Stability study of PD and PI controllers in multiple difference disturbances

This paper discusses the stability study of PD and Pl controllers in multiple difference disturbances. The multiple difference disturbances in this paper are added to the inverted pendulum model that based on robotic leg application such as pendubot. By applying the pendubot model via MATLAB/Simulink block diagram, the performances between the model and disturbances are compared for stability in the simulation results. The simulation results showed that the PD controller could reduce and eliminate disturbances more effective than PI controller in the pendubot model. Overall, the simulation results are based on stability analysis for the degree of stability, steady state performance and transient response

Effect of relining, cement type, and thermocycling on push-out bond strength of fiber reinforced posts.

Statement of the problem: Improving the adaptation of fiber reinforced posts through relining may affect the retention of the posts. Purpose: To investigate the effect of post relining, cement type, and thermocycling on the push-out bond strength of fiber reinforced posts. Materials and methods: (48) endodontically treated human teeth were excessively flared using diamond stones. The teeth were divided into two groups; group (1) (n ¼ 24) received glassix glass fiber posts adapted to the flared canals by relining with composite resin and group (2) (n ¼ 24) received non-relined glassix glass fiber post. Samples of each group were divided into three subgroups (n ¼ 8) according to the type of cement used; subgroup (a): luted using Metacem Refill, a total etch resin cement, subgroup (b): luted using Rely X Unicem, a self-adhesive resin cement and subgroup (c): luted using RelyX Luting, a resin modified glass ionomer cement. Half the samples of each subgroup (n ¼ 4) were subjected to thermocycling. The samples were sectioned horizontally into 2 mm thick slices yielding 3 sections for each sample. Retention was evaluated using push out bond strength test using universal testing machine. The maximum failure load was recorded and used to calculate the push-out bond strength. Data was statistically analyzed and mode of failure was assessed using magnifying glass. Results: Relined posts showed statistically significantly higher mean push-out bond strength than non-relined posts. Rely X Unicem showed the statistically significantly highest mean push-out bond strength among tested cements. Metacem showed significantly lower mean push-out bond strength than Rely X Unicem. Rely X Luting showed the statistically significantly lowest mean push-out bond strengths. There was no statistically significant difference between mean push-out bond strength with and without thermocycling. Most failures occurred at the cementedentin interface in the relined group, while adhesive failure occurred at the cement-post interface in non-relined group

Tool life of TiAlN PVD coated carbide tool in high-speed end milling of untreated inconel 718 under minimum quantity lubrication condition

This paper presents the tool life of the end milled Inconel 718, which is part of a material that is difficult to be machined. Previous researchers found that tool life in machining aged Inconel 718 is shorter compared with other materials. However, this observation required further investigation. Thus, a raw grade Inconel was proposed in this experiment. The experiments were performed using TiAlN-coated carbide. The studied milling parameters were the cutting speed, Vc, from 90 to 150 m/min; feed rate, fz, from 0.15 to 0.25 mm/rev; depth of cut, ap, from 0.3 to 0.5 mm; and radial depth of cut, ae=1 mm. The application of the cutting fluid used in this experiment was a minimum quantity lubricant, which had the advantage of cooling effectiveness and low consumption of cutting fluid. The results showed that the feed rate, fz, was the primary factor controlling the tool life. The combination of Vc=115 m/min, fz=0.15 mm/tooth, as well as ap=0.5 mm and ae=0.15 mm gave the longest tool life that served 95.38 min in operation

Flexible Data Warehouse Parameters: Toward Building an Integrated Architecture

Clinical databases have gathered a huge amount of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Thus it is a difficult task focusing to model a data warehouse, very often, into internal structures and implementation applications. The aim of is project is to find the parameters of medical data warehouse which will be able to construct a flexible framework by analyze the approaches of data warehouse architectures and compare them regarding the cost and integrity. The proposed parameters may assist in achieving continual access to the common data warehouse. The important consideration, however, is that the clinical data record should contain not only longitudinal health summary information but also be used for business intelligence tool such as data mining and OLA

