Modeling, Design and Simulation of Systems

In today’s world, Global Positioning System (GPS) for tracking system plays an important role in tracking required locations. To obtain accurate location in developing the proposed system, several issues. Although Differential-GPS (DGPS) system is expensive, GPS module may have some errors and does not provide the accurate position information of the location. Therefore, the required location needs to have an accurate correction and improvement which can be achieved using DGPS technique. In this research, the proposed technique improves the position of the required location utilizing U-blox Neo 6 M receiver GPS module. With the proposed method, the position of the location is improved within 1–3 m.

In keropok keping industries, most of the production processes are implemented by semi-automated machines. However, the drying process is still conducted using a traditional method where the keropok is arranged under the sunlight. To improve the drying process, a new rotary type of keropok keping drying machine was invented. The new machine will undergo structural analysis via static analysis. The static analysis is only focusing on the vital parts of the new design machine.

This paper compares daily and hourly based generation maintenance scheduling, that both solved using mixed integer linear programming (MILP). Scheduling the maintenance based on hourly gives more advantages as compared to the daily in terms of security and operating cost. In the daily basis, the loading and unloading characteristic of a generator may not be satisfied as it neglects the ramp rates constraints on the consecutive days, while in the hourly basis, these constrains have been considered. Numerical case studies were evaluated on the 6-bus system, IEEE 118-bus system, and practical system. A comparative study is carried out between hourly and daily basis. The result shows that the operating cost obtained was lower when scheduling the maintenance based on hourly as compared to the daily. It can be summarized that a global optimal solution could be achieved using hourly instead of only local optimal achieved in the daily basis.

This paper focuses on charging process of the battery charger for a small wind turbine system to track the highest power point in order to harvest the maximum output in the wide range of wind speed variations. The Maximum Power Point Tracking (MPPT) method with the assistance of classical Proportional-Integral-Derivative (PID) controller is used. The issue regarding how this charging process is managed was studied. Smart management during battery charging process is significantly required since the lack of charging management may affecting the battery’s performance, particularly in terms of its lifetime and the degradation rate. Hence, in this paper, a battery charger with an effective performance is designed using the concept of controlling the pulsating charging current which is implemented by discontinuous conduction mode operation of a power converter. Results show that the efficiency of the charging process is good since the resulting pulsating current is working as expected and protection scheme worked well during the charging process by using buck-converter. This method enables the overcharging and overvoltage to be avoided. Besides that, it provides another feature where the self-discharging process can be avoided by providing small current flow in order to prolong the battery’s lifetime.

Xylitol is one of the bio-based chemical products that received well recognition and highly demanded from both food and pharmaceutical industries which led to various experiment to be carried out on various organism to produce xylitol. Recently, E. coli has become the spotlight to be one of the organisms that can be metabolically engineered to produce xylitol by using gene knockout strategy. However, gene knockout strategy required laborious, expensive and time-consuming when conducted in vivo. Motivated by this, in silico experiment has been done to simulate and manipulate the model of E. coli to construct a new E. coli model that will focus on producing xylitol by using Flux Balance Analysis (FBA). In this paper, a new hybrid method called DNNDSA is proposed which consists of both Deep Neural Network (DNN) method and Differential Search Algorithm (DSA) to do the predictive analysis on a newly constructed model of E. coli to predict which gene knockout condition is the best to be applied in metabolic pathway of E. coli to improve the xylitol production.

The emergence of deep learning as a technique forms a part of artificial intelligence give a huge contribution in machine learning towards the development of powerful tools. Deep learning is potentially being well suited in genomics representations which enable distributed representations’ data from multiple processing layers. Practically, deep learning is capable in demonstrating abstraction within the cell in genomic analysis with high predictive power reinforces leads this research. The enhancement of deep neural network in representing genome-scale data into mathematical model allows predictive analysis to be conducted. This work aims to investigate biological process within E. coli to explore genomics representation in identifying target microbial production. Furthermore, the use of firefly algorithm prevents it from getting stuck at local optima in finding optimal solution during network training. The outcome of this study contributes in identifying the effects of genetic perturbation towards xylitol production of selective metabolic pathway in metabolic network of E. coli.

Deep Learning is a new era of machine learning research that are making major advances in solving problem with powerful computational models. Currently, this new machine learning method is widely used in object detection, visual object and speech recognition and also for making prediction of regulatory genomic and cellular imaging. Here, we review the methodology and applications of deep learning architectures including deep neural network, convolutional neural network and recurrent neural network. Next, we review several existing prediction tools in genomic sequences analysis that use deep learning architectures. In addition, we discuss the future research directions of deep learning.

This study presents an integration of Evolving Spiking Neural - Network (ESNN) with Dynamic Population Particle Swarm Optimization (DPPSO). The original ESNN framework does not automatically modulate its parameters’ optimum values. Thus, an integrated framework is proposed to optimize ESNN parameters namely, the modulation factor (mod), similarity factor (sim), and threshold factor (c). DPPSO improves the original PSO technique by implementing a dynamic particle population. Performance analysis is measured on classification accuracy in comparison with the existing methods. Five datasets retrieved from UCI machine learning are selected to simulate the classification problem. The proposed framework improves ESNN performance in regulating its parameters’ optimum values.

The high demands in the control of force and position implemented in diverse applications have led to the increasing usage of Electro-Hydraulic Actuator (EHA) system. However, the EHA system is commonly exposed to the parameter variations, disturbances, and uncertainties, which are caused by the changes in the operating conditions. Hence, this paper attempts to analyses the impact of changes during the operating condition and a robust control strategy is then formulated based on the control law of the Sliding Mode Control (SMC), where the design of the sliding surface is integrated with the Proportional-Integral-Derivative (PID) controller. Then, the Particle Swarm Optimization (PSO) technique has been utilized to seek for the optimum PID sliding surface parameters. The findings indicate that the proposed robust SMC with PSO-PID sliding surface is preserved to ensure the actuator robust and stable under the variation of the system operating condition, which produce 26% improvement in terms of its robustness characteristic.

