• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.3
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• pp.223-228
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• 2019

This study analyzes the propagation of the path losses between Jeju-do and Jin-do transceivers located in the coastal areas of Korea using the Advanced Refractive Prediction System(AREPS) simulation software based on the actual coastal weather database. The simulated data is used to construct a duct map according to the altitude and thickness of the trap. The duct map is then divided into several regions depending on the altitude parameters of Tx and Rx, which can be used to effectively estimate the abnormal wave propagation characteristics due to duct occurrence in the Jeju-do coastal area. To validate the proposed duct map, two representative atmospheric index samples of the weather database in May 2018 are selected, and the simulated path losses using these atmospheric indices are compared with the measured data. The simulated path losses for abnormal conditions at the Rx point at Jeju-do are 167.7 dB and 192.3 dB, respectively, which are in good agreement with the measured data of 164.4 dB and 194.9 dB, respectively.

• 한국노년학
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• v.29 no.1
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• pp.243-258
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• 2009

Background: Falls are among the most common and serious health problems of older people. The psychological symptoms of falling have received relatively little attention compared to physical problems. Objective: The purpose of this study is to test a model to explain the factors that influence fear of falling among older adults living in a continuing care retirement community (CCRC) in Baltimore city, United States. Methods: A secondary analysis was conducted using data obtained from a Health Promotion Survey done on 149 older adults living in a CCRC. Data was originally obtained during face to face interviews with each participant. Descriptive statistics and bivariate correlations were used to describe the sample and evaluate simple correlations. A path analysis was done using the AMOS 4.0 statistical program. Results: Of the 49 hypothesized paths, 13 were statistically significant, and the model accounted for 22% of the variance in fear of falling among the elderly. There was support for the fit of the model to the data with a nonsignificant chi square at 0.478 (df=2, p=0.79), and the ratio of chi-square to degrees of freedom was 0.24, a CFI of 0.99 and RMSEA of 0.00. In particular, gender, a history of falling, and exercise were significant predictors of fear of falling. Conclusions/Implications: As anticipated, exercise is an important factor to prevent fear of falling. As a modifiable variable, self-efficacy and outcome expectation indirectly influence fear of falling through exercise.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11A
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• pp.936-944
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• 2003

In this paper, we propose an AND/XOR-based technology mapping method for field programmable gate arrays (FPGAs). Due to the fixed size of the programmable blocks in an FPGA, decomposing a circuit into sub-circuits with appropriate number of inputs can achieve excellent implementation efficiency. Specifically, the proposed technology mapping method is based on Davio expansion theorem to decompose a given Boolean circuit. The AND/XOR nature of the proposed method allows it to operate on XOR intensive circuits, such as error detecting/correcting, data encryption/decryption, and arithmetic circuits, efficiently. We conduct experiments using MCNC benchmark circuits. When using the proposed approach, the number of CLBs (configurable logic blocks) is reduced by 67.6% (compared to speed-optimized results) and 57.7% (compared to area-optimized results), total equivalent gate counts are reduced by 65.5 %, maximum combinational path delay is reduced by 56.7 %, and maximum net delay is reduced by 80.5 % compared to conventional methods.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1271-1271
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• 2001

Water pollutants in drainage mainly consist of organic compounds. Hence, total organic carbon (TOC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) were generally used as the indices of pollution. However, these values are determined by special analyzer (TOC), titration method (COD), or microbe culture (BOD). Therefore, the development of simple and easy methods for the determination of water pollution is required. The authors reported the evaluation of water pollution by near infrared (NIR) spectroscopy in a model system with food components (Takamura et al. (200) Near Infrared Spectroscopy: Proceedings of 9th International Conference, pp. 503-507). In this study, the relationship between NIR spectra and drainage was investigated in order to develop a method for evaluation of drainage by NIR. Drainage was obtained in Nara Purification Center. The ranges of TOC, COD, and BOD were 0-130, 0-100 and 0-200, respectively. NIR transmittance spectra were recorded on NIR Systems Model 6250 Research Composition Analyzer in the wavelength range of 680-1235 and 1100-2500 nm with a quartz cell (light path: 0.5, 1, 2, 4 and 10mm) at 10-40. Statistical analysis was performed using NSAS program. A partial least squares (PLS) regression analysis was used for calibration. As the result, a good correlation between the raw NIR spectra and OC was obtained in the calibration. The best light path was 10 and 0.5mm in the wavelength range of 680-1235 and 110-2500nm, respectively. In the calibration, correlation coefficients(R) were 096-0.97 in the both range. In the prediction, however, a good correlation (R=0.89-0.96) was obtained only in the range of 6801235 nm, Similar results were obtained in the cases of COD and BOD. These results suggest the possibility that NIR spectroscopy can be used to evaluate drainage.

