• Structural Engineering and Mechanics
• /
• v.41 no.1
• /
• pp.25-41
• /
• 2012

Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a scheme for crack identification based on a Kriging surrogate model is proposed. A modified rectangular grid (MRG) is introduced to move some sample points lying on the boundary into the internal design region, which will provide more useful information for the construction of Kriging model. The initial Kriging model is then constructed by samples of varying crack parameters (locations and sizes) and their corresponding modal frequencies. For identifying crack parameters, a robust stochastic particle swarm optimization (SPSO) algorithm is used to find the global optimal solution beyond the constructed Kriging model. To improve the accuracy of surrogate model, the finite element (FE) analysis soft ANSYS is employed to deal with the re-meshing problem during surrogate model updating. Specially, a simple method for crack number identification is proposed by finding the maximum probability factor. Finally, numerical simulations and experimental research are performed to assess the effectiveness and noise immunity of this proposed scheme.

• Journal of Communications and Networks
• /
• v.14 no.1
• /
• pp.34-39
• /
• 2012

Collisions between tags greatly reduce the identification speed in radio frequency identification (RFID) systems and increase communication overhead. In particular for an active RFID system, tags are powered by small batteries, and a large number of re-transmissions caused by collisions can deteriorate and exhaust the tag energy which may result in missing tags. An efficient collision-free arbitration protocol for active RFID systems is proposed in this paper. In this protocol, a new mechanism involving collision detection, collision avoidance, and fast tag access is introduced. Specifically, the pulse burst duration and busy-tone-detection delay are introduced between the preamble and data portion of a tag-to-reader (T-R) frame. The reader identifies tag collision by detecting pulses and transmits a busy tone to avoid unnecessary transmission when collision occurs. A polling process is then designed to quickly access the collided tags. It is shown that the use of the proposed protocol results in a system throughput of 0.612, which is an obvious improvement when compared to the framed-slotted ALOHA (FSA) arbitration protocol for ISO/IEC 18000-7 standard. Furthermore, the proposed protocol greatly reduces communication overhead, which leads to energy conservation.

• Smart Structures and Systems
• /
• v.15 no.1
• /
• pp.227-244
• /
• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.2764-2782
• /
• 2021

Existing city-level boundary nodes identification methods need to locate all IP addresses on the path to differentiate which IP is the boundary node. However, these methods are susceptible to time-delay, the accuracy of location information and other factors, and the resource consumption of locating all IPes is tremendous. To improve the recognition rate and reduce the locating cost, this paper proposes an algorithm for city-level boundary node identification based on bidirectional approaching. Different from the existing methods based on time-delay information and location results, the proposed algorithm uses topological analysis to construct a set of candidate boundary nodes and then identifies the boundary nodes. The proposed algorithm can identify the boundary of the target city network without high-precision location information and dramatically reduces resource consumption compared with the traditional algorithm. Meanwhile, it can label some errors in the existing IP address database. Based on 45,182,326 measurement results from Zhengzhou, Chengdu and Hangzhou in China and New York, Los Angeles and Dallas in the United States, the experimental results show that: The algorithm can accurately identify the city boundary nodes using only 20.33% location resources, and more than 80.29% of the boundary nodes can be mined with a precision of more than 70.73%.

• Smart Structures and Systems
• /
• v.29 no.3
• /
• pp.475-484
• /
• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.10
• /
• pp.2722-2727
• /
• 1999

In this paper we propose verifiable self-certified identification and key distribution protocols which has advantages of certificate-based scheme and Girault's self-certified public key. The security of the proposed protocols is based on ${\gamma}$\ulcorner-residuosity problem and discrete logarithm problem.

• Journal of Information Technology Services
• /
• v.16 no.2
• /
• pp.97-110
• /
• 2017

Due to development of IT, various Internet services via the non-face-to-face are increasing rapidly. In the past, the resident registration numbers (RRN) was used a mean of personal identification, but the use of RRN is prohibited by the relevant laws, and the personal identification services using alternative means are activated. According to the prohibition policy of RRN, i-PIN service appeared as an alternative means to identify a person. However, the user's knowledge-based i-PIN service continues to cause fraudulent issuance, account hijacking, and fraud attempts due to hacking accidents. Due to these problems, the usage rate of i-PIN service which performs a nationwide free personal identification service, is rapidly decreasing. Therefore, this paper proposes a technical safety enhancement method for security enhancement in the i-PIN-based personal identification service. In order to strengthen the security of i-PIN, this paper analyzes the encryption key exposure, key exchange and i-PIN authentication model problems of i-PIN and suggests countermeasures. Through the proposed paper, the i-PIN can be expected to be used more effectively as a substitution of RRN by suggesting measures to enhance the safety of personal identification information. Secured personal identification services will enable safer online non-face-to-face transactions. By securing the technical, institutional, and administrative safety of the i-PIN service, the usage rate will gradually increase.

• Journal of Korea Multimedia Society
• /
• v.20 no.9
• /
• pp.1502-1508
• /
• 2017

A musical key is a fundamental concept in Western music theory. It is a collective characterization of pitches and chords that together create a musical perception of the entire piece. It is based on a group of pitches in a scale with which a music is constructed. Each key specifies the set of seven primary chromatic notes that are used out of the twelve possible notes. This paper presents a method that identifies the key of a song using Hidden Markov Models given a sequence of chroma features. Given an input song, a sequence of chroma features are computed. It is then classified into one of the 24 keys using a discrete Hidden Markov Models. The proposed method can help musicians and disc-jockeys in mixing a segment of tracks to create a medley. When tested on 120 songs, the success rate of the music key identification reached around 87.5%.

• Journal of applied mathematics & informatics
• /
• v.26 no.3_4
• /
• pp.519-530
• /
• 2008

This paper studies the multi-domain coupled system of one dimensional Arctic temperature field and establishes identification model about the thermodynamic parameters of sea ice (heat storage capacity, density and conductivity) by the so-called output least-square estimate according to the temperature data acquired by a monitor buoy installed in the Arctic ocean. By the optimal control theory, the existence and dependability of weak solution and the identifiability of identification model have been given. Moreover, necessary optimality condition is proposed. Furthermore, the optimal algorithm for the identification model is constructed. By using the optimal thermodynamic parameters of Arctic sea ice, the numerical simulation is implemented, and the numerical results of temperature distribution of Arctic sea ice are demonstrated.

• Wind and Structures
• /
• v.7 no.3
• /
• pp.173-186
• /
• 2004

Flutter derivatives provide the basis of predicting the critical wind speed in flutter and buffeting analysis of long-span cable-supported bridges. In this paper, one popular stochastic system identification technique, covariance-driven Stochastic Subspace Identification(SSI in short), is firstly presented for estimation of the flutter derivatives of bridge decks from their random responses in turbulent flow. Secondly, wind tunnel tests of a streamlined thin plate model and a ${\Pi}$ type blunt bridge section model are conducted in turbulent flow and the flutter derivatives are determined by SSI. The flutter derivatives of the thin plate model identified by SSI are very comparable to those identified by the unifying least-square method and Theodorson's theoretical values. As to the ${\Pi}$ type section model, the effect of turbulence on aerodynamic damping seems to be somewhat notable, therefore perhaps the wind tunnel tests for flutter derivative estimation of those models with similar blunt sections should be conducted in turbulent flow.