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Program Development For Synthesis Plan Design Of Organic Compounds By Using Artificial Intelligence, Tawatchai Jitporn
Program Development For Synthesis Plan Design Of Organic Compounds By Using Artificial Intelligence, Tawatchai Jitporn
Chulalongkorn University Theses and Dissertations (Chula ETD)
Computer-Assisted Synthesis Planning (CASP) is a computational tool for facilitating organic chemists to design and synthesis organic compounds by calculating the best possible synthesis pathways. Most CASP tools are commercial or closed-source software and thus, they cannot be modified to improve performance or update database. Therefore, it is our aim to develop an open-source CASP tool. Library of reaction rules that was created from the US patent database was taken from literature and was then reclassified to improve its applicability. This modified library was then used for training neural network and 2 important models were obtained for the search engine. …
Flow-Through Reactor For Photomediated Synthesis Of Silver Nanoprisms, Thanasit Laisanguanngam
Flow-Through Reactor For Photomediated Synthesis Of Silver Nanoprisms, Thanasit Laisanguanngam
Chulalongkorn University Theses and Dissertations (Chula ETD)
Silver nanoprisms (AgNPrs) have been widely used due to their outstanding optical properties which is one of an important phenomenon that depends on their size, shape, and compositions with characteristic localized surface plasmon resonance (LSPR). The changes in LSPR of AgNPrs can be simply observed by the color changes of the AgNPrs colloidal solution, therefore, the AgNPrs have been mostly used as chemical sensors as they provide high sensitivity and capable to be functionalized to improve the detection selectivity. In this study, we report a photochemical technique to induce the shape conversion of the spherical silver nanoparticle to AgNPrs by …
Development Of Colorimetric Paper-Based Analytical Device For Cortisol Detection, Thanathip Kosawatphat
Development Of Colorimetric Paper-Based Analytical Device For Cortisol Detection, Thanathip Kosawatphat
Chulalongkorn University Theses and Dissertations (Chula ETD)
Nowadays, the detection of cortisol can play a key role in understanding stress-related diseases. Several competitive immunoassay formats have been reported for the detection of this small hormone molecule. Lateral flow immunoassay (LFIA) is one of the most widely used platforms that offer its simplicity and rapidity for such analysis. Unlike previous sensors for cortisol sensing that require complicated readers, specialized reagents, or even post-treatment methods to amplify the obtained signal, in this work, we employed a concave test zone constructed on the LFIA device (cLFIA) which can directly enhance the signal response as the flowing sample was substantially concentrated …
Application Of Oxygen Scavenger In Gel Electromembrane Extraction For Determination Of Nitrite And Nitrate, Thidarat Samkumpim
Application Of Oxygen Scavenger In Gel Electromembrane Extraction For Determination Of Nitrite And Nitrate, Thidarat Samkumpim
Chulalongkorn University Theses and Dissertations (Chula ETD)
Gel electromembrane microextraction (G-EME) have been proven as a promising extraction method due to its effectiveness for extraction and preconcentration of charged analytes and its compliance with the principle of green chemistry. G-EME consists of two phases (donor and acceptor phases), separated by a gel membrane made of agarose. The driving force is the electrical potential applied across the membrane. Despite of all the advantages, the common problem was the oxygen generated in the acceptor solution due to the electrolysis of water that could affect the analysis of easily oxidized analyte species such as nitrite/nitrate species. In this work, application …
Structural Optimization Under Limited Natural Frequency Constraints Using Comprehensive Learning Phasor Particle Swarm Optimization, Ei Cho Pyone
Chulalongkorn University Theses and Dissertations (Chula ETD)
In this research, a phasor particle swarm optimization with comprehensive learning strategy (CLPPSO) is proposed for the optimal design of dome-like truss structures under the limited frequency-constraints. The proposed scheme is a new variant of PSO techniques with the direct combination of both the phasor theory in mathematics and comprehensive learning strategy to the particle swarm optimization. In order to model particle control parameters, a phase angle incorporating the periodic sine and cosine functions is essentially applied through which only the previous best positions of all particles are used to update the exemplar particle’s velocity during the optimization process. This …
Simultaneous Size And Shape Structural Optimization Using Enhanced Comprehensive Learning Particle Swarm Optimization, Soviphou Muong
