• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.6
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• pp.601-608
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• 2011

This paper presents the damage assessment of a building structure by using a novel optical fiber accelerometer system. Especially, a sub-scaled building model is designed and manufactured to check up the feasibility of the optical fiber accelerometer for structural health monitoring. The novel accelerometer exploits the moir$\acute{e}$ fringe optical phenomenon and two pairs of optical fibers to measure the displacement with a high accuracy, and furthermore a pendulum to convert the displacement into acceleration. A prototype of optical fiber accelerometer system has been successfully developed that consists of a sensor head, a control unit and a signal processing unit. The building model is also designed as a 4-story building with a rectangular shape of $200{\times}300$ mm of edges. Each floor is connected to the next ones by 6 steel columns which are threaded rods. Basically, a random vibration test of the building model is done with a shaker and all of acceleration data is successfully measured at the assigned points by the optical fiber accelerometer. The experiments are repeated in the undamaged state and the damaged state. The comparison of dynamic parameters including the natural frequencies and the eigenvectors is successfully carried out. Finally, the optical fiber accelerometer is proven to be prospective to evaluate dynamic characteristics of a building structure for the damage assessment.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.90-93
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• 2008

In natural language, it is common that repetitive constituents in an expression are to be left out and it is necessary to figure out the constituents omitted at analyzing the meaning of the sentence. This paper is on recognition of boundaries of parallel noun phrases by figuring out constituents omitted. Recognition of parallel noun phrases can greatly reduce complexity at the phase of sentence parsing. Moreover, in natural language information retrieval, recognition of noun with modifiers can play an important role in making indexes. We propose an unsupervised probabilistic model that identifies parallel cores as well as boundaries of parallel noun phrases conjoined by a conjunctive particle. It is based on the idea of swapping constituents, utilizing symmetry (two or more identical constituents are repeated) and reversibility (the order of constituents is changeable) in parallel structure. Semantic features of the modifiers around parallel noun phrase, are also used the probabilistic swapping model. The model is language-independent and in this paper presented on parallel noun phrases in Korean language. Experiment shows that our probabilistic model outperforms symmetry-based model and supervised machine learning based approaches.

• Journal of the Korean Society of Industry Convergence
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• v.13 no.4
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• pp.219-225
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• 2010

A structure of line for producing products is variously changing to be automatic and one-person Cell-Line considering the physical burden for workers in manufacturing industry of electronic goods. However, workers tend to still accuse Work-related Musculoskeletal Disorders (including shoulder, waist, hand, wrist, leg etc.) as a simply repeated work by accelerating of the production speed for productivity improvement in the assembly line. Thus manufacturing engineers in charge of changing and set up newly for an assembly line are necessary to the construct of the guideline on human engineering. Especially. There is no standardized engineering-guide and it is difficult to create the exact work environment because the risk factor analysis and the improvement for assembly line are executed once a year on the current situation. I'd like to reduce the physical burden on workers through the effective improvement of processing by the guideline on working environment fit for a characteristic on manufacturing process when the process is changed or newly installed.

• Journal of Korean Critical Care Nursing
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• v.11 no.1
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• pp.1-14
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• 2018

Purpose : As the intensive care unit (ICU) survival rate increases, interest in the lives of ICU survivors has also been increasing. The purpose of this study was to identify the sentiment of ICU survivors. Method : The author analyzed the quotations from previous qualitative studies related to ICU survivors; a total of 1,074 sentences comprising 429 quotations from 25 relevant studies were analyzed. A word cloud created in the R program was utilized to identify the most frequent adjectives used, and sentiment and emotional scores were calculated using the Artificial Intelligence (AI) program. Results : The 10 adjectives that appeared the most in the quotations were 'difficult', 'different', 'normal', 'able', 'hard', 'bad', 'ill', 'better', 'weak', and 'afraid', in order of decreasing occurrence. The mean sentiment score was negative ($-.31{\pm}.23$), and the three emotions with the highest score were 'sadness'($.52{\pm}.13$), 'joy'($.35{\pm}.22$), and 'fear'($.30{\pm}.25$). Conclusion : The natural language processing of AI used in this study is a relatively new method. As such, it is necessary to refine the methodology through repeated research in various nursing fields. In addition, further studies on nursing interventions that improve the coherency of ICU memory of survivors and familial support for the ICU survivors are needed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.6
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• pp.27-35
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• 2004

