Search results for: fuzzy analytic network process

Authors: Lea Lenhardt, Ivana Zeković, Tatjana Dramićanin, Miroslav D. Dramićanin

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Fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) and artificial neural networks (ANN) were used for characterization and classification of honey. Excitation emission spectra were obtained for 95 honey samples of different botanical origin (acacia, sunflower, linden, meadow, and fake honey) by recording emission from 270 to 640 nm with excitation in the range of 240-500 nm. Fluorescence spectra were described with a six-component PARAFAC model, and PARAFAC scores were further processed with two types of ANN’s (feed-forward network and self-organizing maps) to obtain algorithms for classification of honey on the basis of their botanical origin. Both ANN’s detected fake honey samples with 100% sensitivity and specificity.

Keywords: honey, fluorescence, PARAFAC, artificial neural networks

Procedia PDF Downloads 951

Authors: Barenten Suciu

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In order to explain the damping mechanism, related to the hunting motion of the wheel axle of a high-speed railway vehicle, a generalized dynamic model is proposed. Based on such model, analytic expressions for the damping coefficient and damped natural frequency are derived, without imposing restrictions on the ratio between the lateral and vertical creep coefficients. Influence of the travelling speed, wheel conicity, dimensionless mass of the wheel axle, ratio of the creep coefficients, ratio of the track span to the yawing diameter, etc. on the damping coefficient and damped natural frequency, is clarified.

Keywords: high-speed railway vehicle, hunting motion, wheel axle, damping, creep, vibration model, analysis.

Procedia PDF Downloads 289

Authors: Xu LiYun, Briand Florent, Fan GuoLiang

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The loop-layout design problem (LLDP) aims at optimizing the sequence of positioning of the machines around the cyclic production line. Traffic congestion is the usual criteria to minimize in this type of problem, i.e. the number of additional cycles spent by each part in the network until the completion of its required routing sequence of machines. This paper aims at applying several improvements mechanisms such as a positioned-based crossover operator for the Genetic Algorithm (GA) called a Two Points Crossover (TPC) and an offspring selection process. The performance of the improved GA is measured using well-known examples from literature and compared to other evolutionary algorithms. Good results show that GA can still be competitive for this type of problem against more recent evolutionary algorithms.

Keywords: crossover, genetic algorithm, layout design problem, loop-layout, manufacturing optimization

Procedia PDF Downloads 276

Authors: Djamel–Eddine Yassine Boutiba

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In this present memory, we present the various impacts deflectometer leading to the sizing by strengthening of existing roadways. It reminds that the road network in Algeria plays a major role with regard to drainage in major strategic areas and especially in the fringe northern Algeria. Heavy traffic passing through the northern fringe (between 25% and 30% heavy vehicles) causes substantial degradations at both the surface layer and base layer. The work on site by means within the laboratory CTTP such as deflectographe Lacroix, allowed us to record a large number of deflection localized bending on RN19A (Carrefour CW73-Ain- Merane), whose analysis of the results led us to opt for a building throughout the band's project . By the recorder against HWD (Heavy Weight déflectometer) allowed us to learn about the behavior of the pavement on the banks. In addition, the Software Alize III has been essential in the verification of the increase in the thickness dimensioned.

Keywords: capacity, deflection, deflectograph lacroix, degradation, hwd

Procedia PDF Downloads 279

Authors: Nurudeen Afolami Ayeni, Kasali Bello

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Methylolmelamine with increasing degree of methylol substitution and the phosphates derivatives were used to resinate cotton fabric (CF). The resination was carried out at different curing time and curing temperature. Generally, the results show a reduction in the flame propagation rate of the treated fabrics compared to the untreated cotton fabric (CF). While the flame retardancy of methylolmelamine-treated fibre could be attributed to the degree of crosslinking of fibre-resin network which promotes stability, the methylolmelamine phosphate-treated fabrics show better retardancy due to the intumescences action of the phosphate resin upon decomposition in the resin – fabric network.

