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Articles 3931 - 3960 of 5397
Full-Text Articles in Engineering
Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan
Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan
All Graduate Theses, Dissertations, and Other Capstone Projects
The detection of switching faults of power converters or the Circuit Under Test (CUT) is real-time important for safe and efficient usage. The CUT is a single-phase inverter. This thesis presents two unique methods that rely on backpropagation principles to solve classification problems with a two-layer network. These mathematical algorithms or proposed networks are able to diagnose single, double, triple, and multiple switching faults over different iterations representing range of frequencies. First, the fault detection and classification problems are formulated as neural network-based classification problems and the neural network design process is clearly described. Then, neural networks are trained over …
Development Of A Modeling Algorithm To Predict Lean Implementation Success, Richard Charles Barclay
Development Of A Modeling Algorithm To Predict Lean Implementation Success, Richard Charles Barclay
Doctoral Dissertations
”Lean has become a common term and goal in organizations throughout the world. The approach of eliminating waste and continuous improvement may seem simple on the surface but can be more complex when it comes to implementation. Some firms implement lean with great success, getting complete organizational buy-in and realizing the efficiencies foundational to lean. Other organizations struggle to implement lean. Never able to get the buy-in or traction needed to really institute the sort of cultural change that is often needed to implement change. It would be beneficial to have a tool that organizations could use to assess their …
Studying The Effects Of Various Process Parameters On Early Age Hydration Of Single- And Multi-Phase Cementitious Systems, Rachel Cook
Doctoral Dissertations
”The hydration of multi-phase ordinary Portland cement (OPC) and its pure phase derivatives, such as tricalcium silicate (C3S) and belite (ß-C2S), are studied in the context varying process parameters -- for instance, variable water content, water activity, superplasticizer structure and dose, and mineral additive type and particle size. These parameters are studied by means of physical experiments and numerical/computational techniques, such as: thermodynamic estimations; numerical kinetic-based modelling; and artificial intelligence techniques like machine learning (ML) models. In the past decade, numerical kinetic modeling has greatly improved in terms of fitting experimental, isothermal calorimetry to kinetic-based modelling …
Observer-Based Event-Triggered And Set-Theoretic Neuro-Adaptive Controls For Constrained Uncertain Systems, Abdul Ghafoor
Observer-Based Event-Triggered And Set-Theoretic Neuro-Adaptive Controls For Constrained Uncertain Systems, Abdul Ghafoor
Doctoral Dissertations
"In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon …
Automatic Gaze Classification For Aviators: Using Multi-Task Convolutional Networks As A Proxy For Flight Instructor Observation, Justin Wilson, Sandro Scielzo, Sukumaran Nair, Eric C. Larson
Automatic Gaze Classification For Aviators: Using Multi-Task Convolutional Networks As A Proxy For Flight Instructor Observation, Justin Wilson, Sandro Scielzo, Sukumaran Nair, Eric C. Larson
International Journal of Aviation, Aeronautics, and Aerospace
In this work, we investigate how flight instructors observe aviator scan patterns and assign quality to an aviator's gaze. We first establish the reliability of instructors to assign similar quality to an aviator's scan patterns, and then investigate methods to automate this quality using machine learning. In particular, we focus on the classification of gaze for aviators in a mixed-reality flight simulation. We create and evaluate two machine learning models for classifying gaze quality of aviators: a task-agnostic model and a multi-task model. Both models use deep convolutional neural networks to classify the quality of pilot gaze patterns for 40 …
Multi-Evidence Learning For Medical Diagnosis, Tongjai Yampaka
Multi-Evidence Learning For Medical Diagnosis, Tongjai Yampaka
Chulalongkorn University Theses and Dissertations (Chula ETD)
In recent years, a great many approaches for learning from multiple sources by considering the diversity of different views have been proposed. The most interesting field is medical diagnosis. For example, breast cancer screening normally employs two views of mammography (Cranio-Caudal and Medio-Lateral-Oblique) or two modes of ultrasound (B-mode and Doppler mode) breast images. This study proposes a multi-evidence learning model that combines the multiple evidences of breast images to improve diagnosis. Two views mammography and two modes of ultrasound were used. Our proposed model consists of four stages. First, feature extraction using Convolutional Neuron Networks was operated to extract …
