Search results for: Multicore machines

Authors: R.A.Mahdavinejad

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In this paper, multi-processors job shop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. By using the suitable hybrid of priority dispatching rules, the process of finding the best solution will be improved. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. Thus, the similar work has the same production lines. Other advantage of this algorithm is that the similar machines (not the same) can be considered. So, these machines are able to process a job with different processing and setup times. According to this capability and from this algorithm evaluation point of view, a number of test problems are solved and the associated results are analyzed. The results show a significant decrease in throughput time. It also shows that, this algorithm is able to recognize the bottleneck machine and to schedule jobs in an efficient way.

Keywords: Job shops scheduling, Priority dispatching rules, Makespan, Hybrid heuristic algorithm.

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Authors: K.Manimala, Dr.K.Selvi, R.Ahila

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Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.

Keywords: back propagation network, power quality, probabilistic neural network, radial basis function support vector machine

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Authors: Jalal Soleimannejad, Mohammad Asadizeidabadi, Mahmoud Koorki, Mojtaba Azarpira

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A significant percentage of production costs is the maintenance costs, and analysis of maintenance costs could to achieve greater productivity and competitiveness. With this is mind, the maintenance of machines and installations is considered as an essential part of organizational functions and applying effective strategies causes significant added value in manufacturing activities. Organizations are trying to achieve performance levels on a global scale with emphasis on creating competitive advantage by different methods consist of RCM (Reliability-Center-Maintenance), TPM (Total Productivity Maintenance) etc. In this study, increasing the capacity of Concentration Plant of Golgohar Iron Ore Mining & Industrial Company (GEG) was examined by using of reliability and maintainability analyses. The results of this research showed that instead of increasing the number of machines (in order to solve the bottleneck problems), the improving of reliability and maintainability would solve bottleneck problems in the best way. It should be mention that in the abovementioned study, the data set of Concentration Plant of GEG as a case study, was applied and analyzed.

Keywords: Bottleneck, Golgohar Iron Ore Mining and Industrial Company, maintainability, maintenance costs, reliability.

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Authors: Shuen-Tai Wang, Ying-Chuan Chen, Yu-Ching Lin

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There has been renewal of interest in the relation between Green IT and cloud computing in recent years. Cloud computing has to be a highly elastic environment which provides stable services to users. The growing use of cloud computing facilities has caused marked energy consumption, putting negative pressure on electricity cost of computing center or data center. Each year more and more network devices, storages and computers are purchased and put to use, but it is not just the number of computers that is driving energy consumption upward. We could foresee that the power consumption of cloud computing facilities will double, triple, or even more in the next decade. This paper aims at resource allocation and scheduling technologies that are short of or have not well developed yet to reduce energy utilization in cloud computing platform. In particular, our approach relies on recalling services dynamically onto appropriate amount of the machines according to user’s requirement and temporarily shutting down the machines after finish in order to conserve energy. We present initial work on integration of resource and power management system that focuses on reducing power consumption such that they suffice for meeting the minimizing quality of service required by the cloud computing platform.

Keywords: Cloud computing, energy utilization, power consumption, resource allocation.

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Authors: B. Dora Arul Selvi, .N. Kamaraj

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Transient Stability is an important issue in power systems planning, operation and extension. The objective of transient stability analysis problem is not satisfied with mere transient instability detection or evaluation and it is most important to complement it by defining fast and efficient control measures in order to ensure system security. This paper presents a new Fuzzy Support Vector Machines (FSVM) to investigate the stability status of power systems and a modified generation rescheduling scheme to bring back the identified unstable cases to a more economical and stable operating point. FSVM improves the traditional SVM (Support Vector Machines) by adding fuzzy membership to each training sample to indicate the degree of membership of this sample to different classes. The preventive control based on economic generator rescheduling avoids the instability of the power systems with minimum change in operating cost under disturbed conditions. Numerical results on the New England 39 bus test system show the effectiveness of the proposed method.

Keywords: Fuzzy Support Vector Machine (FSVM), Incremental Cost, Preventive Control, Transient stability

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces  high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: Activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss.

