Case Depth Measurement of Induction Hardening Using Image Processing

INDUCTION HARDENING of medium carbon steel iswidely used to produce automotive parts,agriculturalequipment and other machines.Inductor with highfrequency electric current was used as a heater totransform microstructure of steel surface into Austenite.Then water was sprayed on the heated steel.Austenitetransformed into Martensite.Area of microstructuralchange was considered to be case depth that was animportant parameter to be controlled in productionprocess.The standard procedure to de…     

Navigating a robotic swarm in an uncharted 2D landscape

Navigation is a major issue in robotics due to the necessity for the robots’ course of movement. Navigation consists of two essential components known as localization and planning. Localization in robotics refers to one’s location with reference to a well known position inside the map. Planning is considered as the computation of a path through a map which represents the environment. This given path would be chosen based on the potential of the problem so that the expected destination would be achieved. As such, a reliable map is essential for navigation without which robots would not be able to accomplish the goals. In navigational approaches, reliability of the map would be challenged due to the dynamic and unpredictable nature of real-world applications. It is, consequently, crucial to implement solutions for searching such environments—those affected by dynamic and noisy constraints. In the present study, two enhanced versions of particle swarm optimization (PSO) called area extension PSO (AEPSO) and cooperative AEPSO (CAEPSO) are employed. During the study, AEPSO and CAEPSO are employed as decision-makers and movement controllers of simulated robots (hereafter referred to as agents). The agents’ task is to seek for survivors in realistic simulations based on real-world hostile situations. This study examines the feasibility of AEPSO and CAEPSO on uncertain and time-dependent simulated environments. The simulations follow two phases of training and testing model. Agents use past knowledge gathered during the training phase in their testing phase. The study addresses the impacts of past knowledge, homogeneity and heterogeneity in robotic swarm search. The results demonstrate the feasibility of CAEPSO as robot controller and decision-maker.     

Design and testing of continuous acid-catalyzed esterification reactor for high free fatty acid mixed crude palm oil

Using acid-catalyzed esterification, a continuous reactor, containing four separate continuous stirred tank reactors (CSTR's), was designed and used to reduce the free fatty acid (FFA) content of mixed crude palm oil (MCPO). A six-blade disk turbine and four baffles were installed in each of the four reactors to enhance mixing. The complete reactor was tested using response surface methodology (RSM). A 5-level, 4-factor, central composite design (CCD) was employed to optimize the four important reaction variables (methanol/oil ratio; sulfuric acid/oil ratio; speed of the stirrer; and residence time) to reduce the FFA content of the MCPO to less than 1 wt.% of oil. Multiple regression analysis was used to derive a polynomial equation to predict the FFA content of the product. This was then used to indicate optimal conditions for reducing the FFA in mixed crude palm oil to less than 1 wt.%.     

Porosity enhancement of activated carbon by hydrolyzed lignin from black liquor

Clean Technologies and Environmental Policy - In this work, the porosity enhancement of activated carbon by hydrolyzed lignin extracted from black liquor was studied. Lignin was treated before...     

Quiescent crystallization of natural fibers–polypropylene composites

The effect of natural fibers (vetiver grass and rossells) on quiescent crystallization of polypropylene (PP) composites was analyzed in this study. Also, equilibrium melting temperature (T) of the composites was elucidated. Natural fiber‐PP composites showed lower T when compared to neat PP. Thermal analysis was performed via differential scanning calorimeter to study the crystallization kinetics. Natural fiber‐PP composites exhibited higher rate of crystallization than that of neat PP. Furthermore, spherulitic growth rate and transcrystallinity of the composites were investigated under a polarized light optical microscope. It was found that the growth rates of the composites were lower than that of neat PP. The spherulitic growth rates combined with the crystallization rates were used to calculate number of effective nuclei. It was shown that the number of effective nuclei of the composites was higher than that of neat PP. This suggested that natural fibers could act as a nucleating agent in the composite. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

Applying Area Extension PSO in Robotic Swarm

Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves better performance by balancing its essential intelligent behaviours more intelligently. AEPSO incorporates knowledge with the aim of choosing proper behaviors in each situation. This study provides a comparison between the variations of Basic PSO and AEPSO aiming to address dynamic and time dependent constraint problems in simulated robotic search. The problem is set up in a multi-robot learning scenario. The scenario is based on the use of a team of simulated robots (hereafter referred to as agents) who participate in survivor rescuing missions. The experiments are classified into three simulations. At first, agents employ variations of basic PSO as their decision maker and movement controllers. The first simulation investigates the impacts of swarm size, parameter adjustment, and population density on agents’ performance. Later, AEPSO is employed to improve the performance of the swarm in the same simulations. The final simulation investigates the feasibility of AEPSO in time-dependent, dynamic and uncertain environments. As shown by the results, AEPSO achieves an appreciable level of performance in dynamic, time-dependence and uncertain simulated environments and outperforms the variations of basic PSO, Linear Search and Random Search used in the simulations.     

