Search results for: hybrid electric vehicles

Authors: Mat Syai'in, Adi Soeprijanto, Takashi Hiyama

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An optimal power flow (OPF) based on particle swarm optimization (PSO) was developed with more realistic generator security constraint using the capability curve instead of only Pmin/Pmax and Qmin/Qmax. Neural network (NN) was used in designing digital capability curve and the security check algorithm. The algorithm is very simple and flexible especially for representing non linear generation operation limit near steady state stability limit and under excitation operation area. In effort to avoid local optimal power flow solution, the particle swarm optimization was implemented with enough widespread initial population. The objective function used in the optimization process is electric production cost which is dominated by fuel cost. The proposed method was implemented at Java Bali 500 kV power systems contain of 7 generators and 20 buses. The simulation result shows that the combination of generator power output resulted from the proposed method was more economic compared with the result using conventional constraint but operated at more marginal operating point.

Keywords: Optimal Power Flow, Generator Capability Curve, Particle Swarm Optimization, Neural Network

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Authors: M. Zerikat, S. Chekroun

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This paper proposes an effective adaptation learning algorithm based on artificial neural networks for speed control of an induction motor assumed to operate in a high-performance drives environment. The structure scheme consists of a neural network controller and an algorithm for changing the NN weights in order that the motor speed can accurately track of the reference command. This paper also makes uses a very realistic and practical scheme to estimate and adaptively learn the noise content in the speed load torque characteristic of the motor. The availability of the proposed controller is verified by through a laboratory implementation and under computation simulations with Matlab-software. The process is also tested for the tracking property using different types of reference signals. The performance and robustness of the proposed control scheme have evaluated under a variety of operating conditions of the induction motor drives. The obtained results demonstrate the effectiveness of the proposed control scheme system performances, both in steady state error in speed and dynamic conditions, was found to be excellent and those is not overshoot.

Keywords: Electric drive, Induction motor, speed control, Adaptive control, neural network, High Performance.

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Authors: Oscar Javier Herrera Ochoa, Ivan Dario Romero Fonseca

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This paper presents a design of a model for planning the distribution logistics operation. The significance of this work relies on the applicability of this fact to the analysis of small and medium enterprises (SMEs) of dry freight in Bogotá. Two stages constitute this implementation: the first one is the place where optimal planning is achieved through a hybrid model developed with mixed integer programming, which considers the transhipment operation based on a combined load allocation model as a classic transshipment model; the second one is the specific routing of that operation through the heuristics of Clark and Wright. As a result, an integral model is obtained to carry out the step by step planning of the distribution of dry freight for SMEs in Bogotá. In this manner, optimum assignments are established by utilizing transshipment centers with that purpose of determining the specific routing based on the shortest distance traveled.

Keywords: Transshipment model, mixed integer programming, saving algorithm, dry freight transportation.

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Authors: S. Venkateshwarlu, B. P. Muni, A. D. Rajkumar, J. Praveen

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Custom power is a technology driven product and service solution which embraces a family devices such as Dynamic Voltage Restorer (DVR), Distributed Shunt Compensator (DSTATCOM), Solid State Breaker (SSB) etc which will provide power quality functions at distribution voltages. The rapid response of these devices enables them to operate in real time, providing continuous and dynamic control of the supply including voltage and reactive power regulation, harmonic reduction and elimination of voltage dips. This paper presents the benefits of multilevel inverters when they are used for DPC based custom power devices. Power flow control mechanism, salient features, advantages and disadvantages of direct power control (DPC) using lookup table, SVM, predictive voltage vector and hybrid DPC strategies are discussed in this paper. Simulation results of three level inverter based STATCOM, harmonic analysis of multi level inverters are presented at the end.

Keywords: DPC, DPC-SVM, Dynamic voltage restorer, DSTATCOM, Multilevel inverter, PWM Converter, PDPC, VF-DPC.

