Chi - man Vong

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Restricted Boltzmann Machines (RBM) and auto encoders, learns to represent features in a dataset meaningfully and used as the basic building blocks to create deep networks. This paper introduces Extreme Learning Machine based Auto Encoder (ELM-AE), which learns feature representations using singular values and is used as the basic building block for Multi(More)
This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data," Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for(More)
This paper proposes a novel modelling and optimization approach for steady state and transient performance tune-up of an engine at idle speed. In terms of modelling, Latin hypercube sampling and multiple-input and multiple-output (MIMO) least-squares support vector machines (LS-SVMs) are proposed to build an engine idle-speed model based on experimental(More)
Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every(More)
Modern automotive engines are controlled by the electronic control unit (ECU). The engine performance referred to as outuput torque is significantly affected by the setup of control parameters in the ECU. Traditional ECU tune-up is done by trial-and-error method through repeated dynamometer tests. LS-SVM (Least Squares Support Vector Machines) is a powerful(More)
Automotive engine air-ratio plays an important role of emissions and fuel consumption reduction while maintains satisfactory engine power among all of the engine control variables. In order to effectively control the air-ratio, this paper presents a model predictive fuzzy control algorithm based on online least-squares support vector machines prediction(More)
Precision Medicine in Oncology requires tailoring of therapeutic strategies to individual cancer patients. Due to the limited quantity of tumor samples, this proves to be difficult, especially for early stage cancer patients whose tumors are small. In this study, we exploited a 2.4 × 2.4 centimeters polydimethylsiloxane (PDMS) based microfluidic chip which(More)
This paper describes the design and implementation of an automatic hydraulic circuit design system using case-based reasoning (CBR) as one of successful Knowledge Engineering paradigms. The domain of hydraulic circuit design and case-based reasoning are briefly reviewed. Then a proposed methodology in automatic circuit design and dynamic learning with the(More)