Keywords: cotton fabric, flame retardant, methylolmelamine, crosslinking, resination

Procedia PDF Downloads 382

Authors: Ananchai Ukaew, Choopong Chauypen

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Design concepts of real-time embedded system can be realized initially by introducing novel design approaches. In this literature, model based design approach and in-the-loop testing were employed early in the conceptual and preliminary phase to formulate design requirements and perform quick real-time verification. The design and analysis methodology includes simulation analysis, model based testing, and in-the-loop testing. The design of conceptual drive-by-wire, or DBW, algorithm for electronic control unit, or ECU, was presented to demonstrate the conceptual design process, analysis, and functionality evaluation. The concepts of DBW ECU function can be implemented in the vehicle system to improve electric vehicle, or EV, conversion drivability. However, within a new development process, conceptual ECU functions and parameters are needed to be evaluated. As a result, the testing system was employed to support conceptual DBW ECU functions evaluation. For the current setup, the system components were consisted of actual DBW ECU hardware, electric vehicle models, and control area network or CAN protocol. The vehicle models and CAN bus interface were both implemented as real-time applications where ECU and CAN protocol functionality were verified according to the design requirements. The proposed system could potentially benefit in performing rapid real-time analysis of design parameters for conceptual system or software algorithm development.

Keywords: drive-by-wire ECU, in-the-loop testing, model-based design, real-time embedded system

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

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Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

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Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

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Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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Authors: Kukuri Tavartkiladze, Nana Bolashvili

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The purpose of this paper was separation and study of the part of structure regime, which directly affects the process of desertification. A simple scheme was prepared for the assessment of desertification process; surface air temperature and precipitation for the years of 1936-2009 were analyzed.  The map of distribution of the Desertification Contributing Coefficient in the territory of Georgia was compiled. The simple scheme for identification of the intensity of the desertification contributing process has been developed and the illustrative example of its practical application for the territory of Georgia has been conducted.

Keywords: aridity, climate change, desertification, precipitation

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Authors: Domagoj Hruska, Drazen Milkovic, Maja Darabos

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This paper empirically investigates the performance of Croatian initial public offerings (IPOs) throughout 20 years period, from 1996 until 2016. By proving the comprehensive evaluation of reasons and consequences of IPO initiatives in Croatia we give analytic evidence on the influence of this corporate action on the development of corporate governance. Furthermore, the paper discusses the relationship between internal and external corporate governance mechanisms in companies that initialize entering the financial markets. The paper will provide a synthesis of evidence of IPO-s in Croatia based on in-depth case studies of 13 cases of IPO-s. The major findings of the paper include identification of reasons for conducting IPO-s and calculation of underpricing effect and change of market capitalization. To the best of the author's knowledge, the results of the paper provide the analytical framework for understanding the impact of IPOs on the corporate governance system in transition countries.

Keywords: corporate governance, Croatia, initial public offering, transition economy

Procedia PDF Downloads 163

Authors: Ibrahim Ozkan

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In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading

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Authors: Batuhan Kocaoglu

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Although using powerful systems like ERP (Enterprise Resource Planning), companies still cannot benchmark their processes and measure their process performance easily based on predefined SCOR (Supply Chain Operation References) terms. The purpose of this research is to identify common and corresponding processes to present a conceptual model to model and measure the purchasing process of an organization. The main steps for the research study are: Literature review related to 'procure to pay' process in ERP system; Literature review related to 'sourcing' process in SCOR model; To develop a conceptual model integrating 'sourcing' of SCOR model and 'procure to pay' of ERP model. In this study, we examined the similarities and differences between these two models. The proposed framework is based on the assumptions that are drawn from (1) the body of literature, (2) the authors’ experience by working in the field of enterprise and logistics information systems. The modeling framework provides a structured and systematic way to model and decompose necessary information from conceptual representation to process element specification. This conceptual model will help the organizations to make quality purchasing system measurement instruments and tools. And offered adaptation issues for ERP systems and SCOR model will provide a more benchmarkable and worldwide standard business process.