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Authors: Muhammad Farid Shaari, Zahurin Samad

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This paper presents the design, development and characterization of contractile water jet thruster (CWJT) for mini underwater robot. Instead of electric motor, this CWJT utilizes the Ionic Polymer Metal Composite (IPMC) as the actuator to generate the water jet. The main focus of this paper is to analyze the conceptual design of the proposed CWJT which would determine the thrust force value, jet flow behavior and actuator’s stress. Those thrust force and jet flow studies were carried out using Matlab/Simscape simulation software. The actuator stress had been analyzed using COSMOS simulation software. The results showed that there was no significant change for jet velocity at variable cross sectional nozzle area. However, a significant change was detected for jet velocity at different nozzle cross sectional area ratio which was up to 37%. The generated thrust force has proportional relation to the nozzle cross sectional area.

Keywords: Contractile water jet thruster, IPMC actuator, Thrust force.

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Authors: Dimitrios J. Dimitriou, Maria F. Sartzetaki

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Transport infrastructures are high-cost, long-term investments that serve as vital foundations for the operation of a region or nation and are essential to a country’s or business’s economic development and prosperity, by improving well-being and generating jobs and income. The development of appropriate financing options is of key importance in the decision making process in order develop viable transport infrastructures. The development of transport infrastructure has increasingly been shifting toward alternative methods of project financing such as Public Private Partnership (PPPs) and hybrid forms. In this paper, a methodological decision-making framework based on the evaluation of the financial viability of transportation infrastructure for different financial schemes is presented. The framework leads to an assessment of the financial viability which can be achieved by performing various financing scenarios analyses. To illustrate the application of the proposed methodology, a case study of rail transport infrastructure financing scenario analysis in Greece is developed.

Keywords: Rail transport infrastructure; financial viability, scenario analysis, rail project feasibility.

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and roughsets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: Rough-sets, Classification, Feature Selection, Entropy, Outliers, Frequent itemset mining.

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Authors: Ma Ya-li, Tang Fu-jun, Xue Yu-xiong, Chen Yi-feng, Gao Xin, Wang Yi, Tian Kai, Yan Ze-dong

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Plasma plume will be produced and arrive at spacecraft when the electric thruster operates on orbit. It-s important to characterize the thruster plasma parameters because the plume has significant effects or hazards on spacecraft sub-systems and parts. Through the ground test data of the desired parameters, the major characteristics of the thruster plume will be achieved. Also it is very important for optimizing design of Ion thruster. Retarding Potential Analyzer (RPA) is an effective instrument for plasma ion energy per unit charge distribution measurement. Special RPA should be designed according to certain plume plasma parameters range and feature. In this paper, major principles usable for good RPA design are discussed carefully. Conform to these principles, a four-grid planar electrostatic energy analyzer RPA was designed to avoid false data, and details were discussed including construction, materials, aperture diameter and so on. At the same time, it was designed more suitable for credible and long-duration measurements in the laboratory. In the end, RPA measurement results in the laboratory were given and discussed.

Keywords: Thruster plume ion energy distributions, retarding potential analyzer, ground test.

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both Machine Learning and First Principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed from breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems and humans – including asset maintenance operations – in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: Degradation models, ageing, anomaly detection, soft sensor, incremental learning.

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Authors: Andreas Theissler, Ian Dear

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In the automotive industry test drives are being conducted during the development of new vehicle models or as a part of quality assurance of series-production vehicles. The communication on the in-vehicle network, data from external sensors, or internal data from the electronic control units is recorded by automotive data loggers during the test drives. The recordings are used for fault analysis. Since the resulting data volume is tremendous, manually analysing each recording in great detail is not feasible. This paper proposes to use machine learning to support domainexperts by preventing them from contemplating irrelevant data and rather pointing them to the relevant parts in the recordings. The underlying idea is to learn the normal behaviour from available recordings, i.e. a training set, and then to autonomously detect unexpected deviations and report them as anomalies. The one-class support vector machine “support vector data description” is utilised to calculate distances of feature vectors. SVDDSUBSEQ is proposed as a novel approach, allowing to classify subsequences in multivariate time series data. The approach allows to detect unexpected faults without modelling effort as is shown with experimental results on recordings from test drives.

