• Smart Structures and Systems
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• v.30 no.3
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.164-174
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• 2019

This article presents low altitude small drone identification trends at home and abroad. To reduce the dysfunction caused by the proliferation of drones worldwide, there is a growing interest in remote identification technologies that can identify the basic information of the drone. First, this article introduces policy trends in major countries. US, Europe, and China have recently provided recommendations regarding technologies available for the remote identification and tracking of a drone. Next, standardization activities on identification communications and identification systems are introduced. For this, standards organizations for the small drone identification, such as the International Organization for Standardization, IEEE 802, Radio Technical Commission for Aeronautics, International Civil Aviation Organization, and $3^{rd}$ Generation Partnership Project, are investigated. Finally, drone identification technology trends are introduced. In the US and Europe, various drone identification technologies have been studied to identify a drone owner and drone registration information with a drone identifier. In South Korea, drone identification technology is still in its infancy, whereas drone detection and physical counterattack technologies are somewhat more developed. As such, major drone manufacturers are also currently studying and developing drone identification systems.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.107-118
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• 1998

Two-phase neuro-system identification method is presented. The 1$^{st}$-phase identification uses conventional neural network mapping for modeling an input-output system. The 2$^{nd}$ -phase modeling is also performed sequentially using the 1$^{st}$-phase modeling errors. In the 2$^{nd}$ a phase modeling, newly generated input signals, which are obtained by summing the 1st-phase modeling error and artificially generated uniform series, are utilized as system's I-O mapping elements. The 1$^{st}$-phase identification is interpreted as a “Real Model” system identification because it uses system's real data(i.e., observations and control inputs) while the 2$^{nd}$ -phase identification as a “Artificial Model” identification because of using artificial data. Experimental results are given to verify that the two-phase neuro-system identification could reduce the overall modeling errors.rrors.

• Speech Sciences
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• v.12 no.4
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• pp.31-42
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• 2005

Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Journal of Information Technology Services
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• v.9 no.3
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• pp.179-191
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• 2010

Service identification and composition is one of the key characteristics for a successful Service-Oriented Computing, being receiving a lot of attention from researchers in recent years. In the Service-Oriented Analysis, the identification of business services has to be preceded before application services are identified. Most approaches addressing the derivation of business services are based on heuristic methods and human experts. The manual identification of business services is highly expensive and ambiguous task, and it may result in the service design with bad quality because of errors and misconception. Although a few of approaches of automatic service identification are proposed, most of them are in focus on technical architectures and application services. In this paper, we propose a model on the automatic identification of business services by horizontal and vertical service alignment using Enterprise Architecture as an ontology. We verify the effectiveness of the proposed model of business services identification through a case study based on Department of Defense Enterprise Architecture.

• Journal of Ecology and Environment
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• v.41 no.12
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• pp.345-350
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• 2017

Background: Ecological research often requires monitoring of a specific individual over an extended period of time. To enable non-invasive re-identification, consistent external marking is required. Treefrogs possess lateral lines for crypticity. While these patterns decrease predator detection, they also are individual specific patterns. In this study, we tested the use of lateral lines in captive and wild populations of Dryophytes japonicus as natural markers for individual identification. For the purpose of the study, the results of visual and software assisted identifications were compared. Results: In normalized laboratory conditions, a visual individual identification method resulted in a 0.00 rate of false-negative identification (RFNI) and a 0.0068 rate of false-positive identification (RFPI), whereas Wild-ID resulted in RFNI = 0.25 and RFNI = 0.00. In the wild, female and male data sets were tested. For both data sets, visual identification resulted in RFNI and RFPI of 0.00, whereas the RFNI was 1.0 and RFPI was 0.00 with Wild-ID. Wild-ID did not perform as well as visual identification methods and had low scores for matching photographs. The matching scores were significantly correlated with the continuity of the type of camera used in the field. Conclusions: We provide clear methodological guidelines for photographic identification of D. japonicus using their lateral lines. We also recommend the use of Wild-ID as a supplemental tool rather the principal identification method when analyzing large datasets.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.137-144
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• 2019

The Leaders in organization play various core roles in workers' performance and organizational performance. A leader presents a vision and a direction to enable members to achieve higher performance. Especially, transformational leadership suggests members a strong vision and influences them to have identification with the organization. Based on previous researches, transformational leadership is a close connection to identification with job and organization satisfaction. This study explains the influence of the university professors' transformational leadership on students' university satisfaction and university identification. In this study, the subjects of statistical analysis are Chinese international students studying in Korea. As a result, first, professors' transformational leadership enhanced the level of students' university satisfaction. Second, university identification had a partial mediating influence on the relationship between professors' transformational leadership and students' university satisfaction. Professors' transformational leadership had an influence on the students' identification with their universities. Through this process, students become aware of identification and more satisfied with their universities. As a result of the transformational leadership of the professor, the identification and satisfaction of the international students will be improved and they will be better able to adapt to their study life and achieve high results. The purpose of this study is to emphasize the roles of transformational leadership as a necessary leadership for the successful study abroad life of Chinese students studying in Korea today.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.245-252
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• 2022

Risk identification for bridge projects is a knowledge-based and labor-intensive task involving several procedures and stakeholders. Presently, risk information of bridge projects is unstructured and stored in different sources and formats, hindering knowledge sharing, reuse, and automation of the risk identification process. Consequently, there is a need to develop structured and formalized risk information for bridge projects to aid effective risk identification and automation of the risk management processes to ensure project success. This study proposes a semantic risk breakdown structure (SRBS) to support risk identification for bridge projects. SRBS is a searchable hierarchical risk breakdown structure (RBS) developed with python programming language based on a semantic modeling approach. The proposed SRBS for risk identification of bridge projects consists of a 4-level tree structure with 11 categories of risks and 116 potential risks associated with bridge projects. The contributions of this paper are threefold. Firstly, this study fills the gap in knowledge by presenting a formalized risk breakdown structure that could enhance the risk identification of bridge projects. Secondly, the proposed SRBS can assist in the creation of a risk database to support the automation of the risk identification process for bridge projects to reduce manual efforts. Lastly, the proposed SRBS can be used as a risk ontology that could aid the development of an artificial intelligence-based integrated risk management system for construction projects.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.325-331
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• 2007

Personal identification is essential for the automatic measurement of biosignal information in home healthcare systems. Personal identification is usually achieved with passive radio frequency identification (RFID), which does little more than store a unique identification number. However, passive RFID is not ideal for automatic identification. We present a user identification system based on radio signal strength indication (RSSI) using ZigBee for active RFID tags. Personal identification is achieved by finding the largest RSSI value from aggregated beacon messages that are periodically transmitted by active RFID tags carried by users. Obtaining reliable person!'.! identification without restricting the orientation requires a certain distance between the closest active RFID tag from the ZED and the second closest tag. The results show that the closest active RFID tag from the ZED and the second closest tag must be at least 70 cm apart to achieve reliable personal identification.