Keywords: SCOR, ERP, procure to pay, sourcing, reference model

Procedia PDF Downloads 360

Authors: Allan F. F. Alves, Fernando A. Bacchim Neto, Guilherme Giacomini, Marcela de Oliveira, Ana L. M. Pavan, Maria E. D. Rosa, Diana R. Pina

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Stroke is a worldwide concern, only in Brazil it accounts for 10% of all registered deaths. There are 2 stroke types, ischemic (87%) and hemorrhagic (13%). Early diagnosis is essential to avoid irreversible cerebral damage. Non-enhanced computed tomography (NECT) is one of the main diagnostic techniques used due to its wide availability and rapid diagnosis. Detection depends on the size and severity of lesions and the time spent between the first symptoms and examination. The Alberta Stroke Program Early CT Score (ASPECTS) is a subjective method that increases the detection rate. The aim of this work was to implement an image segmentation system to enhance ischemic stroke and to quantify the area of ischemic and hemorrhagic stroke lesions in CT scans. We evaluated 10 patients with NECT examinations diagnosed with ischemic stroke. Analyzes were performed in two axial slices, one at the level of the thalamus and basal ganglion and one adjacent to the top edge of the ganglionic structures with window width between 80 and 100 Hounsfield Units. We used different image processing techniques such as morphological filters, discrete wavelet transform and Fuzzy C-means clustering. Subjective analyzes were performed by a neuroradiologist according to the ASPECTS scale to quantify ischemic areas in the middle cerebral artery region. These subjective analysis results were compared with objective analyzes performed by the computational algorithm. Preliminary results indicate that the morphological filters actually improve the ischemic areas for subjective evaluations. The comparison in area of the ischemic region contoured by the neuroradiologist and the defined area by computational algorithm showed no deviations greater than 12% in any of the 10 examination tests. Although there is a tendency that the areas contoured by the neuroradiologist are smaller than those obtained by the algorithm. These results show the importance of a computer aided diagnosis software to assist neuroradiology decisions, especially in critical situations as the choice of treatment for ischemic stroke.

Keywords: ischemic stroke, image processing, CT scans, Fuzzy C-means

Procedia PDF Downloads 365

Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

Procedia PDF Downloads 122

Authors: Pierre-Andre Mudry, Jean Decaix, Jeremy Schmid, Cesar Papilloud, Cecile Munch-Alligne

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In most of the Swiss Alpine regions, the availability of water resources is usually adequate even in times of drought, as evidenced by the 2003 and 2018 summers. Indeed, important natural stocks are for the moment available in the form of snow and ice, but the situation is likely to change in the future due to global and regional climate change. In addition, alpine mountain regions are areas where climate change will be felt very rapidly and with high intensity. For instance, the ice regime of these regions has already been affected in recent years with a modification of the monthly availability and extreme events of precipitations. The current research, focusing on the municipality of Val de Bagnes, located in the canton of Valais, Switzerland, is part of a project led by the Altis company and achieved in collaboration with WSL, BlueArk Entremont, and HES-SO Valais-Wallis. In this region, water occupies a key position notably for winter and summer tourism. Thus, multiple actors want to apprehend the future needs and availabilities of water, on both the 2050 and 2100 horizons, in order to plan the modifications to the water supply and distribution networks. For those changes to be salient and efficient, a good knowledge of the current water distribution networks is of most importance. In the current case, the water drinking network is well documented, but this is not the case for the irrigation one. Since the water consumption for irrigation is ten times higher than for drinking water, data acquisition on the irrigation network is a major point to determine future scenarios. This paper first presents the instrumentation and simulation of the irrigation network using custom-designed IoT devices, which are coupled with a digital clone simulated to reduce the number of measuring locations. The developed IoT ad-hoc devices are energy-autonomous and can measure flows and pressures using industrial sensors such as calorimetric water flow meters. Measurements are periodically transmitted using the LoRaWAN protocol over a dedicated infrastructure deployed in the municipality. The gathered values can then be visualized in real-time on a dashboard, which also provides historical data for analysis. In a second phase, a digital clone of the irrigation network was modeled using EPANET, a software for water distribution systems that performs extended-period simulations of flows and pressures in pressurized networks composed of reservoirs, pipes, junctions, and sinks. As a preliminary work, only a part of the irrigation network was modelled and validated by comparisons with the measurements. The simulations are carried out by imposing the consumption of water at several locations. The validation is performed by comparing the simulated pressures are different nodes with the measured ones. An accuracy of +/- 15% is observed on most of the nodes, which is acceptable for the operator of the network and demonstrates the validity of the approach. Future steps will focus on the deployment of the measurement devices on the whole network and the complete modelling of the network. Then, scenarios of future consumption will be investigated. Acknowledgment— The authors would like to thank the Swiss Federal Office for Environment (FOEN), the Swiss Federal Office for Agriculture (OFAG) for their financial supports, and ALTIS for the technical support, this project being part of the Swiss Pilot program 'Adaptation aux changements climatiques'.

