• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.61-66
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• 2008

In railroad operation, turnout is the device designed to provide very critical functions of moving the train to the neighboring rail. It's the only movable section among the rail and track equipment, which has a complicated structure and as rapid movement between the wheel and rail during operation is unavoidable, the safety and the vibration caused by the impact load of the passing train becomes always the major concern. Response to rail vibration tends to vary depending on physical properties of the rail, rail base and the ground, making it difficult to estimate the quantitative outcome through the measurement. Thus, experimental or empirical approach, rather than an analytic method, has been more commonly employed to deal with the ground vibration. To predict the vibration of the turnout, an experimental value and the measured values are applied in parallel to the factors with a high degree of uncertainty. This study hence was intended to compare and analyze the vibration values measured at the crossing part of manganese turnout by type of train and turnout and distance, as well as predict the intensity of vibration generated at the crossing part of manganese turnout when tilting train accelerates.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.21-29
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• 2015

This paper proposes a novel two-tier optimization approach for decision making in many-objective problems. Because the Pareto-optimal solution ratio increases exponentially with an increasing number of objectives, simply finding the Pareto-optimal solutions is not sufficient for decision making in many-objective problems. In other words, it is necessary to discriminate the more preferable solutions from the other solutions. In the proposed approach, user preference-oriented as well as diverse Pareto-optimal solutions can be obtained as candidate solutions by introducing an additional tier of optimization. The second tier of optimization employs the corresponding secondary objectives, global evaluation and crowding distance, which were proposed in previous works, to represent the users preference to a solution and the crowdedness around a solution, respectively. To demonstrate the effectiveness of the proposed approach, decision making for some benchmark functions is conducted, and the outcomes with and without the proposed approach are compared. The experimental results demonstrate that the decisions are successfully made with consideration of the users preference through the proposed approach.

• Progress in Medical Physics
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• v.31 no.2
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• pp.9-19
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• 2020

Purpose: This study aims to develop a multi-purpose electron beam irradiation device for preclinical research and material testing using the research electron linear accelerator installed at the Dongnam Institute of Radiological and Medical Sciences. Methods: The fabricated irradiation device comprises a dual scattering foil and collimator. The correct scattering foil thickness, in terms of the energy loss and beam profile uniformity, was determined using Monte Carlo calculations. The ion-chamber and radiochromic films were used to determine the reference dose-rate (Gy/s) and beam profiles as functions of the source to surface distance (SSD) and pulse frequency. Results: The dose-rates for the electron beams were evaluated for the range from 59.16 Gy/s to 5.22 cGy/s at SSDs of 40-120 cm, by controlling the pulse frequency. Furthermore, uniform dose distributions in the electron fields were achieved up to approximately 10 cm in diameter. An empirical formula for the systematic dose-rate calculation for the irradiation system was established using the measured data. Conclusions: A wide dose-rate range electron beam irradiation device was successfully developed in this study. The pre-clinical studies relating to FLASH radiotherapy to the conventional level were made available. Additionally, material studies were made available using a quantified irradiation system. Future studies are required to improve the energy, dose-rate, and field uniformity of the irradiation system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of the Society of Disaster Information
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• v.16 no.3
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• pp.458-471
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• 2020

Purpose: In Korea, the frequency and frequency of earthquakes are increasing every year. Therefore, the purpose of this study was to compare and analyze the characteristics and examples of earthquakes in Korea and Japan, and to propose improvements to the earthquake prevention policy in Korea. Method: In this study, we investigate and evaluate Japan's response in two cases: the Kobe earthquake and the East Japan earthquake. After surveying and evaluating the nation's countermeasures in the two events, Gyeongju and Pohang, they were compared. Result: When comparing recovery systems in Korea and Japan, there were significant differences in plans for restoration of road transport networks, regional disaster prevention plans, and the introduction of Conclusion: considering the physical distance between Korea's earthquake-prone areas and the capital, the government should quickly come up with countermeasures to ensure that immediate earthquake response in the region is enhanced through the detailed establishment of the Functional Continuity Plan (COOP), and that administrative functions will function normally in the event of a disaster through the introduction of the administrative BCP concept.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.375-380
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• 1999

Ocean Scanning Multispectral Imager (OSMI) is a payload on the KOMPSAT satellite to perform worldwide ocean color monitoring for the study of biological oceanography. The instrument images the ocean surface using a wisk-broom motion with a swath width of 800 km and a ground sample distance (GSD) of<1km over the entire field of view (FOV). The instrument is designed to have an on-orbit operation duty cycle of 20% over the mission lifetime of 3 years with the functions of programmable gain/offset and on-board image data compression/storage. The instrument also performs sun and dark calibration for on-board instrument calibration. The OSMI instrument is a multi-spectral imager covering the spectral range from 400nm to 900nm using CCD Focal Plane Array (FPA). The ocean colors are monitored using 6 spectral channels that can be selected via ground commands. KOMPSAT satellite with OSMI was integrated and the satellite level environment tests and instrument aliveness/functional test as well, such as launch environment, on-orbit environment (Thermal/vacuum) and EMl/EMC test were performed at KARI. Test results met the requirements and the OSMI data were collected and analyzed during each test phase. The instrument is launched on the KOMPSAT satellite in the late 1999 and the image is scheduled to start collecting ocean color data in the early 2000 upon completion of on-orbit instrument checkout.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1089-1096
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• 2019

Recently, small-scale IoT services using a small amount of information through low-performance computing have been spread. It requires low cost, low-power, and long-distance communication technologies with wide communication radius, relatively low power consumption. This paper proposes a MAC layer and routing protocol that supports multi-hop transmission in small-scale IoT environment distributed over a large area based on LoRa communication and delivering a small amount of sensing data. The terminal node is mobile and the communication type provides bidirectional transmission between the terminal node and the network application server. By applying the proposed protocol, a production line monitoring system for smart factory was implemented. It was confirmed that the basic monitoring functions are normally performed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1429-1434
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• 2017

In automatic defect detection problem for spectacle lenses, it is important to extract lens area accurately. Many existing detection methods fail to do it due to insufficient minute noise removal. In this paper, we propose an automatic defect detection method using Bresenham algorithm and adaptive binarization strategy. After usual average binarization, we apply Bresenham algorithm that has the power in extracting ellipse shape from image. Then, adaptive binarization strategy is applied to the critical minute noise removal inside the lens area. After noise removal, We can also compute the influence factor of the defect based on the fuzzy logic with two membership functions such as the size of the defect and the distance of the defect from the center of the lens. In experiment, our method successfully extracts defects in 10 out of 12 example images that include CHEMI, MID, HL, HM type lenses.