Modeling, Design and Simulation of Systems

Image segmentation is the most important steps in image processing process, especially in detecting and segmenting the main focus object from its background or others unwanted image. The objective of this paper is to develop a segmentation technique for pineapple fruit from crop background at the plantation level. Hue value is used to remove the ground and sky from the image. Then, Adaptive Red and Blue chromatic (ARB) is implemented to segmenting the pineapple fruit from the background such as leaves. In this case, the ARB method is still produced misclassifies error. Further segmentation uses Ellipse Hough Transform (EHT) for results enhancement, so that the fruit’s image is completely filtered from misclassify and background. The results obtained show that the proposed technique manages to identify the fruit from the background with better image output compared to conventional method.

Control schemes for nanopositioners typically combine damping and tracking. Due to the positioning performance requirements of the nanopositioning system, it is desirable for the closed-loop frequency response of the nanopositioner to mimic ripple-free pass-band low-pass characteristics. Earlier reports are available on simultaneous damping and tracking control emulating a Butterworth filter design, but this technique only incorporates a single integrator for tracking, which is inadequate for error-free tracking of the triangular and ramp-like signals typically used as input to nanopositioning systems. Double integral tracking guarantees error-free tracking, but is difficult to implement due to phase-related stability issues. In this work, a dual-loop integral tracking algorithm is proposed. Using simulation, it is shown that in the presence of hysteresis, the proposed dual-loop scheme delivers a more accurate positioning performance than the traditional single-loop integral tracking strategy.

This research focus on designing controller for nonlinear system by integration of two robust controllers, back-stepping and sliding mode controller. The dynamics of the system is developed by consider its nonlinearities incorporating external disturbance injected to the system’s actuator. The value of control and reaching law parameters are determined by using particle swarm optimization method such that these parameters are varying automatically according to the changes of the dynamics of the system. The tracking performance of the system yielded by integration of back-stepping and sliding mode controller is compared with its output performance produced by classical sliding mode controller. The results show that assimilation of back-stepping and sliding mode controller for the chosen system generates better performance than sliding mode controller itself which is evaluated in terms of tracking output and tracking error.

Building Research Establishment (BRE), United Kingdom have carried out several full-scale experiments of vehicle fire as to address the fire spread between vehicles. Thus, this paper aims to investigate the capability of the B-RISK zone modelling software to simulate the BRE multiple vehicle fire spread test. Using the information gathered from the work by BRE, series of simulations have been conducted. The results of the simulations are compared with the results from the experiments. Analysis shows that the predicted results from the B-RISK simulations give slightly faster time of ignition to the ones obtained using hand calculation. This could be due to B-RISK includes the radiation effect from the underside of the hot upper layer. As a conclusion, the analysis shows that using the B-RISK simulation software with additional radiation effects does not improve the result as compared to using the hand calculation considering the level of uncertainties which required to be assumed on some input parameters e.g. HRRPUA, heat of combustion, and/or latent heat of gasification.

Route planning is widely used in public transportation, military and other fields. However, the mainstream algorithm adaptability is not comprehensive for the terrain, especially in barrier dense areas, such as A* algorithm, ant colony algorithm and so on. In these algorithms, time-consuming may be too high and planned route may be not optimal. To solve this problem, we propose a novel route planning algorithm based on polygon fusion (RABP). The basic principle of RABP is based on the plane geometry of the shortest line between two points, so the algorithm has strong guidance. Therefore, the algorithm has the advantages of low time-consuming and short route length. At the same time, because of the complex polygon fusion, too strong guidance would not make route planning cannot be accomplished. The algorithm needs to merge the barriers to avoid them and carry on route planning. Meanwhile, it will use the simplified operator to remove the redundant points on the planning route, so the final route is smoother and the route length is shorter. The experiment result shows that the RABP algorithm is more adaptive than A* algorithm and ant colony algorithm to dense barrier areas, the planning route is shorted and consumes less time.

Pedestrian motion behaves stochastically, causing difficulties in modelling the appropriate proxemics for effective and efficient service robot navigation. Intruding the pedestrian social space can affect the social acceptance of a service robot. In this paper, a new proxemics model, Social-Force Gaussian Pedestrian Proxemics Model is presented to model the pedestrian social space and to improve the service robot navigation in dynamic human environment. This model was simulated and validated in a pedestrian simulator with both low and high pedestrian density environments. Results showed that the proposed model (i) improved proxemics representation of pedestrians, (ii) enhanced the robot performance in respecting the social norm and (iii) increased the efficiency in achieving a given task. This paper also presents the methods for parameter selections for the model without the requirement of complex tuning.

Ant colony algorithm can be used for the automatic path planning of complex terrain. However, most of the current ant colony algorithms are based on 2D terrain, without considering the influence of terrain slope on path selection. In addition, the parallelism of the algorithm is not used, which makes the algorithm time-consuming. Aiming at the above problems, this paper proposes an improved ant colony algorithm 3D-PACA. First of all, we raster the map using bilinear interpolation method and translate the 3D terrain into 2D terrain according to the given slope threshold. And then we combine OpenMP parallel programming technology to accelerate this algorithm by mining the concurrency of ant colony algorithm using the idea of parallel computing. The simulation results show that compared with the traditional ant colony algorithm, the improved algorithm can effectively adapt to the three-dimensional terrain, and can get a speedup of about 3 times.

This paper presents the development and application of hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) using cooperative technique (CPSOGSA). In this work, the algorithm is used for functions optimization problem. The CPSOGSA is developed with multiple groups using master-slave architecture. Six benchmark functions were tested to verify the effectiveness of the optimization algorithm. The developed algorithm was compared with the existing PSO, GSA and hybrid PSOGSA. The results indicate that the proposed technique produces better optimization solution compared to the conventional optimization algorithms.

