Peramalan kualiti udara menggunakan kaedah pembelajaran mendalam rangkaian perlingkaran temporal (TCN)

Kajian ini bertujuan untuk membina model kualiti udara untuk meramalkan kepekatan bahan pencemar udara di Malaysia. Kaedah peramalan yang dipilih dalam kajian ini adalah suatu teknik pembelajaran mendalam iaitu Rangkaian Perlingkaran Temporal (TCN). Set data yang digunakan adalah siri masa zarahan terampai bersaiz diameter lebih kecil atau sama dengan 10 mikrometer (PM10) yang diperoleh daripada Jabatan Alam Sekitar Malaysia dari 5 Julai 2017 hingga 31 Januari 2019. Data daripada lima stesen pemantauan kualiti udara di Semenanjung Malaysia dipilih untuk kajian ini. Bagi tujuan perbandingan, kaedah rangkaian memori jangka pendek panjang (LSTM) juga digunakan dalam kajian ini yang mana ketepatan antara kedua-dua model dibandingkan. Secara amnya, nilai model ramalan daripada kedua-dua model adalah menghampiri data asal. Walau bagaimanapun, model yang dibina dengan kaedah TCN adalah lebih baik berbanding model LSTM dari segi ketepatan nilai ramalan. Kajian ini menunjukkan bahawa TCN merupakan teknik yang sesuai digunakan dalam peramalan data siri masa bagi kualiti udara di Semenanjung Malaysia

ORDINAL REGRESSION FOR MODELLING THE FAMILY WELL-BEING AMONG THE MALAYSIANS

Background and Purpose: Understanding factors which affect the level of family well-being is important as it contributes to effective decision making among the policymakers to improve the family lives as well as to strengthen the family institution. Accordingly, this line of research is gaining attention. This study develops an ordinal regression model which identifies demographic, economic and social factors that are significant in explaining the status of family well-being.    Methodology: Data involving 2,808 respondents from a nationwide survey conducted by the National Population and Family Development Board of Malaysia in 2011 were used in this study. Ordinal regression model was implemented to describe the three levels of family well-being.   Findings: The national survey reported that high level of family well-being was experienced by 76.3 per cent of the respondents, followed by moderate (18.4%) and low (5.3%). The fitted ordinal regression model found that ethnic background, family relationship, community relationship, health and safety levels, economic situation of the family, religious practice, housing, and environment are significantly related to family well-being. Meanwhile, it was found that the level of income is not a significant factor in determining the level of family well-being.     Contributions: There are a limited number of studies on the application of ordinal regression for modelling the level of family well-being, particularly with covariates involving the demographic and social characteristics of the respondents. This study fills in the gap in the literature where the ordinal regression model provides useful information for policymakers to enhance the status of family well-being in Malaysia via various policy initiatives.   Keywords: Family well-being, Ordinal Regression Model, ordinal data, Proportional Odds Model.   Cite as: Muhammad Sapri, N. A., Ibrahim, K., Abu Bakar, M. A., & Mohd Ariff, N. (2021). Ordinal regression for modelling the family well-being among the Malaysians.  Journal of Nusantara Studies, 6(2), 424-447. http://dx.doi.org/10.24200/jonus.vol6iss2pp424-44

Bayesian estimation of time to failure distributions based on skew normal degradation model : an application to GaAs laser degradation data

In this paper, the Bayesian method which involves informative and weakly informative priors are considered to estimate the parameters and percentiles of the time to failure distribution. The parameters of the time to failure distribution and its percentiles are determined based on linear degradation model where the degradation parameter is assumed to follow the skew normal distribution. For the prior distributions, location and scale parameters of the skew normal distribution is assumed to follow the uniform distribution while the shape parameter is assumed to follow gamma distribution. Two gamma priors are considered, either informative or weakly informative prior, depending on the assumed values of the hyper parameters. The performance of the method under the different prior assumptions is compared using a simulation study based on Markov Chain Monte Carlo method as well as a real data application. It is found that the parameter estimation based on informative prior is more precise as opposed to the weakly informative prior, especially in the case of small sample size. In addition, the skew normal degradation model is compared to the log-logistic degradation model through a simulation study and a real application of GaAs laser data. When modeling the percentiles of the time to failure distribution, results found based on the skew normal distribution is generally found to be more precise, particularly for the higher percentile values

