• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.369-378
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• 2021

This paper reviewed a few output-only system identification algorithms and identified the shortcomings of those popular blind source separation methods. To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain. The wavelet coefficients with different dominant frequencies can be used to address the under-determinate problem, while complex mode shapes are addressed by introducing the complex wavelet transformation. Numerical simulations with both high and low signal-to-noise ratios validate that CWMSOBI can overcome the above-mentioned issues while obtaining more accurate identified results than other blind identification methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.357-364
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• 1996

This paper presents a new blind identification method of nonminimum phase FIR systems without employing higher-order statistics. It is based on the observation that the absolute mean of a second-order white sequence can measure the higher-order whiteness of the sequence. The proposed method may be a new alternative way to the higher-order statistics approaches. Some computer simulations show that the absolute mean is exactly estimated and the proposed method can overcome the disadvantages of the higher-order statistics approaches.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.69-87
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• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.522-527
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• 2005

We need a transmission of high quality and capacity according to a fast supply of mobile communication terminal. As multipath fading occured in high speed transmission, adaptive array antenna habe been studied to solve such a demand. DOA(Direction of Arrival) estimation play a important .ole in adaptive a..ay antenna. This paper present a space time blind identification using second order statistics and present blind space time adaptive array antenna. Also we verified a effect of the presented method.

• ETRI Journal
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• v.37 no.4
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• pp.727-735
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• 2015

This paper deals with the problem of blind source separation (BSS), where observed signals are a mixture of delayed sources. In reference to a previous work, when the delay time is small such that the first-order Taylor approximation holds, delayed observations are transformed into an instantaneous mixture of original sources and their derivatives, for which an extended second-order blind identification (SOBI) approach is used to recover sources. Inspired by the results of this previous work, we propose to generalize its first-order Taylor approximation to suit higher-order approximations in the case of a large delay time based on a similar version of its extended SOBI. Compared to SOBI and its extended version for a first-order Taylor approximation, our method is more efficient in terms of separation quality when the delay time is large. Simulation results verify the performance of our approach under different time delays and signal-to-noise ratio conditions, respectively.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.317-320
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• 2001

Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling leading to the so-called, second order statistics techniques. And adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed. In this paper, a new approach is proposed that is based on eigenvalue decomposition. And the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.

• Smart Structures and Systems
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• v.13 no.2
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• pp.257-280
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• 2014

In this paper, a novel PARAllel FACtor (PARAFAC) decomposition based Blind Source Separation (BSS) algorithm is proposed for modal identification of structures equipped with tuned mass dampers. Tuned mass dampers (TMDs) are extremely effective vibration absorbers in tall flexible structures, but prone to get de-tuned due to accidental changes in structural properties, alteration in operating conditions, and incorrect design forecasts. Presence of closely spaced modes in structures coupled with TMDs renders output-only modal identification difficult. Over the last decade, second-order BSS algorithms have shown significant promise in the area of ambient modal identification. These methods employ joint diagonalization of covariance matrices of measurements to estimate the mixing matrix (mode shape coefficients) and sources (modal responses). Recently, PARAFAC BSS model has evolved as a powerful multi-linear algebra tool for decomposing an $n^{th}$ order tensor into a number of rank-1 tensors. This method is utilized in the context of modal identification in the present study. Covariance matrices of measurements at several lags are used to form a $3^{rd}$ order tensor and then PARAFAC decomposition is employed to obtain the desired number of components, comprising of modal responses and the mixing matrix. The strong uniqueness properties of PARAFAC models enable direct source separation with fine spectral resolution even in cases where the number of sensor observations is less compared to the number of target modes, i.e., the underdetermined case. This capability is exploited to separate closely spaced modes of the TMDs using partial measurements, and subsequently to estimate modal parameters. The proposed method is validated using extensive numerical studies comprising of multi-degree-of-freedom simulation models equipped with TMDs, as well as with an experimental set-up.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.869-872
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• 2001

Blind identification and equalization of communication channel is important because it does not need training sequence, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel estimator and equalizer length mismatch as well as for its simple adaptive algorithms. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training sequences for data estimation and desired signal for channel estimation.