A Robust System For Core Thai Natural Language Processing Technologies, Can Udomcharoenchaikit
A Robust System For Core Thai Natural Language Processing Technologies, Can Udomcharoenchaikit
Chulalongkorn University Theses and Dissertations (Chula ETD)
As the amount of unstructured textual data grows, it becomes increasingly important to build an intelligent system that can process it. Natural Language Processing (NLP) is a technology that allows a computer to exploit human languages to perform tasks. Deep learning models have shown excellent results across fundamental tasks in NLP, such as word segmentation, part-of-speech tagging, and named-entity recognition. However, in many situations, these proposed methods fail to perform well. For an NLP system to be robust, it must address issues such as out-of-vocabulary and spelling-mistakes. This thesis's research goal is to develop NLP models that can handle malformed …
Semi-Supervised Thai Sentence Segmentation Using Local And Distant Word Representations, Chanatip Saetia
Semi-Supervised Thai Sentence Segmentation Using Local And Distant Word Representations, Chanatip Saetia
Chulalongkorn University Theses and Dissertations (Chula ETD)
A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, …
Deep Sequential Real Estate Recommendation Approach For Solving Item Cold Start Problem, Jirut Polohakul
Deep Sequential Real Estate Recommendation Approach For Solving Item Cold Start Problem, Jirut Polohakul
Chulalongkorn University Theses and Dissertations (Chula ETD)
The item cold-start problem occurs when a recommendation system cannot recommend new items owing to record deficiencies and new listing omissions. When searching for real estate, users can register a concurrent interest in recent and prior projects. Thus, an approach to recommend cold-start and warm-start items simultaneously must be determined. Furthermore, unrequired membership and stop-by behavior cause real estate recommendations to have many cold-start and new users. This characteristic encourages the use of a content-based approach and a session-based recommendation system. Herein, we propose a real estate recommendation approach for solving the item cold-start problem with acceptable warm-start item recommendations …
A Real Estate Valuation Model Using Boosted Feature Selection, Kankawee Chanasit
A Real Estate Valuation Model Using Boosted Feature Selection, Kankawee Chanasit
Chulalongkorn University Theses and Dissertations (Chula ETD)
To estimate real estate values, a complex valuation model based on artificial neural network (ANN) has been established as a successful means in modern machine learning research, specifically when high-dimensional data are available. Unfortunately, the real estate data in many locations, such as Thailand, are quite limited in terms of features. Hence, it becomes mandatory to reduce the complexity using feature selection techniques. These techniques aim to improve performance by identifying significant factors and help decrease the computational overload and model construction. However, due to the lack of explicability and interpretability in ANNs, the analysis of input factors cannot be …
Detection Of Wagyu Beef Sources With Image Classification Using Convolutional Neural Network, Nattakorn Kointarangkul
Detection Of Wagyu Beef Sources With Image Classification Using Convolutional Neural Network, Nattakorn Kointarangkul
Chulalongkorn University Theses and Dissertations (Chula ETD)
Wagyu beef originated in Japan. However, there are many types of Wagyu beef in the market around the globe. Primary sources include Australia, USA, Canada and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor and tenderness. Observing that there are differences in flavor, texture, and quality between distinct sources of Wagyu. This research presents an AI-based approach to identify Wagyu beef sources with image classification. The input images were collected from reliable sources on the internet and augmented with DCGAN. Deep neural networks, CNN, was constructed to detect the marbled fat …
Using Automatic Speech Recognition To Assess Thai Speech Language Fluency In Montreal Cognitive Assessment (Moca), Pimarn Kantithammakorn
Using Automatic Speech Recognition To Assess Thai Speech Language Fluency In Montreal Cognitive Assessment (Moca), Pimarn Kantithammakorn
Chulalongkorn University Theses and Dissertations (Chula ETD)
