Elasticity Model for Easing Peak Hour Demand for Metrorail Transport System

The demand for Urban transportation is characterised by a large scale temporal and spatial variations which causes heavy congestion inside metro trains in peak hours near Centre Business District (CBD) of the city. The conventional approach to address peak hour congestion, metro trains has been to increase the supply by way of introduction of more trains, increasing the length of the trains, optimising the time table to increase the capacity of the system. However, there is a limitation of supply side measures determined by the design capacity of the systems beyond which any addition in the capacity requires huge capital investments. The demand side interventions are essentially required to actually spread the demand across the time and space. In this study, an attempt has been made to identify the potential Transport Demand Management tools applicable to Urban Rail Transportation systems with a special focus on differential pricing. A conceptual price elasticity model has been developed to analyse the effect of various combinations of peak and nonpeak hoursfares on demands. The elasticity values for peak hour, nonpeak hour and cross elasticity have been assumed from the relevant literature available in the field. The conceptual price elasticity model so developed is based on assumptions which need to be validated with actual values of elasticities for different segments of passengers. Once validated, the model can be used to determine the peak and nonpeak hour fares with an objective to increase overall ridership, revenue, demand levelling and optimal utilisation of assets.

An Algorithm for Estimating the Stable Operation Conditions of the Synchronous Motor of the Ore Mill Electric Drive

An algorithm for estimating the stable operation conditions of the synchronous motor of the ore mill electric drive is proposed. The stable operation conditions of the synchronous motor are revealed, taking into account the estimation of the q angle change and the technological factors. The stability condition obtained allows to ensure the stable operation of the motor in the synchronous mode, taking into account the nonlinear character of the mill loading. The developed algorithm gives an opportunity to present the undesirable phenomena, arising in the electric drive system. The obtained stability condition can be successfully applied for the optimal control of the electromechanical system of the mill.

Developing Rice Disease Analysis System on Mobile via iOS Operating System

This research aims to create mobile tools to analyze rice disease quickly and easily. The principle of object-oriented software engineering and objective-C language were used for software development methodology and the principle of decision tree technique was used for analysis method. Application users can select the features of rice disease or the color appears on the rice leaves for recognition analysis results on iOS mobile screen. After completing the software development, unit testing and integrating testing method were used to check for program validity. In addition, three plant experts and forty farmers have been assessed for usability and benefit of this system. The overall of users’ satisfaction was found in a good level, 57%. The plant experts give a comment on the addition of various disease symptoms in the database for more precise results of the analysis. For further research, it is suggested that image processing system should be developed as a tool that allows users search and analyze for rice diseases more convenient with great accuracy.

Self-Tuning Robot Control Based on Subspace Identification

The paper describes the use of subspace based identification methods for auto tuning of a state space control system. The plant is an unstable but self balancing transport robot. Because of the unstable character of the process it has to be identified from closed loop input-output data. Based on the identified model a state space controller combined with an observer is calculated. The subspace identification algorithm and the controller design procedure is combined to a auto tuning method. The capability of the approach was verified in a simulation experiments under different process conditions.

Screening of Factors Affecting the Enzymatic Hydrolysis of Empty Fruit Bunches in Aqueous Ionic Liquid and Locally Produced Cellulase System

The enzymatic hydrolysis of lignocellulosic biomass is one of the obstacles in the process of sugar production, due to the presence of lignin that protects the cellulose molecules against cellulases. Although the pretreatment of lignocellulose in ionic liquid (IL) system has been receiving a lot of interest; however, it requires IL removal with an anti-solvent in order to proceed with the enzymatic hydrolysis. At this point, introducing a compatible cellulase enzyme seems more efficient in this process. A cellulase enzyme that was produced by Trichoderma reesei on palm kernel cake (PKC) exhibited a promising stability in several ILs. The enzyme called PKC-Cel was tested for its optimum pH and temperature as well as its molecular weight. One among evaluated ILs, 1,3-diethylimidazolium dimethyl phosphate [DEMIM] DMP was applied in this study. Evaluation of six factors was executed in Stat-Ease Design Expert V.9, definitive screening design, which are IL/ buffer ratio, temperature, hydrolysis retention time, biomass loading, cellulase loading and empty fruit bunches (EFB) particle size. According to the obtained data, IL-enzyme system shows the highest sugar concentration at 70 °C, 27 hours, 10% IL-buffer, 35% biomass loading, 60 Units/g cellulase and 200 μm particle size. As concluded from the obtained data, not only the PKC-Cel was stable in the presence of the IL, also it was actually stable at a higher temperature than its optimum one. The reducing sugar obtained was 53.468±4.58 g/L which was equivalent to 0.3055 g reducing sugar/g EFB. This approach opens an insight for more studies in order to understand the actual effect of ILs on cellulases and their interactions in the aqueous system. It could also benefit in an efficient production of bioethanol from lignocellulosic biomass.

