• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.361-369
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• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• 한국정보통신학회논문지
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• 제18권6호
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• pp.1469-1474
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• 2014

본 연구는 성인용 아케이드 게임물의 심의에서 게임물의 그래픽적인 외관을 심의의 중요요소로 고려하는 입장과 게임물의 시스템적인 측면을 고려하는 입장간의 차이를 고찰하고자 한다. 지난해 3년간에 걸친 '황금포커성' 게임물에 대한 대법원의 판례는 게임물을 심의 할 때 게임물의 그래픽적인 측면만 고려 할 것인지, 게임물의 시스템적인 측면까지 함께 고려할 것인지에 대한 중요한 판단기준을 제시하고 있다. 이는 성인용 아케이드 게임물에 대한 사행성 게임물 확인에 대한 사항을 어디까지 고려해야 할지에 대한 시사점을 제시하고 있다. 본 연구는 게임물의 등급분류에서 신뢰도와 타당도를 높여 줄 수 있는 방안으로서 과학적이고 체계적인 등급분류를 위한 방향을 모색하고자 한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.81-81
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• 2002

Classification is considered as one of the processes of extracting attributes from satellite imagery and is one of the usual functions in the commercial satellite image processing software. Accuracy of classification plays a key role in deciding the usage of its results. Many tremendous efforts far the higher accuracy have been done in such fields; training area selection, classification algorithm. Our research is one of these effort in different manners. In this research, we conduct classification using multiple satellite image data and evidential approach. We statistically consider the posterior probabilities and certainty in maximum likelihood classification and methodologically Dempster's orthogonal sums. Unfortunately, accuracy for the whole data sets has not assessed yet, but accuracy assessments in training fields and check fields shows accuracy improvement over 10% in overall accuracy and over 0.1 in kappa index.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권3호
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• pp.145-152
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• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

• 대한임베디드공학회논문지
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• 제18권5호
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• pp.217-223
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• 2023

In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.

• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.2023-2028
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• 2013

A number of studies have indicated that Nurr1, which belongs to a novel class of orphan nuclear receptors (the NR4A family), is important for carcinogenesis. Here we investigated expression of Nurr1 protein in benign and malignant human prostate tissues and association with clinicopathologic features using immunohistochemical techniques. Moreover, we also investigated the ability of Nurr1 to influence proliferation, migration, invasion and apoptosis of human prostate cancer cells using small interfering RNA silencing. Immunohistochemical analysis revealed that the expression of Nurr1 protein was higher in prostate cancer tissues than in benign prostate tissue (P<0.001), levels being positively correlated with tumor T classification (P = 0.003), N classification (P = 0.017), M classification (P = 0.011) and the Gleason score (P = 0.020) of prostate cancer patients. In vitro, silencing of endogenous Nurr1 attenuated cell proliferation, migration and invasion, and induced apoptosis of prostate cancer cells. These results suggest that Nurr1 may be used as an indicator for prostate cancer progression and be useful for novel potential therapeutic strategies.

• 대한원격탐사학회지
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• 제34권5호
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• pp.801-810
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• 2018

위성 SAR 영상을 이용한 선박 모니터링은 선박탐지, 선박변별, 선박식별의 세 단계로 분류할 수 있다. 이 중 선박탐지 및 변별에 대해서는 세계적으로 많은 연구가 이루어졌으나, 선박식별에 대해서는 소수의 연구들만이 존재한다. 따라서 향후 고성능의 선박 모니터링 시스템을 구축하기 위해서는 많은 선박식별 연구가 필요한 상황이다. 선박식별 연구를 수행하기 위해서는 먼저 여러 기종의 선박에 대한 위성 SAR 영상과 이에 대응하는 선박 기종 정보를 모두 획득하여 데이터베이스(database: DB)를 구축하는 것이 중요하다. 항공 SAR 영상을 이용한 표적식별의 경우, 지상표적에 대한 미국 moving and stationary target acquisition and recognition(MSTAR) DB를 이용하여 많은 연구들이 수행되었지만, SAR 위성을 이용한 선박식별의 경우, 아직까지 공개적으로 이용 가능한 DB가 없었다. 이에 최근 중국 Shanghai Key Laboratory에서는 유럽우주국(European Space Agency: ESA)에서 운용하는 Sentinel-1 영상과 자동인식시스템(automatic identification system: AIS)으로부터 획득한 선박정보를 결합하여 선박식별 연구용 DB인 OpenSARShip DB를 구축하였다. 이에 먼저 항공 SAR 영상을 이용한 표적식별에서 높은 성능을 보였던 최근 식별 개념들을 위성 SAR DB에 적용하여 OpenSARShip DB의 활용성을 조사해볼 필요가 있다. 따라서 본 논문에서는 기존 항공 SAR 표적식별에서 높은 성능을 보였던 최근 식별 개념들을 OpenSARShip DB에 적용하여 선박식별을 수행한 후, 그 성능을 분석하여 OpenSARShip DB의 활용성을 조사한다.

• Journal of Information Science Theory and Practice
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• 제5권3호
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• pp.31-47
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• 2017

The narrow-down approach, separately composed of search and classification stages, is an effective way of dealing with large-scale hierarchical text classification. Recent approaches introduce methods of incorporating global, local, and path information extracted from web taxonomies in the classification stage. Meanwhile, in the case of utilizing path information, there have been few efforts to address existing limitations and develop more sophisticated methods. In this paper, we propose an expansion method to effectively exploit category path information based on the observation that the existing method is exposed to a term mismatch problem and low discrimination power due to insufficient path information. The key idea of our method is to utilize relevant information not presented on category paths by adding more useful words. We evaluate the effectiveness of our method on state-of-the art narrow-down methods and report the results with in-depth analysis.

• East Asian mathematical journal
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• 제37권4호
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• pp.355-379
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• 2021

In this article, we investigated plane figures introduced in elementary school mathematics in the perspective of traditional classification, and also analyzed plane figures focused on the invariance of plane figures out of traditional classification. In the view of traditional classification, how to treat trapezoids was a key argument. In the current mathematics curriculum of the elementary school mathematics, the concept of sliding, flipping, and turning are introduced as part of development activities of spatial sense, but it is rare to apply them directly to figures. For example, how are squares and rectangles different in terms of symmetry? One of the main purposes of geometry learning is the classification of figures. Thus, the activity of classifying plane figures from a symmetrical point of view has sufficiently educational significance from Klein's point of view.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.