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Exploring the knowledge system in building information modeling (BIM) technology for medical construction projects (MCPS): A bibliometric analysis and prospective direction
(2024-09-01)
Chen, Jiahao
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Laokhongthavorn, Laemthong
This paper investigates the knowledge system in Building Information Modeling (BIM) technology in Medical Construction Projects (MCPs). Specifically, it proposes knowledge graph analysis utilizing scient metric techniques and employs VOSviewer and CiteSpace software to analyze the consequence of relevant statistics from the literature concerning BIM medical construction projects (BIM-MCPs). In order to analyze the collaboration network, the analysis of the co-occurrence network, and the analysis of the co-citation network, 83 journal articles were gathered. The results obtained from the research indicate that (a) the intellectual framework of BIM-MCPs can be categorized into four distinct clusters. (b) The research focus in BIM-MCPs has evolved, including model development, frameworks, implementation, system integration, application throughout the entire lifecycle, emergency medical engineering project applications, and intelligent management of medical facilities and smart buildings. (c) Prospective directions for research are delineated in three distinct areas: enhancement of use for BIM Medical Facility Management (BIM-MFM), integration of BIM technology in sustainability assessment, and application of BIM technology in emergency medical engineering and smart medical construction.
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Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch
(2021-05-01)
Khamsopha, Duangkamolrat
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Woranitta, Sahachairungrueng
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Teerachaichayut, Sontisuk
Fraud creates huge problems for the food industry. One type of fraud is adulteration in order to reduce costs and increase profitability. Fraud occurs in the starch industry, which is difficult or impossible to detect by visual inspection. Therefore this study was to test a possible nondestructive method that could be used to detect the adulterants in tapioca starch by utilizing reflectance near infrared hyperspectral imaging (NIR-HSI) at wavelengths in the range of 935–1720 nm. Pure tapioca starch was adulterated with limestone powder at 0.5% intervals over the range of 0–100% (wt/wt). The samples (n = 201) were divided into a calibration set (n = 140) and a prediction set (n = 61). Chemometrics was investigated and used to establish a calibration model for predicting the concentration of adulterant using partial least squares regression (PLSR). The accuracy of prediction using the model gave excellent results with the correlation coefficient (R) of 0.996 and the root mean square error of prediction (RMSEP) of 2.47%. The model was then used to create the predictive images of pure tapioca starch, adulterated tapioca starch and pure adulterant. It showed different colors based on the concentration of the adulterant. Therefore, NIR-HSI was shown to have potential as a method for rapidly detecting the level of concentration of adulterant in tapioca starch using both the predictive model and visualization.
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Reconfigurable pinart to help the blinds visualization
(2020-07-01)
Chaiwat, Kunlapuch
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Raksasurasan, Wanchanok
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Iamudomchai, Pisit
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Intasam, Anongnat
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Piyawattanametha, Wibool
We developed a reconfigurable pinart model that can transform its topographical surface which is in three-dimension from a stereographic image captured from a webcam. The model relies on a pneumatic pump and solenoid valves to excite each pin stroke via our custom multiplex control system. The travel displacement for each pin stroke is ten centimeters. The reconfiguration time is less than a few minutes with our demonstration reconfigurable pinart prototype of one hundred pixels.
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An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences
(2020-01-01)
Coupet, Matthieu
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Urruty, Thierry
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Leelanupab, Teerapong
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Naudin, Mathieu
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Bourdon, Pascal
Gliomas are the most common central nervous system tumors. They represent 1.3% of cancers and are the 15th most common cancer for men and women. For the diagnosis of such pathology, doctors commonly use Magnetic Resonance Imaging (MRI) with different sequences. In this work, we propose a global framework using convolutional neural networks to create an intelligent assistant system for neurologists to diagnose the brain gliomas. Within this framework, we study the performance of different neural networks on four MRI modalities. This work allows us to highlight the most specific MRI sequences so that the presence of gliomas in brain tissue can be classified. We also visually analyze extracted features from the different modalities and networks with an aim to improve the interpretability and analysis of the performance obtained. We apply our study on the MRI sequences that are obtained from BraTS datasets.
