• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.10
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• pp.435-442
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• 2006

One of the most important topics about the safety of electrical and electronic system is the reliability of the wiring system. The Time-Frequency Domain Reflectometry(TFDR) is a state-of-the-art system for detecting and estimating of the fault on a wiring. In this paper, We've considered the load resistance measurement on a coaxial cable using TFDR in a way of expanded application. The TFDR system was built using commercial Pci extensions for Instrumentation(PXI) and LabVIEW. The proposed real time TFDR system consisted of the reference signal design, signal generation, signal acquisition, algorithm execution and results display part. To implement real time system, all of the parts were programmed by the LabVIEW which is one of the graphical programming languages. Using the application software implemented by the LabVIEW, we were able to design a proper reference signal which is suitable for target cable and control not only the arbitrary waveform generator in the signal generation part but alto the digital storage oscilloscope in the signal acquisition part. By using the TFDR real time system with the terminal resistor on the target cable, we carried out load impedance measurement experiments. The experimental results showed that the proposed system are able not only to detect the location of impedance discontinuity on the cable but also to estimate the load resistance with high accuracy.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.727-739
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• 2003

In this paper, we propose a generation method of regression classes for adaptation in the HM-Net (Hidden Markov Network) system. The MLLR (Maximum Likelihood Linear Regression) adaptation approach is applied to the HM-Net speech recognition system for expressing the characteristics of speaker effectively and the use of HM-Net in various tasks. For the state level sharing, the context domain state splitting of PDT-SSS (Phonetic Decision Tree-based Successive State Splitting) algorithm, which has the contextual and time domain clustering, is adopted. In each state of contextual domain, the desired phoneme classes are determined by splitting the context information (classes) including target speaker's speech data. The number of adaptation parameters, such as means and variances, is autonomously controlled by contextual domain state splitting of PDT-SSS, depending on the context information and the amount of adaptation utterances from a new speaker. The experiments are performed to verify the effectiveness of the proposed method on the KLE (The center for Korean Language Engineering) 452 data and YNU (Yeungnam Dniv) 200 data. The experimental results show that the accuracies of phone, word, and sentence recognition system increased by 34∼37%, 9%, and 20%, respectively, Compared with performance according to the length of adaptation utterances, the performance are also significantly improved even in short adaptation utterances. Therefore, we can argue that the proposed regression class method is well applied to HM-Net speech recognition system employing MLLR speaker adaptation.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10a
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• pp.159-162
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• 2003

The remeshing algorithm using the constrained Delaunay method adapted to the mesh density map is developed. In the finite element simulation of forging process, the numerical error increases as the process goes on. However, it is not desirable to use a uniformly fine mesh in the whole domain. Therefore, it is necessary to reduce the analysis error by constructing locally fine mesh at the region where the error is concentrated such as die corner. In this paper, the point insertion algorithm is used and mesh size is controlled by using a mesh density map constructed with a posteriori error estimation. And an optimized smoothing technique is adapted to have smooth distribution and improve the quality of the mesh.

• Steel and Composite Structures
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• v.50 no.1
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• pp.45-61
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• 2024

This research explores the domain of organic solar cells, a photovoltaic technology employing organic electronics, which encompasses small organic molecules and conductive polymers for efficient light absorption and charge transport, leading to electricity generation from sunlight. A computer simulation is employed to scrutinize resonance and dynamic stability in OSCs, with a focus on size effects introduced by nonlocal strain gradient theory, incorporating additional terms in the governing equations related to displacement and time. Initially, the Navier method serves as an analytical solver to delve into the dynamics of design points. The accuracy of this initial step is verified through a meticulous comparison with high-quality literature. The findings underscore the substantial impact of viscoelastic foundations, size-dependent parameters, and geometric factors on the stability and dynamic deflection of OSCs, with a noteworthy emphasis on the amplified influence of size-dependent parameters in higher values of the different layers' thicknesses.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.187-195
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• 2024