Time series clustering of Malaysia Air quality time series data

Air quality is often associated with the area location and activities where air quality in cities is usually more polluted than in rural areas. This study aims to study the pattern of time series data from air quality stations by performing cluster analysis of air quality station based on the particulate matter 10 micrometres or less in diameter (PM10) and particulate matter 2.5 micrometres or less in diameter (PM2.5) time series data. The clusters obtained from the cluster analysis were compared with the station area category and station location. This study which uses air quality data obtained from the Department of Environment, Malaysia from 5 July 2017 until 30 June 2019, shows five types of air quality patterns in Malaysia. The results also show that none of the clusters is dominated by any station's category. Therefore, it is less appropriate to relate the air quality patterns and the station area category. However, the results show that air quality patterns were related to the station's location, where nearby stations have similar air quality patterns

Wavelet characterizations for investigating nonlinear oscillators

This study investigates the wavelet-based system identification capabilities on determining the system nonlinearity based on the system impulse response function. Wavelet estimates of the instantaneous envelopes and instantaneous frequency are used to plot the system backbone curve. This wavelet estimate is then used to estimate the values of the parameter for the system. Two weakly nonlinear oscillators, which are the Duffing and the Van der Pol oscillators, have been analyzed using this wavelet approach. A case study based on a model of an oscillating flap wave energy converter (OFWEC) was also discussed in this study. Based on the results, it was shown that this technique is recommended for nonlinear system identification provided the impulse response of the system can be captured. This technique is also suitable when the system’s form is unknown and for estimating the instantaneous frequency even when the impulse responses were polluted with noise

Wavelet methods and system identification

I begin with a brief introduction to dynamic systems, the identification of system parameters from records of input and output, and also wave energy converters which provide case studies to motivate the research. The dynamic systems discussed are categorized as linear or nonlinear dynamic systems. I present brief reviews of strategies for identification of dynamic systems which cover the history and also the areas of applications. The discretization of differential equations for dynamic systems is a recurrent theme and I consider forward, backward and central differences in detail for linear systems. The estimation techniques discussed are the principle of least squares, the Kalman filter and spectral analysis. Several system identification techniques for nonlinear dynamic systems in the time domain and in the frequency domain are presented and compared. The main focus of the thesis is estimation methods based on wavelets. I present some introduction to the wavelet transforms, which cover both continuous and discrete wavelet transforms. Wavelet methods for system identification of linear and nonlinear dynamic systems are discussed. Throughout this research, I have published four research articles guided by my supervisors. The first article discusses the wavelet based technique for linear system, and the technique was compared to the spectral analysis technique. The second article compare two types of wave energy converters, where the heaving buoy wave energy converter (HBWEC) is modelled as a linear system and the oscillating flap wave energy converter (OFWEC) as a nonlinear system. The frequency domain technique for system identification of nonlinear dynamic systems have been applied on the OFWEC model. Unscented Kalman filter have been discussed in the third article where the nonlinear OFWEC system have been used as the case study. A wavelet approach for nonlinear system identification has been discussed in the fourth article together with the probing technique. The probing technique was used to find the generalized frequency response functions of the nonlinear dynamic systems based on the nonlinear autoregressive with exogenous input (ARX) model. Both technique were compared for two weakly nonlinear oscillators, the Duffing and the Van der Pol. Once again, we selected the OFWEC system as a case study.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Mathematical Sciences, 2016

