• Asia pacific journal of information systems
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• v.32 no.4
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• pp.707-727
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• 2022

Industrial revolution 4.0 makes business competition more challenging and will impact the instability of the company's financial performance. Dynamic environmental conditions make it difficult for companies to make predictions in making decisions. Investing in information technology (IT) is one way for companies to maintain financial stability and competitive advantage in dynamic competition. Resource-Based Theory (RBT) explains that information technology (IT) is a resource that can create a competitive advantage for the company. This study aims to examine the moderating role of dynamic industrial environments and IT strategic emphasis on the relationship between a lag effect of IT investment and firm's financial performance volatility. Using the data of companies listed on the Indonesia Stock Exchange (IDX) for five years starting from 2013-2017, the method used to estimate the research model's parameters is the generalized method of moments (GMM) approach. The results show that the industrial environment and the emphasis on IT strategy have a role in moderating and strengthening the relationship between the time lag in IT investment in reducing the firm's financial performance volatility.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.53-62
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• 2017

In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.127-132
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• 2009

The feature vectors which are used in conventional speaker recognition (SR) systems may have many correlations between their neighbors. To improve the performance of the SR, many researchers adopted linear transformation method like principal component analysis (PCA). In general, the linear transformation of the feature vectors is based on concatenated form of the static features and their dynamic features. However, the linear transformation which based on both the static features and their dynamic features is more complex than that based on the static features alone due to the high order of the features. To overcome these problems, we propose an efficient method that applies linear transformation and temporal information of the features to reduce complexity and improve the performance in speaker verification (SV). The proposed method first performs a linear transformation by PCA coefficients. The delta parameters for temporal information are then obtained from the transformed features. The proposed method only requires 1/4 in the size of the covariance matrix compared with adding the static and their dynamic features for PCA coefficients. Also, the delta parameters are extracted from the linearly transformed features after the reduction of dimension in the static features. Compared with the PCA and conventional methods in terms of equal error rate (EER) in SV, the proposed method shows better performance while requiring less storage space and complexity.

• STI Policy Review
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• v.6 no.1
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• pp.1-23
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• 2015

Based on the work of Anand et al. (2013) we measure inclusive income growth, which combines growth in gross domestic product (GDP) per capita and growth in the equity of the income distribution. Extending the work of Causa et al. (2014), we estimate a dynamic simultaneous structural equations model of GDP per capita and inclusive income on panel data for 63 countries over the 1990-2013 period. We estimate both equations in error correction form by difference GMM (generalized method of moments). Among the explanatory variables of the level and the distribution of GDP per capita we include R&D (research and development) expenditure per capita. In OECD countries we obtain a large positive effect of R&D on GDP. R&D is found to have a positive effect on the social mobility index but its impact on the income equity index at first decreases, then switches around to become slightly positive in the long run. In non- OECD countries, R&D is found to decrease inclusive income, mostly through a negative growth effect but also because of a slightly increasing income inequity effect.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

• The Korean Journal of Financial Management
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• v.24 no.1
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• pp.109-147
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• 2007

According to the perspective of capital structure theory, we analyzed the dynamism of the capital structure determinants by using panel data of 244 KOSDAQ firms based on two-step GMM system methodology suggested by Blundell Bond(1998). This dynamic methodology had not been used to analyse capital structure determinants in Korea. In the dynamic model of capital structure, profit had negative effect on the book leverage and market leverage, which meant supporting pecking order theory. Growth opportunity (MBR) affected negatively to the market leverage. For the determinants of leverage, earnings volatility had significantly positive effect on KOSDAQ 50 firms. KOSDAQ and KOSDAQ 50 firms had the target leverage. The adjustment speed in KOSDAQ firms was 0.4958 on the book leverage, it was faster than in KOSDAQ 50 firm's 0.2863 on the book leverage and the adjustment speeds for the market leverage were 0.7651 for KOSDAQ firms and 0.5643 for KOSDAQ 50 firms. There was difference in adjustment cost between KOSDAQ firms and KOSDAQ 50 firms.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.375-381
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• 2016

Recently, a foreground-background segmentation using codebook model has been researched actively. The codebook is created one for each pixel in the image. The codewords are vector-quantized representative values of same positional training samples from the input image sequences. The training is necessary for a long time in the most of codebook-based algorithms. In this paper, the initial codebook model is generated simply using median operation with several image frames. The initial codebook is updated to adapt the dynamic changes of backgrounds based on the frequencies of codewords that matched to input pixel during the detection process. We implemented the proposed algorithm in the environment of visual c++ with opencv 3.0, and tested to some of the public video sequences from PETS2009. The test sequences contain the various scenarios including quasi-periodic motion images, loitering objects in the local area for a short time, etc. The experimental results show that the proposed algorithm has good performance compared to the GMM algorithm and standard codebook algorithm.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.79-85
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• 2017

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.657-668
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• 2020

This paper investigates the effect of market structure, including some bank-specific variables and macroeconomic conditions, on the profitability of Indonesian Islamic rural banks. We apply the structure conduct performance (SCP) and the relative market power (RMP) hypothesis. Panel data comprising 142 Islamic rural banks from 2013Q1 to 2018Q4 are employed. This study breaks them apart, associated with the level of economic development consisting of Java as developed regions and outside Java as less developed regions. This study employs static and dynamic panel regression. The GMM method, however, is appropriate because of the dynamic nature of profitability. Our results confirm the SCP hypothesis and fail to support the RMP hypothesis. The higher market concentration allows Islamic rural banks to generate a significantly higher profit by conducting a collusive strategy. More interestingly, the collusive behavior may result in more profit for Islamic rural banks located in the developed regions than those in less developed regions. Evidence also highlights the importance of operating efficiency and impaired financing on profitability. High operating efficiency and low impaired financing can improve profit. Our results suggest that capitalizing market share by improving efficiency and optimizing financing contracts between PLS and non-PLS contracts also improve profit.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.19-29
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• 2021

This study aims to measure financial inclusion and examine its impact on income inequality in a panel of 18 Asian countries over the period 1997-2017. Two alternative approaches for developing financial inclusion index are used: one approach following the methodology of Sarma (2008), while the other is the Dynamic Factor Model (DFM)-based index. The impact of individual indicators and index of financial inclusion on inequality in income is analyzed. The Generalized Method of Moment (GMM) approach is used for empirical analysis. The results indicate that micro-level financial inclusion has a weak negative and statistically significant impact on income inequality. Macro-level index and all individual indicators of financial inclusion do not affect income inequality in the selected sample of economies. The income inequality issues have different natures and cannot be fixed by financial inclusion only. It needs holistic structural reforms to enable fair distribution of income and make an equitable financial system. Financial inclusion is a relatively less important intervention tool regarding fixing the issue of income inequality. This is one of the first studies that used the DFM method for financial inclusion indices construction.