Iterative Estimator-Based Nonlinear Backstepping Control of a Robotic Exoskeleton

A repetitive training movement is an efficient method to improve the ability and movement performance of stroke survivors and help them to recover their lost motor function and acquire new skills. The ETS-MARSE is seven degrees of freedom (DOF) exoskeleton robot developed to be worn on the lateral side of the right upper-extremity to assist and rehabilitate the patients with upper-extremity dysfunction resulting from stroke. Practically, rehabilitation activities are repetitive tasks, which make the assistive/robotic systems to suffer from repetitive/periodic uncertainties and external perturbations induced by the high-order dynamic model (seven DOF) and interaction with human muscle which impact on the tracking performance and even on the stability of the exoskeleton. To ensure the robustness and the stability of the robot, a new nonlinear backstepping control was implemented with designed tests performed by healthy subjects. In order to limit and to reject the periodic/repetitive disturbances, an iterative estimator was integrated into the control of the system. The estimator does not need the precise dynamic model of the exoskeleton. Experimental results confirm the robustness and accuracy of the controller performance to deal with the external perturbation, and the effectiveness of the iterative estimator to reject the repetitive/periodic disturbances.

Mathematical Modeling of Wind Energy System for Designing Fault Tolerant Control

This paper addresses the mathematical model of wind energy system useful for designing fault tolerant control. To serve the demand of power, large capacity wind energy systems are vital. These systems are installed offshore where non planned service is very costly. Whenever there is a fault in between two planned services, the system may stop working abruptly. This might even lead to the complete failure of the system. To enhance the reliability, the availability and reduce the cost of maintenance of wind turbines, the fault tolerant control systems are very essential. For designing any control system, an appropriate mathematical model is always needed. In this paper, the two-mass model is modified by considering the frequent mechanical faults like misalignments in the drive train, gears and bearings faults. These faults are subject to a wear process and cause frictional losses. This paper addresses these faults in the mathematics of the wind energy system. Further, the work is extended to study the variations of the parameters namely generator inertia constant, spring constant, viscous friction coefficient and gear ratio; on the pole-zero plot which is related with the physical design of the wind turbine. Behavior of the wind turbine during drive train faults are simulated and briefly discussed.

Millennial Teachers of Canada: Innovation within the Boxed-In Constraints of Tradition

Every year, schools aim to develop and adopt new technology and pedagogy as a way to equip today's students with the needed 21st Century skills. However, the field of primary and secondary education may not be as open to embracing change in reality. Despite the drive to reform and innovation, the field of education in Canada is still very much steeped in tradition and uses many of the practices that came into effect over 50 years ago. Among those are employment and retention practices. Millennials are the youngest generation of professionals entering the workplace at this time and the ones leaving their jobs within just a few years. Almost half of new teachers leave Canadian schools within their first five years on the job. This paper discusses one of the contributing factors that lead Canadian millennial teachers to either leave or stay in the profession - standardized education system. Using an exploratory case study approach, in-depth interviews with former and current millennial teachers were conducted to learn about their experiences within the K-12 system. Among the findings were the young teachers' concerns about the constant changes to teaching practices and technological implementations that claimed to advance teaching and learning, and yet in reality only disguised and reiterated the same traditional, outdated, and standardized practices that already existed. Furthermore, while many millennial teachers aspired to be innovative with their curriculum and teaching practices, they felt trapped and helpless in the hands of school leaders who were very reluctant to change. While many new program ideas and technological advancements are being made openly available to teachers on a regular basis, it is important to consider the education field as a whole and how it plays into the teachers' ability to realistically implement changes. By the year 2025, millennials will make up approximately 75% of the North American workforce. It is important to examine generational differences among teachers and understand how millennial teachers may be shaping the future of primary and secondary schools, either by staying or leaving the profession.