Finite or Galois fields have been used in numerous applications such as error correcting codes, digital signal processing and cryptography. These applications often require exponetiation on GF(2$^{m}$ ) which is a very computationally intensive operation. Most of the existing methods implemented the exponetiation by iterative methods using repeated multiplications, which leads to much computational load, or needed much hardware cost because of their structural complexity in implementing. In this paper, we present an effective VLSI architecture for exponentiation on GF(2$^{m}$ ). This circuit computes the exponentiation by multiplying product terms, each of which corresponds to an exponent bit. Until now use of this type algorithm has been confined to a primitive element but we generalize it to any elements in GF(2$^{m}$ ).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.197-205
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• 2004

In this paper, we studied on the hardware implementation of a 3${\times}$3 window weighted median filter using bit-level sorting algorithm. The weighted median filter is a generalization of the median filter that is able to preserve :,harp changes in signal and is very effective in removing impulse noise. It has been successfully applied in various areas such as digital signal and video/image processing. The weighted median filters are, for the most part, based on word-level sorting methods, which have more hardware and time complexity, However, the proposed bit-serial sorting algorithm uses weighted adder tree to overcome those disadvantages. It also offers a simple pipelined filter architecture that is highly regular with repeated modules and is very suitable for weighted median filtering. The algorithm was implemented by VHDL and graphical environment in MAX＋PlusII of ALTERA. The simulation results indicate that the proposed design method is more efficient than the traditional ones.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.3
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• pp.47-54
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• 2016

In this paper, we propose a new opportunistic routing scheme based on timed bargaining game. The proposed algorithm effectively formulates the opportunistic routing mechanism as an repeated bargaining model using timed learning method. Additionally, we formulate a new contention window adjusting scheme for reduce collision rate. Simulation results indicate that the proposed scheme has excellent performance than other existing schemes under widely diverse VANET environments.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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• pp.20-23
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• 2004

As the integrated circuit device shrinks to the smaller dimension, the chemical mechanical polishing(CMP) process was required for the global planarization of inter-metal dielectric(IMD) layer with free-defect. However, as the IMD layer gets thinner, micro-scratches are becoming as major defects. Chemical-Mechanical polishing(CMP) of conductors is a key process in Damascene patterning of advanced interconnect structure. The effect of alternative commercial slurries pads, and post-CMP cleaning alternatives are discuss, with removal rate, scratch dentisty, surface roughness, dishing, erosion and particulate density used as performance metrics. Electroplated copper deposition is a mature process from a historical point of view, but a very young process from a CMP perspective. While copper electro deposition has been used and studied for decades, its application to Cu damascene wafer processing is only now gaining complete acceptance in the semiconductor industry. The polishing mechanism of Cu-CMP process has been reported as the repeated process of passive layer formation by oxidizer and abrasion action by slurry abrasives. however it is important to understand the effect of oxidizer on copper passivation layer in order to obtain higher removal rate and non-uniformity during Cu-CMP process. In this paper, we investigated the effects of oxidizer on Cu-CMP process regarding the additional volume of oxidizer.

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Journal of the Korea Society for Simulation
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• v.24 no.3
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• pp.17-26
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• 2015

A cluster tool used in many kinds of semiconductor processes for improving the performance and the quality of wafers has a simple configuration, but its schedule is not easy because of its parallel processing module, a lack of intermediate buffers, and time constraints. While there have been many studies on its schedule, most of them have focused on full cycles in which identical work cycles are repeated under constant task times. In this research, we suggest strategies of start-up transient scheduling which satisfies time constraints and converges into a desirable steady schedule for full work cycle. The proposed schedules are expected robust under the stationary stochastic task times. Finally, we show that the strategies make schedules enters the desirable steady schedule and robust using the simulation.