Recent years, the use of mobile communication has been steadily increases. An important process in mobile communication is the assignment of frequency spectrum called channel to each of the caller and receiver pair in order to communicate. This headed to some problems faced by mobile communication on how to distribute the large number of users efficiently with the limited capital of radio frequency spectrum. Zero interference between channels assigned may contributed to a high quality call service between users. In mobile communication one of the ways to solve the problem is dividing a geographical area into a number of cells in order to reuse the limited frequencies with the aim of supporting more users and also to minimize interference. Hence, a local search method is proposed in this project to solve the channel assignment problem with the minimum span of frequency and zero interference between the channels assigned.

Accurate underwater target tracking during subsea lowering is a complex technological problem in offshore installation and deep ocean mining. It involves the real-time motion compensation of both combined effects from flow-induced vibration on the cable-payload and wave-induced motions on the host vessel. A target tracking mechanism for a planar motion was theoretically derived and simulated in this paper under both regular and irregular wave motions. The simulation results have shown that the proposed target tracking system, by using PID controller integrated with hydrodynamic effects of both surface vessel and subsea payload, has followed the movable subsea target with small vicinity. The findings of this paper can be further implemented in the development of automatizing the subsea operations of the offshore crane system.

A novel tubular linear switched reluctance actuator (TLSRA) without permanent magnet that has 7:7 stator-to-mover pole pairs ratio is presented in this paper. A detailed analysis of the effect of mover parameters on the performances of proposed TLSRA is presented to determine the optimized actuator parameters. As comparison, the performances of the conventional TLSRA with 7:5 stator-to-mover pole pairs ratio is also designed and compared with the proposed TLSRA using the identical dimensions. The differences between the proposed and conventional TLSRA is number of available working pole pairs. The proposed TLSRA has four working pole pairs for a three phases actuator instead of two working pole pairs for conventional TLSRA. The additional working pole pairs in the proposed TLSRA exhibit force improvement, approximate two times higher compared to the conventional TLSRA. The static force characteristics for the proposed TLSRA is calculated and computed by using the three-dimensional finite element method (FEM) with ANSYS Maxwell software.

This paper presents an improved practical controller for enhancing the tracking performance of a ball screw mechanism. Essentially, a controller with practical and easy to design has been preferred for high motion control performance. The existing continuous motion nominal characteristic trajectory following (CM NCTF) controller demonstrates a low accuracy achievement at high frequency motion, where at 5 Hz, the percentage of error are higher than 10% and 15% for amplitude of 1 mm and 10 mm respectively. The NCTF controller comprises of a nominal characteristic trajectory (NCT) and a PI compensator where the controller parameters are easily determined and it is free from exact modeling. In this paper, the CM NCTF controller with velocity feedforward compensator has been proposed in order to enhance the tracking motion accuracy. The simulation results shown that the CM NCTF controller with velocity feedforward compensator achieves better tracking performances than that the CM NCTF controller alone by showing more than three times smaller motion error.

The dominant frequency (DF) of atrial electrogram was claimed by researchers as to be correlated to the electrical activation of the atrial fibrillation (AF). By assessing the DF of AF data of 5 patients with persistent AF, this paper presented some AF signal analysis done to study the performance of the raw data, which is the data without QRS-T subtraction, data with QRS-T subtraction and rectification. The resultant data were mapped in 3-dimensional shapes of respective patients left atrium (LA), showing the DF activity changes during 21 s data recorded. The maps are useful for further analysis to justify which of the mentioned signal processing step is more reliable and also to study the relation between particular sites on the LA with the source (sites) of activation of AF.

Inefficient navigation capability in dynamic agricultural field limits the application of agricultural robot in the field. Probabilistic roadmap however has the robustness for outdoor navigation. A path planning algorithm was established upon an enhanced probabilistic roadmap and this was implemented in a virtual greenhouse environment. A smoothing algorithm for the robot’s navigation has been proposed to improve the existing algorithm in producing an optimal path. A simulation was conducted using a crop inspection mobile robot and tested with suitable turning trajectories for lane changing. Several trajectories were initiated and compared based on travel time, distance and controller error in order to choose the best trajectories for crop inspection. The proposed smoothing algorithm was able to smooth out the initial paths in order to create an optimal path for the robot with error less than 0.1 m.

A finite element simulation for defect depth estimation using the pulsed eddy current (PEC) technique is presented in this paper. In this work, we investigate PEC inspection on defect with different depths, through the transient magnetic flux density profile from eddy current and defect interaction in a stainless steel sample. The investigation is implemented via time-stepping finite element method (FEM) modelled in 3D using COMSOL. The estimation of defect depth was made possible by the peak amplitude feature of the differential magnetic flux density profile acquired by the PEC coil. The underlying phenomena of the acquired results is then observed and discussed through the visualisation of the resultant eddy current density for different defect depths obtained from the simulation. The simulation results indicate the potential of detection and quantitative evaluation of defect using the PEC technique. It is expected the investigation will help in the future work of PEC in terms of sensor development and inversion models for defect characterisation.

An Inverted Pendulum (IP) is one of pendulum-cart laboratory setup consisted of a pole mounted on a cart. The system encompasses complex control problems and greatly challenges the researchers in designing a better and optimal controller. This paper aims to model the IP and to design a PID control that using Quadratic Optimal Tuning (QOT) scheme for stabilization and position control of a single link IP. Firstly, a nonlinear model of IP is derived using Newtonian method. Then an augment model that include PID scheme is formulated. Lastly, the gains of PID scheme is tuned using QOT. Matlab-Simulink environment is utilized to simulate the balancing and position control performance of IP. The results show that the stability of the system had been confirmed and the system had a comparable performance with the one which controlled by LQR controller.