The Internet of Things Ecosystem: Survey of the Current Landscape, Identity Relationship Management, Multifactor Authentication Mechanisms, and Underlying Protocols

A critical component in the Internet of Things (IoT) ecosystem is the need for secure and appropriate transmission, processing, and storage of the data. Our current forms of authentication, and identity and access management do not suffice because they are not designed to service cohesive, integrated, interconnected devices, and service applications. The seemingly endless opportunities of IoT are in fact circumscribed on multiple levels by concerns such as trust, privacy, security, loss of control, and related issues. This paper considers multi-factor authentication (MFA) mechanisms and cohesive identity relationship management (IRM) standards. It also surveys messaging protocols that are appropriate for the IoT ecosystem.

Maximum Power Point Tracking Based on Estimated Power for PV Energy Conversion System

In this paper, a method for maximum power point tracking of a photovoltaic energy conversion system is presented. This method is based on using the difference between the power from the solar panel and an estimated power value to control the DC-DC converter of the photovoltaic system. The difference is continuously compared with a preset error permitted value. If the power difference is more than the error, the estimated power is multiplied by a factor and the operation is repeated until the difference is less or equal to the threshold error. The difference in power will be used to trigger a DC-DC boost converter in order to raise the voltage to where the maximum power point is achieved. The proposed method was experimentally verified through a PV energy conversion system driven by the OPAL-RT real time controller. The method was tested on varying radiation conditions and load requirements, and the Photovoltaic Panel was operated at its maximum power in different conditions of irradiation.

Interpolation Issue in PVNPG-14M Application for Technical Control of Artillery Fire

This paper focused on application support for technical control of artillery units – PVNPG-14M, especially on interpolation issue. Artillery units of the Army of the Czech Republic, reflecting the current global security neighborhood, can be used outside the Czech Republic. The paper presents principles, evolution and calculation in the process of complete preparation. The paper presents expertise using of application of current artillery communication and information system and suggests the perspective future system. The paper also presents problems in process of complete preparing of fire especially problems in permanently information (firing table) and calculated values. The paper presents problems of current artillery communication and information system and suggests requirements of the future system.

Capability Prediction of Machining Processes Based on Uncertainty Analysis

Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Design of IMC-PID Controller Cascaded Filter for Simplified Decoupling Control System

In this work, the IMC-PID controller cascaded filter based on Internal Model Control (IMC) scheme is systematically proposed for the simplified decoupling control system. The simplified decoupling is firstly introduced for multivariable processes by using coefficient matching to obtain a stable, proper, and causal simplified decoupler. Accordingly, transfer functions of decoupled apparent processes can be expressed as a set of n equivalent independent processes and then derived as a ratio of the original open-loop transfer function to the diagonal element of the dynamic relative gain array. The IMC-PID controller in series with filter is then directly employed to enhance the overall performance of the decoupling control system while avoiding difficulties arising from properties inherent to simplified decoupling. Some simulation studies are considered to demonstrate the simplicity and effectiveness of the proposed method. Simulations were conducted by tuning various controllers of the multivariate processes with multiple time delays. The results indicate that the proposed method consistently performs well with fast and well-balanced closed-loop time responses.

Implementing Fault Tolerance with Proxy Signature on the Improvement of RSA System

Fault tolerance and data security are two important issues in modern communication systems. During the transmission of data between the sender and receiver, errors may occur frequently. Therefore, the sender must re-transmit the data to the receiver in order to correct these errors, which makes the system very feeble. To improve the scalability of the scheme, we present a proxy signature scheme with fault tolerance over an efficient and secure authenticated key agreement protocol based on the improved RSA system. Authenticated key agreement protocols have an important role in building a secure communications network between the two parties.

Overview of Different Approaches Used in Optimal Operation Control of Hybrid Renewable Energy Systems

A hybrid energy system is a combination of renewable energy sources with back up, as well as a storage system used to respond to given load energy requirements. Given that the electrical output of each renewable source is fluctuating with changes in weather conditions, and since the load demand also varies with time; one of the main attributes of hybrid systems is to be able to respond to the load demand at any time by optimally controlling each energy source, storage and back-up system. The induced optimization problem is to compute the optimal operation control of the system with the aim of minimizing operation costs while efficiently and reliably responding to the load energy requirement. Current optimization research and development on hybrid systems are mainly focusing on the sizing aspect. Thus, the aim of this paper is to report on the state-of-the-art of optimal operation control of hybrid renewable energy systems. This paper also discusses different challenges encountered, as well as future developments that can help in improving the optimal operation control of hybrid renewable energy systems.