The study on 5G massive MIMO (maMIMO) systems has garnered a lot of research interest. Several research works are ongoing and some of which include, the development of mathematical models, performance evaluation, algorithm development and basic prototyping work. However, the framework for a unified simulation platform and methodology has not been established. The need for a massive MIMO simulation platform that can be used for comparative study and analysis purpose of different deployment scenarios of maMIMO systems is envisaged. Hence, in this paper, a framework for massive MIMO simulation platform (MMSP) for 5G cellular systems is proposed. To achieve this, key research areas related to massive MIMO were identified from the literature and a taxonomy which consists of three non-overlapping branches: (1) System configuration, (2) System processing and (3) System analysis is advocated. Preliminary results are presented using a developed prototype from the proposed framework.

The process of C2 architecture design needs to consider the factors such as changeable battlefield environment, so the designed C2 architecture should be flexible, adaptive and so on. The theory of Enterprise Command and Control (EC2) concerns application of systems science collaborative processes of command and control. It can describe the C2 process and the entity function models of the organization, and analyze the performance of the architecture. However, in the simulation and optimization of the organizational structure is powerless. Multi-Agent Systems (MAS) has become a natural tool for modeling and simulating complex systems, these systems usually contain a great number of entities interacting among themselves, and acting at different levels of abstraction. In this context, it seems unlikely that MAS will be able to faithfully represent complex systems without multiple granularities. The holonic paradigm has proven to be an effective solution to several problems with such complex underlying organizations. The objective of this paper is to propose a preliminary framework of a holonic multi-agent model for the C2 architecture design. Which satisfies the characteristics of Holon’s self-similarity, and combines the advantages of EC2, realized the mechanism of perception, decision-making and execution of C2 architecture and entity function models from different granularity.

This paper presents the optimal formation control for a group of quadrotors based on particle swarm optimization (PSO) algorithm. This is motivated by the conventional approaches that still involve a certain degree of trial and error approach which may not give the optimal performance. The parameter optimization using PSO utilizes the linear quadrotor model obtained from feedback linearization technique. Simulations are conducted on the parameter optimization, followed by implementation of the optimal parameters for formation control of multiple quadrotors. The results show the effectiveness of the proposed technique.

A well-designed modeling framework can improve the efficiency of modeling, which is of great significance to improve the reusability, scalability and combinability of model. The current popular simulation model representation specifications, such as BOM (Base Object Model) and SMP2 (Simulation Model Portability Standards 2), provide some modeling frameworks for modeling work. However, according to these modeling frameworks, either it is difficult to support the behavior description of model, or it is difficult to map the behavior description directly to the implementation of model, reducing the model development efficiency. To solve this problem, this paper presents a component-based modeling framework with hierarchical structure. This framework classifies the components according to their function. In addition, we propose a State Chart based on combined-actions in this framework to realize the mapping from the behavior description to the implementation of model directly. Analysis shows that the framework has good reusability, decoupling, scalability and combinability, which can greatly improve the development efficiency of model.

Simulation result validation is a crucial work in simulation credibility assessment. The result validation metric is a measure of agreement between simulation output and experimental observations. Sometimes the observations maybe derive from different data sources such as the actual system output, credible hardware-in-the-loop simulation system output and the expert opinions and so on. Then the agreement analysis results of system response would be multiple certainly. To solve the simulation result validation with multi-source data, the paper proposes a result validation method based on evidence combination. First, the evidence representation methods for multi-source data on the structure of evidence space are proposed. Then the multiple evidence bodies are aggregated based on evidence combination rules and the integrated validation result could be achieved. In the end, the application process and effectiveness of this validation method is illustrated through a numerical example.

Crowd model has been very important in the investigation and study of the dynamics of a crowd. Crowd analysis using simulation models such as the Social Force Model (SFM) in representing crowd dynamics is instrumental in this study where experiments with real humans are not practical. However, the SFM lacks features that can represent more detailed individual characteristics and realistic movement when dealing with multi-room environment. Therefore, in this paper, we propose some modifications to the original SFM with a magnetic model approach that also integrates a path finding feature to produce a more realistic simulation of individuals’ movements in the crowd. Results show that common collective crowd behaviours have been produced by the modified crowd model in a multi-room environment.

Spatial cluster detection of infected areas is widely used for disease surveillance, prevention and containment. However, the commonly used cluster methods cannot resolve the conflicts between the accuracy and efficiency of detection. We present an improved method for flexibly shaped spatial scanning, which can identify Irregular spatial clusters much more accurately and efficiently. First, we convert geographic information to a graph structure. Next, we approximately locate the disease regions. And then, based on the approximately located regions, we detect arbitrarily shaped and connected clusters in the graph based on likelihood ratio. Finally, we check the significance of the identified regions by Monte Carlo method. The algorithm is tested by an agent based simulation of H1N1 influenza data in Beijing. The results show that compared with the previous spatial scan statistic algorithms, our algorithm performs better in terms of shorter time and higher accuracy.

The software agent technology is one of the human assistive technologies that enables team working. In the process of achieving the team goals, an agent may need help from its teammate to perform its remaining task’s activities in order to meet the task’s deadline. However, certain conditions are needed to be fulfilled for the task transfer even though these would be a burden to the teammate. This paper shows the use of a Workload Manager for handling signals to allow the agent getting help from its teammate. It is also used to identify the available teammate agents that can really help. In this paper also, we simulate the transfer of the remaining task’s activities from one agent to another and demonstrate the process of awarding merit points to the agent that sincerely helps its teammate.

