Dao Vu - Academia.edu (original) (raw)

Papers by Dao Vu

Research paper thumbnail of A Multi-Feature Based Approach Incorporating Variable Thresholds for Detecting Price Spikes in the National Electricity Market of Australia

IEEE Access, 2021

Detecting electricity price spikes is crucial as it helps market participants to gain confidence ... more Detecting electricity price spikes is crucial as it helps market participants to gain confidence and formulate appropriate strategy to maximize their benefits. In this paper, a multi-feature based approach with the incorporation of variable thresholds is developed to detect electricity price spikes in the national electricity market of Australia. The variable thresholds, which are determined using a weighted sliding window average and an adjusted standard deviation, help to segregate spikes from normal price variations. Also, significant features are extracted from the market after thoroughly analyzing the underlying causes resulting into the price spikes. These features are employed as inputs to a support vector machine to classify electricity prices as spikes or non-spikes. A case study is conducted using a dataset acquired from the state of New South Wales, Australia. The results show that the proposed method can successfully detect the price spikes with high accuracy and confide...

Research paper thumbnail of Two Mode - (De)muxer Based on a Symmetric Y Junction Coupler, a \(2\times 2\) MMI Coupler and a Ridge Phase Shifter Using Silicon Waveguides for WDM Applications

Communications in Physics, 2017

In this paper, we introduce a new two-mode (de)multiplexer based on the silicon-oninsulator (SOI)... more In this paper, we introduce a new two-mode (de)multiplexer based on the silicon-oninsulator (SOI) platform. The device is built on a symmetric Y-junction, a 2 × 2 multimode interference (MMI) waveguide and a phaseshifter in the form of a ridge waveguide which is designed using 3D scalar beam propagation method (BPM). The phase evolution in the structure is discussed in details. The simulation results show that the device can operate in a wide wavelength range (150 nm) with a low insertion loss and small crosstalk. A large fabrication tolerance to the width of the input waveguide up to 100 nm is achieved, which is compatible to the current CMOS manufacturing technologies for the photonic integrated circuits. Furthermore, the small footprint (4 µm × 286 µm) makes the device suitable for applications in high bitrate and compact on-chip silicon photonic integrated circuits.

Research paper thumbnail of Political will in Fighting Corruption in Vietnam

Research paper thumbnail of Tiền là Tiên là Phật: Investigating the persistence of corruption in Vietnam

This research aims to examine the persistence of corruption in the public sector in Vietnam and e... more This research aims to examine the persistence of corruption in the public sector in Vietnam and explain why anti-corruption measures have been unsuccessful. It seeks to capture people’s lived experience of corruption in Vietnamese society and their perception of the failure of anti-corruption measures. It demonstrates what government officials and ordinary citizens think about corrupt practices and how they explain corrupt behaviour. The research also draws a clearer picture of Vietnam’s anti-corruption system, the weaknesses of the Anti-Corruption Law (ACL) and its implementation, from insiders’ perspectives. The research illuminates some factors identified in the literature that need to be better understood when dealing with corruption: historical, cultural, economic, administrative and political factors. This project situated Vietnam’s anti-corruption strategy within Jon Quah’s analytical framework, which identifies elements he argues are needed for an effective anti-corruption ...

Research paper thumbnail of Investigation of bioactive chemical constituents and anti-cancer activity of ethanol extract of Curcuma singularis Gagnep rhizomes

Research paper thumbnail of A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables

Applied Energy, Feb 1, 2015

A variance inflation factor and backward elimination based robust regression model for forecastin... more A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables,"

Research paper thumbnail of Short-Term Electricity Demand Forecasting Using a Functional State Space Model

In the past several years, the liberalization of the electricity supply, the increase in variabil... more In the past several years, the liberalization of the electricity supply, the increase in variability of electric appliances and their use, and the need to respond to the electricity demand in real time has made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All of these sources make electricity demand forecasting difficult. To forecast the electricity demand, some proposed parametric methods that integrate main variables that are sources of electricity demand. Others proposed a non parametric method such as pattern recognition methods. In this paper, we propose to take only the past electricity consumption information embedded in a functional vector autoregressive state space model to forecast the future electricity demand. The model we proposed aims to be applied at some aggregation level between regional and nationwide grids. To estimate the parameters of this model, we use likelihood maximization, spline smoothing, functional principal components analysis and Kalman filtering. Through numerical experiments on real datasets, both from supplier Enercoop and from the Transmission System Operator of the French nationwide grid, we show the appropriateness of the approach.

