to damp the oscillations occurred in Power system. This research focuses on adapting the neuro fuzzy controller for the UPFC design by connecting the infinite bus (SMIB - Single machine Infinite Bus) to a linearized model of synchronous machine (Heffron-Phillips) in the power system. This model gains the capability to improve the transient stability and to damp the oscillations of the system.

Keywords: Power System, UPFC, (ANFIS) Adaptive Neuro Fuzzy Inference System, transient, Low frequency oscillations.

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Authors: Qurbanov Huseyn Nuraddin

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The results of scientific research on a machine that pours an equal amount of mineral fertilizer under the soil to increase the productivity of grain in mountain farming and obtain quality grain are substantiated. The average yield of the crop depends on the nature of the distribution of fertilizers in the soil. Therefore, the study of effective energy-saving methods for the application of mineral fertilizers is the actual task of modern agriculture. Depending on the type and variety of plants in mountain farming, there is an optimal norm of mineral fertilizers. Applying an equal amount of fertilizer to the soil is one of the conditions that increase the efficiency of the field. One of the main agro-technical indicators of the work of mineral fertilizing machines is to ensure equal distribution of mineral fertilizers in the field. Taking into account the above-mentioned issues, a combined plough has been improved in our laboratory.

Keywords: Combined plough, mineral fertilizers, sprinkle fluently, fertilizer rate, cereals.

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Authors: D. Hişam, S. İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three ML models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest (RF) Classifier was the most accurate model.

Keywords: Vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting.

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Authors: Hiba Basim Alwan, Ku Ruhana Ku-Mahamud

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A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. Discretizing the continuous value of the parameters is the most common approach in tuning SVM parameters. This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. The first algorithm, ACO_R-SVM, will tune SVM parameters, while the second IACO_MV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.

Keywords: Continuous ant colony optimization, incremental continuous ant colony, simultaneous optimization, support vector machine.

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Authors: M. Duran Toksarı, B. Uçarkuş

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In this paper, we propose two algorithms to optimally solve makespan and total completion time scheduling problems with learning effect and job dependent delivery times in a single machine environment. The delivery time is the extra time to eliminate adverse effect between the main processing and delivery to the customer. In this paper, we introduce the job dependent delivery times for some single machine scheduling problems with position dependent learning effect, which are makespan are total completion. The results with respect to two algorithms proposed for solving of the each problem are compared with LINGO solutions for 50-jobs, 100-jobs and 150- jobs problems. The proposed algorithms can find the same results in shorter time.

Keywords: Delivery times, learning effect, makespan, scheduling, total completion time.

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Authors: Souvik Bhattacharyya, Gautam Sanyal

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Recent years have witnessed the rapid development of the Internet and telecommunication techniques. Information security is becoming more and more important. Applications such as covert communication, copyright protection, etc, stimulate the research of information hiding techniques. Traditionally, encryption is used to realize the communication security. However, important information is not protected once decoded. Steganography is the art and science of communicating in a way which hides the existence of the communication. Important information is firstly hidden in a host data, such as digital image, video or audio, etc, and then transmitted secretly to the receiver.In this paper a data hiding model with high security features combining both cryptography using finite state sequential machine and image based steganography technique for communicating information more securely between two locations is proposed. The authors incorporated the idea of secret key for authentication at both ends in order to achieve high level of security. Before the embedding operation the secret information has been encrypted with the help of finite-state sequential machine and segmented in different parts. The cover image is also segmented in different objects through normalized cut.Each part of the encoded secret information has been embedded with the help of a novel image steganographic method (PMM) on different cuts of the cover image to form different stego objects. Finally stego image is formed by combining different stego objects and transmit to the receiver side. At the receiving end different opposite processes should run to get the back the original secret message.

Keywords: Cover Image, Finite state sequential machine, Melaymachine, Pixel Mapping Method (PMM), Stego Image, NCUT.

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Authors: Jae-Yong Lee, Gyu-Hyun Bae, Yun-Soo Chung, Dae-Sub Kim, Jae-Sung Bae

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In general, fabric is heat-treated using a stenter machine in order to dry and fix its shape. It is important to shape before the heat treatment because it is difficult to revert back once the fabric is formed. To produce the product of right shape, camera type weft straightener has been applied recently to capture and process fabric images quickly. It is more powerful in determining the final textile quality rather than photo-sensor. Positioning in front of a stenter machine, weft straightener helps to spread fabric evenly and control the angle between warp and weft constantly as right angle by handling skew and bow rollers. To process this tricky procedure, the structural analysis should be carried out in advance, based on which, its control technology can be drawn. A structural analysis is to figure out the specific contact/slippage characteristics between fabric and roller. We already examined the applicability of camera type weft straightener to plain weave fabric and found its possibility and the specific working condition of machine and rollers. In this research, we aimed to explore another applicability of camera type weft straightener. Namely, we tried to figure out camera type weft straightener can be used for fabrics. To find out the optimum condition, we increased the number of rollers. The analysis is done by ANSYS software using Finite Element Analysis method. The control function is demonstrated by experiment. In conclusion, the structural analysis of weft straightener is done to identify a specific characteristic between roller and fabrics. The control of skew and bow roller is done to decrease the error of the angle between warp and weft. Finally, it is proved that camera type straightener can also be used for the special fabrics.