Identifying a Drug Addict Person Using Artificial Neural Networks

Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Method of Estimating Absolute Entropy of Municipal Solid Waste

Entropy, as an outcome of the second law of thermodynamics, measures the level of irreversibility associated with any process. The identification and reduction of irreversibility in the energy conversion process helps to improve the efficiency of the system. The entropy of pure substances known as absolute entropy is determined at an absolute reference point and is useful in the thermodynamic analysis of chemical reactions; however, municipal solid waste (MSW) is a structurally complicated material with unknown absolute entropy. In this work, an empirical model to calculate the absolute entropy of MSW based on the content of carbon, hydrogen, oxygen, nitrogen, sulphur, and chlorine on a dry ash free basis (daf) is presented. The proposed model was derived from 117 relevant organic substances which represent the main constituents in MSW with known standard entropies using statistical analysis. The substances were divided into different waste fractions; namely, food, wood/paper, textiles/rubber and plastics waste and the standard entropies of each waste fraction and for the complete mixture were calculated. The correlation of the standard entropy of the complete waste mixture derived was found to be somsw= 0.0101C + 0.0630H + 0.0106O + 0.0108N + 0.0155S + 0.0084Cl (kJ.K-1.kg) and the present correlation can be used for estimating the absolute entropy of MSW by using the elemental compositions of the fuel within the range of 10.3% ≤ C ≤ 95.1%, 0.0% ≤ H ≤ 14.3%, 0.0% ≤ O ≤ 71.1%, 0.0 ≤ N ≤ 66.7%, 0.0% ≤ S ≤ 42.1%, 0.0% ≤ Cl ≤ 89.7%. The model is also applicable for the efficient modelling of a combustion system in a waste-to-energy plant.

Changes in Fish and Shellfish in Thondamanaru Lagoon, Jaffna, Sri Lanka

Current study was conducted for one year from June 2014 to May 2015, with an objective of identification of fish and shellfish diversity in the Thondamanaru lagoon ecosystem. In this study, 11 species were identified from Thondamanaru lagoon, Jaffna, Sri Lanka. There are four fishes, Chanos chanos, Hemirhamphus sp., Nematalosa sp. and Mugil cephalus and seven shell fishes, Penaeus indicus, Penaeus monodon, Penaeus latisulcatus, Penaeus semisulcatus, Metapenaeus monoceros, Portunus pelagicus and Scylla serrata. Species composition of Mugil cephalus, Penaeus indicus and Metapenaeus monoceros was high during rainy seasons. However, lagoon is being subjected to adverse environmental conditions that threaten its fish and shellfish biodiversity due to lack of saline water availability and changes in rainfall pattern.

The Prospective Assessment of Zero-Energy Dwellings

The highest priority of so called, projected passive houses is to meet the appropriate energy demand. Every single material and layer which is injected into a dwelling has a certain energy quantity stored. The passive houses include optimized insulation levels with minimal thermal bridges, minimum of air leakage through the building, utilization of passive solar and internal gains, and good circulation of air which leans on mechanical ventilation system. The focus of this paper is on passive house features, benefits and targets, their feasibility and energy demands which are set up during each project. Numerous passive house-standards outline the very significant role of zero-energy dwellings towards the modern label of sustainable development. It is clear that the performance of both built and existing housing stock must be addressed if the population across the world sets out the energy objectives. This scientific article examines passive house features since the many passive house cases are launched.

Optimum Design of Photovoltaic Water Pumping System Application

The solar power source for pumping water is one of the most promising areas in photovoltaic applications. The implementation of these systems allows to protect the environment and reduce the CO2 gas emission compared to systems trained by diesel generators. This paper presents a comparative study between the photovoltaic pumping system driven by DC motor, and AC motor to define the optimum design of this application. The studied system consists of PV array, DC-DC Boost Converter, inverter, motor-pump set and storage tank. The comparison was carried out to define the characteristics and the performance of each system. Each subsystem is modeled in order to simulate the whole system in MATLAB/ Simulink. The results show the efficiency of the proposed technique.