• ETRI Journal
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• v.36 no.1
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• pp.175-178
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• 2014

We introduce the Energy-Based Blind Separating (EBS) algorithm for extremely fast separation of mixed speech signals without loss of quality, which is performed in two stages: iterative-form separation and closed-form separation. This algorithm significantly improves the separation speed simply due to incorporating only some specific frequency bins into computations. Simulation results show that, on average, the proposed algorithm is 43 times faster than the independent component analysis (ICA) for speech signals, while preserving the separation quality. Also, it outperforms the fast independent component analysis (FastICA), the joint approximate diagonalization of eigenmatrices (JADE), and the second-order blind identification (SOBI) algorithm in terms of separation quality.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.57-81
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• 2008

Knowledge is an essential strategic weapon for sustaining competitive advantage and is the key determinant for organizational growth. When knowledge is shared and disseminated throughout the organization, it increases an organization's value by providing the ability to respond to new and unusual situations. The growing importance of knowledge as a critical resource has forced executives to pay attention to their organizational knowledge. Organizations are increasingly undertaking knowledge management initiatives and making significant investments. Knowledge sourcing is considered as the first important step in effective knowledge management. Most firms continue to make an effort to realize the benefits of knowledge management by using various knowledge sources effectively. Appropriate knowledge sourcing strategies enable organizations to create, acquire, and access knowledge in a timely manner by reducing search and transfer costs, which result in better firm performance. In response, the knowledge management literature has devoted substantial attention to the analysis of knowledge sourcing strategies. Many studies have categorized knowledge sourcing strategies into intemal- and external-oriented. Internal-oriented sourcing strategy attempts to increase firm performance by integrating knowledge within the boundary of the firm. On the contrary, external-oriented strategy attempts to bring knowledge in from outside sources via either acquisition or imitation, and then to transfer that knowledge across to the organization. However, the extant literature on knowledge sourcing strategies focuses primarily on large organizations. Although many studies have clearly highlighted major differences between large and small firms and the need to adopt different strategies for different firm sizes, scant attention has been given to analyzing how knowledge sourcing strategies affect firm performance in small firms and what are the differences between small and large firms in the patterns of knowledge sourcing strategies adoption. This study attempts to advance the current literature by examining the impact of knowledge sourcing strategies on small firm performance from a holistic perspective. By drawing on knowledge based theory from organization science and complementarity theory from the economics literature, this paper is motivated by the following questions: (1) what are the adoption patterns of different knowledge sourcing strategies in small firms (i,e., what sourcing strategies should be adopted and which sourcing strategies work well together in small firms)?; and (2) what are the performance implications of these adoption patterns? In order to answer the questions, this study developed three hypotheses. First hypothesis based on knowledge based theory is that internal-oriented knowledge sourcing is positively associated with small firm performance. Second hypothesis developed on the basis of knowledge based theory is that external-oriented knowledge sourcing is positively associated with small firm performance. The third one based on complementarity theory is that pursuing both internal- and external-oriented knowledge sourcing simultaneously is negatively or less positively associated with small firm performance. As a sampling frame, 700 firms were identified from the Annual Corporation Report in Korea. Survey questionnaires were mailed to owners or executives who were most erudite about the firm s knowledge sourcing strategies and performance. A total of 188 companies replied, yielding a response rate of 26.8%. Due to incomplete data, 12 responses were eliminated, leaving 176 responses for the final analysis. Since all independent variables were measured using continuous variables, supermodularity function was used to test the hypotheses based on the cross partial derivative of payoff function. The results indicated no significant impact of internal-oriented sourcing strategies while positive impact of external-oriented sourcing strategy on small firm performance. This intriguing result could be explained on the basis of various resource and capital constraints of small firms. Small firms typically have restricted financial and human resources. They do not have enough assets to always develop knowledge internally. Another possible explanation is competency traps or core rigidities. Building up a knowledge base based on internal knowledge creates core competences, but at the same time, excessive internal focused knowledge exploration leads to behaviors blind to other knowledge. Interestingly, this study found that Internal- and external-oriented knowledge sourcing strategies had a substitutive relationship, which was inconsistent with previous studies that suggested complementary relationship between them. This result might be explained using organizational identification theory. Internal organizational members may perceive external knowledge as a threat, and tend to ignore knowledge from external sources because they prefer to maintain their own knowledge, legitimacy, and homogeneous attitudes. Therefore, integrating knowledge from internal and external sources might not be effective, resulting in failure of improvements of firm performance. Another possible explanation is small firms resource and capital constraints and lack of management expertise and absorptive capacity. Although the integration of different knowledge sources is critical, high levels of knowledge sourcing in many areas are quite expensive and so are often unrealistic for small enterprises. This study provides several implications for research as well as practice. First this study extends the existing knowledge by examining the substitutability (and complementarity) of knowledge sourcing strategies. Most prior studies have tended to investigate the independent effects of these strategies on performance without considering their combined impacts. Furthermore, this study tests complementarity based on the productivity approach that has been considered as a definitive test method for complementarity. Second, this study sheds new light on knowledge management research by identifying the relationship between knowledge sourcing strategies and small firm performance. Most current literature has insisted complementary relationship between knowledge sourcing strategies on the basis of data from large firms. Contrary to the conventional wisdom, this study identifies substitutive relationship between knowledge sourcing strategies using data from small firms. Third, implications for practice highlight that managers of small firms should focus on knowledge sourcing from external-oriented strategies. Moreover, adoption of both sourcing strategies simultaneousiy impedes small firm performance.