Simultaneous Size And Shape Structural Optimization Using Enhanced Comprehensive Learning Particle Swarm Optimization, Soviphou Muong
Chulalongkorn University Theses and Dissertations (Chula ETD)
This study proposes a simultaneous size and shape structural optimization on truss structures using a metaheuristic algorithm, namely Enhanced Comprehensive Learning Particle Swarm Optimization (ECLPSO) which is based on the CLPSO algorithm. CLPSO itself was originated from a famous Particle Swarm Optimization (PSO) that was invented based on behaviour or movement of a bird flock. CLPSO is an improved PSO algorithm which has good exploration ability but is poor in exploitation. Thus, ECLPSO introduces of two enhancements, including perturbation-based exploitation and adaptive learning probabilities, to improve the exploitation ability and to adjust the learning probabilities of each particle, respectively. The …
A System For Generating Fire Extinguisher Plan And Supporting Fire Evacuation Route In Construction Project, Thiha Nyimin
A System For Generating Fire Extinguisher Plan And Supporting Fire Evacuation Route In Construction Project, Thiha Nyimin
Chulalongkorn University Theses and Dissertations (Chula ETD)
Fires in under-construction threaten worker safety and have a lot of negative impacts on many aspects of construction that were reported the past few years. Due to the dynamic nature of construction, the fire safety plan in construction needed to be reviewed and updated according to construction progress. The conventional approach or manual process to develop the fire safety plan for a construction project can be a time-consuming and enormous labor-intensive task. Furthermore, some of the emergency signs are unclear due to the nature of construction and some are not showing the correct evacuation route because of the structural design …
Incorporating Context Into Non-Autoregressive Model Using Contextualized Ctc For Sequence Labelling, Burin Naowarat
Incorporating Context Into Non-Autoregressive Model Using Contextualized Ctc For Sequence Labelling, Burin Naowarat
Chulalongkorn University Theses and Dissertations (Chula ETD)
Connectionist Temporal Classification (CTC) loss has become widely used in sequence modeling tasks such as Automatic Speech Recognition (ASR) and Handwritten Text Recognition (HTR) due to its ease of use. CTC itself has no architecture constraints, but it is commonly used with recurrent models that predict letters based on histories in order to relax the conditional independent assumption. However, recent sequence models that incorporate CTC loss have been focusing on speed by removing recurrent structures, hence losing important context information. This thesis presents Contextualized Connectionist Temporal Classification (CCTC) loss, which induces prediction dependencies in non-recurrent and non-autoregressive neural networks for …
A Rationale-Based Lifelong Learning Framework With Pseudo-Sample Replay Enhancement, Kasidis Kanwatchara
A Rationale-Based Lifelong Learning Framework With Pseudo-Sample Replay Enhancement, Kasidis Kanwatchara
Chulalongkorn University Theses and Dissertations (Chula ETD)
Lifelong learning (LL) is a machine learning paradigm in which a learner is sequentially trained on a stream of new tasks while preventing learned knowledge from being forgotten. To achieve lifelong language learning, pseudo-rehearsal methods leverage samples generated from a language model to refresh the knowledge of previously learned tasks. Without proper controls, however, these methods could fail to retain the knowledge of complex tasks with longer texts since most of the generated samples are low in quality. To overcome the problem, we propose three specific contributions. First, we utilize double language models, each of which specializes on a specific …
Data Augmentation For Thai Natural Language Processing Using Different Tokenization, Patawee Prakrankamanant
Data Augmentation For Thai Natural Language Processing Using Different Tokenization, Patawee Prakrankamanant
Chulalongkorn University Theses and Dissertations (Chula ETD)
Tokenization is one of the most important data pre-processing steps in the text classification task and also one of the main contributing factors in the model performance. However, getting good tokenizations is non-trivial when the input is noisy, and is especially problematic for languages without an explicit word delimiter such as Thai. Therefore, we proposed an alternative data augmentation method to improve the robustness of poor tokenization by using multiple tokenizations. We evaluated the performance of our algorithms on different Thai text classification datasets. The results suggested our augmentation scheme makes the model more robust to tokenization errors and can …