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Authors: Wayan F. Mahmudy, Romeo M. Marian, Lee H. S. Luong

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This paper and its companion (Part 2) deal with modeling and optimization of two NP-hard problems in production planning of flexible manufacturing system (FMS), part type selection problem and loading problem. The part type selection problem and the loading problem are strongly related and heavily influence the system-s efficiency and productivity. The complexity of the problems is harder when flexibilities of operations such as the possibility of operation processed on alternative machines with alternative tools are considered. These problems have been modeled and solved simultaneously by using real coded genetic algorithms (RCGA) which uses an array of real numbers as chromosome representation. These real numbers can be converted into part type sequence and machines that are used to process the part types. This first part of the papers focuses on the modeling of the problems and discussing how the novel chromosome representation can be applied to solve the problems. The second part will discuss the effectiveness of the RCGA to solve various test bed problems.

Keywords: Flexible manufacturing system, production planning, part type selection problem, loading problem, real-coded genetic algorithm

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

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Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction can be predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels.

Keywords: Bioinformatics, Protein-protein interactions, Bayesian Kernel, Support Vector Machines.

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Authors: Shaukat Wasi, Zubair A. Shaikh, Sajid Qasmi, Hussain Sachwani, Rehman Lalani, Aamir Chagani

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Automatic Extraction of Event information from social text stream (emails, social network sites, blogs etc) is a vital requirement for many applications like Event Planning and Management systems and security applications. The key information components needed from Event related text are Event title, location, participants, date and time. Emails have very unique distinctions over other social text streams from the perspective of layout and format and conversation style and are the most commonly used communication channel for broadcasting and planning events. Therefore we have chosen emails as our dataset. In our work, we have employed two statistical NLP methods, named as Finite State Machines (FSM) and Hidden Markov Model (HMM) for the extraction of event related contextual information. An application has been developed providing a comparison among the two methods over the event extraction task. It comprises of two modules, one for each method, and works for both bulk as well as direct user input. The results are evaluated using Precision, Recall and F-Score. Experiments show that both methods produce high performance and accuracy, however HMM was good enough over Title extraction and FSM proved to be better for Venue, Date, and time.

Keywords: Emails, Event Extraction, Event Detection, Finite state machines, Hidden Markov Model.

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Authors: Samira Vafaye Eslahi

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BCI (Brain Computer Interface) is a communication machine that translates brain massages to computer commands. These machines with the help of computer programs can recognize the tasks that are imagined. Feature extraction is an important stage of the process in EEG classification that can effect in accuracy and the computation time of processing the signals. In this study we process the signal in three steps of active segment selection, fractal feature extraction, and classification. One of the great challenges in BCI applications is to improve classification accuracy and computation time together. In this paper, we have used student’s 2D sample t-statistics on continuous wavelet transforms for active segment selection to reduce the computation time. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier, FKNN (Fuzzy K-Nearest Neighbors), LDA (Linear Discriminate Analysis), and SVM (Support Vector Machines). We resulted that active segment selection method would reduce the computation time and Fractal dimension features with ANFIS analysis on selected active segments is the best among investigated methods in EEG classification.

Keywords: EEG, Student’s t- statistics, BCI, Fractal Features, ANFIS, FKNN.

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Authors: Marcus Banda

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In this article the influence of higher frequency effects in addition to a special damper design on the electrical behavior of a synchronous generator main exciter machine is investigated. On the one hand these machines are often highly stressed by harmonics from the bridge rectifier thus facing additional eddy current losses. On the other hand the switching may cause the excitation of dangerous voltage peaks in resonant circuits formed by the diodes of the rectifier and the commutation reactance of the machine. Therefore modern rotating exciters are treated like synchronous generators usually modeled with a second order equivalent circuit. Hence the well known Standstill Frequency Response Test (SSFR) method is applied to a test machine in order to determine parameters for the simulation. With these results it is clearly shown that higher frequencies have a strong impact on the conventional equivalent circuit model. Because of increasing field displacement effects in the stranded armature winding the sub-transient reactance is even smaller than the armature leakage at high frequencies. As a matter of fact this prevents the algorithm to find an equivalent scheme. This issue is finally solved using Laplace transfer functions fully describing the transient behavior at the model ports.