This paper proposes an efficient PID controller for control of a double-pendulum crane system. Two different fitness functions of a particle swarm optimization (PSO) algorithm are used for the purpose of designing a controller. An accurate positioning with minimum hook and payload oscillations are tested with or without considering the parameters of the payload into the fitness function based on the horizontal distance sways of the crane. To test the effectiveness of the both approaches, extensive simulations are carried out under various crane operating conditions involving different payload masses. Their performances are examined based on the trolley positioning response and hook and payload oscillations reductions. Reductions of mean squared error (MSE) in the oscillations with a better trolley positioning response is obtained. It is envisaged that the appropriate fitness function can be very useful for determining satisfactory responses for double-pendulum crane system.

Principal Component Analysis (PCA) is a popular method used for reduction of large scale data sets in hydrological applications. Typically, PCA scores are applied to hierarchical cluster analysis to redefine region. However, the choice of cumulative percentage of variance for PCA scores and identifying the best number of clusters can be difficult. In this paper, we investigate the effect of determining the number of clusters by comparing (i) standardized and unstandardized PCA scores on different cumulative percentages of variance (ii) to determine number of clusters using Calinski and Harabasz Index. We have found that Calinski and Harabasz Index is most appropriate to determine the best number of clusters and that standardized PCA scores within the range of 65% to 70% cumulative percentage of variance give the most reasonable number of clusters.

This paper first shows how to model an odd-order variable filter (OOVF) such that its stability is absolutely guaranteed under any variable circumstances, and then conducts computer simulations on the optimal design of an odd-order variable bandpass filter by employing the stable mathematical model. Finally, computer simulation results are provided to show the design performance as well as the guaranteed stability. The primary objective of this paper is to demonstrate that the presented mathematical model is useful in the computer design simulations that will never violate the stability conditions. As a result, numerical computer simulations can be conducted without concerning the stability issue. That is, the computer simulations utilizing the stabilized mathematical model will absolutely produce a stable OOVF.

The individual behavior of drivers has a significant influence on the characteristics of vehicular transportation systems such as safety, capacity or traffic flow. Apparently, considering such behaviors in the scope of microscopic traffic simulations is inevitable in order to accomplish simulations close to reality. In recent years, considerable efforts have been put into modeling longitudinal and lateral movements of vehicles or their lane-change behavior, respectively. However, sometimes it is necessary to deviate from the standard behavior prescribed by these models in order to study the effects of exceptional situations in road traffic such as sudden braking maneuvers. This paper addresses this specific use case by introducing a generic behavior injection model, allowing for the integration of predefined driver behaviors into microscopic traffic simulations. Furthermore, it enables the reconstruction of real traffic scenarios by incorporating data gathered from vehicular measurement campaigns. The result is a simple, yet flexible model applicable to a wide range of microscopic traffic simulators.

The purpose of this paper is to improve the efficiency and the level of the crew fire-fighting training and save training costs. The whole framework of fire-fighting training system is designed. Fixed water fire extinguishment system, fixed carbon dioxide extinguishment system, and fire-fighting garment model are developed via the three dimension modeling technology. The action of the virtual character is simulated with the method of Inverse Kinematics (IK) applied, which reflects the imagination of the system. The nebulization of the fire and carbon dioxide is realized with utilizing particle system, enhancing the immersion of the system. The collision detection technology improving the interaction of the system is made use of to determine whether an interaction exists between virtual character and equipment, equipment and equipment, carbon dioxide and fire. The prompt information in the process of training can be provided to assist a trainee to accomplish the training. According to the experimental results, the simulation system has a favorable training effect which can be applied to ship fire-fighting training.

This paper reports the dynamic simulation model of high content CO₂ from natural gas at elevated pressure. The common process of CO₂ modelling are mostly reported in steady state condition at atmospheric pressure. However, disturbances such as startup, shut down, and temperature rise might occur during the absorption process. Therefore, the dynamic study has been conducted in this paper via equilibrium approach with some adjustments to observe the efficiency of CO₂ removal at the top of the column. Input data for the simulation had been acquired from the pilot plant in Universiti Teknologi PETRONAS (UTP). Aspen Dynamic simulator is not able to support the rate based approach and therefore, several adjustments such as the number of stages and Murphree efficiency need to be imposed on the equilibrium stage model to produce similar result as the pilot plant and as well as rate based approach. The error percentage of CO₂ removal observed between actual plant and simulation using equilibrium based approach is less than 5% with several adjustment implemented in the simulator. The results show that the equilibrium approach with some adjustments is able to replicate the pilot plant under dynamic conditions. In dynamic study, the lean solvent flowrate is varied to study the performance of CO₂ removal and it is observed the higher solvent lean solvent flowrate improves the efficiency of CO₂ removal.

Combat Systems Effectiveness Simulation (CESS) is an important supportive means to combat systems analysis and conceptual design. Traditional approaches in developing CESS systems fall into two general categories. One is to apply generic simulation formalisms and platforms to build simulation applications each specific to a set of concrete application requirements. The other is to focus on a certain combat system domain for which a dedicated simulation system is developed. When confronted with non-functional issues like model reusability, simulation composability, and system evolvability, both find their limitations. Based on years experiences in CESS field and best practices found in overseas CESS systems, the model architecture is believed to be the key to develop CESS systems. In this paper, a model architecture-based generic CESS system, named WESS, is introduced. The design rationale, software architecture, application processes, and key aspects of WESS are briefed. A typical case study is given to demonstrate the functionalities of WESS. Practical applications tell WESS is able to help modelers to achieve those aims important to CESS including reusability, composability, and evolvability.

As the volume of accessed information on the World Wide Web is enormous, there might be various web environments of terrorist groups that might comprise various types of information like images, voice, texts which might be a danger for entire web costumers. Thus, a superior technique to detect wicked and non-wicked information is necessitated. This research study provides web sites’ users a solution to prevent them from terrorist threats via developing an intelligent system to recognize the useful contents. The main aim of this study is to understand the behavior of the system, and determine the best solution for securing the susceptible users, state and society. The Naïve Bayes approach (NB) and K-Nearest Neighbor (K-NN) algorithms are investigated on various Kurdish-Sorani data sets as an alternative for replacing traditional approaches. In regards to precision, the Naïve Bayes algorithm demonstrates promising outcomes. The results of this paper will show that the Naïve Bays technique generates greater Kappa Statistics and excellent precision compared with K-Nearest Neighbors.