Keywords: hydraulic digital clone, IoT water monitoring, LoRaWAN water measurements, EPANET, irrigation network

Procedia PDF Downloads 141

Authors: Zekang Lan, Yan Xu, Yingkun Huang, Dian Huang, Shengzhong Feng

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Jobs on high-performance computing (HPC) clusters can suffer significant performance degradation due to inter-job network interference. Topology-aware job allocation problem (TJAP) is such a problem that decides how to dedicate nodes to specific applications to mitigate inter-job network interference. In this paper, we study the window-based TJAP on a fat-tree network aiming at minimizing the cost of communication hop, a defined inter-job interference metric. The window-based approach for scheduling repeats periodically, taking the jobs in the queue and solving an assignment problem that maps jobs to the available nodes. Two special allocation strategies are considered, i.e., static continuity assignment strategy (SCAS) and dynamic continuity assignment strategy (DCAS). For the SCAS, a 0-1 integer programming is developed. For the DCAS, an approach called neural simulated algorithm (NSA), which is an extension to simulated algorithm (SA) that learns a repair operator and employs them in a guided heuristic search, is proposed. The efficacy of NSA is demonstrated with a computational study against SA and SCIP. The results of numerical experiments indicate that both the model and algorithm proposed in this paper are effective.

Keywords: high-performance computing, job allocation, neural simulated annealing, topology-aware

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Authors: Aditya Purohit, Neha Bansal

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Unplanned urbanization and multidirectional sprawl of the cities have resulted in increased motorization and deteriorating transport conditions like traffic congestion, longer commuting, pollution, increased carbon footprint, and above all increased fatalities. In order to overcome these problems, various practices have been adopted including– promoting and implementing mass transport; traffic junction channelization; smart transport etc. However, these methods are found to be primarily focusing on vehicular mobility rather than people accessibility. With this research gap, this paper tries to resolve the mobility issues for Ahmedabad city in India, which being the economic capital Gujarat state has a huge commuter and visitor inflow. This research aims to resolve the traffic congestion and urban mobility issues focusing on Gujarat State Regional Transport Corporation (GSRTC) for the city of Ahmadabad by analyzing the existing operations and network structure of GSRTC followed by finding possibilities of integrating it with other modes of urban transport. The network restructuring (NR) methodology is used with appropriate variations, based on commuter demand and growth pattern of the city. To do these ‘scenarios’ based on priority issues (using 12 parameters) and their best possible solution, are established after route network analysis for 2700 population sample of 20 traffic junctions/nodes across the city. Approximately 5% sample (of passenger inflow) at each node is considered using random stratified sampling technique two scenarios are – Scenario 1: Resolving mobility issues by use of Special Purpose Vehicle (SPV) in joint venture to GSRTC and Private Operators for establishing feeder service, which shall provide a transfer service for passenger for movement from inner city area to identified peripheral terminals; and Scenario 2: Augmenting existing mass transport services such as BRTS and AMTS for using them as feeder service to the identified peripheral terminals. Each of these has now been analyzed for the best suitability/feasibility in network restructuring. A desire-line diagram is constructed using this analysis which indicated that on an average 62% of designated GSRTC routes are overlapping with mass transportation service routes of BRTS and AMTS in the city. This has resulted in duplication of bus services causing traffic congestion especially in the Central Bus Station (CBS). Terminating GSRTC services on the periphery of the city is found to be the best restructuring network proposal. This limits the GSRTC buses at city fringe area and prevents them from entering into the city core areas. These end-terminals of GSRTC are integrated with BRTS and AMTS services which help in segregating intra-state and inter-state bus services. The research concludes that absence of integrated multimodal transport network resulted in complexity of transport access to the commuters. As a further scope of research comparing and understanding of value of access time in total travel time and its implication on generalized cost on trip and how it varies city wise may be taken up.

