Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Control theory. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

A Review on Natural Fiber Reinforced Polymer Composite for Bullet Proof and Ballistic Applications.

Abstract: Even though natural fiber reinforced polymer composites (NFRPCs) have been widely used in automotive and building industries, there is still a room to promote them to high-level structural applications such as primary structural component specifically for bullet proof and ballistic applications. The promising performance of Kevlar fabrics and aramid had widely implemented in numerous ballistic and bullet proof applications including for bullet proof helmets, vest, and other armor parts provides an acceptable range of protection to soldiers. However, disposal of used Kevlar products would affect the disruption of the ecosystem and pollutes the environment. Replacing the current Kevlar fabric and aramid in the protective equipment with natural fibers with enhanced kinetic energy absorption and dissipation has been significant effort to upgrade the ballistic performance of the composite structure with green and renewable resources. The vast availability, low cost and ease of manufacturing of natural fibers have grasped the attention of researchers around the globe in order to study them in heavy armory equipment and high durable products. The possibility in enhancement of natural fiber’s mechanical properties has led the extension of research studies toward the application of NFRPCs for structural and ballistic applications. Hence, this article established a state-of-the-art review on the influence of utilizing various natural fibers as an alternative material to Kevlar fabric for armor structure system. The article also focuses on the effect of layering and sequencing of natural fiber fabric in the composites to advance the current armor structure system.

An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite

Abstract: Engineering properties of rocks such as unconfined compressive strength (UCS) and Young’s modulus (E) are among the essential parameters for the design of tunnel excavations. Many attempts have been made to develop indirect methods of estimating UCS and E. This is generally attributed to the difficulty of preparing and conducting the aforementioned tests in a laboratory. In essence, this study aims to present two predictive models of UCS and E for granite using an adaptive neuro-fuzzy inference system (ANFIS). The required rock samples for model development (45 granite sample sets) were obtained from site investigation work at the Pahang-Selangor raw water transfer tunnel, which was excavated across the Main Range of Peninsular Malaysia. In developing the predictive models, dry density, ultrasonic velocity, quartz content and plagioclase were set as model inputs. These parameters were selected based on simple and multiple regression analyses presented in the article. However, for the sake of comparison, the prediction performances of the ANFIS models were checked against multiple regression analysis (MRA) and artificial neural network (ANN) predictive models of UCS and E. The capacity performances of the predictive models were assessed based on the value account for (VAF), root mean squared error (RMSE) and coefficient of determination (R 2). It was found that the ANFIS predictive model of UCS, with R 2, RMSE and VAF equal to 0.985, 6.224 and 98.455 %, respectively, outperforms the MRA and ANN models. A similar conclusion was drawn for the ANFIS predictive model of E where the values of R 2, RMSE and VAF were 0.990, 3.503 and 98.968 %, respectively.

Watermarking Techniques used in Medical Images: a Survey

Abstract: The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients’ privacy be protected. As a result of this, there is a need for medical image watermarking (MIW). However, MIW needs to be performed with special care for two reasons. Firstly, the watermarking procedure cannot compromise the quality of the image. Secondly, confidential patient information embedded within the image should be flawlessly retrievable without risk of error after image decompressing. Despite extensive research undertaken in this area, there is still no method available to fulfill all the requirements of MIW. This paper aims to provide a useful survey on watermarking and offer a clear perspective for interested researchers by analyzing the strengths and weaknesses of different existing methods.

Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

Abstract: The evaluation and precise prediction of safety factor (SF) of slopes can be useful in designing/analyzing these important structures. In this study, an attempt has been made to evaluate/predict SF of many homogenous slopes in static and dynamic conditions through applying various hybrid intelligent systems namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN and artificial bee colony (ABC)-ANN. In fact, ICA, PSO, GA and ABC were used to adjust weights and biases of ANN model. In order to achieve the aim of this study, a database composed of 699 datasets with 5 model inputs including slope gradient, slope height, friction angle of soil, soil cohesion and peak ground acceleration and one output (SF) was established. Several parametric investigations were conducted in order to determine the most effective factors of GA, ICA, ABC and PSO algorithms. The obtained results of hybrid models were check considering two performance indices, i.e., root-mean-square error and coefficient of determination $$(R^{2})$$ . To evaluate capability of all hybrid models, a new system of ranking, i.e., the color intensity rating, was developed. As a result, although all predictive models are able to approximate slope SF values, PSO-ANN predictive model can perform better compared to others. Based on $$R^{2}$$ , values of (0.969, 0.957, 0.980 and 0.920) were found for testing of ICA-ANN, ABC-ANN, PSO-ANN and GA-ANN predictive models, respectively, which show higher efficiency of the PSO-ANN model in predicting slope SF values.