This paper proposes a method of designing composite nonlinear feedback (CNF) control for under actuated systems. By biasing the output error feedback of the nonlinear part of CNF, a state that is not the reference state but also important, can be given attention not only in the linear part but in the nonlinear part of CNF as well. The proposed scheme is tested on two wheeled inverted pendulum (TWIP) mobile robot which is highly under actuated robot, and shows a better performance in balancing the robot and also in energy consumption.

Three-dimensional sound effect is a relatively weak link in the navigation simulator. The location information, distance attenuation, Doppler Effect and special environment effect cannot be reflected by the sound system at this stage. In this article, a three-dimensional sound effect system is developed based on OpenAL to solve these problems. All kinds of sound sources in simulator are reasonably categorized. Methods are proposed to handle considerable number of sound sources and synchronization problem. The PIMPL mode is applied for completely decoupling the sound module from the simulator main program. The test program of the system is developed under Microsoft Foundation Classes and the test procedure is performed in a real simulator. It can be confirmed that the established system conducted a good simulation of the various sound effects in the navigation environment with a high real-time performance.

In view of the present restriction in complex boundary interaction in real time fluid simulation. A physically based real-time fluid simulation method with complex boundary is proposed and implemented. The pressure projection algorithm is used to solve the N-S equation. The implicit scheme is employed to guarantee the stability of the nonlinear advection term. This paper presents and implements the idea of slicing and voxelization of the solid boundary in a unified way. The algorithm is independent of the specific model, which can simulate the fluid interaction with complex boundary flow field of different 3D models efficiently.

This paper reports a steady state model for CO₂ removal using MEA solvent that operates at elevated pressure and the behaviour that affects the performance of CO₂ absorption process. All the input for the simulation has been acquired from the experimental work using pilot plant which is located at Universiti Teknologi PETRONAS (UTP). Steady state simulation has been demonstrated using Aspen Plus utilizing both equilibrium and rate based approaches. Modifications for the equilibrium based method has been done to ensure similarity between rate based and equilibrium based simulation. Since Aspen Dynamic does not support rate based model, adjustment made to the equilibrium model will enable the model to be used for future studies which involves dynamic and control study. The most relevant input parameters of the equilibrium model are methodically varied and the influence of that variation on the simulation results based on CO₂ removal percentage was monitored. The evaluation has been conducted to observe the percentage of CO₂ removal by setting the Murphree efficiency and varying number of stages of absorber unit.

We present adhesion boundary conditions for smoothed particle hydrodynamics (SPH) with implicit surfaces. An existing method called ghost SPH addresses adhesion boundary conditions and produces plausible liquid animations using ghost particles. The generation of ghost particles, however, takes considerable computation time when it is implemented on graphics processing units (GPUs). The purpose of this paper is to accelerate ghost SPH using GPUs. In order to accelerate the processing of adhesion boundary conditions, we propose a new boundary model that can skip the ghost particle generation process in air and solid objects. The proposed technique is not just efficient but also inherits other advantages of implicit surfaces such as smoothness. Our test results show that the proposed method efficiently produces natural fluid adhesion motion without air or solid particles and achieves more than a hundredfold speed up compared to ghost SPH.

Work integrated learning (WIL) is an innovative teaching pedagogy integrating industrial practical experience with academic learning experience. This paper presents an introduction of OpenStudio® as tool for modelling and simulating building energy. OpenStudio® is an open source building energy software for conducting thermal and energy balance simulation on buildings, with inclusion of weather effects, wind speeds and directions. Hence, a good approximate model on the interactions of the actual environment can be modelled. An industrial collaborative educational case study is presented in this work for the learners’ WIL experience. Preliminary results showed promising use of OpenStudio® for modelling and simulating building energy performance.

In cloud computing, it is desirable for an infrastructure as a service (IaaS) provider to gain more profit by executing more tasks with hybrid clouds scheduling strategy. Most existing methods suggest IaaS provider to execute tasks within its limit processing capacity. This means the excessive tasks are abandoned and revenue is lost. In this paper, the low cost idle instances in public cloud are bided and rented to execute these tasks. Meanwhile, the bidding process is modeled as a VCG auction which can guarantee social welfare maximization. Simulation experiments are carried out with Google task data of 370 min. The profit of our proposed method is compared with the method rejecting excessive tasks and the approach scheduling excessive tasks to on-demand instances, and it shows that our method that using bided instances averagely increase the profit 69.39% and 33.96% respectively.

Existing wireless networks aim to provide communication service between vehicle by enabling the vehicular networks to support wide range applications for enhancing the efficiency of road transportation. As the vehicle moves between the different cell with higher speed than the regular mobile node, a handover process is needed to change its point of attachment to the predicted next cell. When a vehicle moves, the path loss and shadow fading contribute to the large scale variation of reference symbols received quality (RSRQ), especially in an urban area where small cells are located. Since traditional handover decision based on RSRQ induce the ping-pong effect, it is a pressing need to develop an intelligent approach to predict the handover decision process, thus yielding seamless handovers. This paper proposes a Vehicular Location Prediction Handover Algorithm (VLPHA) approach to predict the handover decision and utilize the optimization method by using optimal forwarding probability. The vehicle location and target cell RSRQ are considered as inputs to the handover algorithm to predict the handover decision, hence switching to the best preferable access point. The VLPHA approach has implemented in NS-3 to find the best optimal forwarding probability value. The result shows that the proposed method able to reduce the number of unnecessary handovers as well as ping-pong effect from 35% to 0%.