This paper discusses the application of artificial neural networks (ANN) in terms of eye diagram modeling, where ANN models are trained to predict the eye height and eye width, which are useful information for signal integrity inspection. This paper also presents an improved version of the adaptive sampling method which is used in the data collection process. The proposed adaptive sampling manages to reduce the number of training and testing samples needed to train the neural model, thus reducing the time needed to simulate the data. In addition, the proposed adaptive sampling can generate training and testing samples more evenly across the whole design space, reducing the risk of oversampling and undersampling.

For a complex simulation system with little or no simulation data, the credibility evaluation of simulation system is mainly based on the expert experience to grade the simulation system. However, the existing evaluation methods do not take into account the uncertainty of the evaluation result. To this end, a D number-goal programming method is proposed for complex simulation systems. This method not only handles the uncertainty, but also simplifies the whole evaluation procedure. Finally, the proposed evaluation method is validated by a practical system to illustrate the effectiveness of the proposed method.

Input uncertainty always exists in most engineering problems and leads to output response uncertainty for model predictions. Several global sensitivity analysis methods are utilized to measure the importance of input aleatory uncertainty which influence output the most. However, the aleatory uncertainty often involves epistemic uncertainty in the distribution parameters due to the lack of knowledge. In this paper, an improved moment independent approach coupled with auxiliary variable method is presented to separate aleatory and epistemic terms of hybrid uncertainty. The importance measure is derived to compute the individual contributions of aleatory and epistemic parameters to model output’s uncertainty. Considering the high computation costs of moment independent method, a double loop sampling method is applied in the numerical codes to alleviate simulation. A modified Ishigami function is take for instance for demonstrating the effectiveness and rationality of proposed method and high efficiency of sampling algorithm.

Natural disasters are common nowadays and a major adverse event resulting from natural process of Earth. Most of the natural disaster are beyond control of human beings and cannot be predicted accurately when it occurs. For instance, prediction of a river water level is essential for flood mitigation in order to save people’s lives and property. However, it is very difficult to predict river water level accurately since it is influenced by many factors and the fluctuations are highly non-linear. To address this problem, a river water level predictor utilizing the Radial Basis Function Network (RBFN) is proposed in this study. The goal of this project is to design a neural prediction algorithm that can forecast river water level prediction 7 h ahead with lower error. Result shows Best Fit value of 82.43% and Root Mean Square Error (RMSE) of 1.571.

Polygon model scanner is developed to reconstruct a three-dimension image of an object’s surface. Amongst is by installing five infrared sensors in the sensor array as a medium to harvest data. All components used in the device then are installed in a closed box to avoid any source of light. Next is ellipse, hemisphere, cylinder and rectangle shapes were used to be tested in the developed device. Results from the experiment showed that the device is capable to reconstruct an image of a polygon model. As a whole, it requires 60 min to scan the whole model which covers 10 cm of the height with a diameter of 5 cm. Finally, Butterworth, Mean, Median filters and point fitting were used to determine which filters gives accurate dimension compared to the reference shape.

This paper focuses on the impact of an opened or enclosed residential community on a congested road network. The two-dimensional cellular automata-based model (CA) is used, combined with vehicle behavior such as left-turning and waiting. Also, the traffic controller is an employed model in the intersection. We divide the whole network into several basic modular structures, which could be combined to simulate typical residential communities. Based on these models, simulation of the real-world scenario is carried out. The results include the average traffic density of the network and the running time of probe cars. It is observed that, when we enclose the community during congested traffic, the density is diminished. This means a Braess paradox emerges. However, the average travel time of probe cars with a solid OD (origin and destination) pair will decrease for the reason that added roads give probe cars more choices.

The primary aim of this paper is to assess the performance of k-ε turbulence models by means of adopting various near wall treatments to simulate the flow within a sharp 90° 3-D turning diffuser. The Computational Fluid Dynamics (CFD) results were validated quantitatively and qualitatively with the experimental results (using Particle Image Velocimetry (PIV)). The standard k-ε adopted curvature correction and enhanced wall treatment of y+ ≈ 1.2–1.7 appears as the best validated model, producing minimal deviation with comparable flow structures to the experimental cases.

Based on deep reinforcement learning, an intelligent tactical decision method is proposed to solve the problem of Unmanned Combat Aerial Vehicle (UCAV) air combat decision-making. The increasing complexity of the air combat environment leads to a curse of dimensionality when using reinforcement learning to solve the air combat problem. In this paper, we employed the deep neural network as the function approximator, and combined it with Q-learning to achieve the accurate fitting of action-value function, which is a good way to reduce the curse of dimensionality brought by traditional reinforcement learning. In order to verify the validity of the algorithm, simulation of our deep Q-learning network (DQN) is carried out on the air combat platform. The simulation results show that the DQN algorithm has a good performance in both the reward and action-value utility. The proposed algorithm provides a new idea for the research of UCAV intelligent decision.

Positive Position Feedback (PPF) is a widely used control technique for damping the lightly damped resonant modes of various dynamic systems. Though PPF controller is easy to implement any rigorous mathematical optimization is not possible due to the controller structure. Therefore, almost all PPF designs reported in literature use a trial-and-error approach to push the closed-loop system poles adequately into the left-half plane to achieve adequate damping. In this paper, a full parametric study of the PPF controller based on the closed-loop DC gain vs achievable damping relationship is carried out. It is shown that the PPF controller best suited to only damp the resonance is not the best if both damping and tracking control is required (as is the case in most precision positioning systems). This leads to a more systematic and goal-oriented selection of appropriate PPF controller for specific applications, hitherto unreported in literature. Experiments performed on a piezoelectric-stack actuated nanopositioning platform are presented to support this conclusion.