Keywords: Anomaly detection, fault detection, test drive analysis, machine learning.

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Authors: Saeid Nahi, Seyed Mohammad Hossein Nabavi

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Nowadays, power systems, energy generation by wind has been very important. Noting that the production of electrical energy by wind turbines on site to several factors (such as wind speed and profile site for the turbines, especially off the wind input speed, wind rated speed and wind output speed disconnect) is dependent. On the other hand, several different types of turbines in the market there. Therefore, selecting a turbine that its capacity could also answer the need for electric consumers the efficiency is high something is important and necessary. In this context, calculating the amount of wind power to help optimize overall network, system operation, in determining the parameters of wind power is very important. In this article, to help calculate the amount of wind power plant, connected to the national network in the region Manjil wind, selecting the best type of turbine and power delivery profile appropriate to the network using Monte Carlo method has been. In this paper, wind speed data from the wind site in Manjil, as minute and during the year has been. Necessary simulations based on Random Numbers Simulation method and repeat, using the software MATLAB and Excel has been done.

Keywords: wind turbine, efficiency, wind turbine work points, Random Numbers, reliability

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Authors: Srimuruganandam. B, S.M. Shiva Nagendra

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In this paper the influence of heterogeneous traffic on the temporal variation of ambient PM10, PM2.5 and PM1 concentrations at a busy arterial route (Sardar Patel Road) in the Chennai city has been analyzed. The hourly PM concentration, traffic counts and average speed of the vehicles have been monitored at the study site for one week (19th-25th January 2009). Results indicated that the concentrations of coarse (PM10) and fine PM (PM2.5 and PM1) concentrations at SP road are having similar trend during peak and non-peak hours, irrespective of the days. The PM concentrations showed daily two peaks corresponding to morning (8 to 10 am) and evening (7 to 9 pm) peak hour traffic flow. The PM10 concentration is dominated by fine particles (53% of PM2.5 and 45% of PM1). The high PM2.5/PM10 ratio indicates that the majority of PM10 particles originate from re-suspension of road dust. The analysis of traffic flow at the study site showed that 2W, 3W and 4W are having similar diurnal trend as PM concentrations. This confirms that the 2W, 3W and 4W are the main emission source contributing to ambient PM concentration at SP road. The speed measurement at SP road showed that the average speed of 2W, 3W, 4W, LCV and HCV are 38, 40, 38, 40 and 38 km/hr and 43, 41, 42, 40 and 41 km/hr respectively for the weekdays and weekdays.

Keywords: particulate matter, heterogeneous traffic, fineparticles, coarse particles, vehicle speed, weekend and weekday.

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Authors: V. Vicente E. Mujica, Gustavo Gonzalez

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The global coverage of broadband multimedia and internet-based services in terrestrial-satellite networks demand particular interests for satellite providers in order to enhance services with low latencies and high signal quality to diverse users. In particular, the delay of on-board processing is an inherent source of latency in a satellite communication that sometimes is discarded for the end-to-end delay of the satellite link. The frame work for this paper includes modelling of an on-orbit satellite payload using an agent model that can reproduce the properties of processing delays. In essence, a comparison of different spatial interpolation methods is carried out to evaluate physical data obtained by an GEO satellite in order to define a discretization function for determining that delay. Furthermore, the performance of the proposed agent and the development of a delay discretization function are together validated by simulating an hybrid satellite and terrestrial network. Simulation results show high accuracy according to the characteristics of initial data points of processing delay for Ku bands.

Keywords: Terrestrial-satellite networks, latency, on-orbit satellite payload, simulation.