The fundamental significance of Big Data is in the possibility to enhance effectiveness and advancement for employees with regards to utilize a big volume of data, of various type. If Big Data is defined clearly and strongly characterized, banks can improve in their business, thence prompting to efficiency in various fields. The aim of this study is to identify what are factors that effect on Big Data Analytics skills and further propose a long-term development, self-efficacy and level of analytics as framework for a successful career in Big Data analytics in banking sector. This is because banking sector is one of the most services sector which are having big flow of data. Using quantitative approach to emphasize objective measurements and the statistical, numerical analysis survey in this research, 161 bankers were randomly selected from ten banks in Malaysia. The result of the study revealed that two independent variables significantly affect the successful of Big Data in banking sector which were long-term development and self-efficacy. On the other hand, the third variable (level of analytics) has fairly affect the success of Big Data through the skills indirectly.

Facial expression plays a fundamental role in conveying emotions to a 3D virtual character. This paper presents an automatic and effective approach for 3D face segmentation, feature extraction, rigging and animation. The 3D model we used is a triangular mesh model in OBJ file format. Combining the position information and neighbor coordinates of the points, the facial segmentation and feature extraction can be finished based on Gaussian curvature, shape indexes and face geometry. Furthermore, face rigging mainly includes two sub-models: skeleton and muscle model. Our rigging method is general that people can define their own rig and then quickly apply it to different model. Finally, the rigging model can be driven by emotion data in BVH file format. Also, to validate our approach, we have achieved in running experimentations on 3D facial in Unity3D engine. The result illustrates that our approach is usable and effective.

The behavior modeling is an important part of CGF (Computer Generated Forces) and the helicopter CGF is an indispensable character in the simulation battlefield. In this article, a hierarchical behavior modeling method is raised for the helicopter force. Eleven kinds of atomic behavior models are established on the foundation of the interfaces of our helicopter physical model. Based on the atomic behavior, three types of doctrines for the helicopter force are implemented with the finite state machine. In the case study, the behavior model is proved to be effective with our test scenario and is capable to support the development of a helicopter training system.

This paper addresses transmission delays problem in network control systems. Network-induced delay is an inherent constraint in NCS implementation that could lead to system degradation and destabilization. A particle swarm optimization (PSO) tuning algorithm was adopted to optimally tune the parameters of Generalized Predictive Controller (GPC) to solve networked-induced delay problem. Furthermore, a modified PSO-GPC was designed by replacing the standard GPC objective function with an Integral Time Squared Error (ITSE) performance index in the GPC controller design. A particle swarm optimization based PI controller in the Smith predictor structure is designed to compare the performances of the original PSO-GPC and the modified PSO-GPC. The results show that the modified PSO-GPC performed better than the PSO-GPC in terms of transient response and enhanced NCS performance in the occurrence of network delays.

The demand for rapid system and product innovations pushes the need for the availability of a wide spectrum of computational models, simulations and data (M&S). Efficient and credible M&S applications require model modularity, flexibility, scalability, and reusability, large and diverse model development teams, and above all M&S management tools. Such tools should facilitate and automate not only the coordination of those teams but also the easy, reliable and traceable reuse of model components, in particular regarding model repository search functions and developer team guidance, with emphasis on quality assurance and comprehensive lifecycle documentation. After justifying the needs for availability of a collaborative platform combining all team and M&S management tasks as well as for documenting every phase of the lifecycle of a model in a standardized manner, a generic conceptual architecture of a Model Management Architecture (MMS) meeting these requirements is introduced, along with a demonstrator compatible with current institutional quality assurance approaches for modeling and simulation, such as verification, validation and accreditation (VV&A).

This paper deals with the relationship between pre-learning stress, long term memory, and EEG signals in the brain. Studying the effect of stress is very important especially in academic life for the students. Nowadays; there have been many recent methods evaluating the relationship between stress, learning and memory performance based on different techniques. The most common methods are conducted based on the biological response. Some of these methods have assessed the impact of stress based on biochemical effects by measuring specific hormones such as cortisol, adrenalin and glucocorticoids, or based on physiological effects such as blood pressure, heart rate, skin temperature. However, in all these methods, there are inconsistent findings due to the instability of hormones and a large number of related factors. The aim of this research is to discover the impact of pre-learning stress on long-term memory retrieval using EEG signals. The results indicate that there is a relationship between theta rhythm in the temporal lobe and long-term memory retrieval.

Existing reports of gas transport properties within polymeric membrane as a direct consequence of operating temperature are in a small number and have arrived in diverging conclusion. The scarcity has been associated to challenges in fabricating defect free membranes and empirical investigations of gas permeation performance at the laboratory scale that are often time consuming and costly. Molecular simulation has been proposed as a feasible alternative of experimentally studied materials to provide insights into gas transport characteristic. Hence, a sequence of molecular modelling procedures has been proposed to simulate polymeric membranes at varying operating temperatures in order to elucidate its effect to gas transport behaviour. The simulation model has been validated with experimental data through satisfactory agreement. Solubility has shown a decrement in value when increased in temperature (an average factor of 1.78), while the opposite has been observed for gas diffusivity (an average factor of 1.32) when the temperature is increased from 298.15 K to 323.15 K. In addition, it is found that permeability decreases by 1.36 times as the temperature is increased.

The C matrix in Sliding Mode Control (SMC) is significant to the control performance in MacPherson active suspension system. The SMC was combined with Composite Nonlinear Feedback (CNF) controller due to its characteristics on the transient response and fast settling time. The Neural Network is used to determine the matrix of C based on the road profiles used in this research work. The Proportional Integral (PI) was combined with SMC to overcome the uncertainties, unmatched condition and steady state error occurred in the MacPherson active suspension system. The three road profiles have been applied to this research work. The multi-body dynamics system software called CarSim is used for validation. The numerical experiment results are shown the effect of the C matrix in SMC with CNF controller performance in acceleration of sprung mass.