Keywords: Camera type weft straightener, structure analysis, control, skew and bow roller.

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Features selection, learning with kernels, support vector machine, genetic algorithms and classification.

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Authors: Pushkar Tripathi, Abhishek Sharma, G. N. Pillai, Indira Gupta

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This paper presents a new approach for the protection of Thyristor-Controlled Series Compensator (TCSC) line using Support Vector Machine (SVM). One SVM is trained for fault classification and another for section identification. This method use three phase current measurement that results in better speed and accuracy than other SVM based methods which used single phase current measurement. This makes it suitable for real-time protection. The method was tested on 10,000 data instances with a very wide variation in system conditions such as compensation level, source impedance, location of fault, fault inception angle, load angle at source bus and fault resistance. The proposed method requires only local current measurement.

Keywords: Fault Classification, Section Identification, Feature Selection, Support Vector Machine (SVM), Thyristor-Controlled Series Compensator (TCSC)

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Authors: Eugene Kindler

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The present paper is oriented to classification and application of agent technique in simulation of anticipatory systems, namely those that use simulation models for the aid of anticipation. The main ideas root in the fact that the best way for description of computer simulation models is the technique of describing the simulated system itself (and the translation into the computer code is provided as automatic), and that the anticipation itself is often nested.

Keywords: Agents, Anticipatory systems, Discrete eventsimulation, Simula, Taxonomy.

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. This necessitates increased resource consumption and underscores the importance of addressing sustainable agriculture development along with other environmental considerations. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for 10 different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: Land suitability, machine learning, random forest, sustainable agriculture.

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: Data mining, ensemble, radial basis function, support vector machine, accuracy.

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Authors: İres İskender, Ahmet Karaarslan

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In this study, the performance of a high-frequency arc welding machine including a two-switch inverter is analyzed. The control of the system is achieved using two different control techniques i- fuzzy logic control (FLC) ii- state space averaging based sliding control. Fuzzy logic control does not need accurate mathematical model of a plant and can be used in nonlinear applications. The second method needs the mathematical model of the system. In this method the state space equations of the system are derived for two different “on" and “off" states of the switches. The derived state equations are combined with the sliding control rule considering the duty-cycle of the converter. The performance of the system is analyzed by simulating the system using SIMULINK tool box of MATLAB. The simulation results show that fuzzy logic controller is more robust and less sensitive to parameter variations.

Keywords: Fuzzy logic, arc welding, sliding state space control, PWM, current control.

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: Housing data, feature selection, random forest, Boruta algorithm, root mean square error.

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Authors: Ibrahim Beldjilali, Adel Ghenaiet

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The contra-rotating axial machine is a promising solution for several applications, where high pressure and efficiencies are needed. Also, they allow reducing the speed of rotation, the radial spacing and a better flexibility of use. However, this requires a better understanding of their operation, including the influence of second rotor on the overall aerodynamic performances. This work consisted of both experimental and numerical studies to characterize this counter-rotating fan, especially the analysis of the effects of the blades stagger angle and the inter-distance between the rotors. The experimental study served to validate the computational fluid dynamics model (CFD) used in the simulations. The numerical study permitted to cover a wider range of parameter and deeper investigation on flow structures details, including the effects of blade stagger angle and inter-distance, associated with the interaction between the rotors. As a result, there is a clear improvement in aerodynamic performance compared with a conventional machine.

Keywords: Aerodynamic performance, axial fan, counter rotating rotors, CFD, experimental study.

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Authors: Shilpy Sharma

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As the web continues to grow exponentially, the idea of crawling the entire web on a regular basis becomes less and less feasible, so the need to include information on specific domain, domain-specific search engines was proposed. As more information becomes available on the World Wide Web, it becomes more difficult to provide effective search tools for information access. Today, people access web information through two main kinds of search interfaces: Browsers (clicking and following hyperlinks) and Query Engines (queries in the form of a set of keywords showing the topic of interest) [2]. Better support is needed for expressing one's information need and returning high quality search results by web search tools. There appears to be a need for systems that do reasoning under uncertainty and are flexible enough to recover from the contradictions, inconsistencies, and irregularities that such reasoning involves. In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. Semi-supervised, multi-view algorithms, which reduce the amount of labeled data required for learning, rely on the assumptions that the views are compatible and uncorrelated. This paper describes the use of semi-structured machine learning approach with Active learning for the “Domain Specific Search Engines". A domain-specific search engine is “An information access system that allows access to all the information on the web that is relevant to a particular domain. The proposed work shows that with the help of this approach relevant data can be extracted with the minimum queries fired by the user. It requires small number of labeled data and pool of unlabelled data on which the learning algorithm is applied to extract the required data.

