Second Level Of Decomposition Research Articles

Innovation: A state-of-the-art review and typology

Innovation is often an object of study in economics and management. However, the social and behavioral aspects of innovation acceptance are as important as the economics of product development. A significant portion of the literature considers innovation as a change in the way social actions are conducted, entailing a wide range of social, economic, behavioral, and institutional changes. Various approaches have given rise to the need for a typology. Sundbo (1998) divided innovation into three groups depending on the aspects of the phenomenon: theory of entrepreneurship, technological and social aspects, and strategic aspects. Adopting Sundbo's conceptual framework, this study supplemented and developed it based on recent literature that appeared after 1998. Moreover, this study added new directions at the second level of decomposition and the relationships between different aspects of innovation. In particular, this study analyzed phenomena such as open innovation, agile innovation, and “helix” models. Thus, this study developed a novel typology of innovation that expands the theoretical knowledge in this field. Based on these findings, this study proposed promising areas for future innovation studies.

Reducing computational costs in site response analysis and its application for the nonlinear dynamic analysis of structures

In this paper, the wavelet transform is used for the first time to reduce the cost of site response analysis calculations for different soil profiles in Isfahan city. This downsampling method was used up to three levels to generate approximate waves from the ground motion records. Site response analysis was done using the one-dimensional equivalent linear and nonlinear method. Down-hole seismic tests of 71 boreholes in Isfahan city were used to make different soil profiles. In addition, 30 ground motions compatible with seismic hazard analysis of this city were selected. Due to the lack of comprehensiveness in previous studies, none of them has definitively indicated which level of wave decomposition is the last reliable wavelet filter. But in this article, for the first time, by performing a data reliability review, it is shown that the last reliable filter for the site response is related to the second level of decomposition. Comparison of the site response analysis using approximate waves and the main records, by performing more than 2100 analyses, showed that wavelet transform could be used up to two levels with the maximum error of 8% in predicting amplification factors. In addition, approximate waves were used for the nonlinear dynamic analysis of structures with different heights located at different soil profiles, and results showed that wavelet transform decreases the cost of calculations up to 50% with a 5% error.

Biomedical Signal Denoising Via Permutating, Thresholding and Averaging Noise Components Obtained from Hierarchical Multiresolution Analysis-Based Empirical Mode Decomposition

Biomedical signals are usually contaminated with interfering noise, which may result in misdiagnosis of diseases. Additive white Gaussian noise (AWGN) is a common interfering noise, and much work has been proposed to suppress AWGN. Recently, hierarchical multiresolution analysis-based empirical mode decomposition (EMD) denoising method is proposed and shows potential performance. In order to further improve performance of hierarchical multiresolution analysis-based EMD denoising, this paper combines hierarchical multiresolution analysis-based EMD, thresholding operation and averaging operation together. In particular, EMD is applied to the first intrinsic mode function (IMF) in the first level of decomposition to obtain IMFs in the second level of decomposition. The first IMF in the second level of decomposition is chosen as the noise component. For each realization, this noise component is segmented into various pieces, and these segments are permutated. By summing up this permutated IMF to the rest of IMFs in both the first level of decomposition and the second level of decomposition, new realization of the noisy signal is obtained. Next, for original signal and each realization of newly generated noisy signal, EMD is performed again. IMFs in the first level of decomposition are obtained. Then, consecutive mean squared errors-based criterion is used to classify IMFs in the first level of decomposition into the information group of IMFs or the noise group of IMFs. Next, EMD is applied to IMFs in the noise group in the first level of decomposition and IMFs in the second level of decomposition are obtained. Detrended fluctuation analysis is used to classify IMFs in the second level of decomposition into the information group of IMFs or the noise group of IMFs. After that, thresholding is applied to IMFs in the noise group in the second level of the decomposition to obtain denoised signal. Finally, the above procedures are repeated, and several realizations of denoised signals are obtained. Then, denoised signal obtained by applying thresholding to each realization is averaged together to obtain final denoised signal. The extensive numerical simulations are conducted and the results show that our proposed method outperforms existing EMD-based denoising methods.