The Montreal Cognitive Assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning where scores are based on the number of unique correct words produced by the test-taker. However, with different languages, it is possible that unique words may be counted differently. This study focuses on Thai as a language that differs from English in its type of word combination. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the language fluency test of the MoCA with Thai language support. The …
Accurate Surface Ultraviolet Radiation Forecasting For Clinical Applications With Deep Neural Network, Raksit Raksasat
Accurate Surface Ultraviolet Radiation Forecasting For Clinical Applications With Deep Neural Network, Raksit Raksasat
Chulalongkorn University Theses and Dissertations (Chula ETD)
Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280-360 nm, maximum 320 nm), or narrow-band UV B irradiation (main emission spectrum 310-315nm, maximum 311nm). For patients who cannot access phototherapy centers, sun-bathing, or heliotherapy, can be a safe and effective treatment alternative. However, as sunlight contains the full range of UV radiation (290-400 nm), careful sun-bathing supervised by photodermatologist based on accurate UV radiation forecast is vital to minimize potential adverse effects. Here, using 10-year UV radiation …
Fast Decision-Making Under Time And Resource Constraints, Kyle Gabriel Lassak
Fast Decision-Making Under Time And Resource Constraints, Kyle Gabriel Lassak
Graduate Theses, Dissertations, and Problem Reports (ETD)
Practical decision makers are inherently limited by computational and memory resources as well as the time available in which to make decisions. To cope with these limitations, humans actively seek methods which limit their resource demands by exploiting structure within the environment and exploiting a coupling between their sensing and actuation to form heuristics for fast decision-making. To date, such behavior has not been replicated in artificial agents. This research explores how heuristics may be incorporated into the decision-making process to quickly make high-quality decisions through the analysis of a prominent case study: the outfielder problem. In the outfielder problem, …
Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin
Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin
Electrical & Computer Engineering Faculty Publications
This guest editorial summarizes the Special Section on Machine Learning in Optics.
Generative Adversarial Networks For Visible To Infrared Video Conversion, Mohammad Shahab Uddin, Jiang Li, Chiman Kwan (Ed.)
Generative Adversarial Networks For Visible To Infrared Video Conversion, Mohammad Shahab Uddin, Jiang Li, Chiman Kwan (Ed.)
Electrical & Computer Engineering Faculty Publications
Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) …
Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni
Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni
Dissertations
Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …
The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1
The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1
Tanzania Journal of Engineering and Technology (TJET)
Worldwide, every day human beings generate millions of tons of Faecal Sludge (FS), which is rich in water, nutrients, energy, and organic compounds. Yet FS is not being managed in a way that permits us to derive value from its reuse, while at the same time, millions of farmers struggle with depleted soils and lack of water. In most of the developing countries, energy for cooking is mainly derived from cutting of trees, either as wood or charcoal. Resource recovery and reuse from FS can create livelihoods, enhance food security, support green economies, reduce waste and contribute to cost recovery …
Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger
Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger
Electrical and Computer Engineering Publications
The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …
Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin
Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin
Master of Science in Computer Science Theses
This paper attempts to answer the question of if it’s possible to produce a simple, quick, and accurate neural network for the use in upper-limb prosthetics. Through the implementation of convolutional and artificial neural networks and feature extraction on electromyographic data different possible architectures are examined with regards to processing time, complexity, and accuracy. It is found that the most accurate architecture is a multi-entry categorical cross entropy convolutional neural network with 100% accuracy. The issue is that it is also the slowest method requiring 9 minutes to run. The next best method found was a single-entry binary cross entropy …