Demand Forecasting In Production Planning For Dairy Products Using Machine Learning And Statistical Methods, Chayuth Vithisoontorn
Demand Forecasting In Production Planning For Dairy Products Using Machine Learning And Statistical Methods, Chayuth Vithisoontorn
Chulalongkorn University Theses and Dissertations (Chula ETD)
Demand forecasting is an essential task in manufacturing of every industry. Efficient forecasting relieves the excessive stock and out-of-stock problem, reducing revenue loss. This research performs a direct multistep forecast approach of demand forecasting on 8 dairy products of 5 different dairy production plants with 5-year data. Widely used traditional statistical method and the state of the art deep learning method for sequence problems are picked. ARIMA and LSTM. The models are compared in many aspects, monthly observations against weekly observations, univariate against multivariate, and statistical against deep learning using model error and business metrics. The result shows that both …
Performance Improvement Of Cms Simulation Via Loop Transformation, Teerit Ploensin
Performance Improvement Of Cms Simulation Via Loop Transformation, Teerit Ploensin
Chulalongkorn University Theses and Dissertations (Chula ETD)
High performance processor can tackle bottleneck issues by increasing vector lengths and leveling effectiveness of memory hierarchies to address these issue. Manual optimization of code is a difficult task when having multiple architecturedependent transformation. Our goal is to develop a tool that performs source code transformation based on loop optimization techniques, since a loop plays an important role in improving of performance in scientific simulation software. We implement an source-to-source transformation tool based libTooling, a Clang’s library, based on polyhedral model to simplify a loop transformation of CMSSW building pipeline. The tool also can be used for automatically transformation. The …
Sentiment Analysis Of Messages On Twitter Related To Covid-19 Using Deep Learning Approach, Chotika Imvimol
Sentiment Analysis Of Messages On Twitter Related To Covid-19 Using Deep Learning Approach, Chotika Imvimol
Chulalongkorn University Theses and Dissertations (Chula ETD)
The widespread situation of the Coronavirus-19 (COVID-19) pandemic is a tangible and pressing concern. Many changes in terms of lifestyle are necessary to reduce the chance of infection. While citizens have gone through different emotions, they share their thoughts and interactions on social media, especially on Twitter. COVID-19 related messages can imply social emotion. This study performs sentiment analysis on tweets and annotated them into six classes of positive and negative feelings consisting of anger, disgust, fear, sadness, joy, and surprise. We analyzed both textual information and historical data. We collected 120,642 unique tweets datasets between 1 January 2020 and …
Conformance Checking And Discovery Of Information Service Request Process, Liam Khaosanoi
Conformance Checking And Discovery Of Information Service Request Process, Liam Khaosanoi
Chulalongkorn University Theses and Dissertations (Chula ETD)
Process mining is a form of business process analysis. The approach could support organizations in retrieving structured process information by using recorded process data to discover, monitor and improve the processes. In this work, the process mining technology is applied for conformance checking and discovering an organization’s Information Service Request process in reality as a case study. Currently, the reference model was drawn as a simple flow using Microsoft Word. The proposed method starts with writing a VBA script to extract traces from the drawing that enables the generation of XES file used for modeling the reference process in Petri …
Object Detection In Intelligent Billing System For Conveyor Belt Sushi Restaurant, Rangrak Maitriboriruks
Object Detection In Intelligent Billing System For Conveyor Belt Sushi Restaurant, Rangrak Maitriboriruks
Chulalongkorn University Theses and Dissertations (Chula ETD)
Organization must automate wherever and whenever they can, particularly during today’s global changes in daily lifestyles. Trends regarding the use of technology, especially AI has emerged as a key enabler for disruptive innovation. This thesis thus presents the application programming interface of object detector implemented with YOLOv4 and OpenCV for classifying the prices of sushi plates distinguished by colors. The object detector is part of the smart cross-platform mobile application to facilitate billing process for conveyor belt sushi business. The frontend is developed with Flutter to build single codebase for UIs. To handle the variants of image colors resulting from …
