• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.67-80
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• 2022

The missile early-warning satellite systems have been developed and upgraded by some space-developed nations, under the inevitable trend that the space is more strongly considered as another battle field than before. As the key function of such a satellite-based early warning system, the prediction algorithm of the missile flight trajectory is studied in the paper. In particular, the evolution computation, receiving broad attention in the artificial intelligence area, is applied to the proposed prediction method so that the global optimum-like solution is found avoiding disadvantage of the previous non-linear optimization search tools. Moreover, using the prediction simulator of the launch vehicle flight trajectory which is newly developed in C# and Python, the paper verifies the performance and the feature of the proposed algorithm.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1374-1381
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• 2022

Under the influence of nuclear radiation, the reliability of steam generators (SGs) is an important factor in the efficiency and safety of nuclear power plant (NPP) reactors. Motion planning that remotely manipulates an SG mobile tube-inspection robot to inspect SG heat transfer tubes is the mainstream trend of NPP robot development. To achieve motion planning, conditional traversal is usually used for base position optimization, and then the A^* algorithm is used for path planning. However, the proposed approach requires considerable processing time and has a single expansion during path planning and plan paths with many turns, which decreases the working speed of the robot. Therefore, to reduce the calculation time and improve the efficiency of motion planning, modifications such as the matrix method, improved parent node, turning cost, and improved expanded node were proposed in this study. We also present a comprehensive evaluation index to evaluate the performance of the improved algorithm. We validated the efficiency of the proposed method by planning on a tube sheet with square-type tube arrays and experimenting with Model SG.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2222-2229
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• 2023

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

• Advances in concrete construction
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• v.16 no.6
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• pp.317-325
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• 2023

Strength prediction and correlation of concrete is done using experimental and analytical methods. Main objective is to correlate the experimental and simulated values of compressive strength of concrete mix using Fly Ash (FA) and Silica Fume (SF) by partial replacement of cement in concrete. Mix proportion was determined using IS method for M40grade concrete. Hundred and forty-seven cubes were cast and tested using Universal Testing Machine (UTM). Genetic Algorithm (GA) model was developed using C++ program to simulate the compressive strength of concrete for various proportions of FA and SF replacements individually at 3% increments. Experiments reveal that 12 percent silica fume replacement produced maximum compressive strength of 35.5 N/mm², 44.5 N/mm² and 54.8 N/mm² moreover 9 percent fly ash replacement produced a maximum strength of 31.9 N/mm², 37.6 N/mm² and 51.8 N/mm² during individual material replacement of concrete mix. Correlation coefficient for each curing period of fly ash and silica fume replaced mix were acquired using trend lines. The correlation coefficient is found to be approximately 0.9 in FA and SF replaced mix irrespective of the mix proportion and age of concrete. A higher and positive correlation was found between the experimental and simulated values irrespective of the curing period in all the replacements.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.66-85
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• 2020

The purpose of this study is to explain and predict trends the AI development process based on AI technology patents (total) and AI reporting frames in major newspapers. To that end, a summary of South Korean and U.S. technology patents filed over the past nine years and the AI (Artificial Intelligence) news text of major domestic newspapers were analyzed. In this study, Topic Modeling and Time Series Return Analysis using Big Data were used, and additional network agenda correlation and regression analysis techniques were used. First, the results of this study were confirmed in the order of artificial intelligence and algorithm 5G (hot AI technology) in the AI technical patent summary, and in the news report, AI industrial application and data analysis market application were confirmed in the order, indicating the trend of reporting on AI's social culture. Second, as a result of the time series regression analysis, the social and cultural use of AI and the start of industrial application were derived from the rising trend topics. The downward trend was centered on system and hardware technology. Third, QAP analysis using correlation and regression relationship showed a high correlation between AI technology patents and news reporting frames. Through this, AI technology patents and news reporting frames have tended to be socially constructed by the determinants of media discourse in AI development.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.757-764
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• 2017