Research paper thumbnail of Short-term Load Forecasting using Regression based Moving Windows with Adjustable Window-Sizes

This paper presents a regression based moving window model for solving the short-term electricity... more This paper presents a regression based moving window model for solving the short-term electricity forecasting problem. Moving window approach is employed to trace the demand pattern based on the past history of load and weather data. Regression equation is then formed and least square method is used to determine the parameters of the model. In this paper, a new concept associated with cooling and heating degree is used to establish the relationship between electricity demand and temperature, which is one of the key climatic variables. In addition, Pearson's correlation has been employed to investigate the interdependency of electricity demand between different time periods. These analyses together with the data in the holiday period provide the supportive information for the appropriate selection of the window size. A case study has been reported in this paper by acquiring the relevant data for the state of New South Wales, Australia. The results are then compared with a neural network based model. The comparison shows that the proposed moving window approach with the different window sizes outperforms conventional neural network technique in small time scales i.e., from 30 minuntes to 1 day ahead.

Research paper thumbnail of Load forecasting under changing climatic conditions for the city of Sydney, Australia

Energy

In the current context, climate change has become an unequivocal phenomenon. Although it primaril... more In the current context, climate change has become an unequivocal phenomenon. Although it primarily encompasses change in temperature, nevertheless other weather variables such as rainfall, wind speed, evaporation and humidity can also be affected as a result of climate change. Addressing the impacts of climate change on electricity demand is essential for predicting the future demand. For example, cooling and heating requirements change significantly with respect to climate change that may result to the change in electricity load demand. In this paper, a backward elimination based multiple regression approach is proposed for analyzing the influence of climatic variables on load forecasting. A correlation analysis has been carried out using Pearson's correlation coefficient to examine the interdependency between different climatic variables in the context of Sydney, one of the most densely populated cities in Australia. Regression based analysis has been performed to examine the relationship between per capita electricity demand and associated climatic variables. 'Degree Days' concept has been utilized to determine balance point temperature. Backward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables related to the load demand. Average change in future per capita electricity demand has been predicted using the proposed approach for the city of Sydney, Australia. Results indicate that the demand for Sydney will increase by 6%

Research paper thumbnail of Combinatorial approach using wavelet analysis and artificial neural network for short-term load forecasting

Short term load forecasting is critically important in modern electricity networks since it helps... more Short term load forecasting is critically important in modern electricity networks since it helps provide supportive information for reliable power system operation in competitive electricity market environment. In this paper, the wavelet analysis based neural network model is employed to forecast the electricity demand in short-term period. The wavelet analysis helps to decompose the electricity demand data into different frequency bands. The Fourier transform is then employed to reveal the significant lags of these decomposed components. These lags are then used as inputs of neural network model to forecast the future values of each decomposed component. Finally, the forecasted components are combined together to form the electricity demand forecast. A case study has been reported in the paper by acquiring the data for the state of New South Wales, Australia. MAPE is used to validate the proposed model and the results show that the proposed method is promising for short term load forecasting.

Research paper thumbnail of High throughput FPGA architecture for corner detection in traffic images

2014 IEEE Fifth International Conference on Communications and Electronics (ICCE), 2014

Corner detection is the most computationally intensive step in vehicle tracking and vehicle speed... more Corner detection is the most computationally intensive step in vehicle tracking and vehicle speed estimation algorithms. In order to have real-time vehicle tracking for traffic surveillance applications, high speed architectures for corner detection are needed. This paper presents a high throughput FPGA architecture for detecting special features (corners in more detail) on traffic images which are captured by cameras. The module is implemented based on the FAST (Features from Accelerated Segment Test) algorithm with some modifications to be suitable for traffic images. The proposed architecture is able to reduce a great number of unnecessary detected corner points and maintain a high throughput of more than a thousand of 8-bit gray-scale images per second at 640 × 480 resolution. The resource usage is 21% lower than that of existing work, which allows the architecture to be implemented on almost all types of FPGA.