Keywords: Synchronous exciter machine, Linear transfer function, SSFR, Equivalent Circuit

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Authors: Mrs.K.Kavitha, S.Arivazhagan

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A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.

Keywords: Multi-class, Run Length features, PCA, ICA, classification and Support Vector Machines.

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Authors: Talam K. E, Kainkwa R. M.

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In this study, a field testing has been carried out to assess the power characteristics of some small scale wind turbines fabricated by one native technician from Tanzania. Two Horizontal Axis Wind Turbines (HAWTs), one with five and other with sixteen blades were installed at a height of 2.4m above the ground. The rotation speed of the rotor blade and wind speed approaching the turbines were measured simultaneously. The data obtained were used to determine how the power coefficient varies as a function of tip speed ratio and also the way in which the output power compares with available power in the wind for each turbine. For the sixteen-bladed wind turbine the maximum value of power coefficient of about 0.14 was found to occur at a tip speed ratio of around 0.65 while for the five bladed, these extreme values were respectively attained at approximately 0.2 and 1.7. The five bladed-wind turbine was found to have a higher power efficiency of about 37.5% which is higher compared to the sixteen bladed wind turbine whose corresponding value was 14.37%. This is what would be expected, as the smaller the number of blades of a wind turbine, the higher the electric power efficiency and vice versa. Some of the main reasons for the low efficiency of these machines may be due to the low aerodynamic efficiency of the turbine or low efficiency of the transmission mechanisms such as gearbox and generator which were not examined in this study. It is recommended that some other researches be done to investigate the power efficiency of such machines from different manufacturers in the country. The manufacturers should also be encouraged to use fewer blades in their designs so as to improve the efficiency and at the same time reduce materials used to fabricate the blades. The power efficiency of the electric generators used in the locally fabricated wind turbines should also be examined.

Keywords: Tip speed ratio, Power coefficients and power efficiency.

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Authors: Rajesh Kumar, Puneet Aggarwal

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In context of understanding problems faced by undergraduate students while carrying out laboratory experiments dealing with high voltages, it was found that most of the students are hesitant to work directly on machine. The reason is that error in the circuitry might lead to deterioration of machine and laboratory instruments. So, it has become inevitable to include modern pedagogic techniques for undergraduate students, which would help them to first carry out experiment in virtual system and then to work on live circuit. Further advantages include that students can try out their intuitive ideas and perform in virtual environment, hence leading to new research and innovations. In this paper, virtual environment used is of MATLAB/Simulink for three-phase induction machines. The performance analysis of three-phase induction machine is carried out using virtual environment which includes Direct Current (DC) Test, No-Load Test, and Block Rotor Test along with speed torque characteristics for different rotor resistances and input voltage, respectively. Further, this paper carries out computer aided teaching of basic Voltage Source Inverter (VSI) drive circuitry. Hence, this paper gave undergraduates a clearer view of experiments performed on virtual machine (No-Load test, Block Rotor test and DC test, respectively). After successful implementation of basic tests, VSI circuitry is implemented, and related harmonic distortion (THD) and Fast Fourier Transform (FFT) of current and voltage waveform are studied.

Keywords: Block rotor test, DC test, no-load test, virtual environment, VSI.

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Authors: Amon Lasomboon

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Since the world printing industry has to confront globalization with a constant change, the Thai printing industry, as a small but increasingly significant part of the world printing industry, cannot inevitably escape but has to encounter with the similar change and also the need to revamp its production processes, designs and technology to make them more appealing to both international and domestic market. The essential question is what is the Thai competitive edge in the printing industry in changing environment? This research is aimed to study the Thai level of competitive edge in terms of marketing, technology, environment friendly, and the level of satisfaction of the process of using printing machines. To access the extent to which is the trends in competitiveness of Thai printing industry, both quantitative and qualitative study were conducted. The quantitative analysis was restricted to 100 respondents. The qualitative analysis was restricted to a focus group of 10 individuals from various backgrounds in the Thai printing industry. The findings from the quantitative analysis revealed that the overall mean scores are 4.53, 4.10, and 3.50 for the competitiveness of marketing, the competitiveness of technology, and the competitiveness of being environment friendly respectively. However, the level of satisfaction for the process of using machines has a mean score only 3.20. The findings from the qualitative analysis have revealed that target customers have increasingly reordered due to their contentment in both low prices and the acceptable quality of the products. Moreover, the Thai printing industry has a tendency to convert to ambient green technology which is friendly to the environment. The Thai printing industry is choosing to produce or substitute with products that are less damaging to the environment. It is also found that the Thai printing industry has been transformed into a very competitive industry which bargaining power rests on consumers who have a variety of choices.