Adaptive Image Preprocessing And Augmentation For Disease Screening On Multi-Source Chest X-Ray Datasets, Wasunan Chokchaithanakul
Adaptive Image Preprocessing And Augmentation For Disease Screening On Multi-Source Chest X-Ray Datasets, Wasunan Chokchaithanakul
Chulalongkorn University Theses and Dissertations (Chula ETD)
Research on deep learning models for chest radiology applications has increased attention by the public. However, most works focus on developing models using in-domain data, so the significant drawback, when applied in real-world scenarios, was the mismatched data with the training set. Consequently, some models perform inferior at the deployment stage. This work focused on the effects of dataset mismatch on chest radiography and analyzed the methods the overcome the mismatch issues. The lung balance contrast enhancement technique (lung BCET) automatically identifies the lung region and normalizes the image accordingly to improve the robustness of out-of-domain data developed. Additionally, augmentation …
A Design Of Fpga Framework For Quantum Computing Simulation, Yaninee Jungjarassub
A Design Of Fpga Framework For Quantum Computing Simulation, Yaninee Jungjarassub
Chulalongkorn University Theses and Dissertations (Chula ETD)
We use FPGA to optimize the simulation of quantum computing in two aspects. (a) The if-else state is used in place of tensor product calculation. This allows the tensor product of each quantum operator to be generated in a single clock cycle. (b) The pre-calculated lookup ROM is used for estimating the sine and cosine values. This facilitates the computation of quantum gates that are related to angle. To validate our work, we implement our design in VerilogHDL. The performance is evaluated using an FPGA simulator. The result shows a dramatic improvement in the simulation process comparing to those of …
Cascading Model For Forex Market Forecasting Using Fundamental And Technical Indicator Data Based On Bert, Arisara Pornwattanavichai
Cascading Model For Forex Market Forecasting Using Fundamental And Technical Indicator Data Based On Bert, Arisara Pornwattanavichai
Chulalongkorn University Theses and Dissertations (Chula ETD)
The foreign exchange rate market is the world's biggest and most liquid financial market, and it's where all currency pairs' exchange rates are set. Since foreign exchange (Forex) rates play a critical role in financial technology and business, many researchers are now interested in forecasting them. The characteristics of Forex data, that include fluctuation, non-linearity, and random walk phenomena, make it difficult for forecasting. Several related studies integrate fundamental data (FD) and technical indicator data to generate Forex forecasting signals (TI). TI is a price pattern-based signal, whereas FD is an indicator of the country's economic conditions. Nevertheless, when it …
Travel Time Prediction With Graph Neural Network: A Case Study In Bangkok Thailand, Sathita Buapang
Travel Time Prediction With Graph Neural Network: A Case Study In Bangkok Thailand, Sathita Buapang
Chulalongkorn University Theses and Dissertations (Chula ETD)
Traffic prediction is an essential and challenging task for traffic management and commercial purposes. Machine learning methods for traffic prediction usually treat traffic conditions as time-series due to obvious temporal patterns. Recently, spatial relationships among roads in a road network have also been used to improve traffic prediction. This study proposes a novel method to predict traffic conditions such as speed using a graph convolutional neural network with a spectral adjacency matrix (GCN-Spectral). Unlike a spatial adjacency matrix representing physical connections between road segments, a spectral matrix represents the correlation between road segments regarding traffic conditions. The GCN-Spectral model is …
Pulmonary Lesion Classification Using Convolutional Neural Network For Endobronchial Ultrasonogram, Banphatree Khomkham
Pulmonary Lesion Classification Using Convolutional Neural Network For Endobronchial Ultrasonogram, Banphatree Khomkham
Chulalongkorn University Theses and Dissertations (Chula ETD)
This dissertation aims to develop a method to help classify pulmonary lesions from endobronchial ultrasonography images by proposing new features that are extracted from an EBUS image based on medical knowledge and a pulmonary lesion classification framework. The proposed features, namely the adaptive weighted-sum of the upper triangular gray-level co-occurrence matrix and the adaptive weighted-sum of the lower triangular gray-level co-occurrence matrix are used to determine heterogeneity, which is one of the most important characteristics of malignancy. The proposed features together with other standard features are used as input data for the proposed classification framework that uses the weighted ensemble …