Design Of A Flexible System Simulation Evaluation Framework, Rusheng Ju, Zimin Cai, Wang Song, Wang Peng
Design Of A Flexible System Simulation Evaluation Framework, Rusheng Ju, Zimin Cai, Wang Song, Wang Peng
Journal of System Simulation
Abstract: To meet variable evaluation requirement of complex system simulation, this paper puts forward a design strategy of flexible effectiveness evaluation framework. The composition structure of system simulation evaluation framework is analyzed. To help users design reference dynamically, the method of evaluation reference edit and display is introduced based on Web. To enhance the extensibility of evaluation model, the method of interface design and code generation is investigated. To ensure the flexibility and extensibility of evaluation framework, the relation and mapping mechanism of evaluation references, evaluation model and evaluation result is studied. The framework is realized and verified in …
Research On Simulation Platform For Equipment System Analysis, Yuping Li, Shaojie Mao, Zhenqi Ju, Zhou Fang, Guoqiang Yan
Research On Simulation Platform For Equipment System Analysis, Yuping Li, Shaojie Mao, Zhenqi Ju, Zhou Fang, Guoqiang Yan
Journal of System Simulation
Abstract: With the development of equipment construction from platform-centric to network-centric, simulation analysis of equipment system is an important means and tool to support the transformation and development of equipment construction under the condition of multi-task joint operation. Starting from the requirement of system simulation analysis, a cloud-based equipment system simulation architecture is proposed to realize flexible and configurable simulation environment according to task requirements; and a high-performance simulation framework is conducted, which provides strong support for different application modes, such as parallel hyper-real-time and distributed simulation deduction. The unified description, organization and management method of model and data resources …
Research On Corridor Setting Based On Pedestrian Simulation Of Social Groups, Yiting Xu, Zhang Rui
Research On Corridor Setting Based On Pedestrian Simulation Of Social Groups, Yiting Xu, Zhang Rui
Journal of System Simulation
Abstract: As the connector of each space in the hub, the rail transit hub corridor plays the role of transition and buffer. The existence of social groups makes an important impact on pedestrian traffic and its simulation. The paper supplements the consideration of pedestrian traffic related studies on social groups travel, analyses the characteristics of social groups in rail transit hub corridor, improves Moussaïd social group force model, and redevelops AnyLogic micro-simulation platform based on Python language. Taking a subway station in Beijing as an example, fully considering the influence of social groups, it is obtained that the optimal channel …
Parallel Tasks Optimization Scheduling In Cloud Manufacturing System, Chenwei Feng, Wang Yan
Parallel Tasks Optimization Scheduling In Cloud Manufacturing System, Chenwei Feng, Wang Yan
Journal of System Simulation
Abstract: To solve the problem of unbalanced resource requirements and low resource utilization when the same type of tasks are executed in parallel in the cloud manufacturing system, a task resource scheduling model with the goal of minimizing cost, minimizing time, maximizing reliability and optimizing quality is established. A non-dominated sorting genetic algorithm based on reference points (NSGA-III) is adopted to solve the model by combining real number matrix coding and crossover and mutation based on real number coding instead of common evolutionary strategy. And an optimal decision strategy based on combination of analytic hierarchy process and entropy value method …
Research On The Value Accessing Method For Calibrating Micro Traffic Simulation Model Parameters, Chenjing Zhou, Rong Jian, Kwok Lam
Research On The Value Accessing Method For Calibrating Micro Traffic Simulation Model Parameters, Chenjing Zhou, Rong Jian, Kwok Lam
Journal of System Simulation
Abstract: Parameter calibration is the precondition of the application of micro traffic simulation technology. This study focuses on the value accessing method for parameter calibration in order to further improve the parameter calibration process. The analysis of the distribution characteristics of each parameter calibration results shows that the parameters have different trends in the process of gradual iteration, and there are multiple optimal solutions for the model parameter calibration results. In this paper, the dispersion is used as the quantitative analysis index of the concentration degree of each parameter calibration result, and the parameter value of the model is determined …