Research paper thumbnail of A Multi-Feature Based Approach Incorporating Variable Thresholds for Detecting Price Spikes in the National Electricity Market of Australia

IEEE Access, 2021

Detecting electricity price spikes is crucial as it helps market participants to gain confidence ... more Detecting electricity price spikes is crucial as it helps market participants to gain confidence and formulate appropriate strategy to maximize their benefits. In this paper, a multi-feature based approach with the incorporation of variable thresholds is developed to detect electricity price spikes in the national electricity market of Australia. The variable thresholds, which are determined using a weighted sliding window average and an adjusted standard deviation, help to segregate spikes from normal price variations. Also, significant features are extracted from the market after thoroughly analyzing the underlying causes resulting into the price spikes. These features are employed as inputs to a support vector machine to classify electricity prices as spikes or non-spikes. A case study is conducted using a dataset acquired from the state of New South Wales, Australia. The results show that the proposed method can successfully detect the price spikes with high accuracy and confide...

Research paper thumbnail of Two Mode - (De)muxer Based on a Symmetric Y Junction Coupler, a \(2\times 2\) MMI Coupler and a Ridge Phase Shifter Using Silicon Waveguides for WDM Applications

Communications in Physics, 2017

In this paper, we introduce a new two-mode (de)multiplexer based on the silicon-oninsulator (SOI)... more In this paper, we introduce a new two-mode (de)multiplexer based on the silicon-oninsulator (SOI) platform. The device is built on a symmetric Y-junction, a 2 × 2 multimode interference (MMI) waveguide and a phaseshifter in the form of a ridge waveguide which is designed using 3D scalar beam propagation method (BPM). The phase evolution in the structure is discussed in details. The simulation results show that the device can operate in a wide wavelength range (150 nm) with a low insertion loss and small crosstalk. A large fabrication tolerance to the width of the input waveguide up to 100 nm is achieved, which is compatible to the current CMOS manufacturing technologies for the photonic integrated circuits. Furthermore, the small footprint (4 µm × 286 µm) makes the device suitable for applications in high bitrate and compact on-chip silicon photonic integrated circuits.

Research paper thumbnail of Political will in Fighting Corruption in Vietnam

Research paper thumbnail of Tiền là Tiên là Phật: Investigating the persistence of corruption in Vietnam

This research aims to examine the persistence of corruption in the public sector in Vietnam and e... more This research aims to examine the persistence of corruption in the public sector in Vietnam and explain why anti-corruption measures have been unsuccessful. It seeks to capture people’s lived experience of corruption in Vietnamese society and their perception of the failure of anti-corruption measures. It demonstrates what government officials and ordinary citizens think about corrupt practices and how they explain corrupt behaviour. The research also draws a clearer picture of Vietnam’s anti-corruption system, the weaknesses of the Anti-Corruption Law (ACL) and its implementation, from insiders’ perspectives. The research illuminates some factors identified in the literature that need to be better understood when dealing with corruption: historical, cultural, economic, administrative and political factors. This project situated Vietnam’s anti-corruption strategy within Jon Quah’s analytical framework, which identifies elements he argues are needed for an effective anti-corruption ...

Research paper thumbnail of Investigation of bioactive chemical constituents and anti-cancer activity of ethanol extract of Curcuma singularis Gagnep rhizomes

Research paper thumbnail of A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables

Applied Energy, Feb 1, 2015

A variance inflation factor and backward elimination based robust regression model for forecastin... more A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables,"

Research paper thumbnail of Short-Term Electricity Demand Forecasting Using a Functional State Space Model

In the past several years, the liberalization of the electricity supply, the increase in variabil... more In the past several years, the liberalization of the electricity supply, the increase in variability of electric appliances and their use, and the need to respond to the electricity demand in real time has made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All of these sources make electricity demand forecasting difficult. To forecast the electricity demand, some proposed parametric methods that integrate main variables that are sources of electricity demand. Others proposed a non parametric method such as pattern recognition methods. In this paper, we propose to take only the past electricity consumption information embedded in a functional vector autoregressive state space model to forecast the future electricity demand. The model we proposed aims to be applied at some aggregation level between regional and nationwide grids. To estimate the parameters of this model, we use likelihood maximization, spline smoothing, functional principal components analysis and Kalman filtering. Through numerical experiments on real datasets, both from supplier Enercoop and from the Transmission System Operator of the French nationwide grid, we show the appropriateness of the approach.