Keywords: Competitiveness, Printing Industry

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Authors: M. Mohsenzadeh, F. Shams, M. Teshnehlab

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Current research on semantic web aims at making intelligent web pages meaningful for machines. In this way, ontology plays a primary role. We believe that logic can help ontology languages (such as OWL) to be more fluent and efficient. In this paper we try to combine logic with OWL to reduce some disadvantages of this language. Therefore we extend OWL by logic and also show how logic can satisfy our future expectations of an ontology language.

Keywords: Logical Programming, OWL, Language Extension.

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Authors: Rupali, Anil Kumar Jaiswal

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Cloud computing can be defined as one of the prominent technologies that lets a user change, configure and access the services online. it can be said that this is a prototype of computing that helps in saving cost and time of a user practically the use of cloud computing can be found in various fields like education, health, banking etc.  Cloud computing is an internet dependent technology thus it is the major responsibility of Cloud Service Providers(CSPs) to care of data stored by user at data centers. Scheduling in cloud computing environment plays a vital role as to achieve maximum utilization and user satisfaction cloud providers need to schedule resources effectively.  Job scheduling for cloud computing is analyzed in the following work. To complete, recreate the task calculation, and conveyed scheduling methods CloudSim3.0.3 is utilized. This research work discusses the job scheduling for circulated processing condition also by exploring on this issue we find it works with minimum time and less cost. In this work two load balancing techniques have been employed: ‘Throttled stack adjustment policy’ and ‘Active VM load balancing policy’ with two brokerage services ‘Advanced Response Time’ and ‘Reconfigure Dynamically’ to evaluate the VM_Cost, DC_Cost, Response Time, and Data Processing Time. The proposed techniques are compared with Round Robin scheduling policy.

Keywords: Physical machines, virtual machines, support for repetition, self-healing, highly scalable programming model.

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Authors: Mohammad Yasrebi, Somayeh Khosravi

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

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

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Authors: Shahram Nowrouzieh, Abbas Rezaei Asl, Mohamad Ali Jafari

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Cotton, as a strategic crop, plays an important role in providing human food and clothing need, because of its oil, protein, and fiber. Iran has been one of the largest cotton producers in the world in the past, but unfortunately, for economic reasons, its production is reduced now. One of the ways to reduce the cost of cotton production is to expand the mechanization of cotton harvesting. Iranian farmers do not accept the function of cotton harvesters. One reason for this lack of acceptance of cotton harvesting machines is the number of field losses on these machines. So, the majority of cotton fields are harvested by hand. Although the correct setting of the harvesting machine is very important in the cotton losses, the morphological properties of the cotton plant also affect the performance of cotton harvesters. In this study, the effect of some cotton morphological properties such as the height of the cotton plant, number, and length of sympodial and monopodial branches, boll dimensions, boll weight, number of carpels and bracts angle were evaluated on the performance of cotton picker. In this research, the efficiency of John Deere 9920 spindle Cotton picker is investigated on five different Iranian cotton cultivars. The results indicate that there was a significant difference between the five cultivars in terms of machine harvest efficiency. Golestan cultivar showed the best cotton harvester performance with an average of 87.6% of total harvestable seed cotton and Khorshid cultivar had the least cotton harvester performance. The principal component analysis showed that, at 50.76% probability, the cotton picker efficiency is affected by the bracts angle positively and by boll dimensions, the number of carpels and the height of cotton plants negatively. The seed cotton remains (in the plant and on the ground) after harvester in PCA scatter plot were in the same zone with boll dimensions and several carpels.

Keywords: Cotton, bract, harvester, carpel.

