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Nowadays, electricity consumption especially in Malaysia has increased by the year. One of the factors that led to the increase in electricity consumption of a building is the usage of air conditioners (AC). The inability of conventional AC remote controls to monitor indoor environment conditions of the building will lead to unnecessary extreme temperature setting, then causing unnecessary high electrical consumption. Also, the conventional AC remote control is not able to store and perform analysis on the environment condition data of the building for the use of energy management strategy. The conventional AC remote control is only able to show the temperature set point value of the system, which could not represent the comfort experienced by occupants. In this project, a new device which can monitor the indoor environment condition and estimated electricity consumption on a web server is developed. The device monitors a comprehensive thermal comfort index Predicted Mean Vote (PMV) representing the comfort experienced by the occupants. The device is equipped with wireless sensor modules for indoor and outdoor remote monitoring. The device can monitor the outdoor temperature, indoor temperature, and indoor relative humidity along with indoor PMV value and electricity consumption. This device can grant advance feature to conventional AC remote control without causing physical modification to the existing system.

Analogue meter is a device that has been widely used in a various industry to monitor and obtain the reading of the measurement. Based on the conventional approach, the meter reading will be done continuously by the meter reader that might cause high tendency of human error during the observation. To minimize this fallacy, this approach taken in this paper enables the automation of this the process by obtaining the reading from an analogue meter using an image processing technique and send the output to the central database for further processing. By implementing this approach, observation efficacy can be improved. This paper describes the process on how to obtain the digitized reading of an analogue meter using images captured by a camera. The images are then processed using an image processing method and the Convolutional Neural Network (CNN) is used to determine the reading of the meter. Data is then sent to the MySQL database, as this approach was easily implemented and managed either on-premises or via the cloud. The use case in this study was based on the analogue meter for domestic electricity supply in Malaysia and results show that the meter reading can accurately be recognized using the proposed approach.

The advancement in embedded systems, which includes the mass deployment of internet-connected electronics, allows the concept of Internet of Things (IoT), to become a reality. This paper discusses one example of how an internet-connected embedded system is utilized in an automotive system. An Electronic Control Unit (ECU), which functions as a control unit in a fuel injection system, are equipped with Wi-Fi capability and installed on 110cc motorcycle. The ECU is connected to multiple sensors that is used by the ECU as part of control system, as well as giving raw data in real time to the server by using Wi-Fi as the communication medium. The server will accumulate data transmitted from ECU by using MQTT protocol, chosen due to its minimal data profile. The data can be visualized through web portal, or opened by any other web-enabled devices. The data collected may also be used later for any other purposes, such as On-Board Diagnostics (OBD) system, etc.

Being one of the biggest producers and exporters of palm oil and palm oil products, Malaysia has an important role to play in fulfilling the growing global need for oils and fats sustainably. Quality is an important factor that ensuring palm oil industries fulfill the demands of palm oil product. There has significant relationship between the quality of the palm oil fruits and the content of its oil. Ripe FFB gives more oil content, while unripe FFB give the least content. Overripe FFB shows that the content of oil is deteriorates. There have 4 classes of ripeness stages involves in this paper which are ripe, unripe, underipe and overripe. The proposes approach in this paper uses color features and bag of visual word for classifying oil palm fruit ripeness stages. Experiments conducted in this paper consisted of smartphone camera for image acquisition, python and matlab software for image pre processing and Support Vector Machine for classification. A total of 400 images is taken in a few plant in north Malaysia. Experiments involved on a dataset of 360 images for training for four classes and 40 images for testing. The average accuracy for the 4 classes of the FFB by color features is 57% while the accuracy for ripeness classification by using bag of visual word is 70%.

This paper presents a crowd simulation software that can assist building designers in visualizing and evaluating the building designs. It helps to simulate the movement of crowd in a building, visualize it and hence speed up decision-making process during building layout design stage. The software consists of 4 modules: the environment editor, the pathfinding module, the simulation module and the optimization module. The optimization engine assists the designers in improving their designs so as to ensure good trade-offs between various design parameters. In the example shown in this paper, the optimization feature is able to improve the evacuation process by 17.1% compared to the original design. It is envisaged that the crowd simulation software approach is critical in ensuring the safety of building occupants especially during emergency evacuation.

