Search results for: response time

Authors: Joseph C. Chen, Joshua Cox

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This research focuses on the use of the Taguchi method to reduce the surface roughness and improve dimensional accuracy of parts machined by Wire Electrical Discharge Machining (EDM) with S7 heat treated steel material. Due to its high impact toughness, the material is a candidate for a wide variety of tooling applications which require high precision in dimension and desired surface roughness. This paper demonstrates that Taguchi Parameter Design methodology is able to optimize both dimensioning and surface roughness successfully by investigating seven wire-EDM controllable parameters: pulse on time (ON), pulse off time (OFF), servo voltage (SV), voltage (V), servo feed (SF), wire tension (WT), and wire speed (WS). The temperature of the water in the Wire EDM process is investigated as the noise factor in this research. Experimental design and analysis based on L₁₈ Taguchi orthogonal arrays are conducted. This paper demonstrates that the Taguchi-based system enables the wire EDM process to produce (1) high precision parts with an average of 0.6601 inches dimension, while the desired dimension is 0.6600 inches; and (2) surface roughness of 1.7322 microns which is significantly improved from 2.8160 microns.

Keywords: Taguchi parameter design, surface roughness, dimensional accuracy, Wire EDM.

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Authors: Ajibola O. Akinrinde, Andrew Swanson, Remy Tiako

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There exist significant losses on transmission lines due to distance, as power generating stations could be located far from some isolated settlements. Standalone wind farms could be a good choice of alternative power generation for such settlements that are far from the grid due to factors of long distance or socio-economic problems. However, uncompensated wind farms consume reactive power since wind turbines are induction generators. Therefore, capacitor banks are used to compensate reactive power, which in turn improves the voltage profile of the network. Although capacitor banks help improving voltage profile, they also undergo switching actions due to its compensating response to the variation of various types of load at the consumer’s end. These switching activities could cause transient overvoltage on the network, jeopardizing the end-life of other equipment on the system. In this paper, the overvoltage caused by these switching activities is investigated using the IEEE bus 14-network to represent a standalone wind farm, and the simulation is done using ATP/EMTP software. Scenarios involving the use of pre-insertion resistor and pre-insertion inductor, as well as controlled switching was also carried out in order to decide the best mitigation option to reduce the overvoltage.

Keywords: Capacitor banks, IEEE bus 14-network, Pre-insertion resistor, Standalone wind farm.

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Authors: S. Moshaf, Z. Hamidi-Esfahani, M. H. Azizi

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Xanthan gum is one of the major commercial biopolymers. Due to its excellent rheological properties xanthan gum is used in many applications, mainly in food industry. Commercial production of xanthan gum uses glucose as the carbon substrate; consequently the price of xanthan production is high. One of the ways to decrease xanthan price, is using cheaper substrate like agricultural wastes. Iran is one of the biggest date producer countries. However approximately 50% of date production is wasted annually. The goal of this study is to produce xanthan gum from waste date using Xanthomonas campestris PTCC1473 by submerged fermentation. In this study the effect of three variables including phosphor and nitrogen amount and agitation rate in three levels using response surface methodology (RSM) has been studied. Results achieved from statistical analysis Design Expert 7.0.0 software showed that xanthan increased with increasing level of phosphor. Low level of nitrogen leaded to higher xanthan production. Xanthan amount, increasing agitation had positive influence. The statistical model identified the optimum conditions nitrogen amount=3.15g/l, phosphor amount=5.03 g/l and agitation=394.8 rpm for xanthan. To model validation, experiments in optimum conditions for xanthan gum were carried out. The mean of result for xanthan was 6.72±0.26. The result was closed to the predicted value by using RSM.

Keywords: Optimization, RSM, Waste date, Xanthan gum, Xanthomonas Campestris

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Authors: V.S.Kale, S.R.Bhide, P.P.Bedekar, G.V.K.Mohan

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The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.

Keywords: Artificial neural network, fault detection and classification, parallel transmission lines, wavelet transform.

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: Time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder.