Keywords: mass transportation, multi-modal integration, network restructuring, travel behavior, urban transport

Procedia PDF Downloads 193

Authors: E. Karimi-Sibaki, A. Kharicha, M. Wu, A. Ludwig

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The standard electroslag remelting (ESR) process can ideally control the solidification of an ingot and produce homogeneous structure with minimum defects. However, the melt rate of electrode is rather low that makes the whole process uneconomical especially to produce small ingot sizes. In contrast, continuous casting is an economical process to produce small ingots such as billets at high casting speed. Unfortunately, deep liquid melt pool forms in the billet ingot of continuous casting that leads to center porosity and segregation. As such, continuous casting is not suitable to produce segregation prone alloys like tool steel or several super alloys. On the other hand, the electro slag rapid remelting (ESRR) process has advantages of both traditional ESR and continuous casting processes to produce billets. In the ESRR process, a T-shaped mold is used including a graphite ring that takes major amount of current through the mold. There are only a few reports available in the literature discussing about this topic. The research on the ESRR process is currently ongoing aiming to improve the design of the T-shaped mold, to decrease overall heat loss in the process, and to obtain a higher temperature at metal meniscus. In the present study, a 3D model is proposed to investigate the electromagnetic, thermal, and flow fields in the whole process as well as solidification of the billet ingot. We performed a fully coupled numerical simulation to explore the influence of the electromagnetically driven flow (MHD) on the thermal field in the slag and ingot. The main goal is to obtain some fundamental understanding of the formation of melt pool of the solidifying billet ingot in the ESRR process.

Keywords: billet ingot, magnetohydrodynamics (mhd), numerical simulation, remelting, solidification, t-shaped mold.

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Authors: K. S. Vedasingha, K. K. M. T. Perera, K. I. Hathurusinghe, H. W. I. Akalanka, Nelum Chathuranga Amarasena, Nalaka R. Dissanayake

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Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method among all. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways and caused damages to not only precious lives but also to the economy of the countries. There are some major issues which need to be addressed in railways of South Asian countries since they fall under the developing category. The goal of this research is to minimize the influencing aspect of railway level crossing accidents by developing the “railway process automation system”, as there are high-risk areas that are prone to accidents, and safety at these places is of utmost significance. This paper describes the implementation methodology and the success of the study. The main purpose of the system is to ensure human safety by using the Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. And it is possible to do the above-mentioned process through a decision-making system by using past data. The specialty is both processes working parallel. As usual, if the system fails to close the railway gate due to technical or a network failure, the proposed system can identify the current location and close the railway gate through a decision-making system, which is a revolutionary feature. The proposed system introduces further two features to reduce the causes of railway accidents. Railway track crack detection and motion detection are those features which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype, and it is tested with real-world scenarios to gain the above 90% of accuracy.