Crowd modeling and simulation are very important in the investigation and study of the dynamics of a crowd. They can be used not only to understand the behavior of a crowd in different environments, but also in risk assessment of spaces and in designing spaces that are safer for crowds, especially during emergency evacuations. This paper provides an overview of the use of the crowd simulation model for three main purposes; (1) as a modeling tool to simulate behavior of a crowd in different environments, (2) as a risk assessment tool to assess the risk posed in the environment, and (3) as an optimization tool to optimize the design of a building or space so as to ensure safer crowd movement and evacuation. Result shows that a simulation using the magnetic force model with a pathfinding feature provides a realistic crowd simulation and the use of ABC optimization can reduce evacuation time and improve evacuation comfort. This paper is expected to provide readers with a clearer idea on how crowd models are used in ensuring safer building planning and design.

Payload hoisting and wind disturbance during crane operations are among the challenging factors that affect a payload sway and thus, affect the crane’s performance. This paper proposes a new online adaptive output-based command shaping (AOCS) technique for an effective payload sway reduction of an overhead crane under the influence of those effects. This technique enhances the previously developed output-based command shaping (OCS) which was effective only for a fixed system and without external disturbances. Unlike the conventional input shaping design technique which requires the system’s natural frequency and damping ratio, the proposed technique is designed by using the output signal and thus, an online adaptive algorithm can be formulated. To test the effectiveness of the AOCS, experiments are carried out using a laboratory overhead crane with a payload hoisting in the presence of wind, and with different payloads. The superiority of the method is confirmed by 82% and 29% reductions in the overall sway and the maximum transient sway respectively, when compared to the OCS, and two robust input shapers namely Zero Vibration Derivative-Derivative and Extra-Insensitive shapers. Furthermore, the method demonstrates a uniform crane’s performance under all conditions. It is envisaged that the proposed method can be very useful in designing an effective controller for a crane system with an unknown payload and under the influence of external disturbances.

Antilock braking systems require accurate vehicle speed and acceleration estimations from wheel speed sensors. Adaptive Kalman filtering is used to estimate wheel speed and acceleration for each wheel. Single core or multicore processor-based embedded systems perform the required estimation using a two-stage extended Kalman filter in many clock cycles, which limits Kalman filter speed and accuracy. In the proposed system, purely arithmetic operations used in the process are simplified and implemented as embedded logic to reduce the overhead delays and increase the update rate by 48%. The proposed system is run sequentially using finite-state machines, while a large number of multiplication and division operations are removed through rewiring and logic reuse. The simplifications not only reduce the amount of logic required to perform the operations by 39.7% but also use simple logic, which can run faster than the replaced logic on field-programmable gate array. The results presented show comparable root-mean-square wheel speed error with a high update rate of 20 MHz when running on a 50-MHz board in all scenarios. The system also provides accurate wheel acceleration estimation, which is crucial for many systems, including the reference system.

This work aims at providing a systematic method in producing a safer and optimal factory layout based on a crowd simulation model and the multi-objective artificial bee colony optimization technique. Apart, from ensuring the efficiency of manufacturing processes in planning a factory layout, it is also important that the safety aspect is taken into account. A factory is usually a closed working area consisting of machines, equipment, assembly lines as well as individual working space and other departments within the factory. In this environment, workers move around in the factory to perform different activities, and hence highly complex crowd behaviours that are influenced by the physical, social and psychological factors of the crowd might take place. Therefore, the layout of the factory must be carefully designed so that efficient movements of people can be obtained. Furthermore, during emergency situations that require efficient evacuation of workers from the factory building, a good factory layout will prevent or minimize the possibility of injuries during the evacuation process. This will reduce the evacuation egress time, which is the quantity used to evaluate the evacuation efficiency and the building’s level of safety. One of the techniques to assess the evacuation efficiency of a particular space configuration is by using the crowd simulation model. Recent evidences suggest that the representation of crowd dynamics using a simulation model is useful, where experiments with real humans are too dangerous and not practical to be implemented. This work explains the method to provide optimal exit door configurations for a factory layout by analyzing the crowd evacuation time and the discomfort level, where the proposed optimum exit configurations will be compared with the original configuration for a better evacuation efficiency.