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Authors: R. Resmi, M. R. Baiju

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The thermal damping of a dynamic vibrating micromirror is an important factor affecting the design of MEMS based actuator systems. In the development process of new micromirror systems, assessing the extent of energy loss due to thermal damping accurately and predicting the performance of the system is very essential. In this paper, the depth of the thermal penetration layer at different eigenfrequencies and the temperature variation distributions surrounding a vibrating micromirror is analyzed. The thermal penetration depth corresponds to the thermal boundary layer in which energy is lost which is a measure of the thermal damping is found out. The energy is mainly dissipated in the thermal boundary layer and thickness of the layer is an important parameter. The detailed thermoacoustics is used to model the air domain surrounding the micromirror. The thickness of the boundary layer, temperature variations and thermal power dissipation are analyzed for a Si based torsional mode micromirror. It is found that thermal penetration depth decreases with eigenfrequency and hence operating the micromirror at higher frequencies is essential for reducing thermal damping. The temperature variations and thermal power dissipations at different eigenfrequencies are also analyzed. Both frequency-response and eigenfrequency analyses are done using COMSOL Multiphysics software.

Keywords: Eigen frequency analysis, micromirrors, thermal damping, thermoacoustic interactions.

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Authors: Chen Zhang, Bernhard Schwarz-Röhr, Alexander Härting

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To acquire accurate ship motions at the center of gravity, a single low-cost inertial sensor is utilized and applied on board to measure ship oscillating motions. As observations, the three axes accelerations and three axes rotational rates provided by the sensor are used. The mathematical model of processing the observation data includes determination of the distance vector between the sensor and the center of gravity in x, y, and z directions. After setting up the transfer matrix from sensor’s own coordinate system to the ship’s body frame, an extended Kalman filter is applied to deal with nonlinearities between the ship motion in the body frame and the observation information in the sensor’s frame. As a side effect, the method eliminates sensor noise and other unwanted errors. Results are not only roll and pitch, but also linear motions, in particular heave and surge at the center of gravity. For testing, we resort to measurements recorded on a small vessel in a well-defined sea state. With response amplitude operators computed numerically by a commercial software (Seaway), motion characteristics are estimated. These agree well with the measurements after processing with the suggested method.

Keywords: Extended Kalman filter, nonlinear estimation, sea trial, ship motion estimation.

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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: Dimitrios Ntardas, Alexandros Tzanetos, Georgios Dounias

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High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays - renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of quality and time completion so that new energy producers can quickly and safely intergrade in power grids. The resources needed, such as machines and workers, should be carefully allocated so that the construction of a HVS is completed on time, with the lowest possible cost (e.g. not spending additional cost that were not taken into consideration, because of project delays), but in the highest quality. In addition, there are milestones and several checkpoints to be precisely achieved during construction to ensure the cost and timeline control and to ensure that the percentage of governmental funding will be granted. The management of such a demanding project is a NP-hard problem that consists of prerequisite constraints and resource limits for each task of the project. In this work, a hybrid meta-heuristic method is implemented to solve this problem. Meta-heuristics have been proven to be quite useful when dealing with high-dimensional constraint optimization problems. Hybridization of them results in boost of their performance.

Keywords: High voltage substations, nature-inspired algorithms, project management, meta-heuristics.

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Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: Security, internet of things, cloud computing, Stackelberg security game, machine learning, Naïve Q-learning.

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

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 This paper deals 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. 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. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.

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

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Authors: Maryam Khalili, Fateme Ghanei

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As one of the most significant phenomena of industrialized societies, tourism plays a key role in encouraging regional developments and enhancing higher standards of living for local communities in particular. Traveling is a formative experience endowed with lessons on various aspects of life. It allows us learning how to enhance the social position as well as the social relationships. However, people forget the need to travel and gain first-hand experiences as they have to cope with the ever-increasing rate of stress created by the disorders and routines of the urban dwelling style. In this paper, various spaces of such experiences were explored through a virtual tour with two underlying aims: 1) encouraging, informing, and educating the community in terms of tourism development, and 2) introducing a temporary release from the routines. This study enjoyed a practical-qualitative research methodology, and the required data were collected through observation and using a multiple-response questionnaire. The participants (19-48 years old) included 41 citizens of both genders (63.4% male and 36.6% female) from two regions in Tehran, selected by cluster-probability sampling. The results led to development of a spatial design for a virtual tour experience in Tehran where different areas are explored to both raise people’s awareness and educate them on their cultural heritage.