Keywords: crack detection, decision-making, image processing, Internet of Things, motion detection, prototype, sensors

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Authors: Ahmad Saadiq, Neeraj Sahu

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Sediment, which comprises of solid particles of mineral and organic material are transported by water. In river systems, the amount of sediment transported is controlled by both the transport capacity of the flow and the supply of sediment. The transport of sediment in rivers is important with respect to pollution, channel navigability, reservoir ageing, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. The sediment load transported in a river is a very complex hydrological phenomenon. Hence, sediment transport has attracted the attention of engineers from various aspects, and different methods have been used for its estimation. So, several experimental equations have been submitted by experts. Though the results of these methods have considerable differences with each other and with experimental observations, because the sediment measures have some limits, these equations can be used in estimating sediment load. In this present study, two black box models namely, an SRC (Sediment Rating Curve) and ANN (Artificial Neural Network) are used in the simulation of the suspended sediment load. The study is carried out for Seonath subbasin. Seonath is the biggest tributary of Mahanadi river, and it carries a vast amount of sediment. The data is collected for Jondhra hydrological observation station from India-WRIS (Water Resources Information System) and IMD (Indian Meteorological Department). These data include the discharge, sediment concentration and rainfall for 10 years. In this study, sediment load is estimated from the input parameters (discharge, rainfall, and past sediment) in various combination of simulations. A sediment rating curve used the water discharge to estimate the sediment concentration. This estimated sediment concentration is converted to sediment load. Likewise, for the application of these data in ANN, they are normalised first and then fed in various combinations to yield the sediment load. RMSE (root mean square error) and R² (coefficient of determination) between the observed load and the estimated load are used as evaluating criteria. For an ideal model, RMSE is zero and R² is 1. However, as the models used in this study are black box models, they don’t carry the exact representation of the factors which causes sedimentation. Hence, a model which gives the lowest RMSE and highest R² is the best model in this study. The lowest values of RMSE (based on normalised data) for sediment rating curve, feed forward back propagation, cascade forward back propagation and neural network fitting are 0.043425, 0.00679781, 0.0050089 and 0.0043727 respectively. The corresponding values of R² are 0.8258, 0.9941, 0.9968 and 0.9976. This implies that a neural network fitting model is superior to the other models used in this study. However, a drawback of neural network fitting is that it produces few negative estimates, which is not at all tolerable in the field of estimation of sediment load, and hence this model can’t be crowned as the best model among others, based on this study. A cascade forward back propagation produces results much closer to a neural network model and hence this model is the best model based on the present study.

Keywords: artificial neural network, Root mean squared error, sediment, sediment rating curve

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Authors: Samuel John, S. Sharanya

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Automation has become an important part of our life. It has been used to control home entertainment systems, changing the ambience of rooms for different events etc. One of the main parameters to control in a smart home is the atmospheric comfort. Atmospheric comfort mainly includes temperature and relative humidity. In homes, the desired temperature of different rooms varies from 20 °C to 25 °C and relative humidity is around 50%. However, it varies widely. Hence, automated measurement of these parameters to ensure comfort assumes significance. To achieve this, a fuzzy logic controller using Arduino was developed using MATLAB. Arduino is an open source hardware consisting of a 24 pin ATMEGA chip (atmega328), 14 digital input /output pins and an inbuilt ADC. It runs on 5v and 3.3v power supported by a board voltage regulator. Some of the digital pins in Aruduino provide PWM (pulse width modulation) signals, which can be used in different applications. The Arduino platform provides an integrated development environment, which includes support for c, c++ and java programming languages. In the present work, soft sensor was introduced in this system that can indirectly measure temperature and humidity and can be used for processing several measurements these to ensure comfort. The Sugeno method (output variables are functions or singleton/constant, more suitable for implementing on microcontrollers) was used in the soft sensor in MATLAB and then interfaced to the Arduino, which is again interfaced to the temperature and humidity sensor DHT11. The temperature-humidity sensor DHT11 acts as the sensing element in this system. Further, a capacitive humidity sensor and a thermistor were also used to support the measurement of temperature and relative humidity of the surrounding to provide a digital signal on the data pin. The comfort sensor developed was able to measure temperature and relative humidity correctly. The comfort percentage was calculated and accordingly the temperature in the room was controlled. This system was placed in different rooms of the house to ensure that it modifies the comfort values depending on temperature and relative humidity of the environment. Compared to the existing comfort control sensors, this system was found to provide an accurate comfort percentage. Depending on the comfort percentage, the air conditioners and the coolers in the room were controlled. The main highlight of the project is its cost efficiency.