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Authors: Ya-Chi Yu, Pei-Syuan Ruan, Shao-Wen Chen, Yu-Hsien Chang, Jin-Der Lee, Jong-Rong Wang, Chunkuan Shih

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This study analyzed the transient signals of central region void fraction of air-water two-phase flow in a 3x3 rod bundle. Experimental tests were carried out utilizing a vertical rod bundle test section along with a set of air-water supply/flow control system, and the transient signals of the central region void fraction were collected through the electrical conductivity sensors as well as visualized via high speed photography. By converting the electric signals, transient void fraction can be obtained through the voltage ratios. With a fixed superficial water velocity (J_f=0.094 m/s), two different superficial air velocities (J_g=0.094 m/s and 0.236 m/s) were tested and presented, which were corresponding to the flow conditions of bubbly flows and cap/slug flows, respectively. The time averaged central region void fraction was obtained as 0.109-0.122 with 0.028 standard deviation for the selected bubbly flow and 0.188-0.221with 0.101 standard deviation for the selected cap/slug flow, respectively. Through Fast Fourier Transform (FFT) analysis, no clear frequency peak was found in bubbly flow, while two dominant frequencies were identified around 1.6 Hz and 2.5 Hz in the present cap/slug flow.

Keywords: Central region, rod bundles, transient void fraction, two-phase flow.

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: Corporate credit rating prediction, feature selection, genetic algorithms, instance selection, multiclass support vector machines.

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Authors: Zamri Noranai, Mohammad Zainal Md Yusof

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Sustainable energy usage has been recognized as one of the important measure to increase the competitiveness of the nation globally. Many strong emphases were given in the Ninth Malaysia Plan (RMK9) to improve energy efficient especially to government buildings. With this in view, a project to investigate the potential of energy saving in selected building in Universiti Tun Hussein Onn Malaysia (UTHM) was carried out. In this project, a case study involving electric energy consumption of the academic staff office building was conducted. The scope of the study include to identify energy consumption in a selected building, to study energy saving opportunities, to analyse cost investment in term of economic and to identify users attitude with respect to energy usage. The MS1525:2001, Malaysian Standard -Code of practice on energy efficiency and use of renewable energy for non-residential buildings was used as reference. Several energy efficient measures were considered and their merits and priority were compared. Improving human behavior can reduce energy consumption by 6% while technical measure can reduce energy consumption by 44%. Two economic analysis evaluation methods were applied; they are the payback period method and net present value method.

Keywords: office building, energy, efficiency, economic analyses

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Authors: Simona Dostalova, Ana Maria Jimenez Jimenez, Marketa Vaculovicova, Vojtech Adam, Rene Kizek

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Various nanomaterials can be used as a drug delivery vehicles in nanomedicine, called nanocarriers. They can either be organic or inorganic, synthetic or natural-based. Although synthetic nanocarriers are easier to produce, they can often be toxic for the organism and thus not suitable for use in treatment. From naturalbased nanocarriers, the most commonly used are protein cages or viral capsids. In this work, virus bacteriophage λ was used for delivery of different cytotoxic drugs (cisplatin, carboplatin, oxaliplatin and doxorubicin). Large quantities of phage λ were obtained from phage λ-producing strain of E. coli cultivated in medium with 0.2% maltose. After killing of E. coli with chloroform and its removal by centrifugation, the phage was concentrated by ultracentrifugation at 130 000×g and 4°C for 3 h. The encapsulation of the drugs was performed by infusion method and four different concentrations of the drugs were encapsulated (200; 100; 50; 25 μg·mL-1). Free drug molecules were removed by filtration. The encapsulation was verified using the absorbance for doxorubicin and atomic absorption spectrometry for platinum cytostatics. The amount of encapsulated drug linearly increased with the increasing concentration of applied drug with the determination coefficient R2=0.989 for doxorubicin; R2=0.967 for cisplatin; R2=0.989 for carboplatin and R2=0.996 for oxaliplatin. The overall encapsulation efficiency was calculated as 50% for doxorubicin; 8% for cisplatin; 6% for carboplatin and 10% for oxaliplatin.

Keywords: Bacteriophage λ, doxorubicin, platinum cytostatics, protein-based nanocarrier, viral capsid.