Early detection of anomalies can assist to avoid major losses in term of product degradation, machines’ damages as well as human health issues. This research aims to use Artificial Neural Network to recognize anomalies in the distillation column. The pilot scale distillation column for the ethanol-water system is selected for the study. Faults are generated by variation in feed rate, feed composition and reboiler duty using Aspen Plus® dynamic simulation. The effect of these faults on process variables i.e. changes in distillate and bottom composition, distillate and bottom temperature, bottom flow rate, and the pressure drop is observed. The network is trained using back propagation algorithm to determine root mean square error (RMSE). Based on RMSE minimization, the (6-8-6) net serves as the best choice for the case studied for efficient fault detection. The presented techniques are general in nature and easily applicable to various other industrial problems.

This paper presents modeling and simulation of a wirelessly-controlled thermopneumatic zigzag micromixer. The micromixer is operated by selectively activating two passive wireless heaters with different resonant frequencies using an external magnetic field. Each heater is responsible for heating an air-heating chamber that is connected to a loading reservoir through a microdiffuser element, while the solutions pumped from each reservoir are mixed in a zigzag micromixing element that ends with an outlet hole. The performance of the micromixer is analyzed using finite element method, and mixing is investigated over a low range of Reynold’s number (Re) ⩽ 10 that is suitable various biomedical applications. The optimal activation switching time of the heaters is 10 s, at which the micromixer achieves a maximum mixing efficiency of ~96.1%, after ~65 s. The micromixer provides mixing-ratio controllability with a maximum flow rate and pressure drop of ~3.4 µL/min and ~385.22 Pa, respectively.

This paper analyzed a novel information flow topology (IFT) for vehicle convoy. The topology used two-vehicle look-ahead with an immediate rear-vehicle inclusive. Mass spring damper and Newton’s second law were utilized to provide the behavior and basics for the vehicles motion respectively. The concept of homogeneous vehicle convoy and constant headway time (CHT) policy was in cooperated for the inter-vehicular spacing. The new IFT was compared with the conventional topology of the two-vehicle look-ahead to ascertain its improvement. The novel topology provides good inter-vehicular space of 0.42 m ahead of the conventional topology. Moreover, the proposed topology obeys the rate of change of speed throughout the vehicles journey than the conventional type. Low jerk of $$ 0.44\,{\text{ms}}^{ - 3} $$ was achieved against $$ 0.47\,{\text{ms}}^{ - 3} $$ of the conventional. Finally, the new topology is visible throughout the journey than the earlier, which discontinues after 117 s in all parameters.

One of the advantages of using microchannel reactor in chemical process is enhancement of mass and energy transfer due to the high area-to-volume ratio created by the multiphase droplet. This total interfacial area can be varied by controlling the size of the droplet based on different volumetric flow ratio between continuous and disperse phase. CFD method is used to estimate the size of the droplet using VOF model. In many cases, there is always balance between simulation result accuracy and computational cost. In this study, the grid sensitivity analysis is investigated to see the result accuracy at different meshing quality. From this multiphase simulation, it was found that the simulation is highly dependent on grid size. The droplet size cases showing deviation ±5% and ±20% for highest and lowest meshing quality respectively, which the lowest meshing quality must have at least one layer of mesh refinement at the wall. By knowing this, one can have confidence on using lowest meshing quality for a lower computational cost but still at an acceptable level of result accuracy.

Tobacco endgame is hotly debated worldwide as one of the effort by the World Health Organization Framework Convention on Tobacco Control (WHO FCTC) towards tobacco-free future. Tobacco epidemic involve dynamic interrelationships between multi-disciplinary elements. Hence, it become increasingly difficult to identify the root of the problem in order to solve the issues. System dynamics (SD) has been recognized with its ability to capture the flow and feedback as well as the dynamic relationships between components in a system under study. The holistic view offered by SD will portray the impact of anti-smoking strategies implementation. In order to identify the driving forces in tobacco epidemic, Enhanced Analytic Hierarchy Process (EAHP) has advantages over normal Analytic Hierarchy Process (AHP) based on its capability to eliminate inconsistency in prioritizing the driving forces in tobacco epidemic which is the initiation factors of smoking. SD and EAHP have different strengths and weaknesses, therefore, integrating both modelling approaches will provide more realistic projections of a tobacco control system. This paper uses SD and EAHP to model the complexity of the problem and to identify the dominant linkages between the initiation factors and anti-smoking strategies in assessing the effectiveness of anti-smoking strategies that have been implemented in Malaysia. We proposed a multi-method modelling framework that served as a basis in understanding the complexity of tobacco epidemic due to the interconnections of multiple elements that may range from qualitative to quantitative aspects. Also, we describe the potential benefits of integrating SD and EAHP in demonstrating the interaction between the smoking causes (initiation factors) and its corrector (anti-smoking strategies).

The stock market prediction is a lucrative field of interest with promising profit and covered with landmines for the unprecedented. The markets are complex, non-linear and chaotic in nature which poses huge difficulties to predict the prices accurately. In this paper, a stock trading system utilizing feed-forward deep neural network (DNN) to forecast index price of Singapore stock market using the FTSE Straits Time Index (STI) in t days ahead is proposed and tested through market simulations on historical daily prices. There are 40 input nodes of DNN which are the past 10 days’ opening, closing, minimum and maximum prices and consist of 3 hidden layers with 10 neurons per layer. The training algorithm used is stochastic gradient descent with back-propagation and is accelerated with multi-core processing. A trading system is proposed which utilizes the DNN forecasting results with defined entry and exit rules to enter a trade. DNN performance is evaluated using RMSE and MAPE. The overall trading system shows promising results with a profit factor of 18.67, 70.83% profitable trades and Sharpe ratio of 5.34 based on market simulation on test data.