Keywords: Ecotourism, education, gamification, social interaction, urban design, virtual tour.

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Authors: Abimbola M. Enitan, John O. Odiyo, Feroz M. Swalaha

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The study of microbial ecology and their function in anaerobic digestion processes are essential to control the biological processes. This is to know the symbiotic relationship between the microorganisms that are involved in the conversion of complex organic matter in the industrial wastewater to simple molecules. In this study, diversity and quantity of bacterial community in the granular sludge taken from the different compartments of a full-scale upflow anaerobic sludge blanket (UASB) reactor treating brewery wastewater was investigated using polymerase chain reaction (PCR) and real-time quantitative PCR (qPCR). The phylogenetic analysis showed three major eubacteria phyla that belong to Proteobacteria, Firmicutes and Chloroflexi in the full-scale UASB reactor, with different groups populating different compartment. The result of qPCR assay showed high amount of eubacteria with increase in concentration along the reactor’s compartment. This study extends our understanding on the diverse, topological distribution and shifts in concentration of microbial communities in the different compartments of a full-scale UASB reactor treating brewery wastewater. The colonization and the trophic interactions among these microbial populations in reducing and transforming complex organic matter within the UASB reactors were established.

Keywords: Bacteria, brewery wastewater, real-time quantitative PCR, UASB reactor.

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Authors: Martin Grootveld, Benita Percival, Sarah Moumtaz, Kerry L. Grootveld

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Objectives/Hypotheses: The adverse health effect potential of dietary lipid oxidation products (LOPs) has evoked much clinical interest. Therefore, we employed a ¹H NMR-linked Principal Component Regression (PCR) chemometrics modelling strategy to explore relationships between data matrices comprising (1) aldehydic LOP concentrations generated in culinary oils/fats when exposed to laboratory-simulated shallow frying practices, and (2) the prior saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acid (PUFA) contents of such frying media (FM), together with their heating time-points at a standard frying temperature (180 ^oC). Methods: Corn, sunflower, extra virgin olive, rapeseed, linseed, canola, coconut and MUFA-rich algae frying oils, together with butter and lard, were heated according to laboratory-simulated shallow-frying episodes at 180 ^oC, and FM samples were collected at time-points of 0, 5, 10, 20, 30, 60, and 90 min. (n = 6 replicates per sample). Aldehydes were determined by ¹H NMR analysis (Bruker AV 400 MHz spectrometer). The first (dependent output variable) PCR data matrix comprised aldehyde concentration scores vectors (PC1* and PC2*), whilst the second (predictor) one incorporated those from the fatty acid content/heating time variables (PC1-PC4) and their first-order interactions. Results: Structurally complex trans,trans- and cis,trans-alka-2,4-dienals, 4,5-epxy-trans-2-alkenals and 4-hydroxy-/4-hydroperoxy-trans-2-alkenals (group I aldehydes predominantly arising from PUFA peroxidation) strongly and positively loaded on PC1*, whereas n-alkanals and trans-2-alkenals (group II aldehydes derived from both MUFA and PUFA hydroperoxides) strongly and positively loaded on PC2*. PCR analysis of these scores vectors (SVs) demonstrated that PCs 1 (positively-loaded linoleoylglycerols and [linoleoylglycerol]:[SFA] content ratio), 2 (positively-loaded oleoylglycerols and negatively-loaded SFAs), 3 (positively-loaded linolenoylglycerols and [PUFA]:[SFA] content ratios), and 4 (exclusively orthogonal sampling time-points) all powerfully contributed to aldehydic PC1* SVs (p 10^-3 to < 10^-9), as did all PC1-3 x PC4 interaction ones (p 10^-5 to < 10^-9). PC2* was also markedly dependent on all the above PC SVs (PC2 > PC1 and PC3), and the interactions of PC1 and PC2 with PC4 (p < 10^-9 in each case), but not the PC3 x PC4 contribution. Conclusions: NMR-linked PCR analysis is a valuable strategy for (1) modelling the generation of aldehydic LOPs in heated cooking oils and other FM, and (2) tracking their unsaturated fatty acid (UFA) triacylglycerol sources therein.

