This paper presents investigations into the development of control schemes for end-point vibration
suppression and input tracking of a flexible manipulator. A constrained planar single-link flexible manipulator is considered and the dynamic model of the system is derived using the assumed mode method. To study the effectiveness of the controllers, a Linear Quadratic Regulator (LQR) was initially developed for control of rigid body motion. This is then extended to incorporate a noncollocated PID controller and a feedforward controller based on input shaping techniques to control vibration (flexible motion) of the system. For feedforward controller, positive and modified specified negative amplitude (SNA) input shapers are proposed and designed based on the properties of the system. Results from the simulation of the manipulator responses with the controllers are presented in time and frequency domains. The performances of the control schemes are assessed in terms of level of vibration reduction, input tracking capability and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed.
Proper integrated management of a dam reservoir requires that all components of the water resource system be known. One of these components is the daily reservoir inflow which is the subject matter of this study, i.e. to establish predictions of what is coming in the next rainfall-runoff process over a catchment. The transformation of rainfall into runoff is an extremely complex, dynamic, and more of a non-linear process. The available six-year average daily rainfall data across the Sembrong dam catchment were computed using the well-known Theissen’s polygon method. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Modelling of relationship between rainfall and reservoir inflow data was done using feed-forward back-propagation neural networks. The final selected model has one hidden layer with 11 neurons in the hidden layer. The selected model was applied for an independent data series testing. Results in relation to specific climatic and hydrologic properties of a small tropical catchment suggested that the model is suitable to be used in forecasting the next day’s reservoir inflow. The efficiencies of the model Abtained indicated the validity of using the neural network for modelling reservoir inflow series.
In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, the eager algorithms classify quickly, but they learn very slowly. The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. The method was developed based on the grid structure which was done to create a powerful method for classification. In the current research, the new algorithm was tested and applied to the multiclass classification of two or more categories, which are important for handling problems related to practical classification. The new method was also compared with the Levenberg-Marquardt back-propagation neural network in the learning stage and the Condensed nearest neighbour in the generalization stage to examine the performance of the model. The results from the artificial and real-world data sets (from UCI Repository) showed that the new method could improve both the efficiency and accuracy of pattern classification.
The drying of Piper betle Linn (betel) leaf extract using a lab scale spray dryer was simulated using Computational Fluid Dynamics (CFD). Three different turbulent models (standard k-ε, RNG k-ε and realizable k-ε) were used in the present study to determine the most suitable model for predicting the flow profile. Parametric studies were also conducted to evaluate the effect of process variables on the final moisture content. Four different initial droplet sizes (36, 79, 123 and 166 μm) were tested with four sets of combination of hot air temperature (140 and 160°C) and feed rate (4, 9.5 and 15 ml/min). It was found that standard k-ε is the most suitable turbulent model to predict the flow behaviour Moreover, the lowest final moisture content present in samples was obtained at 140°C and a feed rate of 15.0 ml/min.
In the literature, several methods of ground improvement have been presented including compacted stone columns. The bearing capacity of the granular column is governed mainly by the lateral confining pressure mobilized in the soft soil to restrain or prevent bulging of the granular column. Therefore, the technique becomes unfeasible in peat that does not provide sufficient lateral confinement. This condition can be overcome by encasing the stone column with geogrid. This paper investigates the performance of the geogrid encased vibrocompacted stone column in peat. This study was carried out using PLAXIS software equipped with unit cell concept. The peat was modelled using soft soil model and the stone column using Mohr-Coulomb soil model, respectively. The geogrid was modelled using the geogrid option and could take only tensile force. The results indicate that the geogrid encased stone column can take much higher load in comparison to ordinary stone columns as the stiffness of the column increases. Meanwhile, the length of encasement also varied and it was observed that it was very effective up to about two times the diameter of the column. It also increased the column stiffness, and therefore led to a significant strain reduction. It was also observed that the columns at a spacing of three times the diameter are very effective. The results presented here can be used by the geotechnical engineers to design the geogrid reinforced stone column based on the strength of the soil, diameter of the column, spacing of the columns and stiffness of the geogrid.
Hempedu bumi (Andrographis paniculata, AP), also known as the king of bitters, is an herb commonly found in Asian communities for medicinal usage. The drying behaviour of AP at temperatures of 40, 50, and 60oC with vacuum pressures of 10 and 30 kPa was investigated in this study. The data were then fitted with semi-theoretical and theoretical thin-layer drying models. The results reveal that the drying time is significantly (p
Pulse Shaping Amplifier (PSA) is an essential component in nuclear spectroscopy system. This
amplifier has two functions; to shape the output pulse and performs noise filtering. In this paper,
we describe the procedure for the design and development of a pulse shaping amplifier which can
be used in a nuclear spectroscopy system. This prototype was developed using high performance
electronics devices and assembled on a FR4 type printed circuit board. Performance of this
prototype was tested by comparing it with an equivalent commercial spectroscopy amplifier (Model
Silena 7611). The test results showed that the performance of this prototype was comparable
to the commercial spectroscopic amplifier.