Traffic congestion causes increased vehicular queuing, slower speeds and delay in travel time, continuously claiming many social, economic and environmental problems. While Internet of Vehicles (IoV) advances in equipping vehicles with sensors and actuators that ‘communicates’, classifying and forecasting traffic congestion in real-time and in fast mobility is a sizzling yet challenging research interest. In hemorheology, hypertension can be classified in stages to indicate severity levels, thus a similar analogy need to be tested in traffic to classify congestion levels. This paper attempts to develop a traffic congestion and forecasting model based on hypertension in hemorheology. Traffic congestion was simulated in the city of Shah Alam’s urban area using SUMO urban vehicular mobility simulator. Results show promising and rational adaptation of hemorheology in classifying the severity levels of traffic congestion.

In this research, we propose a lighting control system for environments with multiple light sources, including a natural light source and an artificial light source, based on a self–adaptive software control system. We also propose an algorithm for optimization between control devices in a multi-lighting environment, and evaluation methods for self-adaptive software in an Internet of Things environment. Based on these proposals, a simulation is carried out.

In this paper, a polydimethylsiloxane (PDMS) material based pneumatic actuator with a dual chambered square bellows structure was designed and simulated. Using finite element analysis (FEA), the maximum output displacement, bending angle and input pressure requirements for the actuator were analyzed. The simulation analysis revealed that 9 mm2 square bellows actuator with 17.4 mm length resulted in bidirectional bending. The actuator achieved maximum bending angles of 61° at 38 kPa and 78.5° at 45 kPa under successive and discrete actuation respectively, which makes it suitable for catheter navigation system. The results presented in this work are expected to promote the application of pneumatic based actuators in biomedical application and beyond.

Oceangoing ships produce a roll motion under action of the waves. To absorb and use the energy of roll motion, it’s necessary to analyze the movement of the ship. That’s of great importance to discuss the feasibility of using the wave activated power generation and evaluate its energy conversion efficiency. In this paper, we choose the ITTC single parameter spectrum and adopt linear superposition method to establish the real-time wave model. Then we simulate the model and verify the correctness of the result according to the spectrum analysis. After that, the roll motion model and energy model of the ship are established. The simulation of the roll motion of ship support for the subsequent study of the energy absorption of the roll motion by basic data.

Free kick is a common scoring method on football court. Previous study had not yet constructed an effective system assisting players in training free kicks. On basis of studying Dynamic principle, Bernoulli principle and Magnus effect leading to occurrence of such irregular trajectories, stress analysis is conducted to establish the corresponding mathematical model. Based on that, a free-kick simulation and training system is designed to assist daily training of football players in this paper. Experiments are implemented to analyze the effect of parameters such as the kicking point, the kicking force and the kick angle on trajectory of football. Additionally, suggestions are given on how to shoot a goal successfully avoiding the walls and defense of goalkeeper through optimizing the parameters of the kicking point, the kicking force and the kick angle.

The most important factor in EEG signal processing is the determination of relevant features in encoding the meaning of the signal. Obtaining relevant features for EEG can be done using a spatial filter. The Common Spatial Pattern (CSP) is known to produce discriminative features when processing EEG signals. Yet, CSP is also sensitive to noise and is channel-dependent, as it is considered to be a spatial filter. However, the disadvantage of CSP is that channels containing only noise are also considered as active channels. In this paper, the design of a filter for spatial selection is proposed using CUR decomposition to select important channels or the time segment of EEG trials in order to improve CSP performance. CUR decomposition can also be used as a noise rejection technique because CUR can be used in factorizing the given EEG signals. In other words, CUR decomposition rejects the non-active channels, which typically contain noise, before spatially filtering the EEG signals. Once the EEG signal is decomposed based on the importance of the channels, time segmentation, and EEG factorization, the decomposed signal can be used as input to the CSP. In general, three approaches were proposed in this framework: (1) channel selection, i.e., C selection; (2) time segment selection, R; and (3) signal factorization, U. Furthermore, the performance accuracy between the original CSP and CSP in which the input was spatially filtered by the proposed framework was validated using datasets IVa of BCI competition III. The test results show that the CSP with spatial selection using C selection and U factorization offers 12% and 9% improvement compared to the original CSP, respectively. Hence, the proposed method in this study can be used as a spatial filter to improve the CSP performance.

The Permanent Magnet Linear Synchronous Motor (PMLSM) is designed in this paper to solve the drawbacks of the previous designed PMLSM. The previous designed PMLSM has magnetic flux density, B saturation at lower rated current, I. Therefore, to overcome the saturation of the magnetic flux density, the stator of the PMLSM is designed. Apart from that, the design of the stator help to increase the ratio of thrust, F to cogging force, F_cog. The design of PMLSM is completed in two stages where in the first stage the best model chosen is the model of t_y2 = 3 mm. The thrust of the model has reduced by 14% from the previous designed PMLSM. The model designed and chosen in second stage is compared with the previous designed PMLSM in terms of it performance index. The designed PMLSM has improved in terms of motor constant square density, G compared to the previous designed PMLSM with the increment of 2%.

This paper focuses on load frequency control (LFC) of a single area thermal power system. The purpose of LFC is to minimize the transient varieties due to frequency deviation by ensuring zero steady state error. The frequency deviation normally caused by load perturbation. Hence, the primary goal of this paper is to design LFC for power system stability. Single area thermal power system comprises of governor framework, non-reheat turbine model and generator with load. The closed loop system performances in term of transient and steady state are observed and analyzed by injecting multifarious load perturbation. The simulation results are obtained via simulation works using MATLAB with SIMULINK toolbox.