Membrane bioreactor (MBR) is one of the advanced and new efficient reliable technology that replace the conventional activated sludge process in wastewater treatment plant. Therefore, understanding of dynamic behaviour of membrane filtration process is crucial to ensure good estimation of the filtration process. This paper presents the support vector machines (SVM) and artificial neural network to model and predict the membrane fouling. The predicted models are validated using an experimental data from a pilot scale palm oil mill effluent MBR located at Process Control Laboratory, Universiti Teknologi Malaysia. Simulation results showed that SVM able to produce good prediction as neural network model.

A good layout can streamline transportation within the factory, it contributes to lower cost and delivery time. This research is based on the case in a food company in Indonesia. This company produces snack. The characteristic of this production process is made to stock system with 24-working hours. From the observation of the production process, it indicates that layout planning is ineffective. This showed on long distance to move design items, a high number of worker and low throughput as well. The aim of this research is to re-layout in order to improve throughput and also reduce the number of workers and the distance. A conceptual model was developed to determine factors and responses of the system. Three scenario layouts were developed by using MULTIPLE methods. These scenario layouts were then translated and analyzed into operational models using the ProModel 6.0 Simulation Software. The results indicate an improvement of throughput by 15% for scenario 1, 28% for scenario 2, 21% for scenario 3. And for the number of workers reduce 13% for scenario 1, 2, and 3. For the over distance reduce 83% for scenario 1, 87% for scenario 2, 86% for scenario 3. Generally, scenario 2 give the largest improvement than other although need more expensive cost investment.

Classification of bandwidth-heavy Internet traffic is important for network administrators to throttle network of heavy-bandwidth applications traffic. Statistical methods have been previously proposed as promising method to identify Internet traffic based on packet statistical features. The selection of statistical features still plays an important role for accurate and timely classification. In this work, we propose an approach based on feature selection methods and analytic methods (scatter, one-way analysis of variance) in order to provide optimal features for on-line P2P traffic detection. Feature selection algorithms and machine learning algorithms were implemented using WEKA tool for available traces from University of Brescia, University of Aalborg and University of Cambridge. Experimental results show that the proposed method is able to achieve up to 99.5% accuracy with just six on-line statistical features. These results perform better than other existing approaches in term of accuracy and the number of features.

Krylov subspace methods, such as the Conjugate Gradient (CG) and Conjugate Residual (CR) methods, are treated for efficiently solving a linear system of equations with symmetric matrices. AZMJ variant of Orthomin(2) (abbreviated as AZMJ) [1] has recently been proposed for solving the linear equations. In this paper, an alternative AZMJ variant is redesigned, i.e., an alternative minimum residual method for symmetric matrices is proposed by using the coupled two-term recurrences formulated by Rutishauser. The recurrence coefficients are determined by imposing the A-orthogonality on the residuals as well as CR. Our proposed variant is referred to as MrR. It is mathematically equivalent to CR and AZMJ, but the implementations are different; the recurrence formulae contain alternative expressions for the auxiliary vectors and the recurrence coefficients. Through numerical experiments on the linear equations with real symmetric matrices, it is demonstrated that the residual norms of MrR converge faster than those of CG and AZMJ.

The identification and the quantification of human skill is one of the major characteristics to be considered in designing an algorithm for Human Adaptive Mechatronics (HAM) application. This paper focuses on studying the Racer software, as well as the relationship of data gained and the simulated environment. The fact that Racer is chosen as the tool is described. This paper discusses the details about the software used; Racer as the driving simulator environment during the experiment. The experimental setup, data extraction process and data conversion are explained further in this paper. The experimental results meet the purpose of data collection which provides variety set of data, including many options for cars and tracks. As a conclusion, Racer is a suitable software to be used. The utmost important, the further study of this research will help during the development of car assistance system.

Optimizing parallel discrete event simulation (PDES) on multi-core environments can bring great performance improvement and has become a research hotspot so far. Most of the optimization methods accelerate the simulators by reducing the cost of communication and synchronization with the advantages of shared memory for multi cores. However, both optimistic and conservative simulation algorithms can only support processing events of different entities in parallel, the parallelism of events belonging to one entity is ignored. Focusing on this demand, a deep parallel simulation approach based on conservative simulation algorithm is proposed to explore the parallelism of events belonging to one entity. Besides, a greedy aggregation algorithm is also designed to deal with load balancing problem by reorganizing events into blocks with similar sizes. Phold results show that the parallel simulation approach proposed in this paper gains 15% performance increase comparing to the approach without considering the parallelism of one entity.

The objective of this study is to develop a reliable finite element model to simulate the mixed-mode II+III delamination behavior using six-point bending plate (6PBP) test. Two different cases were studied, which were 6PBP specimens with 60% (6PBP(60)) and 85% (6PBP(85)) of mode III component, respectively. The delamination behavior was simulated using cohesive zone modeling. Results showed good fits between the experimental and numerical force-displacement curves for both cases. In addition, it was found that there were three and one cohesive elements in the fracture process zone of 6PBP(60) and 6PBP(85) models, respectively. Furthermore, for both cases, the first damaged node in both cases was highly mode III dominated. Not only that, the mode III and mode II components in the first two damaged nodes were different. The numerical results from this study signified that the mode ratio of the 6PBP specimens was not constant.

Demand Forecasting is a key to effective inventory management. In forecasting fields, intermittent demand forecasting remains to be a very important but challenging problem. Intermittent demand is characterized by many empty demands, stochastic periods between them, and high variance of non-zero values. These characteristics make intermittent demand forecasting a difficult task, for both parametric and non-parametric approaches. The parametric methods have shown many limitations to provide accurate information. Though non-parametric methods provide better information for decision making than parametric case, they cannot forecast any exact information of point values. This paper proposes a new simulation framework that takes into consideration the correlation structure between demand of assembly and demand of parts, leading to more precise information of point values. In particular, we demonstrate how sub-parts for classification can affect to prediction performance of the overall model via an experiment using artificial data.