Pengitlakan lengkung IDF untuk peristiwa ribut ekstrim di Semenanjung Malaysia

Lengkung keamatan-tempoh-kekerapan (IDF, daripada intensity-duration-frequency) adalah suatu model peristiwa ribut ekstrim yang mampu merumuskan ciri statistik penting dan menggambarkan hubungan antara keamatan, tempoh dan tempoh ulangan sesuatu peristiwa ribut. Tujuan kajian ini adalah untuk mendapatkan suatu taburan serta persamaan IDF teritlak untuk membina lengkung IDF teritlak bagi sebarang stesen curahan hujan di Semenanjung Malaysia. Oleh itu, kajian ini mengenal pasti taburan kebarangkalian terbaik untuk dijadikan taburan teritlak bagi keamatan ribut maksimum tahunan untuk peristiwa ribut di Semenanjung Malaysia menggunakan kaedah momen-L. Seterusnya, dua set lengkung IDF dibina untuk keamatan ribut maksimum tahun di setiap stesen dengan masing-masing menggunakan taburan terbaik bagi stesen tersebut dan taburan teritlak yang dikenal pasti. Kedua-dua set lengkung IDF ini dibandingkan dengan data asal dan antara satu sama lain dengan menggunakan tiga indeks ketepatan padanan, iaitu peratusan pekali variasi punca min ralat kuasa dua, min peratusan perbezaan mutlak dan pekali penentuan. Berdasarkan hasil kajian ini, taburan nilai ekstrim teritlak merupakan taburan terbaik untuk dijadikan taburan teritlak bagi keamatan ribut maksimum tahunan di Semenanjung Malaysia. Selain itu, kedua-dua set lengkung IDF yang dibina didapati sesuai untuk menggambarkan peristiwa ribut ekstrim di Semenanjung Malaysia dan perbezaan antara kedua-dua set lengkung adalah sangat kecil. Oleh itu, pengitlakan lengkung IDF dengan menggunakan taburan dan persamaan yang sama bagi setiap stesen curahan hujan dapat memudahkan pembinaan lengkung IDF di seluruh Semenanjung Malaysia dan membantu dalam pembentukan lengkung IDF di kawasan yang tiada stesen curahan hujan

Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest

Accidents involving heavy vehicles are of significant concern as it poses a higher risk of fatality to both heavy vehicle drivers and other road users. This study is carried out based on the heavy vehicle crash data of 2014, extracted from the MIROS Road Accident and Analysis and Database System (M-ROADS). The main objective of this study is to identify significant variables associated with categories of injury severity as well as classify and predict heavy vehicle drivers’ injury severity in Malaysia using the classification and regression tree (CART) and random forest (RF) methods. Both CART and RF found that types of collision, driver errors, number of vehicles involved, driver’s age, lighting condition and types of heavy vehicle are significant factors in predicting the severity of heavy vehicle drivers’ injuries. Both models are comparable, but the RF classifier achieved slightly better accuracy. This study implies that the variables associated with categories of injury severity can be referred by road safety practitioners to plan for the best measures needed in reducing road fatalities, especially among heavy vehicle drivers

Power Size Biased Two-Parameter Akash Distribution

In this paper, the two-parameter Akash distribution is generalized to size-biased two parameter Akash distribution (SBTPAD). A further modification to SBTPAD is introduced, creating the power size-biased two-parameter Akash distribution (PSBTPAD). Several statistical properties of PSBTPAD distribution are proved. These properties include the following: moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis, the maximum likelihood estimation of the distribution parameters, and finally order statistics. Moreover, plots of the density and distribution functions of PSBTPAD are presented and a reliability analysis is considered. The Rényi entropy of PSBTPAD is proved and the application of real data is discussed

Extreme Value Distributions: An Overview of Estimation and Simulation

The generalized extreme value distribution (GEVD) and various extreme value distributions are commonly applied in air pollution, telecommunications, operational risk management, finance, insurance, material sciences, economics, and hydrology, among many other industries that deal with extreme events. Extreme value distributions (EVDs) typically limit the distribution of maximum and minimum values for many random observations drawn from the same arbitrary distribution. Besides that, it is a crucial method for forecasting future events and emerged as critical method for predicting future events. As a result, prior research is required to select the best estimation method to obtain a reliable value for the parameters of extreme value distributions. This study provides an overview of three-parameter estimation methods based on goodness-of-fit statistics and root mean square error (RMSE). This paper reviewed and compared three estimation methods used to approximate values of parameters for simulated observations taken from the EVD and GEVD. The method of moments (MOMs), maximum likelihood estimator (MLE), and maximum product of spacing (MPS) were the methods investigated in this study. Our findings indicated that the MPS performed better based on the mean square errors (MSEs); meanwhile, the MPS had similar goodness-of-fit statistic values compared to the MLE