This paper presenting an improvement made on material handling system in automotive assembly line in order to investigate the changes or influences that affect the assembly line. Some issues from a case study arose where the current transportation took a long time to supply material at assembly line and a risk of damaging the parts is high. Thus, an improvement is done by changing the current transport equipment into AGV and the storage equipment is changed into semi-automatic pick-to-light system. A method of discrete-event simulation using Delmia Quest software is applied. Based on the simulation result, the total production output increase almost 3 folds from the current output factory can produce. It’s concluded that a combined changes give large influences to the manufacturing system. A part of that, Delmia Quest is a useful software to enable decision-making process and improve system effectively without possibility destroying the elements.

Electromagnetic Field (EMF) is defined as a physical field produced by electrically charged objects. Nowadays there is an extensive concern about the EMF radiation effects towards human body. In this paper, the effect of EMF radiation is studied using a single dipole and 5 × 5 array dipole antennas. The proposed antenna is designed and simulated by using FEKO software. Results and discussions are explored to scrutinize the EMF performance of the antenna particularly on the distance and the frequency of the antenna with respect to the human body. Subsequently, the results of the SAR and power density at various frequency and distance are presented. It is shown that SAR and power density increases as the frequency increases at a fixed distance. The result of power density for the array antenna at different distances is also presented.

Estimating contaminant source has become increasingly important to hazard assessment and emergency management of air contaminant nowadays. In this paper, a source estimation method is proposed to estimate the location and release rate of source. The theoretical basis of this source estimation method is Bayesian inference using the atmospheric dispersion model, Particle Swarm Optimization (PSO) and the observed data. An improved Gaussian dispersion model is proposed to model the continuous emission source. In order to obtain the observed data, a UAV-based air contaminant sensory system is developed consisting of an aerial platform and a sensory system. An experiment is conducted in a chemical industry park to verify the feasibility and credibility of this UAV-based system. Furthermore, the source estimation method proposed recovers the location and release rate of source with a high accuracy, confirming the effectiveness of the method.

The realistic modeling and effective control of drill-strings has been an ongoing research challenge. This has recently come to focus due to the volatility in the oil industry. Owing to the severely nonlinear nature of the drill-string, evolved nonlinear control techniques have recently been proposed to overcome the inherent stick-slip dynamics which are severely detrimental to the drilling performance as well as structural health of any given drill-string. Yet, most of the controller performance is analysed without including the significant delay intrinsic to the overall system. In this paper, the impact of system delay on the overall performance of a controlled drill-string is studied via extensive simulations. The analysis presents the impact of delay on three recently proposed sliding-mode control schemes. A surprising coexistence of attractors is observed from the delayed system on the third controller. This result will potentially impact the design of implementable control schemes proposed in future.

The modeling of aircraft motion is very important for aircraft flight performance evaluation, aero-engine design and air-combat modeling and simulation. However, the flight of aircraft is a complex process, and its flight capability is affected by many factors. The existing modeling process is generally based on analytic methods or energy methods, which leads to complex modeling process or lower model accuracy. For this reason, we deduced the coordinate transformation matrix, and on this basis, established a differential equation of aircraft motion with the earth fixed axis system, and finally built an aircraft motion model based on the numerical integration method. Experiments show that the method is simple and high-precision, and can meet the requirements of rapid modeling of warfare simulation.

In this research work, we design a biosensor using a six-fold photonic quasi-crystal fiber with a fan-shaped analyte channel based on surface plasmon resonance (SPR). We numerically analyze both the dispersion relations and loss spectra for three different refractive indices of the analyte, $$n_{a}$$, using finite element method. Through optimization of the structure, we find that the proposed biosensor exhibits a maximum refractive index sensitivity of 3200 nm/RIU and a resolution of $$3.12\times 10^{-5}$$ RIU when $$n_a$$ is increased from 1.41 to 1.43. We infer that the coupling between the core mode and SPR mode can be explained as a complete coupling of the loss matching condition or an incomplete coupling of the phase matching condition. Owing to the ease of fabrication of the proposed biosensor with an average sensitivity of 2250 nm/RIU, we envisage that this biosensor could turn out to be a versatile instrument for detecting the biomolecules.

Fluid-structure interaction (FSI) is used in the study to predict the vortex-induced vibration (VIV) of a cylinder that is fitted with helical strakes. The aims is to predict the characteristic of VIV after the installation of helical strakes on a cylinder. Two-way coupling through commercial fluid and structural solvers is utilized to develop the simulation. Helical strakes of height, h = 0.10D and pitch, p = 10D is used together with cylinder of diameter, D = 0.018 m. Three different velocities are tested. The amplitude, frequency, fluctuating lift response and vorticity contour are presented. The present study shows capability of FSI in reproducing the VIV characteristic of cylinder fitted with helical strakes. However, improvement is required especially at low reduced velocity as the values are deviated.

Electroencephalography (EEG) monitoring is known to be technically feasible and possibly clinically relevant to determine patients with acute ischemic hemispheric stroke. The EEG is very useful tool in understanding neurological dysfunction of stroke plausible improving the treatment and rehabilitation. Most of the existing techniques to diagnose stroke from the EEG signal is mainly based on Fourier Transform (FT). For instance, the Brain Symmetry Index (BSI) employed Fast Fourier Transform (FFT) as coefficients to measure symmetrical of blood flow between left and right brain hemisphere. The symmetrical index ranges between zero and one where one indicates the highest asymmetrical of blood flow. It is known that the conventional FFT has limitation in analyzing non-linear and non-stationary signal. Therefore, the existing BSI and its variations may also suffer from this transformation properties. In this study, we propose BSI based on Hilbert Huang Transform (HHT) which defined as BSI-HHT. HHT is a way to decompose a signal into so-called intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data. The HHT will be used as coefficients instead off FFT in calculating the BSI index. An experiment to validate the performance of BSI-HHT is conducted in this study as to compare with the existing BSI technique. The EEG signal of Middle Cerebral Artery (MCA) subjects and healthy subjects are used for this investigation. The proposed BSI-HHT has offered better interpretation as it correlates to the stimulation procedure on the gathered data especially at specific frequency band. Also, through the analysis, the HHT coefficient is able to capture the non-stationary and non-linear of the interest electrode.