• Proceedings of the Membrane Society of Korea Conference
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• 1997.10a
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• pp.69-70
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• 1997

Polyimides and related polymers, when synthesized from aromatic monomers, have generally rigid chain structures resulting in a low gas permeability. The rigidity of polymer chains reduces the segmental motion of chains and works as a good barrier against gas transport. To overcome the limit of use as materials of gas separation membranes due to low gas permeability, block copolymers with the incorporation of flexible segments like siloxane linkage and ether linkage have been studied. These block copolymers have microphase-separated structures composed of microdomains of flexible poly(dimethylsiloxane) or polyether segments and of rigid polyimides segments. In case of rigid-flexible block copolymers, the characteristics of both phases for gas permeation are of great difference. The permeation of gas molecules occurs favorably through microdomains of flexible segments, whereas those of rigid segments hinder the permeation of gas molecules. Accordingly the increase of content of flexible segments in a rigid polymer matrix will increase the gas permeability of the membrane linearly. However, this prediction does not satisfy enough many experimental results and in particular the drastic increase of the permeability is observed in a certain volume fraction. It was proposed that the gas transport mechanism is dominated by diffusion rather than gas solubility in a certain content of flexible phase if solution-diffusion mechanism is adopted. However, the transition from solubility-dependent to diffusion-dependent cannot be explained by the understanding of mechanism itself. Therefore, we consider an effective chemical path which permeable phase can form in a microheterogenous medium, and percolation concept is introduced to describe the permeability transition at near threshold where for the first time a percolation path occurs. The volume fraction of both phases is defined as V$_{\alpha}$ and V$_{\beta}$ in block copolymers, and the volume of $\beta$ phase in the threshold forming geometrically a traversing channel is defined as V$_{\betac}$. The formation mechanism of shortest chemical channel is schematically depicted in Fig. 1.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.48-54
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• 2018

With the 4th industry era, smart factories are emerging. In the era of multi-product small scale production, unmanned transportation vehicles are rapidly increasing in utilization of unmanned transportation vehicles that carry and arrange goods in the work space. The conventional unmanned vehicle detected its position by using the guided line method and the position based method for indoor location recognition and movement. This method has disadvantages of initial high cost and maintenance / maintenance. In this paper, to solve the disadvantages, the method of predicting the direct path of the unmanned vehicle through the Kalman filter is verified using the white LED marker of the warehouse and the position data and the image data of the white LED marker recognition image. Through this, the reliability of the linear movement which occupies the most part in the lattice structure is secured. It is also expected that the reliance on additional position sensors will also be reduced.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.5
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• pp.569-576
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• 2011

In this paper a new analysis procedure for evaluation of progressive collapse resisting capacity of a structure was proposed based on the nonlinear static analysis procedure. The proposed procedure produces analysis results identical to those obtained by the linear static analysis procedure specified in the GSA guidelines without iteration, therefore saving a lot of computation time and excluding the possibility of human errors during the procedure. To verify the validity of the proposed procedure, the two methods were applied to the analysis of a reinforced concrete moment frame and a steel braced frame subjected to loss of a first story column and the results were compared. According to the analysis results, the two methods produce identical results in the prediction of progressive collapse and the hinge formation. As iterative analysis is not required in the proposed method, significant amount of analysis time is saved in the proposed analysis procedure.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8S
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• pp.2608-2619
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• 2000

This paper proposes the Handover Scheme for the mobile and describes the result of the performance analysis. In the conventional scheme of handover request, the withdrawal of terminal may occur because handover request is performed based on fixed signal level without considering network load and terminal mobility. The proposed scheme offers the minimization of withdrawal and handover blocking probability by means of the handover request of terminal based on the network load and terminal mobility. Conventional handover scheme has the sequential procedure that network performs resource check and path rerouting on the handover by MT(Mobile Terminal). Proposed handover scheme pre-processes the resource check before the handover request by predicting the handover request timo so that handover latency can be reduced. Moreover, path optimization is executed after the completion of handover in order to reduce handover latency. The rdduction of handover latency prevents the dropping of service by minimizing backward handover blocking. In summary, we propose the prediction of handover request time and decision method based on terminal, validating the performance of proposed scheme considering various cases of simulation.