• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.361-362
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• 2006

This paper presents the robust design of gripper part for a high-speed LCD (Liquid Crystal Display) transfer system. In this paper, the 1st DOE (Design of Experiment) is conducted to find out main-effect factors fur the design of gripper part. Thirty-six experiments are performed using $ANSYS^{(R)}$ and their results are statistically analyzed using $MINITAB^{(R)}$, which shows that the factors, i.e., First-width, Second-width, Rec-width, and thickness of gripper part, are more important than other factors. The main effect plots shows that the maximum deflection and mass of gripper part are minimized by increasing First-width, Second-width, Rec-width and thickness. The 2nd DOE is conducted to obtain RSM (Response Surface Method) equation. The CCD (Central Composite Design) technique with four factors is used. Optimum design is conducted using the RSM equation. Genetic algorithm is used for optimal design. Six sigma robust design is conducted to find out a guideline for control range of design parameter. To obtain six sigma level reliability, the standard deviations of design parameters are shown to be controlled within 5% of average design value.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.162-171
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• 1993

This paper deals with the anisotropy of the mushy region during solidification process of a binary mixture. A theoretical model which specifies a permeability tensor in terms of pricipal values is proposed. Also, the governing equations are modified into convenient forms for the numerical analysis with the existing algorithm. Some test computations are performed for soeidification of aqueous ammonium chloride solution contained in a square cavity. Results show that not only the present model is capable of resolving fundamental characteristics of the tranport phenomena, but also the anisotropy significantly affects the interdendritic flow structure, i.e., double-diffusive convection and macrosegregation patterns.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.3
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• pp.169-175
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• 2014

In this study, it is intended to provide a slip detector is an important function in the research on the slip control can be addressed uncontrollably path withdrawal might during driving of the power wheelchair, slip phenomenon occurs. By detecting and electric wheelchairs, the state of the motor during running, the detection of the slip, slip detection information calculated using an encoder that is connected to the left and right motor with six-axis IMU sensor for the electric wheelchair using an algorithm to calculate the slip ratio. Slip rate calculated in this way is used as control variable for improving the safety of the electric wheelchair. It was confirmed from the slip phenomenon of the path the proposed experiments slim detector proposed in this study. The maximum slip ratio detection zone during the experiment, can occur during turning of the electric wheelchair has been confirmed.

• International Journal of Automotive Technology
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• v.6 no.2
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• pp.149-159
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• 2005

A full nonlinear finite element P185/70Rl4 passenger car radial-ply tire model was developed and run on a 1.7-meter-diameter spinning test drum/cleat model at a constant speed of 50 km/h in order to investigate the tire transient response characteristics, i.e. the tire in-plane free vibration modes transmissibility. The virtual tire/drum finite element model was constructed and tested using the nonlinear finite element analysis software, PAM-SHOCK, a nonlinear finite element analysis code. The tire model was constructed in extreme detail with three-dimensional solid, layered membrane, and beam finite elements, incorporating over 18,000 nodes and 24 different types of materials. The reaction forces of the tire axle in vertical (Z axis) and longitudinal (X axis) directions were recorded when the tire rolled over a cleat on the drum, and then the FFT algorithm was applied to examine the transient response information in the frequency domain. The result showed that this PI 85/70Rl4 tire has clear peaks of 84 and 45 Hz transmissibility in the vertical and longitudinal directions. This result was validated against more than 10 previous studies by either theoretical or experimental approaches and showed excellent agreement. The tire's post-impact response was also investigated to verify the numerical convergence and computational stability of this FEA tire model and simulation strategy, the extraordinarily stable scenario was confirmed. The tire in-plane free vibration modes transmissibility was successfully detected. This approach was never before attempted in investigations of tire in-plane free vibration modes transmission phenomena; this work is believed to be the first of its kind.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.360-366
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• 2008

The initial fuzzy partitions in fuzzy rule-based classification systems are determined by considering the domain region of each attribute with the given data, and the optimal classification boundaries within the fuzzy partitions can be discovered by tuning their parameters using various learning processes such as neural network, genetic algorithm, and so on. In this paper, we propose a selection method for fuzzy partition based on statistical information to maximize the performance of pattern classification without learning processes where statistical information is used to extract the uncertainty regions (i.e., the regions which the classification boundaries in pattern classification problems are determined) in each input attribute from the numerical data. Moreover the methods for extracting the candidate rules which are associated with the partition intervals generated by statistical information and for minimizing the coupling problem between the candidate rules are additionally discussed. In order to show the effectiveness of the proposed method, we compared the classification accuracy of the proposed with those of conventional methods on the IRIS and New Thyroid Cancer data. From experimental results, we can confirm the fact that the proposed method only considering statistical information of the numerical patterns provides equal to or better classification accuracy than that of the conventional methods.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Atmosphere
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• v.31 no.1
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• pp.45-60
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• 2021