The patient flow in the health centres is one of contributing factors to the dissatisfaction and delay in the delivery of healthcare service. This study aims to explore the pattern of dental patient flow in one centre and to solve the delay reason using computer simulation. Three different scenarios has been experimented using FlexSim 4.0 software to simulate the patient’s flow. Three proposed solutions have been experimented, and the best-proposed scenario successfully improved three from seven observed variables: the patient throughput, fasten service in the dental clinic and the length of stay. The length of stay from real data and from simulation output has been compared. The explored times was the longest at registration area for receiving the service from receptionist. The finding shows that we can customise safe interventions without interfering with the operational system using the simulation software.

New forms of collaboration in business world have emerged with the developments of the recent technologies. The success of Web 2.0 usage encouraged business companies to adopt enterprise 2.0 technology. Enterprise 2.0 that use emergent social software platforms (ESSPs) have been adopted by companies around the world. However, although the huge advantages of this technology, the adoption of enterprise 2.0 process is regularly facing end-client resistance due to the lack of empirical evidence of how Enterprise 2.0 is supporting the business objectives. The purpose of this study is to highlight on how successful company like IBM use ESSPs to achieve collaborative efforts that lead to achieve the enterprises strategy goals. Theory of planned behavior has been picked to recount the operation of building awareness and trust to open and share experiences of sharing information and ideas through Enterprise 2.0 platforms. This theory has been applied on a case study that uses social network strategy within IBM Company. The results show that strategies of knowledge sharing, providing resources for instance time and effort, distributing trust, social influence and the use of technology are factors that increase the Enterprise 2.0 adoption in companies and it proof that collaborate, communicate and connection are the most important factors that should be achieved through Enterprise 2.0 to meet a business objectives.

Internet of Things (IoT) enables advance digital services by utilizing data acquisition and controlling device remotely across wireless network infrastructure. It is a primary catalyst for an increasing numbers of application in the fields of cyber-physical systems. In this project, an IoT based guard touring system is proposed. A traditional guard touring system devices lacks the abilities to provide a real-time data acquisition, integration of security related functionalities, summoning features and also a low cost to benefit ratio. Therefore, the proposed IoT based guard touring system provides a solution to the existing systems’ pitfalls at a cost effective price. The system consists of an Android based application for real-time interaction and also an admin monitoring webpage for communication management. The proposed system provides the smart connection and data to information conversion that could be used easily for data mining and subsequent data cognition process.

Brain-Computer Interface (BCI) is a way to translate human thoughts into computer commands. One of the most popular BCI type is Electroencephalography (EEG)-based BCI, where motor imagery is considered one of the most effective ways. Previously, to extract useful information, various filters are introduced, such as spatial, temporal, and spectral filtering. A spatial filtering algorithm called Common Spatial Pattern (CSP) was developed and known to have excellent performance, especially in motor imagery for BCI application. In general, there are several approaches in improving CSP such as regularization approach, analytic approach, and frequency band selection. In general, the existing techniques for band selection is either to select or reject the band by ignoring the importance of the band. For example, Binary Particle Search Optimization Common Spatial Pattern (BPSO-CSP) was proposed to choose multiple possible best bands to be used in processing the data. In this paper, we propose an algorithm called Feature Scaling Common Spatial Pattern (FSc-CSP) to overcome the problem of feature selection. Instead of selecting features, the proposed algorithm employs a feature scaling system to scale the importance of each band by using Genetic Algorithm (GA) altogether with Extreme Learning Machine (ELM) as classifier, with 1 signifying the most important bands, declining until 0 for the unused bands, as opposed to the 1 and 0 selection system used in BPSO-CSP. Conducted experiments show that by employing feature scaling, better results can be achieved especially compared to vanilla CSP and feature selection with 100 hidden nodes in three from five BCI Competition III datasets IVa, namely aa, aw and ay, with around 5–8% better results compared to vanilla CSP and feature selection.

Gravitational search algorithm (GSA) is a metaheuristic population-based optimization algorithm inspired by the Newtonian law of gravity and law of motion. However, GSA has a fundamental problem. It has been reported that the force calculation in GSA is not genuinely based on the Newtonian law of gravity. Based on the Newtonian law of gravity, force between two masses in the universe is inversely proportional to the square of the distance between them. However, in the original GSA, R has been used. In this paper, a modification is done to GSA by considering the square of the distance between masses, which is R2. The CEC2014 benchmark functions for real-parameter single objective optimization problems are employed in the evaluation. An important finding is that by considering the square of the distance between masses, significant improvement over the original GSA is observed provided a large gravitational constant should be used at the beginning of the optimization process.