Research On Source Seeking Methods Of Harmful Gas Leakage In Chemical Industry Parks, Zhao Yong, Bin Chen, Xiaodong Wang, Zhengqiu Zhu, Rongxiao Wang, Xiaogang Qiu
Research On Source Seeking Methods Of Harmful Gas Leakage In Chemical Industry Parks, Zhao Yong, Bin Chen, Xiaodong Wang, Zhengqiu Zhu, Rongxiao Wang, Xiaogang Qiu
Journal of System Simulation
Abstract: Chemical production safety accidents often lead to harmful gas leakage, causing serious environmental damage and casualties. Mastering the information of leaking source quickly can assist in emergency disposal decisions and reduce the harm of accidents. In this paper, Entrotaxis algorithm is used to guide the ground source seeking equipment to move to the vicinity of the leaking source quickly and autonomously in a chemical park scene, and to master the source information. According to the characteristics of the chemical industry park scene, this paper applies intermittent search module into Entrotaxis algorithm and proposes a robust and suitable algorithm (Entrotaxis-Jump …
Numerical Simulation Analysis Of Anti-Blast Impact Of Underground Rescue Capsule Based On Ls-Dyna, Zhang Fan, Yuanhua Yang, Xiaoxu He, Deng Yu
Numerical Simulation Analysis Of Anti-Blast Impact Of Underground Rescue Capsule Based On Ls-Dyna, Zhang Fan, Yuanhua Yang, Xiaoxu He, Deng Yu
Journal of System Simulation
Abstract: Aiming at the strength problem of the underground rescue cabin under explosion impact load, a finite element model of overall explosion impact load and fluid-solid coupling structure response is established in transient dynamic soft LS-DYNA. The flow field impact load is generated by explosion algorithm, and the propagation of the impact load in the air is calculated. The dynamic response of the rescue cabin structure under the impact load is calculated by the fluid-solid coupling method. The results show that the maximum load occurs on the end surface closest to the explosion source, and the structural deformation is small …
The Scheduling Algorithm Of Cloud Job Based On Hopfield Neural Network, Yudong Guo, Jinping Zuo
The Scheduling Algorithm Of Cloud Job Based On Hopfield Neural Network, Yudong Guo, Jinping Zuo
Journal of System Simulation
Abstract: Focusing on the low efficiency of cloud job scheduling and the insufficient utility of resource, a job scheduling algorithm based on Hopfield Neural Network is proposed. In order to improve the resource scheduling ability of the system, The resource characteristics which influence the cloud job scheduling are shown. The mathematical model of resource constraints is established, and the Hopfield energy function is designed and optimized. The average utilization rate of 9 nodes is analyzed by using the standard test cases, and the performance and resource utilization of the proposed strategy are compared with three typical algorithms. …
Simulation Research On Attitude Solution Method Of Micro-Mini Missile, Chunbo Zhao, Junfang Fan, Liu Ning
Simulation Research On Attitude Solution Method Of Micro-Mini Missile, Chunbo Zhao, Junfang Fan, Liu Ning
Journal of System Simulation
Abstract: Aiming at the problem of attitude measurement error in the inertial navigation system of micro and small guided ammunition under eccentric structure, the attitude solution and error compensation optimization are studied by using rotation vector optimization of multiple sub-samples, such as monotone sample, two sub-samples, three sub-samples and four sub-samples, and the fourth-order runge kutta algorithm. Through error compensation and optimization of measured data, the results show that the monomorphic modified algorithm has the worst optimization effect, the fourth-order runge kutta optimization algorithm has the best effect, and the maximum drift error of attitude Angle is better than 10 …
Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren
Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren
Journal of System Simulation
Abstract: Multi-objective optimization of aerodynamic layout is a key technology in the design of vehicles. The overall configuration of the shape parameters is optimized with a double swept-shaped wave shape as the basic configuration. We use NSGA-Ⅱ multi-objective genetic algorithm, take the aircraft double sweep angle as the design variable, consider the maximum takeoff weight, range, volume ratio and other performance indicators, use Elman neural network to establish the relationship between shape parameters and performance parameters, and establish constraints based on mission planning requirements. The Pareto optimal solution set is obtained by using optimized design and the individuals with …