The synoptic structures and precipitation impact of extratropical cyclones (ETCs) influencing on the three adjacent megacities in East Asia, i.e., Beijing (Beijing ETCs), Seoul (Seoul ETCs) and Tokyo (Tokyo ETCs), are analyzed using ERA-interim reanalysis data from 1979 to 2018. Individual ETC tracks are identified with the automated tracking algorithm applied to 850-hPa relative vorticity field. Among four seasons, ETCs are the most frequent in spring. In this season, Beijing ETCs are mainly generated at the leeside of Altai-Sayan Mountains and primarily develop through interaction between the upper-level trough and lower-level cyclonic circulation. For Seoul ETCs, the leesides of Altai-Sayan Mountains (Seoul-N ETCs) and Tibetan Plateau (Seoul-S ETCs) are main genesis regions and the features of ETCs are different according to the genesis regions. While Seoul-N ETCs mainly develope by the same mechanism of Beijing ETCs, strong diabatic heating due to vapor transport is responsible for the genesis of Seoul-S ETCs. Tokyo ETCs are originated from the leesides of Tibetan Plateau and Kuroshio-Oyashio Extension regions, and strong diabatic heating as well as interaction between upper and lower levels determines the genesis of these ETCs. The precipitation impact resulting from ETCs become strong in the order of Beijing ETCs, Seoul-N ETCs, Seoul-S ETCs, and Tokyo ETCs and accounts for up to 40%, 27%, 52%, and 70% of regional precipitation, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1147-1161
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• 2023

The Internet of Things (IoT) requires a new processing model that will allow scalability in cloud computing while reducing time delay caused by data transmission within a network. Such a model can be achieved by using resources that are closer to the user, i.e., by relying on edge computing (EC). The amount of IoT data also grows with an increase in the number of IoT devices. However, building such a flexible model within a heterogeneous environment is difficult in terms of resources. Moreover, the increasing demand for IoT services necessitates shortening time delay and response time by achieving effective load balancing. IoT devices are expected to generate huge amounts of data within a short amount of time. They will be dynamically deployed, and IoT services will be provided to EC devices or cloud servers to minimize resource costs while meeting the latency and quality of service (QoS) constraints of IoT applications when IoT devices are at the endpoint. EC is an emerging solution to the data processing problem in IoT. In this study, we improve the load balancing process and distribute resources fairly to tasks, which, in turn, will improve QoS in cloud and reduce processing time, and consequently, response time.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.38.2-38.2
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• 2020

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1 kpc. This enormous dynamic range allows us to simultaneously capture the physics of the cosmic web on very large scales and account for the formation and evolution of dwarf galaxies on much smaller scales. Inside the simulation box. we zoom-in on a high-resolution cuboid region with a volume of 1049 × 114 × 114 Mpc3. The subgrid physics chosen to model galaxy formation includes radiative heating/cooling, reionization, star formation, supernova feedback, chemical evolution tracking the enrichment of oxygen and iron, the growth of supermassive black holes and feedback from active galactic nuclei (AGN) in the form of a dual jet-heating mode. For this simulation we implemented a hybrid MPI-OpenMP version of the RAMSES code, specifically targeted for modern many-core many thread parallel architectures. For the post-processing, we extended the Friends-of-Friend (FoF) algorithm and developed a new galaxy finder to analyse the large outputs of HR5. The simulation successfully reproduces many observations, such as the cosmic star formation history, connectivity of galaxy distribution and stellar mass functions. The simulation also indicates that hydrodynamical effects on small scales impact galaxy clustering up to very large scales near and beyond the baryonic acoustic oscillation (BAO) scale. Hence, caution should be taken when using that scale as a cosmic standard ruler: one needs to carefully understand the corresponding biases. The simulation is expected to be an invaluable asset for the interpretation of upcoming deep surveys of the Universe.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.