Keywords: Frying oils, frying episodes, lipid oxidation products, cytotoxic/genotoxic aldehydes, chemometrics, principal component regression, NMR Analysis.

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Authors: Vikas Tewari, Shivendra Tewari, K. R. Pardasani

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Calcium is a vital second messenger used in signal transduction. Calcium controls secretion, cell movement, muscular contraction, cell differentiation, ciliary beating and so on. Two theories have been used to simplify the system of reaction-diffusion equations of calcium into a single equation. One is excess buffer approximation (EBA) which assumes that mobile buffer is present in excess and cannot be saturated. The other is rapid buffer approximation (RBA), which assumes that calcium binding to buffer is rapid compared to calcium diffusion rate. In the present work, attempt has been made to develop a model for calcium diffusion under excess buffer approximation in neuron cells. This model incorporates the effect of [Na+] influx on [Ca2+] diffusion,variable calcium and sodium sources, sodium-calcium exchange protein, Sarcolemmal Calcium ATPase pump, sodium and calcium channels. The proposed mathematical model leads to a system of partial differential equations which have been solved numerically using Forward Time Centered Space (FTCS) approach. The numerical results have been used to study the relationships among different types of parameters such as buffer concentration, association rate, calcium permeability.

Keywords: Excess buffer approximation, Na+ influx, sodium calcium exchange protein, sarcolemmal calcium atpase pump, forward time centred space.

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Authors: Dorota Habrat

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The aim of study was to analyze the functioning the new model of criminal corporate responsibility in Poland. The need to introduce into the Polish legal system liability of corporate (collective entities) has resulted, among others, from the Polish Republic's international commitments, in particular related to membership in the European Union. The study showed that responsibility of collective entities under the Act has a criminal nature. The main question concerns the ability of the collective entity to be brought to guilt under criminal law sense. Polish criminal law knows only the responsibility of individual persons. So far, guilt as a personal feature of action, based on the ability of the offender to feel in his psyche, could be considered only in relation to the individual person, while the said Act destroyed this conviction. Guilt of collective entity must be proven under at least one of the three possible forms: the guilt in the selection or supervision and so called organizational guilt. In addition, research in article has resolved the issue how the principle of proportionality in relation to criminal measures in response of collective entities should be considered. It should be remembered that the legal subjectivity of collective entities, including their rights and freedoms, is an emanation of the rights and freedoms of individual persons which create collective entities and through these entities implement their rights and freedoms. The whole study was proved that the adopted Act largely reflects the international legal regulations but also contains the unknown and original legislative solutions.

Keywords: Criminal corporate responsibility, Polish criminal law.

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Authors: María José Benítez Jiménez

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Article 3 of the European Convention for the Protection of Human Rights and Fundamental Freedoms (E.C.H.R.) proclaims that no one may be subjected to torture, punishment or degrading treatment. The legislative correlate in Spain is embodied in Article 15 of the Spanish Constitution, and there must be an overlapping interpretation of both precepts on the ideal plane. While it is true that there are not many cases in which the European Court of Human Rights (E.C.t.H.R. (The Strasbourg Court)) has sanctioned Spain for its failure to investigate complaints of torture, it must be emphasized that the tendency to violate Article 3 of the Convention appears to be on the rise, being necessary to know possible factors that may be affecting it. This paper addresses the analysis of sentences that directly or indirectly reveal the violation of Article 3 of the European Convention. To carry out the analysis, sentences of the Strasbourg Court have been consulted from 2012 to 2016, being able to address any previous sentences to this period if it provided justified information necessary for the study. After the review it becomes clear that there are two key groups of subjects that request a response to the Strasbourg Court on the understanding that they have been tortured or degradingly treated. These are: immigrants and terrorists. Both phenomena, immigration and terrorism, respond to patterns that have mutated in recent years, and it is important for this study to know if national regulations begin to be dysfunctional.

Keywords: European convention for the protection of human rights and fundamental freedoms, European Court of Human Rights, sentences, Spanish Constitution, torture.