Keywords: arduino, DHT11, soft sensor, sugeno

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Authors: Abdel-Aziz M. Mohamed

Abstract:

This research proposes a methodology to estimate the customer order lead time in the supply chain based on the process capability index. The cases when the process output is normally distributed and when it is not are considered. The relationships between the system capability indices in both service and manufacturing applications, delivery system reliability and the percentages of orders delivered after their promised due dates are presented. The proposed method can be used to examine the current process capability to deliver the orders before the promised lead-time. If the system was found to be incapable, the method can be used to help revise the current lead-time to a proper value according to the service reliability level selected by the management. Numerical examples and a case study describing the lead time estimation methodology and testing the system capability of delivering the orders before their promised due date are illustrated.

Keywords: lead-time estimation, process capability index, delivery system reliability, statistical analysis, service achievement index, service quality

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Authors: Habib Gorine, Rabia Saleh

Abstract:

Mobile Ad-Hoc Networks are the special type of wireless networks which share common security requirements with other networks such as confidentiality, integrity, authentication, and availability, which need to be addressed in order to secure data transfer through the network. Their routing protocols are vulnerable to various malicious attacks which could have a devastating consequence on data security. In this paper, three types of attacks such as selfish, gray hole, and black hole attacks have been applied to the two most important routing protocols in MANET named dynamic source routing and ad-hoc on demand distance vector in order to analyse and compare the impact of these attacks on the Network performance in terms of throughput, average delay, packet loss, and consumption of energy using NS2 simulator.

Keywords: MANET, wireless networks, routing protocols, malicious attacks, wireless networks simulation

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Authors: Sathapath Kilaso

Abstract:

Thailand has a large range of geographic diversity. Thailand can be divided into 5 regions which are North Region, East Region, West Region, South Region and North-East Region which each region has a different geographic and climate. Especially in North Region, the geographic is mountain and intermontane plateau which will be a reason that the roads in the North Region have a lot of bends. So the driver in the North Region road will have to have a very high skill of driving. If the accident is occurred, the emergency rescue will have a hard time to reach the accident area and rescue the victim of the accident as the long distance and steep road. This article will apply the concept of the wireless sensor network with the micro-controller to alert the driver when the driver reaches the very dangerous bend.

Keywords: wireless sensor network, motion sensor, smart alert, dangerous bend

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Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

Abstract:

India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

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Authors: Lina Dagilienė

Abstract:

The article explores the impact of normative institutional factors on the development of sustainability reporting. The vast majority of research in the scientific literature focuses on mandatory institutional factors, i.e. how public institutions and market regulators affect sustainability reporting. Meanwhile, there is lack of empirical data for the impact of normative institutional factors. The effect of normative factors in this paper is based on the role of non-governmental organizations (NGO) and institutional theory. The case of Global Compact Local Network in the developing country was examined. The research results revealed that in the absence of regulated factors, companies were not active with regard to social disclosures; they presented non-systemized social information of a descriptive nature. Only 10% of sustainability reports were prepared using the GRI methodology. None of the reports were assured by third parties.

Keywords: institutional theory, normative, sustainability reporting, Global Compact Local Network

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Authors: Durga Prasad Sapkota, Madhu Sudan Kayastha, Koichi Wakita

Abstract:

In this paper, we have compared and analyzed the electron absorption properties between with and without excitonic effect bulk in high purity GaAs spatial light modulator for an optical fiber communication network. The electroabsorption properties such as absorption spectra, change in absorption spectra, change in refractive index and extinction ratio have been calculated. We have also compared the result of absorption spectra and change in absorption spectra with the experimental results and found close agreement with experimental results.

