3 research outputs found
Prediction of the Compressive Strength of Alkali-Activated Mortar for Block-Work Binders Using Adaptive Neuro-Fuzzy Inference System
No full textThis study employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the compressive strength of alkali-activated mortars (AAMs) designed for blockwork applications. The model, configured with 225 fuzzy rules, evaluates key mix parameters including activator ratio, alkali concentration, and liquid-to-solid ratio, optimizing strength predictions. Experimental validation confirms that ANFIS achieves high accuracy, with an RMSE of 0.97 and an a20-index of 0.74, indicating strong predictive reliability. The research utilizes waste cellular lightweight concrete (CLC) as a precursor, demonstrating its feasibility for sustainable construction. By reducing the need for extensive laboratory testing, ANFIS improves mix design efficiency, making AAMs more viable for commercial applications. The findings highlight the effectiveness of ANFIS in optimizing AAM formulations, ensuring a balance between material efficiency, sustainability, and structural performance. This research supports the transition toward more eco-friendly, high-performance construction materials, facilitating the broader adoption of alkali-activated mortars in real-world applications
Trademark image retrieval by local features
Full text linkThe challenge of abstract trademark image retrieval as a test of machine vision algorithms has attracted considerable research interest in the past decade. Current
operational trademark retrieval systems involve manual annotation of the images
(the current ‘gold standard’). Accordingly, current systems require a substantial
amount of time and labour to access, and are therefore expensive to operate. This
thesis focuses on the development of algorithms that mimic aspects of human
visual perception in order to retrieve similar abstract trademark images
automatically. A significant category of trademark images are typically highly
stylised, comprising a collection of distinctive graphical elements that often
include geometric shapes. Therefore, in order to compare the similarity of such
images the principal aim of this research has been to develop a method for solving
the partial matching and shape perception problem.
There are few useful techniques for partial shape matching in the context of
trademark retrieval, because those existing techniques tend not to support multicomponent
retrieval. When this work was initiated most trademark image
retrieval systems represented images by means of global features, which are not
suited to solving the partial matching problem. Instead, the author has
investigated the use of local image features as a means to finding similarities
between trademark images that only partially match in terms of their subcomponents.
During the course of this work, it has been established that the
Harris and Chabat detectors could potentially perform sufficiently well to serve as
the basis for local feature extraction in trademark image retrieval. Early findings
in this investigation indicated that the well established SIFT (Scale Invariant
Feature Transform) local features, based on the Harris detector, could potentially
serve as an adequate underlying local representation for matching trademark
images.
There are few researchers who have used mechanisms based on human
perception for trademark image retrieval, implying that the shape representations
utilised in the past to solve this problem do not necessarily reflect the shapes
contained in these image, as characterised by human perception. In response, a
ii
practical approach to trademark image retrieval by perceptual grouping has been
developed based on defining meta-features that are calculated from the spatial
configurations of SIFT local image features. This new technique measures certain
visual properties of the appearance of images containing multiple graphical
elements and supports perceptual grouping by exploiting the non-accidental
properties of their configuration.
Our validation experiments indicated that we were indeed able to capture
and quantify the differences in the global arrangement of sub-components evident
when comparing stylised images in terms of their visual appearance properties.
Such visual appearance properties, measured using 17 of the proposed metafeatures,
include relative sub-component proximity, similarity, rotation and
symmetry. Similar work on meta-features, based on the above Gestalt proximity,
similarity, and simplicity groupings of local features, had not been reported in the
current computer vision literature at the time of undertaking this work.
We decided to adopted relevance feedback to allow the visual appearance
properties of relevant and non-relevant images returned in response to a query to
be determined by example. Since limited training data is available when
constructing a relevance classifier by means of user supplied relevance feedback,
the intrinsically non-parametric machine learning algorithm ID3 (Iterative
Dichotomiser 3) was selected to construct decision trees by means of dynamic
rule induction. We believe that the above approach to capturing high-level visual
concepts, encoded by means of meta-features specified by example through
relevance feedback and decision tree classification, to support flexible trademark
image retrieval and to be wholly novel.