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Authors: Ju Young Lee, Jung-Seok Yang, Jaeyoung Choi

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The purpose of this paper primarily intends to develop GIS interface for estimating sequences of stream-flows at ungauged stations based on known flows at gauged stations. The integrated GIS interface is composed of three major steps. The first, precipitation characteristics using statistical analysis is the procedure for making multiple linear regression equation to get the long term mean daily flow at ungauged stations. The independent variables in regression equation are mean daily flow and drainage area. Traditionally, mean flow data are generated by using Thissen polygon method. However, method for obtaining mean flow data can be selected by user such as Kriging, IDW (Inverse Distance Weighted), Spline methods as well as other traditional methods. At the second, flow duration curve (FDC) is computing at unguaged station by FDCs in gauged stations. Finally, the mean annual daily flow is computed by spatial interpolation algorithm. The third step is to obtain watershed/topographic characteristics. They are the most important factors which govern stream-flows. In summary, the simulated daily flow time series are compared with observed times series. The results using integrated GIS interface are closely similar and are well fitted each other. Also, the relationship between the topographic/watershed characteristics and stream flow time series is highly correlated.

Keywords: Integrated GIS interface, spatial interpolation algorithm, FDC.

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: Autonomous surveillance, Bayesian reasoning, decision-support, interventions, patterns-of-life, predictive analytics, predictive insights.

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Authors: S. Z. Moussavi, A. Badri, F. Rastegar Kashkooli

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Wind farms (WFs) with high level of penetration are being established in power systems worldwide more rapidly than other renewable resources. The Independent System Operator (ISO), as a policy maker, should propose appropriate places for WF installation in order to maximize the benefits for the investors. There is also a possibility of congestion relief using the new installation of WFs which should be taken into account by the ISO when proposing the locations for WF installation. In this context, efficient wind farm (WF) placement method is proposed in order to reduce burdens on congested lines. Since the wind speed is a random variable and load forecasts also contain uncertainties, probabilistic approaches are used for this type of study. AC probabilistic optimal power flow (P-OPF) is formulated and solved using Monte Carlo Simulations (MCS). In order to reduce computation time, point estimate methods (PEM) are introduced as efficient alternative for time-demanding MCS. Subsequently, WF optimal placement is determined using generation shift distribution factors (GSDF) considering a new parameter entitled, wind availability factor (WAF). In order to obtain more realistic results, N-1 contingency analysis is employed to find the optimal size of WF, by means of line outage distribution factors (LODF). The IEEE 30-bus test system is used to show and compare the accuracy of proposed methodology.

Keywords: Probabilistic optimal power flow, Wind power, Pointestimate methods, Congestion management

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: Classification, singing, spectral analysis, vocal emission, vocal register.

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Authors: Mahnaz Hosseinzadeh, Shaghayegh Rezaee Amiri

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This paper presents a methodology for serial production planning problem in wire and cable manufacturing process that addresses the problem of input-output imbalance in different consecutive stations, hoping to minimize the halt of machines in each stage. To this end, a linear Goal Programming (GP) model is developed, in which four main categories of constraints as per the number of runs per machine, machines’ sequences, acceptable inventories of machines at the end of each period, and the necessity of fulfillment of the customers’ orders are considered. The model is formulated based upon on the real data obtained from IKO TAK Company, an important supplier of wire and cable for oil and gas and automotive industries in Iran. By solving the model in GAMS software the optimal number of runs, end-of-period inventories, and the possible minimum idle time for each machine are calculated. The application of the numerical results in the target company has shown the efficiency of the proposed model and the solution in decreasing the lead time of the end product delivery to the customers by 20%. Accordingly, the developed model could be easily applied in wire and cable companies for the aim of optimal production planning to reduce the halt of machines in manufacturing stages.

Keywords: Serial manufacturing process, production planning, wire and cable industry, goal programming approach.