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: Artificial Neural Networks, ANNs, classifier algorithms, credit risk assessment, logistic regression, machine learning, support vector machines.

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Authors: C. L. Toh, N. R. N. Idris, A. H. M. Yatim

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This paper introduces a new digital logic design, which combines the DSP and FPGA to implement the conventional DTC of induction machine. The DSP will be used for floating point calculation whereas the FPGA main task is to implement the hysteresis-based controller. The emphasis is on FPGA digital logic design. The simulation and experimental results are presented and summarized.

Keywords: DTC, DSP, FPGA, induction machine

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Authors: David G. Maxínez, Francisco Javier Sánchez Rangel, Guillermo Castillo Tapia, Petra Baldovinos Noyola, M. Antonieta García Galván, Moisés G Sierra

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Describes the current situation of educational Robotics "the State of the art" its concept, its evolution their niches of opportunity, academic and business and the importance of education and academic outreach. It shows that the development of high-tech automated educational materials influence the teaching-learning process and that communication between machines and humans is a reality.

Keywords: Education, robotics, robots, technology, innovation, educational constructivism

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Authors: Alan C. Lin, Tzu-Kuan Lin, Tsong Der Lin

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This paper proposes a new method to find the equations of transformation matrix for the rotation angles of the two rotational axes and the coordinates of the three linear axes of an orthogonal multi-axis milling machine. This approach provides intuitive physical meanings for rotation angles of multi-axis machines, which can be used to evaluate the accuracy of the conversion from CL data to NC data.

Keywords: CAM, multi-axis milling machining.

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Authors: Saule Mussabekova

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Recent advances in genetics have allowed increasing acutely the capacities of the formation of reliable evidence in conducting forensic examinations. Thus, traces of biological origin are important sources of information about a crime. Currently, around the world, sexual offenses have increased, and among them are those in which the criminals use various detergents to remove traces of their crime. A feature of modern synthetic detergents is the presence of biological additives - enzymes. Enzymes purposefully destroy stains of biological origin. To study the nature and extent of the impact of modern washing powders on saliva stains on the physical evidence, specially prepared test specimens of different types of tissues to which saliva was applied have been examined. Materials and Methods: Washing machines of famous manufacturers of household appliances have been used with different production characteristics and advertised brands of washing powder for test washing. Over 3,500 experimental samples were tested. After washing, the traces of saliva were identified using modern research methods of forensic medicine. Results: The influence was tested and the dependence of the use of different washing programs, types of washing machines and washing powders in the process of establishing saliva trace and identify of the stains on the physical evidence while washing was revealed. The results of experimental and practical expert studies have shown that in most cases it is not possible to draw the conclusions in the identification of saliva traces on physical evidence after washing. This is a consequence of the effect of biological additives and other additional factors on traces of saliva during washing. Conclusions: On the basis of the results of the study, the feasibility of saliva traces of the stains on physical evidence after washing is established. The use of modern molecular genetic methods makes it possible to partially solve the problems arising in the study of unlaundered evidence. Additional study of physical evidence after washing facilitates detection and investigation of sexual offenses against women and children.

Keywords: Saliva research, modern synthetic detergents, laundry detergents, forensic medicine.

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Authors: Wasiu O. Ajagbe, Habeeb O. Hamzat, Waris A. Adebisi