Thai Tokenizer Invariant Classification Based On Bi-Lstm And Distilbert Encoders, Noppadol Kongsumran
Thai Tokenizer Invariant Classification Based On Bi-Lstm And Distilbert Encoders, Noppadol Kongsumran
Chulalongkorn University Theses and Dissertations (Chula ETD)
Natural language processing (NLP) is a topic in artificial intelligence to teach computer to understand human language. Researchers can feed text of some particular language in any length and type such as characters, words, and sentences into the algorithm to extract a summarized context in terms of numbers. To accept a word array in Thai language, tokenization process is needed to split a text into words because each sentence is written consecutively without any space between words. In general, different tokenizers can produce different sets of words from a single sentence, resulting in uncontrolled accuracies in NLP and related tasks. …
Warehouse Processes Improvement Using Lean Six Sigma And Rfid Technology, Pakkaporn Rungruengkultorn
Warehouse Processes Improvement Using Lean Six Sigma And Rfid Technology, Pakkaporn Rungruengkultorn
Chulalongkorn University Theses and Dissertations (Chula ETD)
The warehouse processes are critical to the success of every organization. Thus, a well-managed warehousing process increases an organization's efficiency. The objective of this thesis is to improve warehouse processes in garment manufacturing using Lean Six Sigma and RFID Technology to reduce waste time and non value-added activities of the current processes. Lean Six Sigma is business process improvement technique that combines the tools and principles of Lean and Six Sigma. The DMAIC methodology was employed in this research, which stands for Define, Measure, Analyze, Improve, and Control. Additionally, Model analysis, Value Stream Mapping (VSM), SIPOC charts, and Cause and …
Classification Of Abusive Thai Messages In Social Networks Using Deep Learning, Ruangsung Wanasukapunt
Classification Of Abusive Thai Messages In Social Networks Using Deep Learning, Ruangsung Wanasukapunt
Chulalongkorn University Theses and Dissertations (Chula ETD)
Social media has improved on traditional news sources by allowing increased access to information. However, the anonymity social media provides can lead to abusive and hateful speech without detection or repercussion from individuals with malicious intentions. This research develops a binomial and a multinomial classification model for classifying Thai social media text for five categories of abusive content detection in social media that include Rude, Figurative, Dirty, Offensive and Non-Abusive. The experiments demonstrated that DistilBERT achieved the highest F1 score with 0.8510 for the binomial model and 0.9067 for the multinomial model. BiLSTM performed second best with an F1 score …
Designing Answer Sets For Thai Advisory Chatbot With Different Talking Styles For Covid-19 Pandemic, Sakolwan Peetaneelavat
Designing Answer Sets For Thai Advisory Chatbot With Different Talking Styles For Covid-19 Pandemic, Sakolwan Peetaneelavat
Chulalongkorn University Theses and Dissertations (Chula ETD)
This research focuses on designing a set of answers for Thai advisory chatbot using a different conversational approach from conventional chatbot for COVID-19 pandemic in order to improve user’s chatbot experience. The research studies how to modify the underlying interactive chatbot language to suit those extrovert and introvert personalities. To evaluate the correctness of answer set design, the author builds two classification models based on Extroversion Text Classification Model (ETCM). Both models utilize the same Logistic Regression algorithm, but different feature selection techniques. The first model relies solely on Term Frequency-Inverse Document Frequency (TF-IDF) property. In contrast, the second model …
Considering Neighbor Projection On Neural Based Recommender Systems, Thitiporn Neammanee
Considering Neighbor Projection On Neural Based Recommender Systems, Thitiporn Neammanee
Chulalongkorn University Theses and Dissertations (Chula ETD)
Now, CF is applied with a neural network to make the model more flexible and more accurate. Different neighbors have a different influence on the target user, and different users usually have different rating patterns. Therefore, the proposed method needs to consider two major issues when applying CF with a neural network: the similarity levels between the neighbors and the target user and the user's rating pattern conversion. Thus, the proposed method consists of three main modules to solve the issues mentioned above: rating conversion, similarity module uses, and prediction module. In the experiment, the proposed method is evaluated and …
Thai Variable-Length Question Classification For E-Commerce Platform Using Machine Learning With Topic Modeling Feature, Wasu Chunhasomboon