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Authors: Yong W. Foo, Lai M. Tang

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Artificial Intelligence (AI), particularly the usage of deep neural networks for hierarchical representations from data, has found numerous complex applications across various domains, including computer vision, robotics, autonomous vehicles, and other scientific fields. However, their inherent “black box” nature can sometimes make it challenging for early researchers or school students of various levels to comprehend and trust the results they produce. Consequently, there has been a growing demand for reliable visualization tools in engineering and science education to help learners understand, trust, and explain a deep learning network. This has led to a notable emphasis on the visualization of AI in the research community in recent years. AI visualization tools are increasingly being adopted to significantly improve the comprehension of complex topics in deep learning. This paper presents an approach to empower students to actively explore the inner workings of deep neural networks by integrating the student-centered learning approach of flipped classroom models with the investigative capabilities of AI visualization tools, namely, the TensorFlow Playground, the Local Interpretable Model-agnostic Explanations (LIME), and the SHapley Additive exPlanations (SHAP), for delivering an AI education curriculum. Integrating these two factors is crucial for fostering ownership, responsibility, and critical thinking skills in the age of AI.

Keywords: Deep Learning, Explainable AI, AI Visualization, Representation Learning.

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Authors: T. Shyam Ramanath, A. Sudharsan, A. Kavitha

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As is needless to say; a majority of accidents, which occur, are due to drunk driving. As such, there is no effective mechanism to prevent this. Here we have designed an integrated system for the same purpose. Alcohol content in the driver-s body is detected by means of an infrared breath analyzer placed at the steering wheel. An infrared cell directs infrared energy through the sample and any unabsorbed energy at the other side is detected. The higher the concentration of ethanol, the more infrared absorption occurs (in much the same way that a sunglass lens absorbs visible light, alcohol absorbs infrared light). Thus the alcohol level of the driver is continuously monitored and calibrated on a scale. When it exceeds a particular limit the fuel supply is cutoff. If the device is removed also, the fuel supply will be automatically cut off or an alarm is sounded depending upon the requirement. This does not happen abruptly and special indicators are fixed at the back to avoid inconvenience to other drivers using the highway signals. Frame work for integration of sensors and control module in a scalable multi-agent system is provided .A SMS which contains the current GPS location of the vehicle is sent via a GSM module to the police control room to alert the police. The system is foolproof and the driver cannot tamper with it easily. Thus it provides an effective and cost effective solution for the problem of drunk driving in vehicles.

Keywords: Global system monitoring, global positioning system.

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Authors: Mohamed Abdallah, Mostafa Warith, Roberto Narbaitz, Emil Petriu, Kevin Kennedy

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Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.

Keywords: Adaptive neural fuzzy inference system (ANFIS), gas production, landfill

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Authors: Qing Dai, Zhengkui Lin, Jiajia Zhang, Yi Qu

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Based on activity method, this paper focuses on morning commuting behavior when commuters travel with autonomous vehicles (AVs). Firstly, a net utility function of commuters is constructed by the activity utility of commuters at home, in car and at workplace, and the disutility of travel time cost and that of schedule delay cost. Then, this net utility function is applied to build an equilibrium model. Finally, under the assumption of constant marginal activity utility, the properties of equilibrium are analyzed. The results show that, in autonomous driving, the starting and ending time of morning peak and the number of commuters who arrive early and late at workplace are the same as those in manual driving. In automatic driving, however, the departure rate of arriving early at workplace is higher than that of manual driving, while the departure rate of arriving late is just the opposite. In addition, compared with manual driving, the departure time of arriving at workplace on time is earlier and the number of people queuing at the bottleneck is larger in automatic driving. However, the net utility of commuters and the total net utility of system in automatic driving are greater than those in manual driving.

Keywords: Autonomous cars, bottleneck model, activity utility, user equilibrium.

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Authors: H. Shayeghi, M. Mahdavi, H. Haddadian

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Transmission network expansion planning (TNEP) is an important component of power system planning that its task is to minimize the network construction and operational cost while satisfying the demand increasing, imposed technical and economic conditions. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, the lines adequacy rate has not been studied after the planning horizon, i.e. when the expanded network misses its adequacy and needs to be expanded again. In this paper, in order to take transmission lines condition after expansion in to account from the line loading view point, the adequacy of transmission network is considered for solution of STNEP problem. To obtain optimal network arrangement, a decimal codification genetic algorithm (DCGA) is being used for minimizing the network construction and operational cost. The effectiveness of the proposed idea is tested on the Garver's six-bus network. The results evaluation reveals that the annual worth of network adequacy has a considerable effect on the network arrangement. In addition, the obtained network, based on the DCGA, has lower investment cost and higher adequacy rate. Thus, the network satisfies the requirements of delivering electric power more safely and reliably to load centers.