The use of the Light Rail Transit (LRT) system is currently preferred because LRT is sustainable,
improves travel options and facilitates swift mobility in urban areas. Hence, the structural stability
and safety of this public transportation system against seismic occurrences are indispensable. Given
that these structures cannot be considered conventional frames because of their complex architectural
design, focussing meticulously on reliable seismic design codes and structural rehabilitation techniques
is vital for the design of the lateral resistance system. One Malaysian LRT station is considered in this
study, and the seismic response of this train station when equipped with supplementary viscous damper
devices is evaluated. Thus, the LRT station is modelled through finite element simulation. The methods
of seismic analysis are limited to linear seismic analyses, namely, response spectrum and time history
analyses. Results derived in this study show a significant improvement in structural response when the
station is fitted with dampers; approximately 40% reduction in displacement is observed at the top joint
of the roof. Furthermore, the lateral base shears decrease by approximately 70%.
Numerical studies of blood flow system of aorta coronary sinus conduit were carried out using ANSYSTMCFD simulation. A different model of conduit, which differs in the inlet diameter, was investigated. Theinvestigated inlet diameters are 3 mm, 4 mm and 5 mm. Pressure drop from 80 mmHg to 15 mmHgwas achieved for all the models. The comparison chart was produced to compare the pattern of pressurereduction as well as velocity distribution in each model. From the analysis of coronary sinus conduit,it was found that a narrow tube needs to be incorporated into the conduit produced. This is to inducea venturi effect to reduce the pressure drop of blood within a specific throat length. As conclusion, amodel of 3 mm inlet and a throat diameter of 1.13 mm show satisfactory result for pressure reductionfrom 80 mmHg to 15 mmHg. This particular model also has a lower peak velocity at the inlet zone ofthe throat section, which is more preferable in terms of Reynolds number.
This research focuses on the ASTER DEM generation for visual and mathematical analysis of topography, landscapes and landforms, as well as modeling of surface processes of Central Alborz, Iran. ASTER DEM 15 m generated using tie points over the Central Alborz and Damavand volcano with 5671 m height from ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) satellite data using PCI Geomatica 9.1. Geomorphic parameters are useful to identify and describe geomorphologic forms and processes, which were extracted from ASTER DEM in GIS environment such as elevation, aspect, slope angle, vertical curvature, and tangential curvature. Although the elevation values are slightly low in altitudes above 5500 m asl., the ASTER DEM is useful in interpretation of the macro- and meso-relief, and provides the opportunity for mapping especially at medium scales (1:100,000 and 1:50,000). ASTER DEM has potential to be a best tool to study 3D model for to geomorphologic mapping and processes of glacial and per glacial forms above 4300 m asl.
Controller tuning is needed to select the optimum response for the controlled process. This work presents a new tuning procedure of PID controllers with safety and response quality measures on a non-linear process model by optimization procedure, with a demonstration of two tanks in series. The model was developed to include safety constraints in the form of path constraints. The model was then solved with a new optimization solver, NLPOPT1, which uses a primal-dual interior point method with a novel non-monotone line search procedure with discretized penalty parameters. This procedure generated a grid of optimal PID tuning parameters for various switching of steadystates to be used as apredictor of PID tunings for arbitrary transitions. The interpolation of tuning parameters between the available parameters was found to be capable to produce state profiles with no violation on the safety measures, while maintaining the quality of the solution with the final set points targeted achievable.
Digital elevation model (DEM) generation from stereo images is an effective and economical method in topography mapping. This paper used the stereo pair methodology to generate the digital elevation model (DEM) from PRISM (Panchromatic Remote-Sensing Instrument Satellite) sensor which is onboard of ALOS (Advanced Land Observing Satellite). The pair of forward-backward is used as stereoscopic imagery in this study. Ten ground control points (GCPs) are collected with residual error 0.49 pixels to generate an absolute DEM. This generated DEM with 2.5 m spatial resolution is then matched with the 90 m spatial resolution of
SRTM (Space Shuttle Radar Topography Mission) DEM to compare the result. Although SRTM-DEM has a much coarser resolution, the positional accuracy of the matching is found. The difference of the height from the mean sea level (MSL) between the SRTM-DEM and the PRISM-DEM is analyzed and the correlation between the two DEMs is R²=0.8083. The accuracy of the DEM generated is given by the RMSE value of 0.8991 meter.
Malaysia is a tropical country and it is subjected to flooding in both the urban and rural areas. Flood
modelling can help to reduce the impacts of flood hazard by taking extra precautions. HEC-RAS model was used to predict the flood levels at selected reach of the Langat River with a total length of 34.4 km. The Langat River is located in the state of Selangor, Malaysia and it is subjected to regular flooding. The selected reach of the Langat River has insufficient data and a methodology was proposed to overcome this particular problem. Since complete floodplain data for the area are not available, the modelling therefore assumed vertical walls at the left and right banks of the Langat River and all the predicted flood levels above the banks were based on this assumption. The HECRAS model was calibrated and the values of Manning’s coefficients of roughness for the Langat River were found to range from 0.04 to 0.10. The discharge values were calculated for 5, 10, 25, 50, and 100 year return periods and the maximum predicted flood depth ranged from 2.1m to 7.8m. Meanwhile, the model output was verified using the historical record and the error between the recorded and predicted water levels was found to range from 3% to 15%.
