• 사물인터넷융복합논문지
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• 제10권2호
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• pp.17-23
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• 2024

국내 상수도관의 파열, 결함 등으로 인한 누수율로 인한 손실이 매우 크고, 이런 누수를 예방을 위한 방지 대책이 필요한 상황이다. 본 논문에서는 진동 센서를 활용한 누수 탐지 센서를 개발하고 인공지능 기술을 활용한 최적의 누수 탐지 알고리듬을 제시하고자 한다. 상수도 배관에서 취득한 진동음은 FFT(Fast Fourier Transform)를 이용한 전처리 과정을 거친 뒤, 최적화된 트리 기반 부스팅 알고리듬을 적용하여 누수 분류를 하였다. 다양한 실증 환경에서 취득한 약 26만여 개의 실험 데이터에 적용한 결과 기존의 SVM(Support Vector Machine) 방법에 비해약 4%가 향상된 97%의 정확도를 얻었고, 연산 처리속도는 약 1,362배가 향상되어 엣지 디바이스 적용에도 적합함을 확인하였다.

• Steel and Composite Structures
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• 제37권2호
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3507-3522
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• 2022

Model pruning methods have attracted huge attention owing to the increasing demand of deploying models on low-resource devices recently. Most existing methods use the weight norm of filters to represent their importance, and discard the ones with small value directly to achieve the pruning target, which ignores the contribution of the small norm filters. This is not only results in filter contribution waste, but also gives comparable performance to training with the random initialized weights [1]. In this paper, we point out that the small norm filters can harm the performance of the pruned model greatly, if they are discarded directly. Therefore, we propose a novel filter contribution recycle (FCR) method for structured model pruning to resolve the fore-mentioned problem. FCR collects and reassembles contribution from the small norm filters to obtain a mixed contribution collector, and then assigns the reassembled contribution to other filters with higher probability to be preserved. To achieve the target FLOPs, FCR also adopts a weight decay strategy for the small norm filters. To explore the effectiveness of our approach, extensive experiments are conducted on ImageNet2012 and CIFAR-10 datasets, and superior results are reported when comparing with other methods under the same or even more FLOPs reduction. In addition, our method is flexible to be combined with other different pruning criterions.

• 수산해양교육연구
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• 제26권2호
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• pp.368-381
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• 2014

The purpose of this study is to diagnose the lifelong education promotion system in Busan and to establish a desirable promotion system. In the study, we search for the optimal alternative to manage lifelong education exclusive organization(Busan Institute for lifelong Education) and seek ways to vitalize the lifelong education promotion system in Busan. The focus is also placed on completing a network-type governance system by strengthening the connection and cooperation among the parties. In order to make the promotion system function efficiently, the vitalizing methods of lifelong education promotion system can be roughly categorized into some kind as follows : strengthening the network between the interested parties and establishing their roles, restructuring legal as well as administrative and financial support system; enhancing education and public relations; intensifying local infrastructure of lifelong education; and boosting accessibility and expanding exchange and cooperation.

• ETRI Journal
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• 제32권5호
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• pp.761-765
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• 2010

In this paper, we introduce an improved adaptive boosting (AdaBoost) classifier and its application, a disguised-face discriminator that discriminates between bare and disguised faces. The proposed classifier is based on an AdaBoost learning algorithm and regression technique. In the process, the lookup table of AdaBoost learning is utilized. The proposed method is verified on the captured images under several real environments. Experimental results and analysis show the proposed method has a higher and faster performance than other well-known methods.

• 조명전기설비학회논문지
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• 제29권7호
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• pp.87-94
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• 2015

Recently, renewable energy sources are under the spotlight. due to the depletion of fossil fuels and environmental problem for the carbon dioxide. Among them, research on the Photovoltaic System using solar energy systems has been actively conducted. In this paper, we propose boosting the insulated DC/DC converter topologies Applied to soft-switching methods used in photovoltaic PCS. The proposed topology is of a type that combines a series of full-bridge converter and a boost converter, a full bridge converter and applying the insulation and soft switching system, the output voltage boost stage is carried out for the boost control. The proposed circuit validity was verified through the PSIM simulation and 5kW PV PCS Prototype and experiments.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.31-33
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• 2009

Face detection using AdaBoost algorithm is capable of processing images rapidly while having high detection rates. It seemed to be the fastest and the most robust and it is still today. Many improvements or extensions of this method have been proposed. However, previous approaches only deal with upright faces. They suffer from limited discriminant capability for rotated faces as these methods apply the same features for both upright and rotated faces. To solve this problem, it is necessary that we rotate input images or make independently trained detectors. However, this can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. This paper proposes a robust algorithm for finding rotated faces within an image. It reduces the computational and sample complexity, by finding common features that can be shared across the classes. And it will be able to apply with multi-class object detection.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.151-165
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• 2006

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(III)
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• pp.519-522
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• 2003

'KRS 6330-3256라' standard regulates performance test of insulating transformer for protecting the electronic interlock linkage equipment which is being done by test procedures and methods according to the standard. The balance/unbalance test evaluating the performance of transformer are applied by impulse voltage($l2/200{\mu}s$) but affected by many other factors. Therefore, the review about these is necessary to have the reliability and reappearance of test results. In this paper, we have simulated about several phenomenon appearing in real balance/unbalance test and compared simulated results with real ones. With the basis of these results, the influence to test result of inner and outer factors are investigated and analyzed and established the plan boosting the reliability and reappearance of test results.

• 한국IT서비스학회지
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• 제14권4호
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• pp.121-135
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• 2015

Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.