Keywords: STNEP Problem, Network Adequacy, DCGA.

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Authors: Mansoor Ahsan, Khalid Rafique, Farrukh Mazhar

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Unmanned Aerial Vehicles (UAVs) have gained tremendous importance, in both Military and Civil, during first decade of this century. In a UAV, onboard computer (autopilot) autonomously controls the flight and navigation of the aircraft. Based on the aircraft role and flight envelope, basic to complex and sophisticated controllers are used to stabilize the aircraft flight parameters. These controllers constitute the autopilot system for UAVs. The autopilot systems, most commonly, provide lateral and longitudinal control through Proportional-Integral-Derivative (PID) controllers or Phase-lead or Lag Compensators. Various techniques are commonly used to ‘tune’ gains of these controllers. Some techniques used are, in-flight step-by-step tuning, software-in-loop or hardware-in-loop tuning methods. Subsequently, numerous in-flight tests are required to actually ‘fine-tune’ these gains. However, an optimization-based tuning of these PID controllers or compensators, as presented in this paper, can greatly minimize the requirement of in-flight ‘tuning’ and substantially reduce the risks and cost involved in flight-testing.

Keywords: Unmanned aerial vehicle (UAV), autopilot, autonomous controls, PID controler gains tuning, optimization.

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Authors: Ali Asadian, Behzad Moshiri, Ali Khaki Sedigh, Caro Lucas

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Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with respect to the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS integration in comparison with conventional ANFIS specially in the cases of satellites- outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

Keywords: Adaptive Network based Fuzzy Inference System (ANFIS), Genetic optimization, Global Positioning System (GPS), Inertial Navigation System (INS).

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Authors: Galina Mihaleva, C. Koh

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Fashion, like many other design fields, undergoes numerous evolutions throughout the ages. This paper aims to recognize and evaluate the significance of advance technology in fashion design and examine how it changes the role of modern fashion designers by modifying the creation process. It also touches on how modern culture is involved in such developments and how it affects fashion design in terms of conceptualizing and fabrication. The methodology used is through surveying the various examples of technological applications to fashion design and drawing parallels between what was achievable then and what is achievable now. By comparing case studies, existing fashion design examples and crafting method experimentations; we then spot patterns in which to predict the direction of future developments in the field. A breakdown on the elements of technology in fashion design helps us understand the driving force behind such a trend. The results from explorations in the paper have shown that there is an observed pattern of a distinct increase in interest and progress in the field of fashion technology, which leads to the birth of hybrid crafting methods. In conclusion, it is shown that as fashion technology continues to evolve, their role in clothing crafting becomes more prominent and grows far beyond the humble sewing machine.

Keywords: Fashion design, functional aesthetics, smart textiles, 3D printing.

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Authors: Cinna Soltanpur, Mohammad Ghamari, Behzad Momahed Heravi, Fatemeh Zare

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Low-density parity-check (LDPC) codes have been shown to deliver capacity approaching performance; however, problematic graphical structures (e.g. trapping sets) in the Tanner graph of some LDPC codes can cause high error floors in bit-error-ratio (BER) performance under conventional sum-product algorithm (SPA). This paper presents a serial concatenation scheme to avoid the trapping sets and to lower the error floors of LDPC code. The outer code in the proposed concatenation is the LDPC, and the inner code is a high rate array code. This approach applies an interactive hybrid process between the BCJR decoding for the array code and the SPA for the LDPC code together with bit-pinning and bit-flipping techniques. Margulis code of size (2640, 1320) has been used for the simulation and it has been shown that the proposed concatenation and decoding scheme can considerably improve the error floor performance with minimal rate loss.

Keywords: Concatenated coding, low–density parity–check codes, array code, error floors.