Drying characteristics of Shiitake mushroom and Jinda chili during vacuum heat pump drying

Drying characteristics of the Shiitake mushroom and Jinda chili, a commonly grown variety in the Northeast of Thailand, was investigated under varying conditions of the drying temperatures (50, 55, 60 and 65 °C) and the vacuum pressures (0.1, 0.2, 0.3 and 0.4 bar) in a new design of a vacuum heat pump dryer. Nine different thin layer mathematical drying models were compared according to their correlation coefficient, reduced chi-square and root mean square error to estimate vacuum heat pump drying curves. The result indicates that the Midilli model can present better predictions than the others. The constants and coefficients of this model could be explained by the effect of the drying temperature and the drying pressure. The drying temperature and pressure significantly affects color degradation (probability P < 0.05). Drying temperature has little effects on rehydration capacity (probability P > 0.05). Rehydration capacity notably decreases with an increase in the vacuum pressure.     

A cascaded classifier approach for improving detection rates on?rare attack categories in network intrusion detection

Network intrusion detection research work that employed KDDCup 99 dataset often encounter challenges in creating classifiers that could handle unequal distributed attack categories. The accuracy of a classification model could be jeopardized if the distribution of attack categories in a training dataset is heavily imbalanced where the rare categories are less than 2% of the total population. In such cases, the model could not efficiently learn the characteristics of rare categories and this will result in poor detection rates. In this research, we introduce an efficient and effective approach in dealing with the unequal distribution of attack categories. Our approach relies on the training of cascaded classifiers using a dichotomized training dataset in each cascading stage. The training dataset is dichotomized based on the rare and non-rare attack categories. The empirical findings support our arguments that training cascaded classifiers using the dichotomized dataset provides higher detection rates on the rare categories as well as comparably higher detection rates for the non-rare attack categories as compared to the findings reported in other research works. The higher detection rates are due to the mitigation of the influence from the dominant categories if the rare attack categories are separated from the dataset.     

Properties of Bayesian student model for INQPRO

Employing a probabilistic student model in a scientific inquiry learning environment often presents two challenges. First, what constitute the appropriate variables for modeling scientific inquiry skills in such a learning environment, considering the fact that it practices exploratory learning approach? Following exploratory learning approach, students are granted the freedom to navigate from one GUI to another. Second, do causal dependencies exist between the identified variables, and if they do, how should they be defined? To tackle the challenges, this research work attempted the Bayesian Networks framework. Leveraging on the framework, two student models were constructed to predict the acquisition of scientific inquiry skills for INQPRO, a scientific inquiry learning environment developed in this research work. The student models can be differentiated by the variables they modeled and the causal dependencies they encoded. An on-field evaluation involving 101 students was performed to assess the most appropriate structure of the INQPRO’s student model. To ensure fairness in model comparison, the same Dynamic Bayesian Network (DBN) construction approach was employed. Lastly, this paper highlights the properties of the student model that provide optimal results for modeling scientific inquiry skill acquisition in INQPRO.     

Electrical resistivity and corrosion potential of reinforced concrete: influencing factors and prediction models

Abstract

This paper presents two models: one for predicting the electrical resistivity of concrete and the other for the corrosion potential of reinforcing steel. The prediction models were developed based on experimental results, considering various influencing factors. The experiment approach included the concrete mix proportion, chloride content, concrete cover thickness and time of exposure as the parameters. The results revealed that fly ash concrete showed significantly high electrical resistivity even in the presence of chloride ions. The effects of fly ash became more significant when the water to binder ratio was lower. Chloride ions also decreased the corrosion potential of steel in both the OPC and fly ash concrete. The corrosion potential was found less negative for fly ash concrete due to higher electrical resistivity. The prediction results show good agreement with the experimental results of this study and some other researchers.