In this work, a theoretical analysis on the electromagnetic actuated valveless micropump for bidirectional flow is reported. The microchannel module is optimized to increase the smoothness of stream flow inside the chamber by modified the tangential angle to an optimum angle of 79.80°, with microchannel specifications of 10 mm chamber diameter and 1 mm channel width. In addition, the numerical simulation study determines the best shape selection of the micropump actuator: NdFeB magnet involved in membrane displacement. It is observed that cylindrical shaped magnet gives the lowest membrane stress when a force is applied. The obtained optimum geometrical parameters and best magnet shape were then being used for the dual chamber design for bidirectional flow micropump. From the analysis obtained, the micropump was capable for bidirectional flow application. Hence, the optimized design geometry for the microchannel and the best NdFeB magnet size can serve as a design guideline for bidirectional flow micropump without a complex structure.

In this paper, a steady state simulation for hydrogen production from steam gasification of Palm kernel shell was developed and studied. The gasification pilot plant process has been modelled in Aspen Plus® using Gibbs reactor (R-Gibbs). The effects of different operating parameters using sensitivity analysis, including gasification temperature 600–900 °C and steam flow rate (1 to 2 kg/hr.), on hydrogen yields and Syngas composition were investigated. The simulation results have shown the main gas components in Synthesis gas were H₂, CO, CO₂, CH₄. The product gas hydrogen yield increases with the increase in temperature. The hydrogen concentration improved from 22.52 vol. % to 36.06 vol.%, but the CO concentration decreased from 37.53 vol.% to 28.37% with increasing temperature from 650–900 °C under the operating parameters of the steam flow rate of 1.56 kg/hr.

A non-invasive condition monitoring system for diagnosis of faults is vital for induction motors to operate safely and reliably. The currently used invasive techniques need direct access to the motor to collect and analyze data. Furthermore, the sensors used in invasive techniques are relatively expensive. This paper presents the development of hardware and software integrations for non-invasive diagnostic system to monitor specifically motor-coupled gear defects. The proposed system employs instantaneous power analysis, a unique technique for diagnostic condition monitoring which allows real-time non-stop tracking as well as assesses the severity of the defects. This technique can be adopted for decision-making that is not only fast but reliable. The severity of different gear defects have been studied experimentally, and the results were analyzed. The effectiveness of the proposed method has been verified through experimentation from the actual hardware implementation through the system-design platform and development environment software tool, LabVIEW.

In the development of new energy, a new type of marine gyro power plant has a higher wave energy conversion efficiency in the field of wave energy, the key component of which is the gyroscope speed control system, but designing a motor system for a heavy inertia gyro rotor is rare, it is a challenge for engineering design methods under limited output power and other design conditions. In this paper, we design the dual-loop speed control system by using the engineering method, and aim at the problem of the proportional integral coefficient caused by the heavy moment of inertia. Analyzing the design principle and the MATLAB simulation results, we propose a method for parameter modification using the Bode diagram. In ensuring the overshoot, stability at the proper adjusting range, we not only effectively reduce the proportional integral coefficient, but also provide a reference for engineering design ideas.

An effective state feedback stabilizing controller plays an important role to ensure the reliability and robustness of nonlinear steer-by-wire (SbW) system. This paper addresses a new state feedback controller designed to ensure stability of SbW system. The SbW systems modeling is further studied where the additive of nonlinearities and disturbance need to be taken into account and compensated effectively. The state feedback control law can be designed based on the bound information of nonlinearity in the system in the sense that not only the robustness with respect to nonlinearity can be obtained but also the front steering wheel angle can converge to the hand-wheel reference angle asymptotically. The state feedback controller K is obtained by solving a linear matrix inequality (LMI) condition which formulated based on Lyapunov functional candidate. The efficacy of the proposed method is verified by applying the theorem on the SbW system simulated on Matlab/Simulink. The simulation work validated that the proposed controller results in excellent system performance.

Air pollution model is commonly used to predict the pollutant level in the air for the upcoming days based on the previous data. In this paper, a new model for predicting ozone, nitrogen dioxide and sulphur dioxide will be developed using the previous data of pollutants agents such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ozone, particulate matter and the meteorological data includes wind speed, temperature and humidity. It is developed to improve the estimation values for a low cost setup of air pollution measurement system. The models are constructed using the Levenberg-Marquardt training algorithms in the neural network tool. Different input parameters are investigated to develop better performance model for predicting air pollution. The proposed model is capable to predict the air pollution level with high accuracy and the meteorological data are dominantly influenced the accuracy of the model.

Modelling and simulation is an alternative way of testing the dynamic behaviour of a real system – in some situation, testing the real system are expensive, time consuming, not comfortable, and dangerous. Mathematical model describing the dynamic behaviour of a system can be represented by using white, black, or grey box model. This study focuses on developing a simplified Auto Regressive Moving Average (ARMA) model (a type of linear black model) to represent the dynamic thermal behaviour of iHouse – simplification is done based on the theoretical knowledge of the building. The performance of the simplified ARMA model developed in this study is compared with the performance of the models developed in previous studies, which are: (1) House Thermal Simulator; (2) and ARMA model. Result shows that the simplified ARMA model developed in this study consists of simpler set of mathematical equations, but can still simulate the dynamic thermal behaviour of iHouse with the accuracy that is almost on par with the models developed in previous studies.

This paper presents the design approach of Multirate Output Feedback (MROF) based Discrete Integral Sliding Mode Control (DISMC) for system with uncertainties. Firstly, the state representing the MROF has to be identified. The discrete integral sliding surface were selected in order to design the controller. The MROF that used output feedback with two different sampling times which are slow and fast rate is then combined with the DISMC to control the uncertain system. The reachability condition and stability are also considered and presented. Through extensive computer simulation, it shows that the proposed controller’s capable to track the reference input even though uncertainties present in the system. The findings demonstrated that the MROF based DISMC provided better system response as compared to the Discrete Linear Quadratic Regulator (DLQR) and discrete Proportional Integral Derivative (PID).