Biocatalytic reaction is a type of reaction which uses enzyme or whole-cell as a (bio)-catalyst to achieve a desired conversion, under controlled conditions in a bioreactor. Temperature produces opposed effects on enzyme activity and stability, and is therefore a key variable in any biocatalytic processes. An exothermic biocatalytic reaction, in a continuous-stirred-tank reactor (CSTR), was analyzed where dynamic equations (non-linear differential equations) could be derived from the Michaelis-Menten and Arrhenius equations, by performing mass and energy balances on the reactor. In this work, the effects of the different parameters such as dilution rate, proportional control constant and dimensionless total enzyme concentration, on the stability of the system, were studied. The stability of the reaction could be analyzed, based on the ODE (ordinary differential equation), solved using the numerical technique in MATLAB® and the analytical investigation using Mathematica.® The numerical analysis can be carried out by considering the hase-plane behaviour and bifurcation diagrams of the dynamic equations, while the analytical analysis using Mathematica® can be undertaken by evaluating the eigenvalues of the system. In order to model the operational stability of biocatalysts, modulation factors need to be considered so that a proper design of bioreactors can be done. Temperature, as a key variable in such bioprocess systems, can be conveniently optimized through the use of appropriate models.
Spatio-temporal datasets are a collection of datasets where data can vary in both space and time. Theoretically, such datasets can be considered as continuous and discrete. For example, specification of the function, F: Ed T Rn, where Ed denotes d-dimensional Euclidean space, T = R* ∩ {} the domain of time and Rn an n-dimensional scalar field. Examples of such data sets include time-varying simulation results, film and videos, time-varying medical datasets, geometry models with motion or deformation, meteorological measurements, and many more. It is therefore highly desirable to use visualisation to summarize meaningful information in higher dimensional spatio-temporal datasets. Our aim is to conceive an efficient visual study to facilitate scientists in identifying temporal association among complex and chaotic atom movements in ion trajectories. An application that uses a streamline for spatial motion of ion trajectories and Colour Number Coding Scheme for temporal encoding of high degree of timeline events among mobile ions is proposed. With an anthology of the visual examples, it was revealed that this application would be beneficial for scientists to visually mine any 3D spatio-temporal dataset.
The takraw ball is a very unique interwoven ball used in the action game of sepak takraw. The traditional takraw ball is manufactured by conventionally weaving split rattan strips into a spherical basket. Modern takraw balls are manufactured by forming strips of plastics materials into interwoven hoop. These interwoven hoops form 12 pentagon holes and 30 intersections. The purpose of this study is to construct a finite-element (FE) model of a takraw ball in particular for normal impact simulation on flat surfaces under low speed conditions. Two FE models were developed to observe the dynamic behavior including impact forces, contact time, coefficient of restitution and deformation of the ball. The first model consists of a single solid hollow ball with 12 pentagon holes and the second model consists of six center strips and 12 side edge strips of
extrusion hoops to form 12 pentagon holes and 270 cross-sections. The models were also compared with results of experimental impact tests whereby the ball was impacted normal to a rigid plate at three different heights. The ball is described in the FE model as a linear elastic material.
It was found that the FE analysis solution of the ball model was found to be reasonably close with the experimental results. However further improvement need to be done by taking into consideration the nonlinearity of the takraw ball under large deformation as well as at high impact velocity.
Groundwater is the main source of water in the Kingdom of Saudi Arabia (KSA). A larger part of groundwater is founded in alluvial (unconfined) aquifers. Prediction of water table elevations in
unconfined aquifers is very useful in water resources planning and management. During the last two
decades, many aquifers in different regions of the KSA experienced significant groundwater decline.
The declines in these aquifers raised concerns over the quantity and quality of groundwater, as well
as concerns over the planning and management policies used in KSA. The main objective of this study was to predict water table fluctuations and to estimate the annual change in water table at an alluvial aquifer at wadi Hada Al Sham near Makkah, KSA. The methodology was achieved using numerical groundwater model (MODFLOW). The model was calibrated and then used to predict water table elevations due to pumping for a period of 5 years. The output of the model was found to be in agreement with the previous records. Moreover, the simulation results also show reasonable declination of water table elevations in the study area during the study period.
The aims of this study were to demonstrate "order error" in the calculation of continuous relative phase (CRP) and to suggest two alternative methods-(i) constructing phase-plane portraits by plotting position over velocity; and (ii), the Hilbert transform-to rectify it. Order error is the change of CRP order between two degrees of freedom (e.g., body segments) when using the conventional method of constructing phase-plane portraits (i.e., velocity over position). Both sinusoidal and non-sinusoidal simulated signals as well as signals from human movement kinematics were used to investigate order error and the performance of the two alternative methods. Both methods have been shown to lead to correct results for simulated sinusoidal and non-sinusoidal signals. For human movement data, however, the Hilbert transform is superior for calculating CRP.
Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.