INVESTIGATION OF THE INFLUENCE OF ADAPTIVE NOISE ON THE EFFICIENCY OF DISCRETE THRESHOLDFUL WAVELET FILTRATION OF MODULATED SIGNALS

To study the filtering of modular signals, a specially generated model signal was used, which consists of attenuating harmonics of a random initial frequency and a phase with half overlap. With the help of the window ganning provides smoothness at the end of the generated signal. Due to the use of Euclidean norms of vectors formed from wavelet coefficients of approximation and detail for all levels of decomposition of the model signal with noise and without obtaining a mathematical model, the change of noise power for filtration efficiency is taken into account. The study of the obtained mathematical model proved the existence of two different levels of decomposition distributed error from the influence of noise for the coefficients of appropriation and detail depending on the number of harmonics in the signal model. For the proposed model signal is indeterminate from the initial frequencies and attenuation coefficients of changes in the error of the error determined through the Euclidean norms of vectors for the coefficients of approximation and detail occurs when the number of harmonics of twenty families is exceeded. An important result of this analysis is that it allows to identify such levels of decomposition at which the relative error of both coefficients of both detail and approximation will be minimal. This confirms the presence of a minimum average dramatic MSE of the threshold filtering efficiency in which remains powerful at the second level of decomposition for the generated model signal when the noise changes. In addition, this error value is further reduced by selecting a discrete wavelet. The non-threshold method of discrete filter wavelet is compared with the methods of general and universal thresholds for the proposed model function in the conditions of noise power change. The unconditional advantage of the method without a threshold is the minimal use of computing resources. The above wavelet filtering technique can be used for other signal functions. The proposed model function is given as a general example.

WAVELET-CONVERSION IN ELECTROCARDIO SIGNAL PROCESSING

In the present work, processing of an electro cardio signal using a wavelet transform is consi-dered. In electrocardiography, various digital signal-processing techniques are used to detect, extract, and analyze the various components of an electrocardiogram. Among them, the wavelet transform technique gives promising results in the analysis of the time-frequency characteristics of the electrocardiogram components. The urgency of solving the problem of improving the quality of life of people with the help of early diagnosis and timely treatment of various cardiac diseases is obvious. The process of automated analysis of a huge database of electrocardiographic data is especially important. Wavelet analysis can be successfully used to smooth and remove noise in the ECG signal. Electrocardiogram signal, cleaned from noise components, looks clearer, while its volume is from 10 to 5% of the original signal, which largely solves the problem of storing cardiac records. Aim. Development of an algorithm for threshold processing of wavelet coefficients and filtering of an electrocardiography signal. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the MATLAB environment that implements continuous and discrete wavelet transform. Results. The work shows the result of filtering the ECG signal with the addition of noise with a signal-to-noise ratio of 35 and 45 dB using the decomposition levels N = 2, N = 3, N = 4. Conclusion. Based on the analysis of the data obtained, it can be concluded that the second level of decomposition is the most optimal for filtering the ECG signal. With an increase in the level of decomposition, the output ratio decreases, at the level N = 4 the output signal-to-noise almost does not exceed the input one, therefore, the filtering becomes ineffective. The correlation coefficient to the fourth level is significantly reduced, which means a significant increase in the distortion introduced by the filtering algorithm.

Empirical Mode Decomposition-Based Hierarchical Multiresolution Analysis for Suppressing Noise

This paper proposes a strategy for suppressing noise via empirical mode decomposition (EMD)-based hierarchical multiresolution analysis. First, an intrinsic mode function (IMF) index that can differentiate the noise-dominant IMFs and the information-dominant IMFs in the first level of decomposition is determined by the conventional EMD-based denoising method. Next, the EMD is applied to those discarded IMFs in the first level of decomposition to obtain the IMFs in the second level of decomposition. Then, the detrended fluctuation analysis (DFA) is employed to define the IMF index separating the noise-dominant IMFs and the information-dominant IMFs in the second level of decomposition. Information-dominant IMFs in both the first and second levels of decompositions are summed together to obtain the denoised signal. The study is limited to signals that were corrupted by additive white Gaussian noise (AWGN). Extensive computer numerical simulations are conducted on both synthetic signals and practical signals. The results show that our proposed method is promising in some cases, especially at high-input SNR levels. For example, in the case of the CH4 signal with 12-dB input SNR, the processed SNR based on our proposed strategy with the consecutive mean squared error (CMSE) is 23.27 dB, while that based on the conventional CMSE method is only 23.14 dB. Also, our proposed method can extract information from more than one level of decomposition to reconstruct the denoised signal compared to only one level of decomposition in the conventional EMD-based denoising method.