The energy efficiency of a heating, ventilating and air conditioning (HVAC) system optimized using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC) method is reported. The new method adopts the advanced algorithm of RBFNN for the HVAC system to estimate the residual errors, increase the control signal and reduce the error results. The objective of this study is to develop and simulate the EC-RBFNN for a self tuning PID controller for a decoupled bilinear HVAC system to control the temperature and relative humidity (RH) produced by the system. A case study indicates that the EC-RBFNN algorithm has a much better accuracy than optimization PID itself and PID-RBFNN, respectively.

Direct yaw control of four-wheel vehicles using optimal controllers such as the linear quadratic regulator (LQR) and the sliding mode controller (SMC) either considers only certain parameters constant in the nonlinear equations of vehicle model or totally neglect their effects to obtain simplified models, resulting in loss of states for the system. In this paper, a modified state-dependent Ricatti equation method obtained by the simplification of the vehicle model is proposed. This method overcomes the problem of the lost states by including state integrals. The results of the proposed system are compared with the sliding mode slip controller and state-dependent Ricatti equation method using high fidelity vehicle model in the vehicle simulation software package, Carsim. Results show 38% reduction in the lateral velocity, 34% reduction in roll and 16% reduction in excessive yaw by only increasing the fuel consumption by 6.07%.

This paper proposes a new control scheme for a 3-dimensional (3D) overhead crane having simultaneous payload hoisting and persistent wind disturbance. Control of the 3D crane system under those effects is highly challenging as it results in a high unwanted payload sway and inaccurate payload positioning. The proposed control structure which combines a new adaptive command shaping technique designed based on output signals and an integral sliding mode (ISM) control provides a precise crane positioning and low payload sway. The two independent controllers are designed such that they can practically be combined and implemented without affecting the overall stability of the closed-loop system. To test the effectiveness of the proposed method, simulations and experiments on a laboratory overhead crane are carried out under various conditions with different payload masses, various cable lengths, payload hoisting and external wind disturbance. Under all operating conditions, by comparing with an ISM control, the combined controller gives smoother and more satisfactory transient responses with two to four-fold reductions in the overall payload sway. In addition, the controller provides substantial reductions in the overall chattering of the control signal and sliding manifold which resulted in smoother control signals and system responses.

Overcrowding during emergency evacuation can cause crowd stampede and trampling that will lead to serious injuries or fatalities. These situations are likely to occur in natural or man-made disasters in open or closed spaces. The inefficient building design such as the unsuitable placement of an exit door is one of the key factors that contribute to the above tragedies. Most existing works used a trial and error method for a single room scenario in finding optimal locations of the exit doors. The main limitation in these works is that it is not suitable for handling multi-room problems and more complex room arrangements, since such problems would require a tedious process. To overcome this limitation, Artificial Bee Colony (ABC) optimization algorithm based on stochastic method is proposed in this paper. Compared to other optimization techniques, this algorithm requires less control parameters to be tuned in finding the most optimal exit door locations. A crowd evacuation model based on the Social Force Model (SFM) is used in representing the crowd dynamics and becomes the basis for the cost functions for the optimizer. The presented methodology has shown that the optimum locations of exit doors for a multi-room scenario can improve the evacuation efficiency; by minimizing crowd evacuation times and maximizing the number of people being evacuated. It is also clearly demonstrated that the optimized design is remarkable in improving the evacuation efficiency under different desired speed of crowd to evacuate.

This paper presents an intelligent control strategy based on internal model control (IMC) to control nonlinear systems. In particular, a wavelet neural network (WNN)-based nonlinear autoregressive moving average (NARMA-L2) network is used to acquire the forward dynamics of the controlled system. Subsequently, the control law can be directly derived. In this approach, a single NARMA-L2 with only one training phase is required. Hence, unlike other related works, this design approach does not require an additional training phase to find the model inversion. In the literature, gradient descent methods are the most widely applied training techniques for the neural network-based IMC. However, these methods are characterized by the slow convergence speed and the tendency to get trapped at local minima. To avoid these limitations, the newly developed modified micro-artificial immune system (modified Micro-AIS) is employed in this work to train the NARMA-L2. The simulation results have demonstrated the effectiveness of the proposed approach in terms of accurate control and robustness against external disturbances. In addition, a comparative study has shown the superiority of the WNN over the multilayer perceptron and the radial basis function based IMC. Moreover, compared with the genetic algorithm, the modified Micro-AIS has achieved better results as the training method in the IMC structure.