The retrieval performance the above system was compared with two other
state-of-the-art image trademark retrieval systems: Artisan developed by Eakins
(Eakins et al., 1998) and a system developed by Jiang (Jiang et al., 2006). Using
relevance feedback, our system achieves higher average normalised precision
than either of the systems developed by Eakins’ or Jiang. However, while our
trademark image query and database set is based on an image dataset used by
Eakins, we employed different numbers of images. It was not possible to access to
the same query set and image database used in the evaluation of Jiang’s trademark
iii
image retrieval system evaluation. Despite these differences in evaluation
methodology, our approach would appear to have the potential to improve
retrieval effectiveness
Development of Heat Resistance Properties of Geopolymer Bricks from Fly Ash and Metakaolin by Bagasse Ash Additionการพัฒนาสมบัติการทนความร้อนของอิฐจีโอโพลิเมอร์จากเถ้าลอยและดินขาวเผาโดยการเติมเถ้าชานอ้อย
No full textงานวิจัยนี้มุ่งศึกษาการพัฒนาอิฐจีโอโพลิเมอร์จากเถ้าลอยผสมดินขาวเผาและเถ้าชานอ้อยเพื่อเพิ่มประสิทธิภาพการป้องกันความร้อน โดยศึกษาผลของการแทนที่เถ้าลอยด้วยเถ้าชานอ้อยในอัตราส่วนร้อยละ 0 10 20 และ 30 โดยน้ำหนัก ต่อคุณสมบัติทางกายภาพและเชิงกล ได้แก่ กำลังรับแรงอัด ความหนาแน่น ความพรุน การดูดซึมน้ำ ค่าสัมประสิทธิ์การนำความร้อน และความสามารถในการต้านทานความร้อนสูง ผลการวิจัยพบว่าการเพิ่มปริมาณเถ้าชานอ้อยส่งผลให้ค่าการนำความร้อนลดลงอย่างมีนัยสำคัญ โดยสูตร BA30 มีค่าการนำความร้อนลดลงมากที่สุดถึง 41.1% (0.277 W/m·K) ขณะที่กำลังรับแรงอัดมีแนวโน้มลดลงเมื่อเพิ่มปริมาณเถ้าชานอ้อย โดยที่อายุ 28 วัน สูตร BA0 BA10 BA20 และ BA30 มีค่ากำลังรับแรงอัดเฉลี่ย 14.23 13.29 8.53 และ 9.18 MPa ตามลำดับ ทั้งนี้ เมื่อพิจารณาคุณสมบัติโดยรวมพบว่าสูตร BA10 มีความเหมาะสมที่สุดโดยมีกำลังอัดสูงกว่าเกณฑ์มาตรฐาน มอก.77-2565 ถึง 32.9% ค่าการนำความร้อนลดลง 12.8% มีความทนทานต่อความร้อนสูงสุด โดยรักษากำลังไว้ได้ 18.7% ที่อุณหภูมิ 1000°C การวิเคราะห์ต้นทุนพบว่าอิฐจีโอโพลิเมอร์มีต้นทุนประมาณ 4 บาทต่อก้อน ทั้งยังมีค่าการนำความร้อนต่ำกว่าอิฐมอญและอิฐบล็อกทั่วไปกว่า 40% และสามารถทนไฟได้นานกว่า 4 ชั่วโมง แสดงถึงคุณสมบัติในการป้องกันความร้อนที่เหนือกว่าอย่างชัดเจนThis research aimed to develop geopolymer bricks using fly ash blended with metakaolin and bagasse ash to enhance thermal insulation properties. The study examined the effects of replacing fly ash with bagasse ash at proportions of 0%, 10%, 20%, and 30% by weight on the physical and mechanical properties, including compressive strength, density, porosity, water absorption, thermal conductivity coefficient, and high-temperature resistance. The results showed that increasing the bagasse ash content significantly reduced thermal conductivity, with the BA30 formulation achieving the greatest reduction of 41.1% (0.277 W/m·K). Meanwhile, compressive strength tended to decrease with increasing bagasse ash content. At 28 days, formulations BA0, BA10, BA20, and BA30 exhibited average compressive strengths of 14.23, 13.29, 8.53, and 9.18 MPa, respectively. Overall, the BA10 formulation was found to be optimal, with compressive strength exceeding the TIS 77-2565 standard by 32.9%, thermal conductivity reduced by 12.8%, and maximum heat resistance, retaining 18.7% of its strength at 1000°C. Cost analysis revealed that geopolymer bricks cost approximately 4 baht per unit. It also exhibited thermal conductivity values over 40% lower than those of conventional clay and concrete bricks, and withstood fire exposure for more than 4 hours, clearly demonstrating superior thermal insulation performance