Thai Variable-Length Question Classification For E-Commerce Platform Using Machine Learning With Topic Modeling Feature, Wasu Chunhasomboon
Chulalongkorn University Theses and Dissertations (Chula ETD)
Nowadays, e-commerce platform continuously grows every year and becomes a part of our daily life. However, the application changes from time to time. Either new users or experienced users could face a problem. Several channels, which are FAQ, email, live chat, and call, are provided by e-commerce platform to cope with the problem. FAQ is usually ignored because it is hard to search for the desired answer. The rest channels are applicable. However, the huge number of users causes a bottleneck especially in the special events which delays the users to receive help because customer service agent can reply to …
Single Image Super-Resolution Using Capsule Generative Adversarial Network, Amir Hajian
Single Image Super-Resolution Using Capsule Generative Adversarial Network, Amir Hajian
Chulalongkorn University Theses and Dissertations (Chula ETD)
The current research aims to investigate and propose a Generative Adversarial Network (GAN) architecture [53] using capsule network architecture [76] in the discriminator module of the proposed model (Caps-GAN) for Single Image Super-Resolution. Besides, the study aims to develop the proposed SR framework in three scale factors. Finally, the performance of Caps-GAN is compared with other state-of-the-art models. Our Caps-GAN model consists of three fundamental components: the generator module, capsule discriminator module, and combinations of loss functions based on the GAN concept. The proposed generator utilizes the residual in residual dense blocks (RRDB) architecture [28] under a progressively up-sampling framework …
Multi-Modal Biometric-Based Human Identification Using Deep Convolutional Siamese Neural Network, Hsu Mon Lei Aung
Multi-Modal Biometric-Based Human Identification Using Deep Convolutional Siamese Neural Network, Hsu Mon Lei Aung
Chulalongkorn University Theses and Dissertations (Chula ETD)
Biometric recognition is a critical task in security control systems. Although face biometric has long been granted the most accepted and practical biometric for human recognition, it can be easily stolen and imitated. It also has challenges getting reliable facial information from the low-resolution camera. In contrast, a gait physical biometric has been recently used for recognition. It can be more complicated to replicate and can also be taken from reliable information from the poor-quality camera. However, human body recognition has remained a problem since the lack of full-body detail within a short distance. Moreover, the unimodal biometric system still …
Deep Learning With Attention Mechanism For Iterative Face Super-Resolution, Krit Duangprom
Deep Learning With Attention Mechanism For Iterative Face Super-Resolution, Krit Duangprom
Chulalongkorn University Theses and Dissertations (Chula ETD)
Face images are widely used in many applications, such as face recognition and face identification. Regarding security, face identification is used to track the crimes. However, the camera's low resolution and environmental degradation problem hinders the face application's performance. In this thesis, we study face image super-resolution to restore the image from low-resolution to high-resolution. We proposed deep learning with an attention mechanism for iterative face super-resolution that included an image super-resolution network and face alignment network combined. The input low-resolution image is enlarged into a super-resolution face image. Then, the image has repeatedly estimated the alignment to enhance the …
Simplified Tone Reservation-Based Techniques For Peak-To-Average Power Ratio Reduction Of Orthogonal Frequency Division Multiplexing Signals, Rafee Al Ahsan
Simplified Tone Reservation-Based Techniques For Peak-To-Average Power Ratio Reduction Of Orthogonal Frequency Division Multiplexing Signals, Rafee Al Ahsan
Chulalongkorn University Theses and Dissertations (Chula ETD)
Orthogonal Frequency Division Multiplexing (OFDM) is one of the preferred modulation techniques for modern wireless communications networks, due to its high spectral efficiency and immunity to frequency selective channels. However, OFDM signals are known to suffer from a large peak-to-average power ratio (PAPR). OFDM signals with high PAPR values will inevitably be clipped by the power amplifiers (PA), causing signal distortion and out-of-band radiation, that would lead to the deterioration of bit error rate performance. This thesis focuses on a class of PAPR reduction techniques called tone reservation (TR) techniques, which possesses three desirable features, namely high PAPR reduction gain, …