METHODS OF DESIGNING HARDWARE DECORATION OF PRODUCTIONS OF CARBON NANOTUBES AND BY-PRODUCTS ON THEIR BASIS

From the positions of system analysis and the theory of optimal design the approach to the development of hardware design of industrial production of carbon nanotubes and by-ptoducts on the basis of their functionalized forms is proposed. Functionalization is carried out by oxidation of nanotubes with concentrated nitric acid and their subsequent modification by titanium stearate. Functionalized by this method, carbon nanotubes have increased hydrophobic properties and are semi-products for use in composites based on non-polar matrices. Material flows of production of nanotubes and semi-products on their basis are analyzed. Interrelations between them are established. The cost of the product is used as a global economic criterion of optimality. A three-level decomposition of the technological scheme of nanotubes and their functionalized forms production on the principle of "production – stage of production – hardware design stage" is made. At the second level of decomposition, the following stages are identified: catalyst preparation; preparation of carbonaceous raw materials; synthesis of nanotubes; utilization of gaseous pyrolysis products; treatment of material after synthesis; oxidation of nanotubes; modification of oxidized nanotubes with titanium stearate. Economic optimality criteria are determined for them, taking into account the return of by-products from the stages of purification of carbon nanotubes and neutralization of gaseous pyrolysis products at the stage of catalyst production and preparation of the initial carbon-containing raw materials, respectively. The interrelation of information and coordinating signals of the first and second level of the task of designing a new production is established. The correlation of initial design data (qualitative composition of carbon-containing raw materials, production capacity, complex quality index of carbon nanotubes and their morphological characteristics) and the main design and operating parameters of the hardware design of industrial production of carbon nanotubes and semi-products based on them is shown. The task of designing a new production taking into account the combined production of catalyst-free and functionalized carbon nanotubes with the use of the same hardware design is formulated.

Does forest matter regarding Chilean CO2 international abatement commitments? A multilevel decomposition approach

ABSTRACTThis paper assesses the role of the forestry sector in the CO2-eq emissions change in Chile from 1990 to 2013. Due to its relevance, this sector is considered as a proxy LULUCF activities. A second objective of this paper is to explore whether the forestry sector has always contributed to a decoupling process between CO2-eq emissions and economic growth. To address the first objective, the LMDI method has been used. For the second objective, the decoupling status between CO2-eq emissions comes from the Tapio index, which was used for the first step. For the second step, a second level of decomposition was carried out. Major findings from the LMDI and second-level decomposition analysis reveal that Chile's forestry sector clearly acted as a sink but failed to outweigh the role played by all other sectors taken into consideration. The results show that Chile has also failed to reach decoupling between economic growth and CO2-eq emissions. Efforts made in the forestry sector to reduce CO2 emissions were interesting but not strong enough to meet decoupling objectives. It is recommended that native forest management be reinforced and strengthened, mainly by reforestation with oak, raulí and coigüe varieties.

Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances

Detecting discontinuities in electrical signals from recorded oscillograms makes it possible to segment them. This is the first step in implementing automated methods which will ensure disturbances in electrical power systems are detected, classified and stored. In this context, this paper presents a way of determining an adaptive threshold based on the decomposition of electrical signals through the Discrete Wavelet Transform (DWT) using Daubechies family filter banks, allowing for the segmentation of signals and, as a consequence, the analysis of disturbances related to Power Quality (PQ). Considering this, the proposed approach was initially evaluated for signals originating from mathematical models representing short-term voltage fluctuations, transients (impulsive and oscillatory) and harmonic distortions. In the synthetic signal database, either single or combined occurrences of more than one disturbance were considered. By applying the DWT, the amount of energy and entropy of energy were then calculated for the leaves of the second level of decomposition. Based on these calculations, a unique adaptive threshold could be determined for each analyzed signal. Afterwards, the amount of existing intersections between the threshold and the curve of details obtained for the second level of decomposition was then defined. Thus, the intersections determine the beginning and end of the segments. In order to validate the approach, the performance of the proposed methodology was analyzed considering the signals obtained from oscillograms provided by IEEE 1159.3 Task Force, as well as real oscillograms obtained from a regional distribution utility. After these analyses, it was observed that the proposed approach is efficient and applicable to automatic segmentation of events related to PQ.