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Authors: Mutuku Muvengei, John Kihiu

Abstract:

In this paper, a bond graph dynamic model for a valvecontrolled hydraulic cylinder has been developed. A simplified bond graph model of the inter-actuator interactions in a multi-cylinder hydraulic system has also been presented. The overall bond graph model of a valve-controlled hydraulic cylinder was developed by combining the bond graph sub-models of the pump, spool valve and the actuator using junction structures. Causality was then assigned in order to obtain a computational model which could be simulated. The causal bond graph model of the hydraulic cylinder was verified by comparing the open loop state responses to those of an ODE model which had been developed in literature based on the same assumptions. The results were found to correlate very well both in the shape of the curves, magnitude and the response times, thus indicating that the developed model represents the hydraulic dynamics of a valve-controlled cylinder. A simplified model for interactuator interaction was presented by connecting an effort source with constant pump pressure to the zero-junction from which the cylinders in a multi-cylinder system are supplied with a constant pressure from the pump. On simulating the state responses of the developed model under different situations of cylinder operations, indicated that such a simple model can be used to predict the inter-actuator interactions.

Keywords: Bond graphs, Inter-actuator interactions, Valvecontrolledhydraulic cylinder.

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Authors: A. Joseph, D.B.L. Bong, D.A.A. Mat

Abstract:

Security has been an important issue and concern in the smart home systems. Smart home networks consist of a wide range of wired or wireless devices, there is possibility that illegal access to some restricted data or devices may happen. Password-based authentication is widely used to identify authorize users, because this method is cheap, easy and quite accurate. In this paper, a neural network is trained to store the passwords instead of using verification table. This method is useful in solving security problems that happened in some authentication system. The conventional way to train the network using Backpropagation (BPN) requires a long training time. Hence, a faster training algorithm, Resilient Backpropagation (RPROP) is embedded to the MLPs Neural Network to accelerate the training process. For the Data Part, 200 sets of UserID and Passwords were created and encoded into binary as the input. The simulation had been carried out to evaluate the performance for different number of hidden neurons and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the results obtained, using Tansig and Purelin in hidden and output layer and 250 hidden neurons gave the better performance. As a result, a password-based user authentication system for smart home by using neural network had been developed successfully.

Keywords: Neural Network, User Authentication, Smart Home, Security

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Authors: Sunitha Ramanujam, Hany El Yamany, Miriam A. M. Capretz

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Software reliability, defined as the probability of a software system or application functioning without failure or errors over a defined period of time, has been an important area of research for over three decades. Several research efforts aimed at developing models to improve reliability are currently underway. One of the most popular approaches to software reliability adopted by some of these research efforts involves the use of operational profiles to predict how software applications will be used. Operational profiles are a quantification of usage patterns for a software application. The research presented in this paper investigates an innovative multiagent framework for automatic creation and management of operational profiles for generic distributed systems after their release into the market. The architecture of the proposed Operational Profile MAS (Multi-Agent System) is presented along with detailed descriptions of the various models arrived at following the analysis and design phases of the proposed system. The operational profile in this paper is extended to comprise seven different profiles. Further, the criticality of operations is defined using a new composed metrics in order to organize the testing process as well as to decrease the time and cost involved in this process. A prototype implementation of the proposed MAS is included as proof-of-concept and the framework is considered as a step towards making distributed systems intelligent and self-managing.

Keywords: Software reliability, Software testing, Metrics, Distributed systems, Multi-agent systems

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Authors: Raja Das, M. K. Pradhan

Abstract:

This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.

Keywords: Electrical-discharge machining, General Regression Neural Network, Back-propagation Neural Network, Radial Overcut.

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Authors: Theodore N.S. Karfakis, Anna Andrade, Carolina Volkmer-Castilho, Dennis R. Valle, Eric Arets, Paul van Gardingen

Abstract:

The aim of the work presented here was to either use existing forest dynamic simulation models or calibrate a new one both within the SYMFOR framework with the purpose of examining changes in stand level basal area and functional composition in response to selective logging considering trees > 10 cm d.b.h for two areas of undisturbed Amazonian non flooded tropical forest in Brazil and one in Peru. Model biological realism was evaluated for forest in the undisturbed and selectively logged state and it was concluded that forest dynamics were realistically represented. Results of the logging simulation experiments showed that in relation to undisturbed forest simulation subject to no form of harvesting intervention there was a significant amount of change over a 90 year simulation period that was positively proportional to the intensity of logging. Areas which had in the dynamic equilibrium of undisturbed forest a greater proportion of a specific ecological guild of trees known as the light hardwoods (LHW’s) seemed to respond more favorably in terms of less deviation but only within a specific range of baseline forest composition beyond which compositional diversity became more important. These finds are in line partially with practical management experience and partiality basic systematics theory respectively.