Light detection and ranging (LiDAR), a widely used sensor in mobile robots and autonomous vehicles, has its most important function as measuring the range of objects in three-dimensional space and generating point clouds. These point clouds consist of the coordinates of each reflection point and can be used for various tasks, such as obstacle detection and environment recognition. However, several processing steps are required, such as three-dimensional modeling, mesh generation, and rendering. Efficient data processing is crucial because LiDAR provides a large number of real-time measurements with high sampling frequencies. Despite the rapid development of controller computational power, simplifying the computational algorithm is still necessary. This paper presents a method for estimating the presence of curbs, humps, and ground tilt using range measurements from a single horizontal or vertical scan instead of point clouds. These features can be obtained by data segmentation based on linearization. The effectiveness of the proposed algorithm was verified by experiments in various environments.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.299-308
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• 2006

As recently XML is becoming the standard of exchanging web documents and public documentations, XML data are increasing in many areas. To retrieve the information about XML documents efficiently, the semantic web based on the ontology is appearing. The existing ontology has been constructed manually and it was time and cost consuming. Therefore in this paper, we propose the semi-automatic ontology generation technique using the data mining technique, the association rules. The proposed method solves what type and how many conceptual relationships and determines the ontology domain level for the automatic ontology generation, using the data mining algorithm. Appying the association rules to the XML documents, we intend to find out the conceptual relationships to construct the ontology, finding the frequent patterns of XML tags in the XML documents. Using the conceptual ontology domain level extracted from the data mining, we implemented the semantic web based on the ontology by XML Topic Maps (XTM) and the topic map engine, TM4J.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.968-980
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• 2023

The dynamic structural responses are sensitive to the time-frequency content of seismic waves, and seismic input motions in time-history analysis are usually required to be compatible with design response spectra according to nuclear codes. In order to generate spectra-compatible input motions while maintaining the intrinsic non-stationarity of seismic waves, an improved time-domain approach is proposed in this paper. To maintain the nonstationary characteristics of the given seismic waves, a new time-frequency envelope function is constructed using the Hilbert amplitude spectrum. Based on the intrinsic mode functions (IMFs) obtained from given seismic waves through variational mode decomposition, a new corrective time history is constructed to locally modify the given seismic waves. The proposed corrective time history and time-frequency envelope function are unique for each earthquake records as they are extracted from the given seismic waves. In addition, a dimension reduction iterative technique is presented herein to simultaneously superimpose corrective time histories of all the damping ratios at a specific frequency in the time domain according to optimal weights, which are found by the genetic algorithm (GA). Examples are presented to show the capability of the proposed approach in generating spectra-compatible time histories, especially in maintaining the nonstationary characteristics of seismic records. And numerical results reveal that the modified time histories generated by the proposed method can obtain similar dynamic behaviors of AP1000 nuclear power plant with the natural seismic records. Thus, the proposed method can be efficiently used in the design practices.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.454-457
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• 2005

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. And calendar based on temporal association rules proposes the discovery of association rules along with their temporal patterns in terms of calendar schemas, but this approach is also adopt an Apriori-like candidate set generation. In this paper, we propose an efficient temporal frequent pattern mining using TFP-tree (Temporal Frequent Pattern tree). This approach has three advantages: (1) this method separates many partitions by according to maximum size domain and only scans the transaction once for reducing the I/O cost. (2) This method maintains all of transactions using FP-trees. (3) We only have the FP-trees of I-star pattern and other star pattern nodes only link them step by step for efficient mining and the saving memory. Our performance study shows that the TFP-tree is efficient and scalable for mining, and is about an order of magnitude faster than the Apriori algorithm and also faster than calendar based on temporal frequent pattern mining methods.

• Land and Housing Review
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• v.1 no.1
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• pp.51-57
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• 2010

The importance of seismic design is greatly emphasized recently in Korea, resulting in an increase in the number of dynamic analysis being performed. One of the most important input parameters for the dynamic seismic analysis is input ground motion. However, it is common practice to use recorded motions from U.S. or Japan without considering the seismic environment of Korea or synthetic motions generated in the frequency domain. The recorded motions are not suitable for the seismic environment of Korea since the variation in the duration and energy with the earthquake magnitude cannot be considered. The artificial motions generated in frequency domain used to generated design response spectrum compatible ground motion has the problem of generating motions that have different frequency characteristics compared to real recordings. In this study, an algorithm that generates target response spectrum compatible ground motions in time domain is used to generate a suite of input ground motions. The generated motions are shown to preserve the non-stationary characteristics of the real ground motion and at the same, almost perfectly match the design response spectrum.