• Journal of Broadcast Engineering
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• v.14 no.3
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• pp.322-331
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• 2009

This paper proposes an efficient data representation of stereo images using edge-based mesh optimization. Mash-based two dimensional warping for stereo images mainly depends on the performance of a node selection and a disparity estimation of selected nodes. Therefore, the proposed method first of all constructs the feature map which consists of both strong edges and boundary lines of objects for node selection and then generates a grid-based mesh structure using initial nodes. The displacement of each nodal position is iteratively estimated by minimizing the predicted errors between target image and predicted image after two dimensional warping for local area. Generally, iterative two dimensional warping for optimized nodal position required a high time complexity. To overcome this problem, we assume that input stereo images are only horizontal disparity and that optimal nodal position is located on the edge include object boundary lines. Therefore, proposed iterative warping method performs searching process to find optimal nodal position only on edge lines along the horizontal lines. In the experiments, we compare our proposed method with the other mesh-based methods with respect to the quality by using Peak Signal to Noise Ratio (PSNR) according to the number of nodes. Furthermore, computational complexity for an optimal mesh generation is also estimated. Therefore, we have the results that our proposed method provides an efficient stereo image representation not only fast optimal mesh generation but also decreasing of quality deterioration in spite of a small number of nodes through our experiments.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.175-195
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• 2005

사례기반추론(case-based reasoning)은 사례간 유사도를 평가하여 유사한 이웃사례를 찾아내고, 이웃사례의 결과를 이용하여 새로운 사례에 대한 예측결과를 생성하는 전통적인 인공지능기법 중 하나다. 이러한 사례기반추론이 최근 적용이 쉽고 간단하다는 장점과 모형의 갱신이 실시간으로 이루어진다는 점 등으로 인해, 온라인 환경에서의 고객관계관리를 위한 도구로 학계와 실무에서 주목을 받고 있다 하지만, 전통적인 사례기반추론의 경우, 타 인공지능기법에 비해 정확도가 상대적으로 크게 떨어진다는 점이 종종 문제점으로 제기되어 왔다. 이에, 본 연구에서는 사례기반추론의 성과를 획기적으로 개선하기 위한 방법으로 유전자 알고리즘을 활용한 사례기반추론의 동시 최적화 모형을 제안하고자 한다. 본 연구가 제안하는 모형에서는 기존 연구에서 사례기반추론의 성과에 중대한 영향을 미치는 요소들로 제시된 바 있는 사례 특징변수의 상대적 가중치 선정(feature weighting)과 참조사례 선정(instance selection)을 유전자 알고리즘을 이용해 최적화함으로서, 사례간 유사도를 보다 정밀하게 도출하는 동시에 추론의 결과를 왜곡할 수 있는 오류사례의 영향을 최소화하고자 하였다. 제안모형의 유용성을 검증하기 위해, 본 연구에서는 국내 한 전문 인터넷 쇼핑몰의 구매예측모형 구축사례에 제안모형을 적용하여 그 성과를 살펴보았다. 그 결과, 제안모형이 지금까지 기존 연구에서 제안된 다른 사례기반추론 개선모형들은 물론, 로지스틱 회귀분석(LOGIT), 다중판별분석(MDA), 인공신경망(ANN), SVM 등 다른 인공지능 기법들에 비해서도 상대적으로 우수한 성과를 도출할 수 있음을 확인할 수 있었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• The Transactions of the Korea Information Processing Society
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• v.13 no.10
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• pp.537-544
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• 2024

The rapid advancement of artificial intelligence and machine learning technologies is driving innovation across various industries, with natural language processing offering substantial opportunities for the analysis and processing of text data. The development of effective text classification models requires several complex stages, including data exploration, preprocessing, feature extraction, model selection, hyperparameter optimization, and performance evaluation, all of which demand significant time and domain expertise. Automated machine learning (AutoML) aims to automate these processes, thus allowing practitioners without specialized knowledge to develop high-performance models efficiently. However, current AutoML frameworks are primarily designed for structured data, which presents challenges for unstructured text data, as manual intervention is often required for preprocessing and feature extraction. To address these limitations, this study proposes a web-based AutoML platform that automates text preprocessing, word embedding, model training, and evaluation. The proposed platform substantially enhances the efficiency of text classification workflows by enabling users to upload text data, automatically generate the optimal ML model, and visually present performance metrics. Experimental results across multiple text classification datasets indicate that the proposed platform achieves high levels of accuracy and precision, with particularly notable performance when utilizing a Stacked Ensemble approach. This study highlights the potential for non-experts to effectively analyze and leverage text data through automated text classification and outlines future directions to further enhance performance by integrating Large language models.

• Analytical Science and Technology
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• v.25 no.5
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• pp.292-299
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• 2012

GC/MS has been utilized for many applications due to great resolution and reproducibility, which made it possible to build up the database of mass spectrum, while HS-SPME has the advantage of solventfree extraction of volatile compounds. The combination of these two methods, HS-SPME GC/MS, enabled many scientific applications with various possibilities. In this study, the analysis of trail pheromone excreted from live Camponotus japonicus with the feature of solvent-free extraction was carried out and the optimization for this analysis was performed. The major compounds detected were n-decane, n-undecane, and n-tridecane. Optimization for the best detection of these hydrocarbons was processed in the point of SPME parameter (selection of fiber, extraction temperature, extraction time, etc.). The advantage of the analysis of live sample is to analyze phenomenon right after it is excreted by ants. But the experimental process has restriction of extraction temperature and time because of the analysis of live ants. Establishing the process of HS-SPME GC/MS applied to live samples shown in this study can be a breakthrough for the ecofriendly and ethical research of live things.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.8
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• pp.449-455
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• 2017

Process input data including material and energy, process output data including product, co-product and its environmental emissions of the reference and target processes were collected and analyzed to evaluate the process performance. Environmentally problematic input/environmental emissions of the manufacturing processes were identified using these data. Significant process inputs contributing to each of the environmental emissions were identified using multiple regression analysis between the process inputs and environmental emissions. Optimum combination of the end-of-pipe technologies for treating the environmental emissions considering economic aspects was made using the linear programming technique. The cement manufacturing processes in Korea and the EU producing same type of cement were chosen for the case study. Environmentally problematic input/environmental emissions of the domestic cement manufacturing processes include coal, dust, and $SO_x$. Multiple regression analysis among the process inputs and environmental emissions revealed that $CO_2$ emission was influenced most by coal, followed by the input raw materials and gypsum. $SO_x$ emission was influenced by coal, and dust emission by gypsum followed by raw material. Optimization of the end-of-pipe technologies treating dust showed that a combination of 100% of the electro precipitator and 2.4% of the fiber filter gives the lowest cost. The $SO_x$ case showed that a combination of 100% of the dry addition process and 25.88% of the wet scrubber gives the lowest cost. Salient feature of this research is that it proposed a method for identifying environmentally problematic input/environmental emissions of the manufacturing processes, in particular, cement manufacturing process. Another feature is that it showed a method for selecting the optimum combination of the end-of-pipe treatment technologies.