Keywords: exciton, refractive index change, extinction ratio, GaAs

Procedia PDF Downloads 571

Authors: Kai-Jui Kou, Tzu-Ling Shen, Ying-Fang Wang

Abstract:

The objectives of the present study are to characterize nanoparticles generated from the different working type in 3D printing room and the stack flue during 3D printing process. The studied laboratory (10.5 m× 7.2 m × 3.2 m) with a ventilation rate of 500 m³/H is installed a 3D metal printing machine. Direct-reading instrument of a scanning mobility particle sizer (SMPS, Model 3082, TSI Inc., St. Paul, MN, USA) was used to conduct static sampling for nanoparticle number concentration and particle size distribution measurements. The SMPS obtained particle number concentration at every 3 minutes, the diameter of the SMPS ranged from 11~372 nm when the aerosol and sheath flow rates were set at 0.6 and 6 L/min, respectively. The concentrations of background, printing process, clearing operation, and screening operation were performed in the laboratory. On the other hand, we also conducted nanoparticle measurement on the 3D printing machine's stack flue to understand its emission characteristics. Results show that the nanoparticles emitted from the different operation process were the same distribution in the form of the uni-modal with number median diameter (NMD) as approximately 28.3 nm to 29.6 nm. The number concentrations of nanoparticles were 2.55×10³ count/cm³ in laboratory background, 2.19×10³ count/cm³ during printing process, 2.29×10³ count/cm³ during clearing process, 3.05×10³ count/cm³ during screening process, 2.69×10³ count/cm³ in laboratory background after printing process, and 6.75×10³ outside laboratory, respectively. We found that there are no emission nanoparticles during the printing process. However, the number concentration of stack flue nanoparticles in the ongoing print is 1.13×10⁶ count/cm³, and that of the non-printing is 1.63×10⁴ count/cm³, with a NMD of 458 nm and 29.4 nm, respectively. It can be confirmed that the measured particle size belongs to easily penetrate the filter in theory during the printing process, even though the 3D printer has a high-efficiency filtration device. Therefore, it is recommended that the stack flue of the 3D printer would be equipped with an appropriate dust collection device to prevent the operators from exposing these hazardous particles.

Keywords: nanoparticle, particle emission, 3D printing, number concentration

Procedia PDF Downloads 176

Authors: Weikang Gong, Chunhua Li

Abstract:

Dynamics plays an essential role in function exertion of proteins. Elastic network model (ENM), a harmonic potential-based and cost-effective computational method, is a valuable and efficient tool for characterizing the intrinsic dynamical properties encoded in biomacromolecule structures and has been widely used to detect the large-amplitude collective motions of proteins. Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. In recent years, many ENM variants have been proposed. Here, we propose a small but effective modification (denoted as modified mENM) to the multiscale ENM (mENM) where fitting weights of Kirchhoff/Hessian matrixes with the least square method (LSM) is modified since it neglects the details of pairwise interactions. Then we perform its comparisons with the original mENM, traditional ENM, and parameter-free ENM (pfENM) on reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM achieves the best performance among the four ENM models. Additionally, it is noted that with the weights of the multiscale Kirchhoff/Hessian matrixes modified, interestingly, the modified mGNM/mANM still has a much better performance than the corresponding traditional ENM and pfENM models. As to dynamical cross-correlation map (DCCM) calculation, taking the data obtained from MD trajectories as the standard, mENM performs the worst while the results produced by the modified mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Generally, ANMs perform better than the corresponding GNMs except for the mENM. Thus, pfANM and the modified mANM, especially the former, have an excellent performance in dynamical cross-correlation calculation. Compared with GNMs (except for mGNM), the corresponding ANMs can capture quite a number of positive correlations for the residue pairs nearly largest distances apart, which is maybe due to the anisotropy consideration in ANMs. Furtherly, encouragingly the modified mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while mANM fails in all the cases. This suggests that the consideration of long-range interactions is critical for ANM models to produce protein functional motions. Based on the analyses, the modified mENM is a promising method in capturing multiple dynamical characteristics encoded in protein structures. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.

Keywords: elastic network model, ENM, multiscale ENM, molecular dynamics, parameter-free ENM, protein structure

Procedia PDF Downloads 115