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Automated attribute inference for IOT data visualization service
(2019-01-01)
Sangpetch, Orathai
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Sangpetch, Akkarit
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Nartnorakij, Jittinat
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Vejprasitthikul, Narawan
As data becomes vital to urban development of modern cities, Thailand has initiated a smart city project on pilot cities around the country. We have implemented an interoperable data platform for smart city to enable Internet of Things (IoT) data exchanges among organizations through APIs. One of the key success is that people can access and visual the data. However, data can have various attributes since standard has not completely established and adopted. Therefore, it is difficult to automate the process to achieve comprehensive visualization. Traditionally, we require developers to manually examine data streams to determine which data attribute should be presented. This process can be very time consuming. The visualization system must be manually updated whenever a source stream modifies its data attributes. This problem becomes an impediment to implement a scalable cloud-based visualization service. To mitigate this challenge, we propose an automated attribute inference approach to automatically select key visualizable attribute from heterogeneous streams of data sources. We have experimented with different data attribute selection algorithms, namely an empirical rule-based system and the chosen machine learning algorithms. We implement and evaluate the proposed selection algorithms through our 3D visualization program in order to get the feedback from users.
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Visualization and Estimation of Temperature from Glowing Hot Object by Artificial Neural Network and Image Analysis Technique
(2018-01-01)
Jirasuwankul, N.
This paper proposes alternative technique to estimate temperature of glowing hot objects with application of artificial neural network (ANN) and image analysis techniques. Regardless of using the cutting edge technology or sophisticated sensor such as 2D thermo-imaging equipment, an approximated thermo-imagery of the glowing hot object can be reconstructed by a well-trained ANN model together with image analysis in RGB color space. By training the model with data along the Blackbody locus from the CIE-1931 chromaticity chart and using three normalized individual monochromatic R, G and B images as inputs, the processed image having correlated color temperature (CCT) is finally obtained. Experimental results show that averaging error of the estimated temperature can be achieved with 10% for the reddish-yellowish hot objects and less than 10% for the bright-yellow one respectively.
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Implementation of facial visualization for robotic indicator by using fuzzy emotion system
(2007-12-01)
Purahong, Boonchana
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Archevapanich, Tuanjai
This paper shows the result of the [1] experiment and shows how to design and status of the robot in Face emotion. We implement with the a vacuum robot, and we will show how to setup the system into hardware, which consists of Fuzzy Emotion Generator that processes the inputs from the heat sensors of the main board and the battery indicator to the outputs for using in visualization step which is a displaying board. Visualization has been developed to show the robot's emotion through the face completely by limited hardware. The result of the experiment of showing the emotion through the face of the robot works naturally. ©ICROS.
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Facial visualization for robotic indicator by using Fuzzy Emotional System
(2006-12-01)
Archevapanich, Tuanjai
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Purahong, Boonchana
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Klingajay, Mongkorn
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Sooraksa, Pitikhate
The issue of this paper is to show working status of a robot in the form of Face emotion which shows the creation of Fuzzy Emotion Generator. This Fuzzy Emotion Generator uses Takagi-Sugeno (TS) Fuzzy System to process input data from heat sensors on CPU and Batter Indicator into emotional output of a robot for visualization. The visualization system that was built can completely show robot's emotions. The experimental results show the advantages of the application of Fuzzy system in presenting the output and its advantages are natural continuation which is the result of successful implementation. © 2006 ICASE.
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Accelerating ray-casting for semitransparent image using Bi-directional skipped distance transforms
(2002-01-01)
Sepsirisuk, Kasemsuk
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Ungpinitpong, Phittaya
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Lertprasert, Polsart
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Sangworasil, Manas
This paper shows a method for speeding up the ray-traversal process of volume viewing. The algorithm acceleration is achieved by skipping empty regions defined for each background voxel of the volume in pre-processing step. Background voxels are labeled by a value, which is equal to the voxel distance to the nearest foreground voxel. The algorithm starts by backward traverse of the volume to find an existing point of the foreground and follows by forward traverse of the volume from an entry point of volume until it meets the existing point. This method enables a faster Tay-traversal time than traditional distance transforms.
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