Keywords: Search engines; machine learning, Informationretrieval, Active logic.

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Authors: Markus Siepermann, Christoph Siepermann

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This paper presents the concept and realisation of an e-learning tool that provides predefined or automatically generated exercises concerning logistics cost accounting. Students may practise where and whenever they like to via the Internet. Their solutions are marked automatically by the tool while considering consecutive faults and without any intervention of lecturers.

Keywords: Automatic marking, e-learning environment, onlinepracticing, randomly-generated exercises.

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Authors: A. Chebak, N. Barka, A. Menou, J. Brousseau, D. S. Ramdenee

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This paper presents the study of hardness profile of spur gear heated by induction heating process in function of the machine parameters, such as the power (kW), the heating time (s) and the generator frequency (kHz). The global work is realized by 3D finite-element simulation applied to the process by coupling and resolving the electromagnetic field and the heat transfer problems, and it was performed in three distinguished steps. First, a Comsol 3D model was built using an adequate formulation and taking into account the material properties and the machine parameters. Second, the convergence study was conducted to optimize the mesh. Then, the surface temperatures and the case depths were deeply analyzed in function of the initial current density and the heating time in medium frequency (MF) and high frequency (HF) heating modes and the edge effect were studied. Finally, the simulations results are validated using experimental tests.

Keywords: Induction heating, simulation, experimental validation, 3D model, hardness profile.

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Authors: Quratulain N. Rajput, Sajjad Haider

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This paper applies Bayesian Networks to support information extraction from unstructured, ungrammatical, and incoherent data sources for semantic annotation. A tool has been developed that combines ontologies, machine learning, and information extraction and probabilistic reasoning techniques to support the extraction process. Data acquisition is performed with the aid of knowledge specified in the form of ontology. Due to the variable size of information available on different data sources, it is often the case that the extracted data contains missing values for certain variables of interest. It is desirable in such situations to predict the missing values. The methodology, presented in this paper, first learns a Bayesian network from the training data and then uses it to predict missing data and to resolve conflicts. Experiments have been conducted to analyze the performance of the presented methodology. The results look promising as the methodology achieves high degree of precision and recall for information extraction and reasonably good accuracy for predicting missing values.

Keywords: Information Extraction, Bayesian Network, ontology, Machine Learning

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Authors: Seung Woo Lee, So Jeong Nam, Jai-Kyung Lee

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It can be said that the business sector is faced with a range of challenges–a rapidly changing business environment, an increase and diversification of customers- demands and the consequent need for quick response–for having in place flexible management and production info systems. As a matter of fact, many manufacturers have adopted production info management systems such as MES and ERP. Nevertheless, managers are having difficulties obtaining ever-changing production process information in real time, or responding quickly to any change in production related needs on the basis of such information. This is because they rely on poor production info systems which are not capable of providing real-time factory settings. If the manufacturer doesn-t have a capacity for collecting or digitalizing the 4 Ms (Man, Machine, Material, Method), which are resources for production, on a real time basis, it might to difficult to effectively maintain the information on production process. In this regard, this paper will introduce some new alternatives to the existing methods of collecting the 4 Ms in real time, which are currently comprise the production field.

Keywords: 4M, Acquisition of Data on shop-floor, Real-time machine interface

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Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: Image processing, Illumination equalization, Shadow filtering, Object detection, Colour models, Image segmentation.

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Authors: Amin Almasi

Abstract:

Composite laminates are relatively weak in out of plane loading, inter-laminar stress, stress concentration near the edge and stress singularities. This paper develops a new analytical formulation for laminated composite rotating disc fabricated from symmetric sequential quasi isotropic layers to predict three dimensional stress and deformation. This analysis is necessary to evaluate mechanical integrity of fiber reinforced multi-layer laminates used for high speed rotating applications such as high speed impellers. Three dimensional governing equations are written for rotating composite disc. Explicit solution is obtained with "Frobenius" expansion series. Based on analytical results, there are two separate zones of three dimensional stress fields in centre and edge of rotating disc. For thin discs, out of plane deformations and stresses are small in comparison with plane ones. For relatively thick discs deformation and stress fields are three dimensional.

Keywords: Composite Disc, Rotating Machine.

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Authors: H. Anıl, G. Kar

Abstract:

In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations, since the geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning and time series methods, the total generation of the power plants belonging to Zorlu Doğal Electricity Generation, which has a high installed capacity in terms of geothermal, was predicted for the first one-week and first two-weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: Machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting.

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