• 산업경영시스템학회지
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• 제43권1호
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.196-212
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• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.

• 한국지능시스템학회논문지
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• 제21권3호
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• pp.341-346
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• 2011

공백기술예측은 기술경영 분야에서 중요하게 다루어지는 주제이다. 다양한 분야에서 현재까지의 기술개발결과를 분석하여 상대적으로 연구개발이 이루어지지 못한 분야를 찾아내어 개발하는 것은 국가와 기업의 발전에 중요한 영향을 미친다. 현재 특허는 기술개발결과에 대한 가장 객관적인 데이터 중 하나이다. 본 논문에서는 특허데이터를 이용하여 공백기술을 정량적으로 예측할 수 있는 방법에 대하여 연구한다. 하나의 정량적 기술예측모형이 완벽하다는 보장을 할 수 없기 때문에 본 연구에서는 여러 가지 모형들의 결과를 결합하여 예측하는 앙상블모형을 제안한다. 통계적 분석기법과 기계학습 알고리즘을 결합하여 보다 객관적이고 정확한 공백기술예측모형을 구축한다. 제안방법의 객관적인 성능평가를 위하여 각 기술분야에 대하여 최초 특허가 이루어진 시점부터 최근까지 출원, 등록된 특허데이터를 이용한다.

• 천문학회지
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• 제53권6호
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1301-1316
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• 2024

In modern warfare, missiles play a pivotal role but typically spend the majority of their lifecycle in long-term storage or standby mode, making it difficult to detect failures. Preemptive detection of missiles that will fail is crucial to preventing severe consequences, including safety hazards and mission failures. This study proposes a contamination-based stacking ensemble model, employing the local outlier factor (LOF), to detect such missiles. The proposed model creates multiple base LOF models with different contamination values and combines their anomaly scores to achieve a robust anomaly detection. A comparative performance analysis was conducted between the proposed model and the traditional single LOF model, using production-related inspection data from missiles deployed in the military. The experimental results showed that, with the contamination parameter set to 0.1, the proposed model exhibited an increase of approximately 22 percentage points in accuracy and 71 percentage points in F1-score compared to the single LOF model. This approach enables the preemptive identification of potential failures, undetectable through traditional statistical quality control methods. Consequently, it contributes to lower missile failure rates in real battlefield scenarios, leading to significant time and cost savings in the military industry.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• 응용통계연구
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• 제13권2호
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• pp.415-425
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• 2000

붓스트랩 표본추출과 pairwise coupling의 알고리즘을 결합한 새로운 분류 알고리즘을 제안하고, 이를 선형판별분석과 2차 판별분석에 적용하였다. 그리고 새로운 분류 알고리즘의 정확도를 비교하기위해 널리 사용되는 waveform 자료 등을 분석한 후, 그 결과를 기존 분류 방법과 비교하였다.

• 한국통신학회논문지
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• 제34권8C호
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• pp.772-781
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• 2009

This paper proposes a novel algorithm for the efficient classification and retrieval of medical images. After color and edge features are extracted from medical images, these two feature vectors are then applied to a multi-class Support Vector Machine, to give membership vectors. Thereafter, the two membership vectors are combined into an ensemble feature vector. Also, to reduce the search time, Correlated Categories Vector is proposed for similarity matching. The experimental results show that the proposed system improves the retrieval performance when compared to other methods.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1990년도 봄 학술발표회 논문집
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• pp.24-30
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• 1990

An improved algorithm for simulation of multi-variate random processes has been presented. It is based on the spectral representation method. The conventional methods give sample time histories which satisfy the target spectral density matrix only in the sense of ensemble average. However, the present method can generate sample functions which satisfy the target spectra in the ergodic sense. Example analysis is given for the simulation of earthquake accelerations with three components.

• 대한공간정보학회지
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• 제22권4호
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• pp.47-52
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• 2014

종분포모형은 생물다양성 평가, 보호지역 지정, 서식지 관리 및 복원, 기후변화 예측 등의 다양한 분야에 활용되고 있으나 공공이나 정책분야에서는 모형의 불확실성으로 인하여 활용이 제한적이었다. 최근에는 이러한 모형의 불확실성을 저감하기 위하여 앙상블이나 합의모형 등의 다중모형을 적용하는 연구가 증가하고 있다. 이에 본 연구에서는 히어리를 대상으로 단일모형과 앙상블(다중) 모형을 적용하고 이를 비교하는 연구를 수행하였다. 모형은 AUC와 kappa, TSS를 이용하여 적합도를 평가하였으며, 이 중 모형 간의 비교가 용이하고 이항형 지도로 바로 변환할 수 있는 TSS가 효과적이었다. 단일모형과 앙상블 모형 모두 높은 모형적합도를 나타내었으며, 다중 모형 중에서는 RF, Maxent, GBM이 높게, GAM, SRE는 비교적 낮게 평가되었다. 예측지도에서는 단일모형에 비해 다중모형의 예측범위가 과대 추정되는 경향이 있었다. 이는 여러 모형이 중첩된 결과로 현장전문가와 모형전문가들 간의 협력연구를 통하여 적절한 모형 선택과 가중치 부여 등을 통하여 문제를 해결할 수 있다. 앙상블모형을 공간의사결정이나 보호지역계획에 활용하기 위해서는 불확실성의 정도와 원인을 파악하고, 이를 저감하려는 개선작업과 함께 결과의 불확실성이나 위험성을 인지하고 의사결정을 해야 한다.