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Structural failures occur due to a number of reasons. These may include under design, poor workmanship, substandard materials, misleading laboratory tests and lots more. Reinforcing steel bar is an important construction material, hence its properties must be accurately known before being utilized in construction. Understanding this property involves carrying out mechanical tests prior to design and during construction to ascertain correlation using steel testing machine which is usually not readily available due to the location of project. This study was conducted to determine the reliability of reinforcing steel testing machines. Reconnaissance survey was conducted to identify laboratories where yield and ultimate tensile strengths tests can be carried out. Six laboratories were identified within Ibadan and environs. However, only four were functional at the time of the study. Three steel samples were tested for yield and tensile strengths, using a steel testing machine, at each of the four laboratories (LM, LO, LP and LS). The yield and tensile strength results obtained from the laboratories were compared with the manufacturer’s specification using a reliability analysis programme. Structured questionnaire was administered to the operators in each laboratory to consider their impact on the test results. The average value of manufacturers’ tensile strength and yield strength are 673.7 N/mm² and 559.7 N/mm² respectively. The tensile strength obtained from the four laboratories LM, LO, LP and LS are given as 579.4, 652.7, 646.0 and 649.9 N/mm² respectively while their yield strengths respectively are 453.3, 597.0, 550.7 and 564.7 N/mm². Minimum tensile to yield strength ratio is 1.08 for BS 4449: 2005 and 1.15 for ASTM A615. Tensile to yield strength ratio from the four laboratories are 1.28, 1.09, 1.17 and 1.15 for LM, LO, LP and LS respectively. The tensile to yield strength ratio shows that the result obtained from all the laboratories meet the code requirements used for the test. The result of the reliability test shows varying level of reliability between the manufacturers’ specification and the result obtained from the laboratories. Three of the laboratories; LO, LS and LP have high value of reliability with the manufacturer i.e. 0.798, 0.866 and 0.712 respectively. The fourth laboratory, LM has a reliability value of 0.100. Steel test should be carried out in a laboratory using the same code in which the structural design was carried out. More emphasis should be laid on the importance of code provisions.

Keywords: Reinforcing steel bars, reliability analysis, tensile strength, universal testing machine, yield strength.

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Authors: Andrea Gelmini

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This paper considers the advent of 5G and the use of it in a Based Control System (BCS), posing as a basic concept the question of what the real differences and practical improvements are compared to 4G. To this purpose, a testbed hardware simulator has been designed and built where identical machines with the same sensors and management systems will communicate with different radio access network connections. This allows an objective statistical comparison of performance on the real functioning and improvement of the infrastructure with the Industrial Internet of Things (IIoT) connected to it.

Keywords: 4G, 5G, BCS, eSIM, IIoT, SCADA, Testbed.

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Authors: Monjed Ezzeddinne, Nicolas Lefebvre, Régis Lengellé

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This paper presents a new version of the SVM mixture algorithm initially proposed by Kwok for classification and regression problems. For both cases, a slight modification of the mixture model leads to a standard SVM training problem, to the existence of an exact solution and allows the direct use of well known decomposition and working set selection algorithms. Only the regression case is considered in this paper but classification has been addressed in a very similar way. This method has been successfully applied to engine pollutants emission modeling.

Keywords: Identification, Learning systems, Mixture ofExperts, Support Vector Machines.

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Authors: N. Javadian, J. Rezaeian, Y. Maali

Abstract:

In this paper a multi-objective nonlinear programming model of cellular manufacturing system is presented which minimize the intercell movements and maximize the sum of reliability of cells. We present a genetic approach for finding efficient solutions to the problem of cell formation for products having multiple routings. These methods find the non-dominated solutions and according to decision makers prefer, the best solution will be chosen.

Keywords: Cellular Manufacturing, Genetic Algorithm, Multiobjective, Life-Cycle.

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Authors: A. Ourici

Abstract:

Doubly fed induction machines DFIM are used mainly for wind energy conversion in MW power plants. This paper presents a new strategy of field oriented control ,it is based on the principle of a double flux orientation of stator and rotor at the same time. Therefore, the orthogonality created between the two oriented fluxes, which must be strictly observed, leads to generate a linear and decoupled control with an optimal torque. The obtained simulation results show the feasibility and the effectiveness of the suggested method.

Keywords: Doubly fed induction machine, double fluxorientation control , vector control , PWM inverter.

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Authors: E. Ramprasath, P. Manojkumar, P. Veena

Abstract:

DC motors have been widely used in the past centuries which are proudly known as the workhorse of industrial systems until the invention of the AC induction motors which makes a huge revolution in industries. Since then, the use of DC machines has been decreased due to enormous factors such as reliability, robustness and complexity but it lost its fame due to the losses. In this paper a new methodology is proposed to construct a DC motor through the simulation in LabVIEW to get an idea about its real time performances, if a change in parameter might have bigger improvement in losses and reliability.

Keywords: Direct Current motor, LabVIEW software, modelling and analysis, overall characteristics of Direct Current motor.

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