Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Adaptive thresholding in dynamic scene analysis for extraction of fine line

This paper presents an adaptive threshold method whereby a fine thin line of one-pixel width lines could be detected in a gray level images. The proposed method uses the percentage difference between the mean of the pixels within a window and the center pixel. The minimum threshold value however is heuristically set to 32. If the percentage difference is greater than 40% then the threshold value will be set to the difference value. This method has been applied in detecting moving objects with fine lines and the results showed that the method was able to pickup straight thin edges that belong to the moving objec

Fingerprint center point location using directional field

This paper presents a reliable fingerprint center point (CP) location algorithm for the alignment of fingerprints to construct a shift invariant fingerprint recognition system. The proposed algorithm is based on Alteration Tracking (AT) and CP estimation (CPE). AT is proposed to extract a track that records the transition from one quantized direction to another. CPE is aimed to find the bending point with highest transition of direction from the transition track. This algorithm is tested against fingerprints captured from SAGEM MSO100 optical scanner and the second database from University of Bologna. Experimental result shows that the proposed algorithm is capable of reliably locating fingerprint CP

Characterizations on microencapsulated sunflower oil as self-healing agent using In situ polymerization method

This paper emphasizes the characterization on the microencapsulation of sunflower oil as self-healing agent. In-situ polymerization method mainly implicates in the microencapsulation process. The analysis of microencapsulated sunflower oil via prominent characterization of yield of microcapsules, microcapsules characteristics and Fourier Transmission Infa-Red Spectroscopy (FTIR). The prime optimization used was reaction time of microencapsulation process in the ranges of 2, 3 and 4 h. The higher reaction time of microencapsulation process resulted in a higher yield of microcapsules. The yield of microcapsules increases from 46 to 53% respectively by the increasing of reaction time from 2 to 4 h. The surface morphology study associating the diameter of microcapsules measured to analyse the prepared microcapsules. It was indicated that microcapsules were round in shape with smooth micro-surfaces. It was discovered that the diameter of microcapsules during microencapsulation process after 4 h reaction time was in average of 70.53 μm. This size was measured before filtering the microcapsules with solvent and dried in vacuum oven. Apparently, after filtering and drying stage, the diameter of microcapsules specifically identified under Field Emission Scanning Electron Microscopy (FESEM) showing the size of 2.33 μm may be due to the removing the suspended oil surrounded the microcapsules. Sunflower oil as core content and urea formaldehyde (UF) as shell of microcapsules demonstrated the proven chemical properties on characterization by FTIR with the stretching peak of 1537.99 - 1538.90 cm-1 (-H in -CH2), 1235.49 - 1238.77 cm-1 (C-O-C Vibrations at Ester) and 1017.65 - 1034.11 cm-1 (C-OH Stretching Vibrations). It was showed that sunflower oil can be considered as an alternative nature resource for self-healing agent in microencapsulation process. The characterization of microencapsulated sunflower oil using in-situ polymerization method showed that sunflower oil was viable self-healing agent to be encapsulated and incorporated in metal coating

Business success and psychological traits of housing developers

Although the issues on entrepreneurship in the real estate industry are disparaging and have received little interest from global researchers, the significant roles played by the industry players warrant further investigation. The personal traits of the owners/managers are deemed to be the key factors to the business success of housing development firms. This paper examined the main determinants of successful business in the housing development sector under the lens of psychological traits of the owners/managers. To identify the success factors of housing development firms, interviews were conducted on 10 housing developers in Peninsular Malaysia. The study shows the significant psychological traits that distinguish the business success of housing developers compared with those in the existing literature. These factors are (1) high confidence and ambition, (2) vision and foresight, (3) industriousness, (4) perseverance and (5) integrity. As housing development is a risky industry, developers can use these findings as a guideline in managing their business toward superior performance

Warna kontemporari dalam karya seni / Aimi Atikah Roslan dan Dr Syed Alwi Syed Abu Bakar