It is of critical importance in many physics fields to compare results both from simulation and experiments. In order to make this comparison efficient, visualization of experimental and simulation data is rather essential. In this paper, we focus on the colliding plasma plumes using laser produced plasmas to understand the basic physics in a nuclear laser fusion reactor. To visually compare dynamical data acquired through experiments and simulations, we propose a visualization method to highlight overlapping space-time regions of XYT-space volumes. The visualization of the XYT-space volumes is conducted based on particle-based techniques. First, input data for an experiment and a simulation are both converted into the XYT-space volumes and then further converted into particle data. Second, we perform the particle-based volume fusion. Third, we conduct the particle-based evaluation of the degrees of overlapping and highlight highly overlapping regions using colors with large whiteness. We demonstrate the effectiveness of our method by applying it to the plasma plume collision of tungsten. Our method has visually proven that the univalent ionized plasma is the most dominant plasma in the experiment.

Data visualization describes any effort to help people understand the raw data by changing it into an interactive way of data presentation. In this study, researchers applied the data visualization concept to identify the trends or patterns of the human capital and Halal training in the Halal industry. The visualization tool used in this study is known as Tableau, which is a good visualization tool with a variety of interactive graphs. The human capital dataset used in this study was gathered from Jobstreet portal. This study has analyzed the growth of human capital in the Halal industry and the relationship between the human capital and Halal training in Malaysia. The graph analysis shown in this study will enable people such as halal training providers and job seekers to make a wise decision.

The application of the simulation model in examining a road section’s potential capacity. The varying degree to overtake provision and diverse traffic features on capacity and speed/flow relationships effects were evaluated. The application of the model was to evaluate the potential effects of different number of junctions and different turning volumes level of on the traffic operation effectiveness. Generally, the model applied shows the need for more experiential studies as the simulation outcomes proposed that higher capacity is achievable.

The needs to develop the traffic simulation model for evaluating the effects of various road layout and road traffic characteristics on traffic operations are required as traffic simulation models are becoming an attractive option in evaluating traffic matters. The present traffic operations simulation models on single carriageway roads similarly suffering from a comparable fault. Consequently, it is necessary for a complete traffic simulation model to be developed to accomplish this task. It is crucial for re-development of the procedure to be incorporated with the Malaysian traffic conditions and characteristics for precise valuation and investigation. Such a model must have the capability to simulate traffic behavior for a variety of geometry and road layout, evaluate the effect of turning vehicles at unsignalised intersections and compositions of traffic which also include motorcycles on single carriageway roads.

Cloud computing is a fastly developing technology, which will be a ubiquitous service in coming days. Cloud has additionally focalized numerous apparently unique components, for example, storage, compute, and so forth into a unified infrastructure. OpenStack is one of the eminent cloud computing programming in the cloud group. It is conveyed as Infrastructure as a Service, which implies and permits the clients to provision their own machines in cloud by utilizing its components, similar to computation, storage, and so on. Keeping in mind about the end goal to give such services, whereas OpenStack needs to verify its clients. The component in OpenStack that plays out this capacity is called Keystone. In Keystone, the present component has to give a token to the requesting clients, which is then given to different services from where the clients ask for particular services (e.g. storage, compute and so forth). In this paper, ECC-Kerberos based authentication model is examined and formulated for OpenStack. The key distribution of this examination is to increase the comprehension of the possibility of Kerberos in OpenStack with the end goal of authentication. A noteworthy advantage is that the authentication model in OpenStack can then be founded as an outstanding and very much high in standard. This proposed authentication model is implemented. The evaluation and demonstration of this implementation is also presented.

The use of scientific visualization technique in real-time rendering environment has great potential to enhance user interaction in visualizing blood flow; therefore the integration algorithm of chosen scientific visualization technique and real-time algorithm should be developed and implemented in virtual environment. It is a basic fact that the effectiveness of blood flow visualization are reliant on two fundamental issues. First is how to present an improved visualization of cardiovascular flow, and the second is how to interactively visualize the fluidic blood flow in gaining insight into the cardiovascular physiology. This research proposed a framework on based on idea from previous research that improve visualization of blood flow by integrates with particle based simulation method in order to allow more user interaction. Despite the variety and number of existing method, there are still demands for new improved visualization technique with a mission to provide better information. Good understanding on blood flow pattern will aid clinician, engineer and researcher during the diagnosis and prognosis of pathology as well as the assessment of risk and follow-up finding.

Debugging is essential in parallel discrete event simulation (PDES) application development, directly determining the correctness of simulation application and the development efficiency. However, programmers prefer to add some debugging code into application code to speed up debugging. Besides, lots of debugging data generated from simulation execution need to be analyzed manually, resulting in debugging time-consuming and laborious. This paper proposes a debugging framework for PDES application named SUPE-Debug, which is built on the PDES engine SUPE. It provides automatic debugging code generation and debugging data visualization analysis function. Experimental results show that SUPE-Debug can automatically insert debugging code into the existing application code and provide a more intuitional way to display the debugging data for programmers to analyze. It is helpful for searching and locating errors, accelerating debugging progress and improving application development efficiency.

There are two major strategies to schedule real-time tasks in multiprocessor systems; partitioning and global scheduling. The partitioning approach has acceptable overhead but cannot guarantee to be optimal. The global approach can provide this guarantee but it has considerable overhead. Thus, a multiprocessor real-time scheduling approach for weakly hard real-time tasks is proposed that employs hybrid scheduling. Studies have shown that current multiprocessor scheduling of weakly hard real-time tasks used imprecise computation model based on iterative algorithms. This algorithm decomposed into two parts; mandatory and optional, unfortunately, the result analysis is precise only if its mandatory and optional parts are both executed. Even, the use of hierarchical scheduling algorithm, such as two-level scheduling under PFair algorithm may cause high overhead due to frequent preemptions and migrations. In this paper, an alternative scheduling approach will be proposed, which is, its combines elements of the two well-known multiprocessor scheduling approaches. It aims to employs benefits and advantages of the partitioning and global scheduling. Accordingly, the proposed hybrid multiprocessor real-time scheduling is use the best algorithm of each of partitioning and global approaches, R-BOUND-MP-NFRNS and RM-US (m/3m−2) with multiprocessor response time test. Schedulability experiments and simulation results using Matlab show the proposed hybrid multiprocessor scheduling approach to be effective for weakly hard real-time tasks.