Keywords: Amazonbasin, ecological species guild, selective logging, simulation modeling.

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Authors: N. Akhavan, Sh. Tavousi Tafreshi, A. Ghasemi

Abstract:

Seismic behavior of irregular structures through the past decades indicate that the stated buildings do not have appropriate performance. Among these subjects, the current paper has investigated the behavior of special steel moment frame with different configuration of soft storey vertically. The analyzing procedure has been evaluated with respect to incremental dynamic analysis (IDA), and numeric process was carried out by OpenSees finite element analysis package. To this end, nine 2D steel frames, with different numbers of stories and irregularity positions, which were subjected to seven pairs of ground motion records orthogonally with respect to Ibarra-Krawinkler deterioration model, have been investigated. This paper aims at evaluating the response of two-dimensional buildings incorporating soft storey which subjected to bi-directional seismic excitation. The IDAs were implemented for different stages of PGA with various ground motion records, in order to determine maximum inter-storey drift ratio. According to statistical elements and fracture range (standard deviation), the vulnerability or exceedance from above-mentioned cases has been examined. For this reason, fragility curves for different placement of soft storey in the first, middle and the last floor for 4, 8, and 16 storey buildings have been generated and compared properly.

Keywords: Special steel moment frame, soft storey, incremental dynamic analysis, fragility curve.

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Authors: Gökhan Polat, Dicle Kocaoğlu Yılmazer, Muhlis Nezihi Sarıdede

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Iron oxides are the main input to produce iron in integrated iron and steel plants. During production of iron from iron oxides, some wastes with high iron content occur. These main wastes can be classified as basic oxygen furnace (BOF) sludge, flue dust and rolling scale. Recycling of these wastes has a great importance for both environmental effects and reduction of production costs. In this study, recycling experiments were performed on basic oxygen furnace sludge, flue dust and rolling scale which contain 53.8%, 54.3% and 70.2% iron respectively. These wastes were mixed together with coke as reducer and these mixtures are pressed to obtain cylindrical briquettes. These briquettes were pressed under various compacting forces from 1 ton to 6 tons. Also, both stoichiometric and twice the stoichiometric cokes were added to investigate effect of coke amount on reduction properties of the waste mixtures. Then, these briquettes were reduced at 1000°C and 1100°C during 30, 60, 90, 120 and 150 min in a muffle furnace. According to the results of reduction experiments, the effect of compacting force, temperature and time on reduction ratio of the wastes were determined. It is found that 1 ton compacting force, 150 min reduction time and 1100°C are the optimum conditions to obtain reduction ratio higher than 75%.

Keywords: Iron oxide wastes, reduction, coke, recycling.

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Authors: J. Fenianos, C. Khater, D. Brouillet

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Ecological projects are often faced with reluctance from local communities hosting the project, especially when this project involves variation from preset ideas or classical practices. This paper aims at appreciating the contribution of environmental psychology through cognitive flexibility exercises to improve the acceptability of local communities in adopting more ecological rehabilitation scenarios. The study is based on a quarry site located in Bekaa- Lebanon. Four groups were considered with different levels of involvement, as follows: Group 1 is Training (T) – 50 hours of on-site training over 8 months, Group 2 is Awareness (A) – 2 hours of awareness raising session, Group 3 is Flexibility (F) – 2 hours of flexibility exercises and Group 4 is the Control (C). The results show that individuals in Group 3 (F) who followed flexibility sessions accept comparably the ecological rehabilitation option over the more classical one. This is also the case for the people in Group 1 (T) who followed a more time-demanding “on-site training”. Another experience was conducted on a second quarry site combining flexibility with awareness-raising. This research confirms that it is possible to reduce resistance to change thanks to a limited in-time intervention using cognitive flexibility. This methodological approach could be transferable to other environmental problems involving local communities and changes in preset perceptions.

Keywords: Acceptability, ecological restoration, environmental psychology, Lebanon, local communities, resistance to change.

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