Penulisan ini adalah mengenai kajian terhadap warna kontemporari di dalam karya seni. Warna kontemporari telah menjadi penghubung dalam mengaplikasi teori dan praktikal terutamanya dalam menghasilkan karya seni lukis. Walaupun sudah banyak kajian mengenai warna namun kajian ini mengupas hubung kait antara karya seni dan warna kontemporari. Ini membolehkan pembaca memahami bahawa warna kontemporari adalah sebahagian daripada dunia seni lukis dan seni reka bentuk. Penyelidik menggunakan karya seni dalam kajian warna kontemporari bagi menjawab persoalan kepentingan warna kontemporari. Hasilnya, peranan warna kontemporari ini sangat penting yang menjadi pergerakan sesuatu era seni kontemporari. Penulisan ini adalah satu cubaan untuk membawa perkara yang berkaitan dengan warna dengan menggunakan satu bahasa yang sama

Business Success and Psychological Traits of Housing Developers

Although the issues on entrepreneurship in the real estate industry are disparaging and have received little interest from global researchers, the significant roles played by the industry players warrant further investigation. The personal traits of the owners/managers are deemed to be the key factors to the business success of housing development firms. This paper examined the main determinants of successful businesses in the housing development sector under the lens of psychological traits of the owners/managers. To identify the success factors of housing development firms, interviews were conducted on 10 housing developers in Peninsular Malaysia. The study shows the significant psychological traits that distinguish the business success of housing developers compared with those in the existing literature. These factors are (1) high confidence and ambition, (2) vision and foresight, (3) industriousness, (4) perseverance and (5) integrity. As housing development is a risky industry, developers can use these findings as a guideline in managing their business toward superior performance

Extraction of biological apatite from cow bone at different calcination temperatures: a comparative study

The purpose of this study is to extract natural hydroxyapatite (HAP) from cow bone. The hydrothermal method followed by calcination treatment at different temperatures is used in this current research. Cow bone has the potential for producing hydroxyapatite, a chief component present in bone and teeth of vertebrates. HAP is an excellent material used in bone restoration and tissue regeneration. Characterizations of the cow bone natural HAP powder were done by X-ray diffraction (XRD) and Thermogravimetric analysis (TGA). TGA data revealed that biological apatite is thermally stable at 1100ºC. XRD data showed that the extracted HAP is, highly crystalline and hexagonal crystal structure having a crystallite size in the range of 10-83 nm. The extracted HAP material is found to be thermally stable up to 1300ºC

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

An innovative Dynamic Voltage Restorer (DVR) system based on Artificial Neural Network (ANN) technology, implemented in MATLAB Simulink, accurately detects, and dynamically restores voltage sags, significantly improving power quality and ensuring a reliable supply to critical loads, contributing to the advancement of power quality enhancement techniques. Voltage sags are a prevalent power quality concern that can have a significant impact on sensitive electrical equipment. An innovative approach to address voltage sags through the operation of a Dynamic Voltage Restorer (DVR) based on Artificial Neural Network (ANN) technology. The proposed system, developed using MATLAB Simulink, leverages the ANN's capabilities to accurately detect voltage sags and dynamically restore the voltage to the affected load. The ANN is trained using a comprehensive dataset comprising voltage sag events, enabling it to learn the intricate relationships between sag characteristics and optimal compensation techniques. By integrating the trained ANN into the DVR control scheme, real-time compensation for voltage sags is achieved. The effectiveness of the proposed system is rigorously evaluated through extensive simulations and performance analysis. The results demonstrate the superior performance of the ANN-based DVR in terms of voltage sag detection accuracy and restoration precision. Consequently, the proposed system presents an intelligent and adaptive solution for voltage sag compensation, ensuring a reliable and high-quality power supply to critical loads. This research contributes to the advancement of power quality enhancement techniques, facilitating the implementation of intelligent power system