Tensile Strength Analysis of High Density Polyethylene for Injection Moulded Parts

This paper investigates the performance of pure high density polyethylene (p-HDPE) and recycled high density polyethylene (r-HDPE) by comparing the tensile strength of both materials. The specimens were injected by injection moulding machine and the parameters investigated were melting temperature (200-240°C), injection pressure (75-95 MPa), and holding time (20-30 s). Response Surface Methodology (RSM) was used to accommodate the experimental run as well as to analyse the experimental results. The result from Analysis of Variance (ANOVA) showed that the melting temperature is the most significant parameters affecting the tensile strength of both materials with the F-value is 307.58, followed by injection pressure (77.32) and holding time (19.67). The result also showed that the tensile strength of both materials increase with increasing of melt temperature, injection pressure and holding time. The optimal tensile strength of p-HDPE (27.04 MPa) was obtained at the melting temperature of 240°C, injection pressure of 95 MPa and holding time of 20 s. On the other hand, the optimal tensile strength of r-HDPE (16.058 MPa) was achieved at the melting temperature of 240°C, injection pressure of 95 MPa, and holding time of 29 s. The reduction percentage of tensile strength for r-HDPE as compared to p-HDPE was in the range of 43.478% - 40.703%. Even though the tensile strength of r-HDPE has been reduced by around 40% as compared to p-HDPE, the r-HDPE can still be utilised for packaging application such as containers, bottles, and jars. Therefore, this will help to significantly reduce waste in order to sustain the environment

Single and Multi-Sources Energy Sizing for Electric Vehicle: A Case Study

The automotive industry has introduced various renewable-energy based technologies such as battery electric vehicles (BEV) and fuel-cell electric vehicles (FCEV). However  the main concern is addressing issues to determine which vehicle with different energy sources are more efficiency and cost saving than the others.  In order to overcome this issue detailed analysis need to be performed on the important criterions in vehicle sizing like energy cost, dissipated energy and effective energy source (EES). This paper deals with the modeling, evaluation and analysis of single and multi-source electric vehicle (EV) on three classes of EV, namely the light electric vehicle (LEV), medium electric vehicle (MEV) and electric vehicle (EV). A comparison on dissipated energy with different EES, charging cost and weight were made based on a linear mathematical calculation. The results have shown that multi-sources energy powered-vehicle deliver among the best dissipated energy and EES percentage. Findings of this energy sizing under various combination of EV would be helpful for further research on the EV energy applications

Oil Palm Mapping Over Peninsular Malaysia Using Google Earth Engine and Machine Learning Algorithms

Oil palm plays a pivotal role in the ecosystem, environment, economy and without proper monitoring, uncontrolled oil palm activities could contribute to deforestation that can cause high negative impacts on the environment and therefore, proper management and monitoring of the oil palm industry are necessary. Mapping the distribution of oil palm is crucial in order to manage and plan the sustainable operations of oil palm plantations. Remote sensing provides a means to detect and map oil palm from space effectively. Recent advances in cloud computing and big data allow rapid mapping to be performed over large a geographical scale. In this study, 30 m Landsat 8 data were processed using a cloud computing platform of Google Earth Engine (GEE) in order to classify oil palm land cover using non-parametric machine learning algorithms such as Support Vector Machine (SVM), Classification and Regression Tree (CART) and Random Forest (RF) for the first time over Peninsular Malaysia. The hyperparameters were tuned, and the overall accuracy produced by the SVM, CART and RF were 93.16%, 80.08% and 86.50% respectively. Overall, the SVM classified the 7 classes (water, built-up, bare soil, forest, oil palm, other vegetation and paddy) the best. However, RF extracted oil palm information better than the SVM. The algorithms were compared and the McNemar's test showed significant values for comparisons between SVM and CART and RF and CART. On the other hand, the performance of SVM and RF are considered equally effective. Despite the challenges in implementing machine learning optimisation using GEE over a large area, this paper shows the efficiency of GEE as a cloud-based free platform to perform bioresource distributions mapping such as oil palm over a large area in Peninsular Malaysia