Adaptive Threshold for Electrical Disturbances Segmentation

The detection of the electrical signal discontinuities in the oscillographies recorded in substations and/or points of common coupling allows their segmentation, which is crucial for implementation of automated methods for detection, classification, location and storage by classes of disturbances in electric power systems. In this context, this study provides a way of determining an adaptive threshold that allows the segmentation of voltage or current signals based on Wavelets decomposition. The disturbances considered in this work were the short-duration voltage variations, impulsive and oscillatory transients, and harmonic distortions. The signals were synthetically generated. Moreover, white noise was added on the signals. Thus, a Symlet Wavelet was applied to the signals in order to denoise them. In the sequence, a Daubechies Wavelet was used to decompose the filtered signals. So, to determine the initial and final points of each segment, an adaptive threshold was established based on the energy and entropy of energy for the second level of decomposition. Thus, the number and position of each segment were determined according to the intersections of the detail curves and the thresholds found.

The Possibilities of Difference Analysis Utilisation in Profit Rate Assessment

The main aim of this article is the application of selected methods of difference analysis to the chosen profit indicator of Return on Equity (ROE). First, the importance of the ROE indicator is described in the paper and then, also, the possible methods of pyramidal decomposition in multiplicative structures – ie. the method of gradual changes, the decomposition method with residue, the logarithmic method and the functional method. The pyramidal decomposition of the ROE top indicator is implemented using the functional method, the only one that can be used in the case of a negative volume index indicator and, at the same time, this method is not sensitive to the order of the factors. In conclusion, the assessment of the impact of partial analytical indicators of pyramidal decomposition on the top analysed indicator of ROE is accomplished. The degree of various influences of sub-indicators on the ROE top indicator, in the analysed periods, was different. The ROE indicator was most affected by the Sales Profitability indicator on the second level of decomposition. In the first and last assessment periods, the Sales Profitability indicator was determined by the Cost Efficiency indicator, in the first, and in the second period, by the cost of sales indicator. The partial factor Financial Leverage affected the ROE the least within the assessment period. The Tax Earnings Reduction indicator following the Interest Earnings Reduction indicator presented the stronger influence on the ROE after the Financial Leverage indicator.

Pyramid Vector Quantization of Video Subband With Dithering

This paper describes the development of a low complexity and fixed–rate video compression scheme based on three–dimensional subband coding of video signals. The video codec first performs three–dimensional subband decomposition on a group of video frames, and then encode high frequency subbands with pyramid vector quantization and lowest tempo–spatial band with a DPCM coding in time and space. To improve the visual quality of reconstructed video, different types of subtractive and non–subtractive dithering of pyramid vector quantizers were experimented and its effectiveness was proved by a standard pair comparison subjective test. Coder complexity was reduced by using longer filters in the first level of spatial decomposition for better selectivity and coding gain and shorter filter in the second level of decomposition for lower complexity. Results at different low bit–rate (64, 128 and 384 Kbps) for several standard video sequences are reported and compared with ITU standard H.263.

Image recognition via Bayesian likelihood analysis of wavelet coefficients

This study experiments with a Bayesian approach in recognition of images utilizing a joint-form likelihood of wavelet coefficients built from decomposition. Images of handwritten numerals are attacked via the Mallet decomposition algorithm with Daubechies wavelets to extract the feature vectors of coefficients. The model assumes the coefficient vectors by multivariate normal distributions and employs a Bayesian approach for classification based on the joint form of distributions. The results demonstrate marked improvement in recognition performance at the second level of decomposition.