In this article, a nonlinear autoregressive with exogenous input (NARX) network was utilized to model a conveyor belt grain dryer using a set of input–output data collected during an experiment to dry paddy grains. The resulting NARX model has achieved a remarkable modeling accuracy compared to other previously reported modeling techniques. To control the considered dryer, a simplified type 2 adaptive neuro-fuzzy inference system (ANFIS) controller was proposed. The effectiveness of this controller was demonstrated by several performance tests conducted by computer simulations. Moreover, a comparative study with other related controllers further confirmed the superiority of the proposed dryer controller.

A type of parameter estimation technique based on the linear integral filter (LIF) method, the least-absolute error with variable forgetting factor (LAE+VFF) estimation method, is proposed in this paper to estimate the railway wheelset parameters modelled as a time-varying continuous-time (C-T) system. The inputs to the parameter estimator are the control signal and the railway wheelset system output, which is the wheelset’s lateral velocity. The algorithm includes an instrumental variable (IV) element to reduce estimation bias and a variable forgetting factor for good parameter tracking and smooth steady state. Simulation results have shown that the LAE with fixed forgetting factor gives better parameter estimates compared to the recursive least-squares error (RLSE) method, whereas the LAE+VFF offers even better estimation and tracking of system parameters that are subject to abrupt changes, provided that the fs and lf values are chosen accordingly. It has also been proven that the estimation error of the proposed LAE+VFF estimation algorithm is bounded.

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

Irrational crowds tend to adapt herd mentality, having group behaviour and high suggestion through interaction. It is important to see how an irrational crowd can be controlled to prevent undesirable crowd attitude. This paper reviews existing models and the controllers to provide a comprehensive study for crowd control. It focuses on a comprehensive analysis of the control of psychological crowd, modelled using LeBon’s theory; which defines the crowd behaviour in terms of crowd attitude. The crowd attitude is defined in terms of suggestibility and prestige and the crowd interaction is defined in terms of the interaction of prestige and suggestibility, which is naturally unstable. A controller is required to achieve stability. In this paper several control approaches are described and the best control approach is highlighted. The results conclude, the best control approach is using multiple control agents, since the control effort is reduced and the stabilizing time is improved.

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.

The use of Inertial Navigational System (INS) has been proven to be suitable for vehicular stability and control. The same system can be used for inertial based navigation in the absence of GPS. In this paper, the problem of obtaining good attitude estimates from low cost sensors used for car navigation in the absence of GPS data is discussed. The states to be estimated are using angular velocity and linear accleration signals obtained from the sets of gyros and accelerometers of the INS. The special orthogonal group, the SO(3)-based complementary filters, have been used as the estimators as they are most suited for embedded systems to generate highly efficient algorithms for navigation. The INS has also been integrated with a set of magnetometers to assist in achieving global navigation. This integration requires kinematics equations as well as the inclusion of the gyro and accelerometer calibration and filtering. By using the quatronion representation, not only highly compact algorithms for integration can be generated, but it can also estimate and remove the effects of other biases and misalignments caused by, for instance, inaccurate installations and inherent sensors problems. The results obtained through simulation indicate better performance then Kalman filter approach as well as iterative recursive least square approach even with low grade sensors. The results are comparable with attitude estimation using roll index but with much less computations and better performance.

Most motorcycles in developing countries use carburettors as the fuel delivery system especially for models with cubic capacity of less than 350 cc. However, small gasoline carburetted engines suffer from low operating efficiency, high fuel consumption and produce high level of hazardous emissions. A retrofit fuel injection system (FIS) is a system that is developed to totally replace the conventional carburettor system to improve its fuel economy and exhaust emissions, providing a low-cost alternative in an effort to reduce fuel costs and air pollution. This paper provides a comprehensive review on the retrofit fuel injection technology developed for small gasoline spark ignition (SI) motorcycle engines from 50 cc to 350 cc. Three main retrofit FIS schemes – the throttle body injection (TBI), port fuel injection (PFI) and direct injection (DI) – are compared, in terms of configurations, complexity, costs and performances.

