• Structural Engineering and Mechanics
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• v.55 no.5
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• pp.901-911
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• 2015

The goal of this study is to investigate computational convergence of optimal solutions, with respect to optimality criteria (OC) method and methods of moving asymptotes (MMA) as optimization model for non-linear programming of material topology optimization using an acceleration method that makes design variables rapidly move toward almost 0 and 1 values. 99 line topology optimization MATLAB code uses loop vectorization and memory pre-allocation as properly exploiting the strengths of MATLAB and moves portions of code out of the optimization loop so that they are only executed once as restructuring the program. Numerical examples of a simple beam under a lateral load and a given material density limitation provide merits and demerits of the present OC and MMA for 99 line topology optimization code of continuous material topology optimization design.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.283-289
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• 2003

This paper proposes an expert system which can determine automatically the shunting routes corresponding to the given shunting works by considering totally the train operating environments in the station. The expert system proposes the multiple shunting routes with priority of selection based on heuristic search strategy. Accordingly, system operator can select a shunting route with the safety and efficiency among the those shunting routes. The expert system consists of a main inference engine and a sub inference engine. The main inference engine determines the shunting routes with selection priority using the segment routes obtained from the sub inference engine. The heuristic rules are extracted from operating knowledges of the veteran route operator and station topology. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique. And, the validity of the builted expert system is proved by a test case for the model station.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.149-157
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• 2012

In this thesis, the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated. It is shown formally that neither approximation can be addressed effectively for a sliding-window join of arbitrary input streams. Previous work has addressed only the maximum-subset problem, and has implicitly used a frequency based model of stream arrival. There exists a sampling problem for this model. More importantly, it is shown that a broad class of applications for which an age-based model of stream arrival is more appropriate, and both approximation scenarios under this new model are addressed. Finally, for the case of multiple joins being executed with an overall memory constraint, an algorithm for memory allocation across the join that optimizes a combined measure of approximation in all scenarios considered is provided.

• Journal of the Korean Society for Marine Environment & Energy
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• v.14 no.2
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• pp.121-129
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• 2011

The EFDC model has been simplified to enhance the computing performance in hydrodynamic modeling. Water quality module and unnecessary conditional statements were deleted in subroutine list and memory allocation. The performance of the enhanced model (EFDC-E) was checked by applying EFDC and EFDC-E models to simulating the tidal flow in Mokpo coastal zone. Both two-dimensional models and threedimensional models have been applied and compared. Three-dimensional models showed better simulation results agreeing with observed currents than two-dimensional models. The simulation results of EFDC-E model gave good results agreeing with the simulation results of EFDC model and the observed data. The computing speed of EFDC-E model is improved 3 times faster than that of EFDC model in modeling hydrodynamic flow for real time of 3 days in both 2-dimensional modeling and 3-dimensional modeling. The EFDC-E model can be used widely for hydrodynamic modeling because of improved simulation speed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Nuclear Engineering and Technology
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• v.31 no.3
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• pp.344-363
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• 1999

A multi-dimensional thermal-hydraulic system analysis code, MARS 1.3.1, has been developed in order to have the realistic analysis capability of two-phase thermal-hydraulic transients for pressurized water reactor (PWR) plants. As the backbones for the MARS code, the RELAP5/MOD3.2.1.2 and COBRA-TF codes were adopted in order to take advantages of the very general, versatile features of RELAP5 and the realistic three-dimensional hydrodynamic module of COBRA-TF. In the MARS code, all the functional modules of the two codes were unified into a single code first. Then, the source codes were converted into the standard Fortran 90, and then they were restructured using a modular data structure based on "derived type variables" and a new "dynamic memory allocation" scheme. In addition, the Windows features were implemented to improve user friendliness. This paper presents the developmental work of the MARS version 1.3.1 including the hydrodynamic model unification, the heat structure coupling, the code restructuring and modernization, and their verifications.their verifications.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1457-1463
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• 1999

This paper develops an real-time expert system for the electronic interlocking system. it obtains the higher safety by determining the railway interlocking strategy in order to prevent trains from colliding, and derailing in the viewpoint of veteran expert, considering the situation of station in real-time. The expert system determines the real-time interlocking strategy by confirming the interlocking relationships among signal facilities based on the interlocking knowledge base from input information such as signal, points, and it is implemented as the rule-based system in order to represented accurately and effectively the interlocking relationships. Especially in case of emergency the function which determines the rational route coordinating with IIKBAG on the workstation is designed in order to minimize the spreading effect. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the build and interface of the station structure database. And, the validity of the built expert system is proved by simulating the diversity cases which may occur in the real system for the typical station model.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.6
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• pp.58-64
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• 2009

For the success of the reconfigurable computing, the algorithm for mapping operations onto coarse-grained reconfigurable architecture is very important. This paper proposes a resource-aware mapping system for the coarse-grained reconfigurable architecture and its own underlying heuristic algorithm. The operation assignment and the routing path allocation are simultaneously performed with a cycle-accurate time-exclusive resource model. The proposed algorithm minimizes the communication resource usage and the global memory access with the list scheduling heuristic. The operation to be mapped are prioritized with general properties of data flow. The evaluations of the proposed algorithm show that the performance is significantly enhanced in several benchmark applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1107-1114
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• 2004

System Packet Interface Level 4 Phase 2(SPI-4.2) is an interface for packet and cell transfer between a physical layer(PHY) device and a link layer device, for aggregate bandwidths of OC-192 ATM and Packet Over Sonet/SDH(POS), as well as 10Gbps Ethernet applications. SPI-4.2 core consists of Tx and Rx modules and supports full duplex communication. Tx module of SPI-4.2 core writes 64-bit data word and 14-bit header information from the user interface into asynchronous FIFO and transmits DDR(Double Data Rate) data over PL4 interface. Rx module of SPI-4.2 core operates in vice versa. Tx and Rx modules of SPI-4.2 core are designed to support maximum 256-channel and control the bandwidth allocation by configuring the calendar memory. Automatic DIP4 and DIP-2 parity generation and checking are implemented within the designed core. The designed core uses Xilinx ISE 5.li tool and is described in VHDL Language and is simulated by Model_SIM 5.6a. The designed core operates at 720Mbps data rate per line, which provides an aggregate bandwidth of 11.52Gbps. SPI-4.2 interface core is suited for line cards in gigabit/terabit routers, and optical cross-connect switches, and SONET/SDH-based transmission systems.