• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.53-56
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• 2001

Reverse engineering has been widely used for the shape reconstruction of an object without CAD data and it includes some steps such as scanning of a clay or wood model, and generating some manufacturing data in an STL file. A new approach to remove point data with Delaunay triangulation is introduced to deal with the size problems of STL file and the difficulties in the operation of RP process. This approach can be used to reduce a number of measuring data from laser scanner within a specified tolerance, thus it can avoid the time for handing point data during modeling process and the time for verifying and slicing STL model during RP process. Developed software enables the user to specify the criteria for the selection of group of triangles either by the angle between triangles or the percentage of triangles reduced, and thus RP models with accuracy will be helpful to automated process.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1340-1346
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• 2020

The aim of this paper is to explore the 8th-order Adams-Bashforth-Moulton (ABM8) method in the solution of the point reactor kinetics equations. The numerical experiment considers feedback reactivity by Doppler effects, and insertions of reactivity. The Doppler effects is approximated with an adiabatic nuclear reactor that is a typical approximation. The numerical results were compared and discussed with several solution methods. The CATS method was used as a benchmark method. According with the numerical experiments results, the ABM8 method can be considered as one of the main solution method for changes reactivity relatively large.

• Nuclear Engineering and Technology
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• v.49 no.8
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• pp.1636-1644
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• 2017

The main purpose of this article is to describe the magnetohydrodynamic stagnation point flow of Walter-B nanofluid over a stretching sheet. The phenomena of heat and mass transfer are based on the involvement of thermal radiation and chemical reaction. Characteristics of Newtonian heating are given special attention. The Brownian motion and thermophoresis models are introduced in the temperature and concentration expressions. Appropriate variables are implemented for the transformation of partial differential frameworks into sets of ordinary differential equations. Plots for velocity, temperature, and nanoparticle concentration are displayed and analyzed for governing parameters. The skin friction coefficient and local Nusselt and Sherwood numbers are studied using numerical values. The temperature and heat transfer rate are enhanced within the frame of the thermal conjugate parameter.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.1095-1099
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• 2017

A fission source can act as a stabilization element in coupled Monte Carlo simulations. We have observed this while studying numerical instabilities in nonlinear steady-state simulations performed by a Monte Carlo criticality solver that is coupled to a xenon feedback solver via fixed-point iteration. While fixed-point iteration is known to be numerically unstable for some problems, resulting in large spatial oscillations of the neutron flux distribution, we show that it is possible to stabilize it by reducing the number of Monte Carlo criticality cycles simulated within each iteration step. While global convergence is ensured, development of any possible numerical instability is prevented by not allowing the fission source to converge fully within a single iteration step, which is achieved by setting a small number of criticality cycles per iteration step. Moreover, under these conditions, the fission source may converge even faster than in criticality calculations with no feedback, as we demonstrate in our numerical test simulations.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.335-347
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• 2024

Open-vocabulary 3D point cloud instance segmentation (OV-3DIS) is a challenging visual task to segment a 3D scene point cloud into object instances of both base and novel classes. In this paper, we propose a novel model Open3DME for OV-3DIS to address important design issues and overcome limitations of the existing approaches. First, in order to improve the quality of class-agnostic 3D masks, our model makes use of T3DIS, an advanced Transformer-based 3D point cloud instance segmentation model, as mask proposal module. Second, in order to obtain semantically text-aligned visual features of each point cloud segment, our model extracts both 2D and 3D features from the point cloud and the corresponding multi-view RGB images by using pretrained CLIP and OpenSeg encoders respectively. Last, to effectively make use of both 2D and 3D visual features of each point cloud segment during label assignment, our model adopts a unique feature ensemble method. To validate our model, we conducted both quantitative and qualitative experiments on ScanNet-V2 benchmark dataset, demonstrating significant performance gains.

• Tunnel and Underground Space
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• v.19 no.6
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• pp.473-478
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• 2009

The accuracy of point load apparatus is depend on point to point coaxial fitting. Also, the estimation of applied point load using the pressure gauge frequently lead to erroneous results. An improved point load apparatus has been developed in this study by mounting linear bearing on polished support rod, and eccentric error of point to point axis has been sustained less than 0.1 mm even under series of extreme work load conditions. Two digital displacement gauges are attached to measure the distance from point to point with sample specimen. A load cell mounted at the end of upper conical platen measure the applied net load on sample instead of preassure gauge. Total of 107 point load tests has been achieved to assure the quality and performance of developed apparatus. This exercise turned out to be successful.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.506-516
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• 2005

This paper deals with an investment scheduling problem of maximizing net present value of dividend with reinvestment allowed, where each investment has certain capital requirement and generates deterministic profit. Such deterministic profit is calculated at completion of each investment and then allocated into two parts, including dividend and reinvestment, at each predetermined reinvestment time point. The objective is to make optimal scheduling of investments over a fixed planning horizon which maximizes total sum of the net present values of dividends subject to investment precedence relations and capital limit but with reinvestment allowed. In the analysis, the scheduling problem is transformed to a kind of parallel machine scheduling problem and formulated as an integer programming which is proven to be NP-complete. Thereupon, a depth-first branch-and-bound algorithm is derived. To test the effectiveness and efficiency of the derived algorithm, computational experiments are performed with some numerical instances. The experimental results show that the algorithm solves the problem relatively faster than the commercial software package (CPLEX 8.1), and optimally solves the instances with up to 30 investments within a reasonable time limit.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.5
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• pp.85-91
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• 1989

In this paper, we propose a neural net system for speech recognition, which is composed of two neural networks. Firstly the self-supervised BP(Back Propagation) network generates the distributed concept corresponding to the activity pattern in the hidden units. And then the self-organizing neural network forms a concept map which directly displays the similarity relations between concepts. By doing the above, the difficulty in learning the conventional BP network is solved and the weak side of BP falling into a pattern matcher is gone, while the strong point of generating the various internal representations is used. And we have obtained the concept map which is more orderly than the Kohonen's SOFM. The proposed neural net system needs not any special preprocessing and has a self-learning ability.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.6
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• pp.899-908
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• 2023

Droughts can alter the dynamics of aquatic ecosystems, including fish communities. This study compared the variations in fish communities in Juam reservoir before and after drought events. Fish sampling was conducted five times from April 2021 to April 2023 using three different sampling methods (kick net, cast net, and gill net). The water level in the reservoir reached its peak (103.73 EL.m, 62.2% capacity) in September 2021, before the drought, and dropped to its lowest point (88.84 EL.m, 17.6% capacity) in April 2023. The dissolved oxygen content in the reservoir decreased from 27 to 6.3 mg/L between the period with the lowest water level (April 2023) and the period with the highest water level (September 2021). In September 2021, 466 fish were collected, but after one year of drought, the number of individuals decreased to 105. Further, the number of fish collected and water levels were positively correlated. Dominant species exhibited a population decline of over 60% with decreasing water levels. These findings highlight the importance of fishery resource management during drought periods.