• Journal of Preventive Medicine and Public Health
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• 제44권5호
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• pp.201-209
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• 2011

Objectives: This study examined the use of health impact assessment (HIA) as a tool for intersectoral collaboration using the case of an HIA project conducted in Gwang Myeong City, Korea. Methods: A typical procedure for rapid HIA was used. In the screening step, the Aegi-Neung Waterside Park Plan was chosen as the target of the HIA. In the scoping step, the specific methods and tools to assess potential health impacts were chosen. A participatory workshop was held in the assessment step. Various interest groups, including the Department of Parks and Greenspace, the Department of Culture and Sports, the Department of Environment and Cleansing, civil societies, and residents, discussed previously reviewed literature on the potential health impacts of the Aegi-Neung Waterside Park Plan. Results: Potential health impacts and inequality issues were elicited from the workshop, and measures to maximize positive health impacts and minimize negative health impacts were recommended. The priorities among the recommendations were decided by voting. A report on the HIA was submitted to the Department of Parks and Greenspace for their consideration. Conclusions: Although this study examined only one case, it shows the potential usefulness of HIA as a tool for enhancing intersectoral collaboration. Some strategies to formally implement HIA are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3962-3980
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• 2019

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to $[1,1,{\cdots},1]^T$. The solutions of all the mapping matrices are integrated into an overall objective function, which uses ${\ell}_{2,1}$-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.

• ETRI Journal
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• 제41권4호
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• 한국전통조경학회지
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• 제32권3호
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• pp.201-212
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• 2014

이 연구는 근대 부산의 대정공원에서 3년간에 걸쳐 개최된 국낙원의 준비 과정과 국낙원의 볼거리인 국화로 만든 인형인국인형 그리고 예기(藝妓)들이 꾸민 연무 공연의 내역을 밝혀 지금까지 전혀 알려지지 않았던 대정공원 국낙원의 전모를 밝히고 상업적 성공을 위하여 사용한 홍보 전략을 고찰한 것이다. 1926년부터 1928년까지 3년간에 걸쳐 부산 대정공원에서 개최된 국낙원은 국화를 이용하여 일본 역사의 극적인 장면이나 카부키 등 대중 예술의 한 장면을 인형으로 재현한 국인형, 대륜국과 현애국 그리고 일반 시민들이 가꾼 국화와 분재로 구성한 국화단, 그리고 미도리마치 유곽에서 선발되어 한 달 정도 강습을 받은 일본인 조선인 예기들의 카부키, 구극 등 연무 공연으로 구성되었다. 예기들은 일본과 동래에서 초청한 전문 예능인으로부터 예능을 단기간에 배워 무대에 올랐다. 국낙원의 홍보 전략은 주최자 부산일보가 기획한 것으로 지속적인 보도와 예기들의 인기투표, 경품 행사, 그리고 시민의 참가였다. 국낙원은 3회라고 하는 단명으로 끝났다. 이 연구는 근대도시 시설인 공원의 역할과 기능을 이해하는 데에 필요한 공원의 생활사를 발굴한 것이다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권3호
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• pp.192-201
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• 2002

디지털 형태의 문서가 널리 퍼지고 끊임없이 증가함에 따라 이를 자동으로 가공하고 처리하는 문서 자동분류의 중요성이 널리 인식되고 있다. 최근의 문서 자동분류는 k-최근접 이웃, 결정트리, Support Vector Machine, 신경망 등의 다양한 기계학습 기법을 이용하여 연구되고 있다. 그러나 많은 연구가 잘 조직된 데이타 집합을 이용하여 연구결과를 보여주고 있으며, 실제 문제에의 응용성에는 큰 비중을 두지 않고 있다. 본 논문에서는 문서분류의 응용시스템인 질의 자동응답시스템에 적용할 수 있는 다중분류기 결합 방법을 제안하고 실제 전자우편 문서의 분류문제를 해결한다. 첫째로, 다중신경 망을 이용한 문서분류를 제안한다. 제안한 방법은 최대값 결합, 신경망 결합을 통해 성능의 향상을 가져온다. 둘째로, 여러 분류기의 결합을 통해 문서분류의 성능을 개선한다. 본 논문에서는 투표 결합방법, Borda 결합, 신경망 결합방법 등을 적용하여 여러 분류기의 결합을 수행하였다. 실용 가능성을 분석한 실험결과 90%이상의 정확율을 보여 제안한 방법이 실용적일 수 있음을 알 수 있었다.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]