Research paper thumbnail of Short-term Load Forecasting using Regression based Moving Windows with Adjustable Window-Sizes

This paper presents a regression based moving window model for solving the short-term electricity... more This paper presents a regression based moving window model for solving the short-term electricity forecasting problem. Moving window approach is employed to trace the demand pattern based on the past history of load and weather data. Regression equation is then formed and least square method is used to determine the parameters of the model. In this paper, a new concept associated with cooling and heating degree is used to establish the relationship between electricity demand and temperature, which is one of the key climatic variables. In addition, Pearson's correlation has been employed to investigate the interdependency of electricity demand between different time periods. These analyses together with the data in the holiday period provide the supportive information for the appropriate selection of the window size. A case study has been reported in this paper by acquiring the relevant data for the state of New South Wales, Australia. The results are then compared with a neural network based model. The comparison shows that the proposed moving window approach with the different window sizes outperforms conventional neural network technique in small time scales i.e., from 30 minuntes to 1 day ahead.

Research paper thumbnail of Load forecasting under changing climatic conditions for the city of Sydney, Australia

Energy

In the current context, climate change has become an unequivocal phenomenon. Although it primaril... more In the current context, climate change has become an unequivocal phenomenon. Although it primarily encompasses change in temperature, nevertheless other weather variables such as rainfall, wind speed, evaporation and humidity can also be affected as a result of climate change. Addressing the impacts of climate change on electricity demand is essential for predicting the future demand. For example, cooling and heating requirements change significantly with respect to climate change that may result to the change in electricity load demand. In this paper, a backward elimination based multiple regression approach is proposed for analyzing the influence of climatic variables on load forecasting. A correlation analysis has been carried out using Pearson's correlation coefficient to examine the interdependency between different climatic variables in the context of Sydney, one of the most densely populated cities in Australia. Regression based analysis has been performed to examine the relationship between per capita electricity demand and associated climatic variables. 'Degree Days' concept has been utilized to determine balance point temperature. Backward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables related to the load demand. Average change in future per capita electricity demand has been predicted using the proposed approach for the city of Sydney, Australia. Results indicate that the demand for Sydney will increase by 6%

Research paper thumbnail of Combinatorial approach using wavelet analysis and artificial neural network for short-term load forecasting

Short term load forecasting is critically important in modern electricity networks since it helps... more Short term load forecasting is critically important in modern electricity networks since it helps provide supportive information for reliable power system operation in competitive electricity market environment. In this paper, the wavelet analysis based neural network model is employed to forecast the electricity demand in short-term period. The wavelet analysis helps to decompose the electricity demand data into different frequency bands. The Fourier transform is then employed to reveal the significant lags of these decomposed components. These lags are then used as inputs of neural network model to forecast the future values of each decomposed component. Finally, the forecasted components are combined together to form the electricity demand forecast. A case study has been reported in the paper by acquiring the data for the state of New South Wales, Australia. MAPE is used to validate the proposed model and the results show that the proposed method is promising for short term load forecasting.

Research paper thumbnail of High throughput FPGA architecture for corner detection in traffic images

2014 IEEE Fifth International Conference on Communications and Electronics (ICCE), 2014

Corner detection is the most computationally intensive step in vehicle tracking and vehicle speed... more Corner detection is the most computationally intensive step in vehicle tracking and vehicle speed estimation algorithms. In order to have real-time vehicle tracking for traffic surveillance applications, high speed architectures for corner detection are needed. This paper presents a high throughput FPGA architecture for detecting special features (corners in more detail) on traffic images which are captured by cameras. The module is implemented based on the FAST (Features from Accelerated Segment Test) algorithm with some modifications to be suitable for traffic images. The proposed architecture is able to reduce a great number of unnecessary detected corner points and maintain a high throughput of more than a thousand of 8-bit gray-scale images per second at 640 × 480 resolution. The resource usage is 21% lower than that of existing work, which allows the architecture to be implemented on almost all types of FPGA.