• 한국지반공학회논문집
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• 제16권1호
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• pp.83-97
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• 2000

본 연구에서는, 흙막이 벽체의 변위 예측시스템 개발을 위하여 다층퍼셉트론을 이용해 임의의 인공신경망 모델을 구축하고 그 성능을 평가하여 최적의 모델을 선정하였다. 인공신경망모델의 학습과 검증을 위해 국내 도심지에 실제 시공이 완료된 다양한 현장의 계측자료를 수집하였고, 수집된 계측자료의 분석을 통해 흙막이벽체의 거동에 영향을 미치는 인자를 조사하였다. 아울러 실행비를 기준으로 선별한 신뢰성 있는 계측자료를 조사된 영향인자를 토대로 데이터 베이스화하여 인공신경망 모델의 학습과 검증에 사용하였으며, 학습은 최급강하법을 기초로 하는 역전파 알고리즘을 이용하여 수행하였다. 학습에 포함되지 않은 현장들에 대하여 흙막이벽체의 최대수평변위와 그 발생위치를 예측하고 이를 계측치와 비교하여, 제시한 변위 예측시스템의 적용성을 부분적으로 확인하였다.

• 한국통신학회논문지
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• 제42권4호
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• pp.939-949
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• 2017

고장 예지 및 건전성 관리 기술(Prognostics and Health Management; PHM)은 시스템의 현재 상태를 진단하고 향후 발생 가능한 고장 시점을 신뢰성 있게 예지하는 기술로써 유지 보수 비용의 절감 및 시스템의 안정성 향상을 꾀하고자 하는 다양한 산업분야에서 활발하게 이용되고 있다. 스마트 그리드의 에너지 저장장치, 전기차, 스마트폰, 항공우주산업 등 광범위한 사용처에서 중요한 에너지원으로 사용되고 있는 배터리 또한 성능 저하 및 폭발의 위험성으로부터 자유로울 수 없기 때문에 이러한 고장 예지 및 건전성 관리 기술이 반드시 적용되어야 할 어플리케이션이다. 본 논문에서는 PHM의 기본적인 개념을 소개함과 동시에 배터리의 잔존 유효 수명(Remaining Useful Life; RUL)을 예측하는 각종 알고리즘 및 성능 평가 지표 서술에 초점을 맞추도록 한다. 더불어 배터리의 기능적 동작 원리 및 전기화학 기반의 모델링에 대한 설명을 통해 향후 잠재적인 가능성을 지닌 배터리의 전반적인 특성에 대한 깊은 이해 및 응용 기술에 대한 통찰력을 제시하고자 한다.

• 통합자연과학논문집
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• 제12권4호
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• 디지털산업정보학회논문지
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• 제17권3호
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• 한국건축시공학회지
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• 제2권4호
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• pp.105-112
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• 2002

The EVMS(Earned Value Management System) comes from C/SCSC which was first released by the United States Department of Defense in December 1967, and preyed very powerful and efficient project management tool from a lot of practices. Although it is an excellent tool, we can not be succeed appling foreign system due to the differences of construction culture and law between the Korean and US construction industries. EAC(Estimate at Completion) is one of the most important functions in the EVMS. The purpose of this study is to propose the improved EAC method according to the performance indices better than old that and to prove from examples. In advance, the improved EAC method is to estimate more exactly costs and to promote efficiency in construction projects.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 추계학술대회
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• pp.489-498
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• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• 한국구조물진단유지관리공학회 논문집
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• 제22권4호
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• pp.27-36
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• 2018

현재 적용되고 있는 평가체계는 1996년 개정되어 이후 4차례 개정을 실시하였으나, 구조적 안전성의 확보 목표에 국한된 평가체계로 인해 장기적 관점의 성능저하 예측 및 예산투입을 위한 우선순위 의사결정의 근거로 활용하기에는 부족하다. 따라서, 본 연구에서는 델파이 기법과 AHP기법을 활용하여 기존 도로터널의 여러 구조형식 중 재래식(ASSM) 터널의 성능평가에 적합한 평가체계를 새롭게 제시하고자 한다. 국 내외의 기존 평가체계 및 평가항목을 검토한 결과, 도출가능한 평가항목이 한정적인 것으로 판단되어 기존에 적용되고 있는 항목에 대한 폐쇄적인 설문과 새로운 항목 도출을 위한 개방형 설문을 병행하여 설문조사를 실시하였다. 조사한 설문결과를 내용타당도 검증을 거쳐 재래식(ASSM)터널에 적합한 성능평가인자를 도출하였으며, AHP기법을 활용하여 도출된 평가항목에 대한 가중치를 산정한 후 구조물의 특성에 맞는 성능평가 체계를 새롭게 제시하여, 향후 성능평가 세부지침 수정 및 보완 시 참고자료로 활용될 수 있도록 하고자 한다.

• Korean Journal of Radiology
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• 제25권1호
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• pp.74-85
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• 2024

Objective: Idiopathic intracranial hypertension (IIH) is a condition of unknown etiology associated with venous sinus stenosis. This study aimed to develop a magnetic resonance venography (MRV)-based radiomics model for predicting a high trans-stenotic pressure gradient (TPG) in IIH patients diagnosed with venous sinus stenosis. Materials and Methods: This retrospective study included 105 IIH patients (median age [interquartile range], 35 years [27-42 years]; female:male, 82:23) who underwent MRV and catheter venography complemented by venous manometry. Contrast enhanced-MRV was conducted under 1.5 Tesla system, and the images were reconstructed using a standard algorithm. Shape features were derived from MRV images via the PyRadiomics package and selected by utilizing the least absolute shrinkage and selection operator (LASSO) method. A radiomics score for predicting high TPG (≥ 8 mmHg) in IIH patients was formulated using multivariable logistic regression; its discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC). A nomogram was constructed by incorporating the radiomics scores and clinical features. Results: Data from 105 patients were randomly divided into two distinct datasets for model training (n = 73; 50 and 23 with and without high TPG, respectively) and testing (n = 32; 22 and 10 with and without high TPG, respectively). Three informative shape features were identified in the training datasets: least axis length, sphericity, and maximum three-dimensional diameter. The radiomics score for predicting high TPG in IIH patients demonstrated an AUROC of 0.906 (95% confidence interval, 0.836-0.976) in the training dataset and 0.877 (95% confidence interval, 0.755-0.999) in the test dataset. The nomogram showed good calibration. Conclusion: Our study presents the feasibility of a novel model for predicting high TPG in IIH patients using radiomics analysis of noninvasive MRV-based shape features. This information may aid clinicians in identifying patients who may benefit from stenting.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.652-657
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• 2004

As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• Journal of Gastric Cancer
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• 제21권4호
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.