This paper provides a detailed review on heating, ventilation and air conditioning (HVAC) scheduling techniques for buildings towards energy-efficient and cost-effective operations. The scheduling techniques can be divided into 3 main classes, which are the basic techniques, conventional techniques and advanced techniques. The basic scheduling technique involves only the manipulation of the ‘ON’ and ‘OFF’ states of the HVAC system whereas the conventional scheduling technique uses pre-cooling or pre-heating techniques to reduce the peak demand with the use of several setpoint temperatures. The characteristics, as well as the energy and cost saving potentials for each strategy are presented. The advanced scheduling technique, which is the improved version of the basic and the conventional scheduling techniques is found to have the highest energy and cost saving potential. The limitations of the scheduling techniques have also been identified and possible solutions to overcome these limitations and improve the scheduling performance further have also been briefly stated.

Assembly sequence planning (ASP) plays an important role in the production planning and should be optimized to minimize production time and cost when large numbers of parts and sub-assemblies are involved in the assembly process. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. In this study, an approach using particle swarm optimization (PSO) is proposed to tackle one of the ASP problems which are optimizing the assembly sequence time. PSO uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. Each bird, called particle, learns from its own best position and the globally best position. Experimental results show that PSO algorithm can produce good results in optimizing the assembly time, has a powerful global searching ability and fast rate of convergence.

This paper presents outcomes of the usage of a developed prototype of PFI retrofit-kit for small 4-stroke gasoline engine. The developed PFI retrofit-kit produced good and high brake power and brake mean effective pressure compared to the carburetor system with over 50% improvement. Exhaust-out emissions such as carbon monoxide, carbon dioxide and hydrocarbon have been reduced in the range of 39%, 185%, and 57% respectively. However, brake specific fuel consumption was found to be higher (125%) as compared to carburetor system.

In this paper, a new Artificial Neural Network (ANN) model that relates human comfort and electrical power consumption of a building with temperature, illumination and carbon dioxide (CO2) level inside the building is developed. The model has been developed using samples of simulated data representing the indoor environment variables. Results have shown that neural network with 14 hidden layer neurons produces outputs that is closest to the actual system outputs.

Semi-active control of dynamic response of civil structures with magneto-rheological (MR) fluid dampers has emerged as a novel revolutionary technology in recent years for designing “smart structures.” A small-scale MR damper model with the valve mode mechanism has been examined in this research using dynamic recurrent neural network modeling approach to reproduce its hysteretic nonlinear behavior. Modified Bouc–Wen model based on nonlinear differential equations has not only been employed as the reference model to provide a comprehensive training data for the neural network but also for comparison purposes. A novel frequency and amplitude varying displacement input signal (modulated chirp signal) associated with a random supply voltage has been introduced for persistent excitation of the damper in such a way to cover almost all of its operating conditions. Finally a series of validation tests were conducted on the proposed model which proved the appropriate performance of the model in terms of accuracy and capability for realization.

Fuel injection system is a promising technology that enhances positively the fuel economy, engine performances and emission reduction, as compared to the conventional carburetor system. Currently, motorcycles using carburetor system are widely used as a mean of transportation especially in urban areas. This conventional fuelling system produces more harmful emissions and consumes more fuel compared to the fuel injection system. It is therefore desirable to have a fuel injection system that can easily be retrofitted to the current on-road motorcycles. This paper presents a review and comparative study using 1-D simulation software - GT-Power, on electronic fuel injection (EFI) system between port-fuel injection (PFI) and direct injection (GDI) system for retrofitment purpose of small 125cc 4-stroke gasoline engine. From this study, PFI system has been selected based on its high brake power, brake torque, and brake mean effective pressure with low brake specific fuel consumption.

The behavior of human crowd is an interesting phenomenon in which individuals are set as a collection that comprises of a highly dynamic social group. The crowd behaviors have been investigated by researchers over the years. Recent works include the study in modeling and controlling of the dynamic psychological behavior of crowds such as students’ behavior in a classroom or people’s behavior in a one-dimensional queue. In this paper, an improved version of the psychological crowd model has been proposed, where the social interaction between two individuals in a crowd is represented by a weightage, called the weight of social interaction. It has been shown that the inclusion of the social interaction weight has allowed social interactions between individuals to be included and results in a more accurate representation of the crowd’s psychological factors propagations. Since the psychological dynamics of crowd is naturally unstable, this paper also discusses the application of two nonlinear control approaches to stabilise the crowd to make it calm. Results show that for a crowd of n number of agents, the single-agent controller gives similar performance with the n-agent controller but with much less resources. The simulation results also show that it takes less amount of time to stabilise a crowd when the crowd model includes social interaction weights

