• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Journal of Radiation Protection and Research
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• v.40 no.1
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• pp.55-64
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• 2015

Dose calculations which are a crucial requirement for radiotherapy treatment planning systems require accuracy and rapid calculations. The conventional radiotherapy treatment planning dose algorithms are rapid but lack precision. Monte Carlo methods are time consuming but the most accurate. The new combined system that Monte Carlo methods calculate part of interesting domain and the rest is calculated by neural can calculate the dose distribution rapidly and accurately. The preliminary study showed that neural networks can map functions which contain discontinuous points and inflection points which the dose distributions in inhomogeneous media also have. Performance results between scaled conjugated gradient algorithm and Levenberg-Marquardt algorithm which are used for training the neural network with a different number of neurons were compared. Finally, the dose distributions of homogeneous phantom calculated by a commercialized treatment planning system were used as training data of the neural network. In the case of homogeneous phantom;the mean squared error of percent depth dose was 0.00214. Further works are programmed to develop the neural network model for 3-dimensinal dose calculations in homogeneous phantoms and inhomogeneous phantoms.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• 한국사회정책
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• v.23 no.1
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• pp.61-93
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• 2016

This research aims to analyze the role of social care services after the unification, assuming that the unification would occur in 2020 in a peaceful manner. While much has been discussed about the unification in recent years inside or outside academia, most of discussion tends to focus on political and economic dimensions. Also, social policy studies on North Korean defectors have increased, but few pay attention to social policy strategies after the possible unification. In this context, this study explores various explicit and implicit roles of social care services and possible strategies after the unification. As research methodology, it employs one of the scenario methods, 'TAIDA', for projecting and simulating uncertain future. In so doing, first, it reviews South and North Korean socio-economic experiences during last two decades as feedback and German unification experiences as feedforward. In addition, it utilizes a expert survey. Based on the reviews together with the survey result, it discusses various influences of social care services after the unification and draws policy implications. This research aruges that social care services could have profound effects on the stability of socio-economic conditions after the unification.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.6
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• pp.449-469
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• 2011

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigerating Engineering during 2010. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) Research trends of thermal and fluid engineering have been surveyed as groups of general thermal and fluid flow, fluid machinery, and new and renewable energy. Various topics were presented in the field of general thermal and fluid flow. Research issues mainly focused on the thermal reliability of axial fan and compressor in the field of fluid machinery. Studies on the design of ground source heat pump systems and solar chemical reactors were executed in the field of new and renewable energy. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics and industrial heat exchangers. Researches on heat transfer characteristics included heat transfer in thermoelectric cooling/power generation systems, combined heat and power systems, carbon nano fluid with PVP, channel filled with metal foam and smoke ventilation in a rescue station of a railroad tunnel. Also the studies on flow boiling of R123/oil mixture in a plain tube bundle and R410A charge amount in an air cooled mini-channel condenser were reported. In the area of industrial heat exchangers, researches on plate heat exchanger, shell and tube heat exchanger, enthalpy exchanger, micro channel PCHE were performed. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics and industrial heat exchangers. Researches on heat transfer characteristics included heat transfer in thermoelectric cooling/power generation systems, combined heat and power systems, carbon nano fluid with PVP, channel filled with metal foam and smoke ventilation in a rescue station of a railroad tunnel. Also the studies on flow boiling of R123/oil mixture in a plain tube bundle and R410A charge amount in an air cooled mini-channel condenser were reported. In the area of industrial heat exchangers, researches on plate heat exchanger, shell and tube heat exchanger, enthalpy exchanger, micro channel PCHE were performed. (3) Refrigeration systems with alternative refrigerants such as hydrocarbons, mixed refrigerants, and CO2 were studied. Performance improvement of refrigeration systems are tried applying various ideas of refrigerant subcooling, dual evaporator with hot gas bypass control and feedforward control. The hybrid solar systems combining the solar collection devices with absorption chillers or compression heat pumps are simulated and studied experimentally as well to improve the understanding and the feasibility for actual applications. (4) Research trend in the field of mechanical building facilities has been found to be mainly focused on field applications rather than performance improvements. Various studies on heating and cooling systems, HVAC facilities, indoor air environments and energy resources were carried to improve the maintenance and management of building service equipments. In the field of heating and cooling systems, papers on a transformer cooling system, a combined heat and power, a slab thermal storage and a heat pump were reported. In the field of HVAC facilities, papers on a cooling load, an ondol and a drying were presented. Also, studies on HVAC systems using unutilized indoor air environments and energy resources such as air curtains, bioviolence, cleanrooms, ventilation, district heating, landfill gas were studied. (5) In the field of architectural environment and energy, studies of various purposes were conducted such as indoor environment, building energy, renewable energy and green building. In particular, renewable energy and building energy-related researches have mainly been studied reflecting the global interest. In addition, many researches which related the domestic green building certification of school building were performed to improve the indoor environment of school.