This paper presents the impact of dielectric substrates with different relative permittivity ‎used in designing a square ring patch antenna. Choice of suitable substrate material is essential while configuring the square ring microstrip patch antenna, since ‎dielectric constant of a substrate is a basic parameter over regulating return loss, gain, ‎bandwidth, voltage standing wave ratio (VSWR), and effectiveness of radiation ‎pattern. Two fundamental properties of substrate material is considered in this research i.e. dietetic ‎constant and loss tangent. Therefore, increasing the permittivity of substrate ‎material permits compact size of antenna, but return loss, gain and directivity of ‎antenna diminishing with admiration to the increment of dielectric substrate. The ‎proposed geometric antennas have been designed utilising Computer Simulated ‎Technology (CST) Microwave and MATLAB as a tool for the application, the antenna is ‎acknowledged utilising a square ring patch simulated with different dielectric ‎constant and contains radiating patch and ground plane. It has been considered ‎several of dielectric material whose permittivity ranges from 1.96 to 6.15 relying ‎on demand of GSM at desired frequency 1.8 GHz.

Many researchers have proposed notches and strips for band ‎enhancement or ‎interference reduction of ultra‎–‎wideband (UWB) antenna. Previous studies ‎illustrated the ‎advantage of using slot technologies and strips to change the ‎antenna impedance matching. ‎This study presents a comprehensive overview of ‎the performances of reconfiguration techniques on UWB ‎antennas based on filtering ‎and matching band. Several studies are considered, and ‎the performance of ‎antennas designs that applied slot, strips, parasitic ‎stubs, and spiral loop ‎resonators are compared. Unwanted bands, ‎such as WLAN and WiMAX ‎systems, are interfered with UWB communications. ‎The notches are used for ‎either band rejection or bandwidth enhancement. ‎Moreover, a hexagonal UWB ‎patch antenna with the coplanar waveguide is demonstrated in this paper. The ‎improvement of the UWB antenna bandwidth is realised by incising the patch ‎size. Loading two L-shape strips on the radiation patch achieved band rejection at 5.15 GHz. The simulated parametric study represents different results achieved with ‎different side–‎patch lengths. The best achievements of the proposed design are the ‎UWB bandwidth from 2.8 GHz to 11.5 GHz, and the radiation gain of 4 dB at ‎‎5.8 GHz. Good omnidirectional radiation patterns observe on E-plane by ‎bidirectional patterns provide on H-plane. Finally, the performance and ‎implementing the studied UWB antennas are discussed in details.‎

Because the synthetic natural environment data has many characteristics of multi-source, heterogeneous and massive, the data cube technology which can handle multi-dimensional data is applied to the synthetic natural environment data management system. While the traditional data cube technology has the problem of low construction and query efficiency. This paper presents a data cube based on XML B+ tree structure. The data cube leads into XML data files. By dividing the source data dimensionively, the B+ tree is constructed layer by layer, and binary coding is established for each hierarchy node to preserve the association and inheritance relationship between the dimensions. At the same time, the construction and query algorithm is given.

In wireless Network-on-Chip (WiNoC), radio frequency (RF) transceivers account for a significant power consumption, particularly its transmitter, out of its total communication energy. In current WiNoC architectures, high transmission power consumption with constant maximum power suffers from significant energy and load imbalance among RF modules which leads to hotspot formation, thus affecting the reliability requirement of the network system. This paper proposes an energy-aware adaptive packet relocator mechanism, in which, based on transmission energy consumption and predefined energy threshold, packets are routed to adjacent transmitter for communication with receiver radio hub, aiming an optimized energy distribution in WiNoC. The proposed strategy alone achieves total communication energy savings of about $$8\%$$.

Sustainability is currently becoming a trend in technologies such as telecommunications. The goal is to reduce power consumption and increase the battery life of mobile nodes. The highest power consumer in the telecommunication module, the power amplifiers were designed using inefficient configurations as the older emphasize is only to achieve highest linearity possible at the output. This work proposes designs for Class E power amplifiers operating at 2.4 GHz based on nonlinear SPICE FET models using two different Sokal’s empirical equations. One emphasizes considerations of output power with quality factor, the other does not. Clear differences can be observed through simulation results as the newer Sokal’s equation proved to be more efficient. This analytical solution can be used to design future power amplifiers where efficiency is the prime concern.

To improve effluent quality of a wastewater treatment plant (WWTP), an optimized model order reduction (MOR) for the high order WWTP system is proposed. A high order model may lead to inefficient analysis of the system and can be computationally expensive. Hence, an accurate and suitable reduced order model needs to be obtained. In this research, an optimized MOR algorithm is proposed by the combination of Frequency Domain Gramian based Model Reduction (FDIG) and Singular Perturbation Approximation (SPA). To reduce the high order model to lower order model with minimum reduction error, optimization techniques of Firefly Algorithm (FFA) and Gravitational Search Algorithm (GSA) is applied. To show the effectiveness of the proposed technique, a case study on WWTP is utilized. From the results obtained, the optimized reduced order models obtained is a 9th order system which yield the lowest reduction error while preserving the stability of the original system.

Logistics operators face the dilemma about how to increase the efficiency of logistics networks in high demand volatility environments, reaching the best possible cost, especially in distribution centers in the supply chain which significantly absorbs capital of the organizations. This work is based on a Colombian logistic operator. The article approaches the contrast of scenarios for the development of infrastructures at the level of heights and lease contract time of the warehouse from a dynamic perspective using systems dynamics modelling, as a support tool for financial and contractual decisions as well as for the allocation of resources as collaborators and logistics equipment which, by integrating market variability, provides a strategic vision in different planning horizons for the evaluation of performance measures, depending on the use of resources, demand served, lower operating costs and growth in financial results.