Most motorcycles in developing countries use carburetor systems as the fuel delivery method especially for models with the cubic capacity of less than 125cc. However, small gasoline fuelled engines operating using carburetor system suffer from low operating efficiency, waste of fuel and produce higher level of hazardous emissions to the environment. In this study, an electronic control unit (ECU) is designed and simulated for a retrofit fuel injection (FIS) system. The ECU is targeted to have a simple design, reliable and offers all of the necessary functions of the modern ECU. The simulation results shows that the designed ECU can determine the injection period as close to the proposed value and can drive the injector efficiently based on the generated PWM pulse.

This paper proposes an intelligent segmentation technique for pineapple fruit using Convolutional Neural Network (CNN). Cascade Object Detector (COD) method is used to detect the position of the pineapple from the captured image by returning the bounding box around the detecting pineapple. Image background such as ground, sky and other unwanted objects have been removed using Hue value, Adaptive Red and Blue Chromatic Map (ARB) and Normalized Difference Index (NDI) methods. However, the ARB and NDI methods are still producing misclassified error and the edge is not really smooth. In this case Template Matching Method (TMM) has been implemented for image enhancement process. Finally, an intelligent CNN is developed as a decision maker to select the best segmentation image ouput from ARB and NDI. The results obtained show that the proposed intelligent method has successfully verified the fruit from the background with high accuracy as compared to the conventional method.

The 1MWth Reactor TRIGA PUSPATI (RTP) Mark II type has undergone more than 35 years of operation. The existing core power control uses feedback control algorithm (FCA). It is challenging to keep the core power stable at the desired value within acceptable error bands to meet the safety demand of RTP due to the sensitivity of nuclear research reactor operation. Currently, the system is not satisfied with power tracking performance and can be improved. Therefore, a new design core power control is very important to improve the current performance in tracking and regulate reactor power by control the movement of control rods. In this paper, the adaptive controller and focus on Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) were applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, adaptive controller model, and control rods selection programming. The mathematical models of the reactor core were based on point kinetics model, thermal hydraulic models, and reactivity models. The adaptive control model was presented using Lyapunov method to ensure stable close loop system and STC Generalised Minimum Variance (GMV) Controller was not necessary to know the exact plant transfer function in designing the core power control. The performance between proposed adaptive control and FCA will be compared via computer simulation and analysed the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

The instability caused by the conical (or profiled) shape of a solid-axle railway wheelset can be overcome by proper design of the vehicle's primary suspension system but is generally difficult as some of the wheelset parameters, namely the conicity and creep coefficients, are time-varying. To maintain the wheelset stability at high speeds and satisfactory curving performance simultaneously over the whole range of the parameters' variations, the self-tuning linear-quadratic regulator (S-T LQR) for the primary suspension system of a high-speed two-axle railway vehicle has been developed. The objective of the controller was to minimize the lateral displacement of the wheelset relative to track centerline and its yaw angle, on straight and curved tracks. The Continuous-time Least-Absolute Error with Variable Forgetting Factor (C-T LAE+VFF) estimation algorithm has been used to estimate the wheelset parameters before being used in the calculation of the linear quadratic feedback control gain matrix. The simulation results show that the S-T LQR performed better than the fixed-gain LQR for both the conical and profiled wheelset, suggesting that the ability to estimate the time-varying wheelset parameters and use them in the feedback controller design is necessary to produce better primary suspension control performance.

Psychological dynamics that are created by the interactions among persons within One-Dimensional Queue, specially the spread of actions from person to person is discussed in this paper. The instability of psychological dynamic factors can be overcome by persons' interactions in a crowd dynamic. Surowiecki's theory states that the individuals in a crowd are not affected by each other in terms of attitudes. This state indicates wisdom of crowd while Gustav Le Bon's theory is in contrast with. The purpose of this paper is to prove that Surowiecki's theory is originated from Gustav Le Bon's theory if there is a large distance between the members of group in the one dimensional queue. This paper proposes a dynamic crowd model by Bergey, Spieser and Davison based on Gustav Le Bon's theory for static crowd model with variable distance interaction; resulting into better control of crowd dynamics using distance interaction.