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Authors: Gbenga M. Ayininuola, Olasunkanmi A. Adekitan

Abstract:

Calcined kaolinite clay (CKC) is a pozzolanic material that is current drawing research attention. This work investigates the conditions for the best performance of a CKC from a kaolinite clay source in Ajebo, Abeokuta (southwest Nigeria) known for its commercial availability. Samples from this source were subjected to X-ray diffractometry (XRD) and differential scanning calorimetry (DSC). XRD shows that kaolinite is the main mineral in the clay source. This mineral is responsible for the pozzolanic behavior of CKC. DSC indicates that the transformation from the clay to CKC occurred between 550 and 750 ^oC. Using this temperature range, clay samples were milled and different CKC samples were produced in an electric muffle furnace using temperatures of 550, 600, 650, 700, 750 and 800 ^oC respectively for 1 hour each. This was also repeated for 2 hours. The degree of de-hydroxylation (d_tg) and strength activity index (SAI) were also determined for each of the CKC samples. The d_tg and SAI tests were repeated two more times for each sample and averages were taken. Results showed that peak d_tg occurred at 750 ^oC for 1 hour calcining combination (94.27%) whereas marginal differences were recorded at some lower temperatures (90.97% for 650 ^oC for 2 hours; 91.05% for 700 ^oC for 1 hour and 92.77% for 700 ^oC for 2 hours). Optimum SAI was reported at 700 ^oC for 1 hour (99.05%). Rating SAI as a better parameter than d_tg, 700 ^oC for 1 hour combination was adopted as the best calcining condition. The paper recommends the adoption of this clay source for pozzolan production by adopting the calcining conditions established in this work.

Keywords: Calcined kaolinite clay, calcination, optimum-calcining conditions, pozzolanity.

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

Abstract:

Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: Intrusion detection system, decision tree, support vector machine, feature selection.

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Authors: Saqib Mahmood, Rolf Radespiel

Abstract:

This paper aims at numerically analysing the effect of an active flow control (AFC) by a vortex generator jet (VGJ) submerged in a boundary layer via Chimera Grids and Detached- Eddy Simulation (DES). The performance of DES results are judged against Reynolds-Averaged Navier-Stokes (RANS) and compared with the experiments that showed an unsteady vortex motion downstream of VGJ. Experimental results showed that the mechanism of embedding logitudinal vortex structure in the main stream flow is quite effective in increasing the near wall momentum of separated aircraft wing. In order to simulate such a flow configuration together with the VGJ, an efficient numerical approach is required. This requirement is fulfilled by performing the DES simulation over the flat plate using the DLR TAU Code. The DES predictions identify the vortex region via smooth hybrid length scale and predict the unsteady vortex motion observed in the experiments. The DES results also showed that the sufficient grid refinement in the vortex region resolves the turbulent scales downstream of the VGJ, the spatial vortex core postion and nondimensional momentum coefficient
RVx .

Keywords: VGJ, Chimera Grid, DES, RANS.

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Authors: N. Massoum, B. Bouazza, H. Tahir, C. Sayah, A. Guen Bouazza

Abstract:

to simulate the phenomenon of electronic transport in semiconductors, we try to adapt a numerical method, often and most frequently it’s that of Monte Carlo. In our work, we applied this method in the case of a ternary alloy semiconductor GaInP in its cubic form; The Calculations are made using a non-parabolic effective-mass energy band model. We consider a band of conduction to three valleys (ΓLX), major of the scattering mechanisms are taken into account in this modeling, as the interactions with the acoustic phonons (elastic collisions) and optics (inelastic collisions). The polar optical phonons cause anisotropic collisions, intra-valleys, very probable in the III-V semiconductors. Other optical phonons, no polar, allow transitions inter-valleys. Initially, we present the full results obtained by the simulation of Monte Carlo in GaInP in stationary regime. We consider thereafter the effects related to the application of an electric field varying according to time, we thus study the transient phenomenon which make their appearance in ternary material

Keywords: Monte Carlo simulation, steady-state electron transport, transient electron transport, alloy scattering.

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