As the reduction of greenhouse gases(GHGs) emission has become a global issue, the microgrid markets are growing rapidly. With the sudden changes in the market, Korean government suggested a new business model called 'Self-Sufficient Energy Islands'. Its main concern is a stand-alone microgrid composed of Distributed Energy Resources(DERs) such as Renewable Energy Sources(RESs), Energy Storage System(ESS) and Fuel Cell, in order to minimize the emission of GHGs. According to these trend, this paper is written to propose an optimal sizing method of DERs in a stand-alone microgrid by using Genetic Algorithm(GA), one of the representative stochastic methods. It is to minimize the net present cost with the variables, size of RESs and ESS. In the process for optimization, the sunless days are considered as additional constraints. Through the case study analysis, the size of DERs installed in a microgrid system has been computed using the proposed method in MATLAB. And the result of MATLAB is compared with that of HOMER(Hybrid Optimization of Multiple Energy Resources), a well-known energy modeling software.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.128-136
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• 2003

In today's design trend of vehicle structure, crush zone is fiequently reinforced by adding a box-shaped sub-frame in order to avoid an excessive deformation against a high-speed offset barrier such as EU Directive 96/97 EC, IIHS offset test. That kind of vehicle structure design results in a relatively monotonic crash pulse for airbag ECU(Electronic Control Unit) located at non-crush zone. As for an angular crash event, the measured crash signal using a single-axis accelerometer in a longitudinal direction is usually weaker than that of frontal barrier crash. Therefore, it is not so easy task to achieve a satisfactory crash discrimination performance for offset and angular crash events. In this paper, we introduce a new crash discrimination algorithm using an electronic X-Y 2-axis accelerometer in order to improve crash discrimination performance especially for those crash events. The proposed method uses a crash signal in lateral direction(Y-axis) as well as in longitudinal direction(X-axis). A crash severity measure obtained from Y-axis acceleration is used to improve the discrimination between fire and no-fire events. The result obtained by the proposed measure is logically ORed with an existing algorithm block using X-axis crash signal. Simulation and pulse injection test have been conducted to verify the performance of proposed algorithm by using real crash data of a 2,000cc passenger vehicle.

• Spatial Information Research
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• v.12 no.1
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• pp.101-110
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• 2004

Research that establish new cadastral survey model that use GPS to introduce GPS observation technique in cadastral survey and research that develop connection technologies are now abuzz. The purpose of this research is to keep in step in such trend and grasp present condition and performance of surveying connection to common use GPS data processing software, and analyze data processing algorithm, and develop suitable GPS data processing software in our real condition regarding GPS data processing and result of control point calculation. This research studies analysis common use software and error occurrence by data processing method that college and company have. Also, It analyzes algorithm that is applied to existing GPS data processing software. After that we study algorithm that is most suitable with cadastral survey and then develop cadastral survey calculation software for new cadastral control points.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.435-444
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• 2005

This paper presents a lane-departure identification (LDI) algorithm for a traveling vehicle on a structured road. The algorithm makes up for the weak points of the former method based on EDF[1] by introducing a Lane Boundary Pixel Extractor (LBPE), the well known Hough transform, and liner regression. As a filter to extract pixels expected to be on lane boundaries, the LBPE plays an important role in enhancing the robustness of LDI. Utilizing the pixels from the LBPE the Hough transform provides the lane-related parameters composed of orientation and distance, which are used in the LDI. The proposed LDI is based on the fact the lane-related parameters of left and right lane boundaries are symmetrical as for as the optical axis of a camera mounted on a vehicle is coincident with the center of lane; as the axis deviates from the center of lane, the symmetrical property is correspondingly lessened. In addition, the LDI exploits a linear regression of the lane-related parameters of a series of successive images. It plays the key role of determining the trend of a vehicle's traveling direction and minimizing the noise effect. Except for the two lane-related parameters, the proposed algorithm does not use other information such as lane width, a curvature, time to lane crossing, and of feet between the center of a lane and the optical axis of a camera. The system performed successfully under various degrees of illumination and on various road types.