• Structural Engineering and Mechanics
• /
• 제86권5호
• /
• pp.673-686
• /
• 2023

Determining the properties of pile from cone penetration test (CPT) is costly, and need several in-situ tests. At the present study, two novel hybrid learning models, namely PSO-RF and HHO-RF, which are an amalgamation of random forest (RF) with particle swarm optimization (PSO) and Harris hawks optimization (HHO) were developed and applied to predict the pile set-up parameter "A" from CPT for the design aim of the projects. To forecast the "A," CPT data along were collected from different sites in Louisiana, where the selected variables as input were plasticity index (PI), undrained shear strength (S_u), and over consolidation ratio (OCR). Results show that both PSO-RF and HHO-RF models have acceptable performance in predicting the set-up parameter "A," with R² larger than 0.9094, representing the admissible correlation between observed and predicted values. HHO-RF has better proficiency than the PSO-RF model, with R² and RMSE equal to 0.9328 and 0.0292 for the training phase and 0.9729 and 0.024 for testing data, respectively. Moreover, PI and OBJ indices are considered, in which the HHO-RF model has lower results which leads to outperforming this hybrid algorithm with respect to PSO-RF for predicting the pile set-up parameter "A," consequently being specified as the proposed model. Therefore, the results demonstrate the ability of the HHO algorithm in determining the optimal value of RF hyperparameters than PSO.

• 영어영문학
• /
• 제55권1호
• /
• pp.3-24
• /
• 2009

The Harry Potter series is considered to represent the multicultural aspect of contemporary British society and to show critical perspectives of racism. This series, however, also includes many elements of British imperialism. This paper examines the ideas about education and Harry's role in relation to British imperialism. One of the main ideas prevalent in 19th century British boys' public schools was that people's blood origin is the most important element in determining their characteristics, ability and moral qualities. The students' inherited capacity and their family background are more highly regarded than their secondary learning and training. This reflects a 19th century concept that ultimately, inborn quality makes 'a hero', a truth presented in the educational policies of Hogwarts. Hogwarts' educational policies and systems can also be related to 'developmentalism', which defines children as imperfect, in-progress and incomplete, thus needing proper training and discipline. As this concept functioned to justify the control of children while educating them, Hogwarts adopts diverse controlling devices and oppressive policies, which are mainly justified in the name of education. On the one hand, child characters are controlled and oppressed by the school authorities, on the other hand, some of the students such as Harry have remarkable magic powers enough to resist the adult authority and even to save the magic society from the evil power. Harry plays dual roles, which the British boys of the Empire were assigned from their society; they are important heirs to conquer the 'evil' or 'barbarous' world but need to be obedient to a 'good' authority to achieve the mission. Harry's magic power and self-discipline ultimately contribute to fulfilling Dumbledore's mission, which mirrors 19th century British boys' roles as the heirs of the British Empire.

• International Journal of Computer Science & Network Security
• /
• 제23권12호
• /
• pp.151-160
• /
• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• 한국수자원학회논문집
• /
• 제53권12호
• /
• pp.1159-1172
• /
• 2020

본 연구에서는 강우예측을 위해 U-Net과 SegNet에 기반한 합성곱 신경망 네트워크 구조에 장기간의 국내 기상레이더 자료를 활용하여 심층학습기반의 강우예측을 수행하였다. 또한, 기존 외삽기반의 강우예측 기법인 이류모델의 결과와 비교 평가하였다. 심층신경망의 학습 및 검정을 위해 2010부터 2016년 동안의 기상청 관악산과 광덕산 레이더의 원자료를 수집, 1 km 공간해상도를 갖는 480 × 480의 픽셀의 회색조 영상으로 변환하여 HDF5 형태의 데이터를 구축하였다. 구축된 데이터로 30분 전부터 현재까지 10분 간격의 연속된 레이더 영상 4개를 이용하여 10분 후의 강수량을 예측하도록 심층신경망 모델을 학습하였으며, 학습된 심층신경망 모델로 60분의 선행예측을 수행하기 위해 예측값을 반복 사용하는 재귀적 방식을 적용하였다. 심층신경망 예측모델의 성능 평가를 위해 2017년에 발생한 24개의 호우사례에 대해 선행 60분까지 강우예측을 수행하였다. 임계강우강도 0.1, 1, 5 mm/hr에서 평균절대오차와 임계성공지수를 산정하여 예측성능을 평가한 결과, 강우강도 임계 값 0.1, 1 mm/hr의 경우 MAE는 60분 선행예측까지, CSI는 선행예측 50분까지 참조 예측모델인 이류모델이 보다 우수한 성능을 보였다. 특히, 5 mm/hr 이하의 약한 강우에 대해서는 심층신경망 예측모델이 이류모델보다 대체적으로 좋은 성능을 보였지만, 5 mm/hr의 임계 값에 대한 평가결과 심층신경망 예측모델은 고강도의 뚜렷한 강수 특징을 예측하는 데 한계가 있었다. 심층신경망 예측모델은 예측시간이 길어질수록 공간 평활화되는 경향이 뚜렷해지며, 이로 인해 강우 예측의 정확도가 저하되었다. 이류모델은 뚜렷한 강수 특성을 보존하기 때문에 강한 강도 (>5 mm/hr)에 대해 심층신경망 예측모델을 능가하지만, 강우 위치가 잘못 이동하는 경향이 있다. 본 연구결과는 이후 심층신경망을 이용한 레이더 강우 예측기술의 개발과 개선에 도움이 될 수 있을 것으로 판단된다. 또한, 본 연구에서 구축한 대용량 기상레이더 자료는 향후 후속연구에 활용될 수 있도록 개방형 저장소를 통해 제공될 예정이다.

• 지능정보연구
• /
• 제29권1호
• /
• pp.121-142
• /
• 2023

수입식품의 수입 건수와 수입 중량이 꾸준히 증가함에 따라 식품안전사고 방지를 위한 수입식품의 안전관리가 더욱 중요해지고 있다. 식품의약품안전처는 통관단계의 수입검사와 더불어 통관 전 단계인 해외제조업소에 대한 현지실사를 시행하고 있지만 시간과 비용이 많이 소요되고 한정된 자원 등의 제약으로 데이터 기반의 수입식품 안전관리 방안이 필요한 실정이다. 본 연구에서는 현지실사 전 부적합이 예상되는 업체를 사전에 선별하는 기계학습 예측 모형을 마련하여 현지실사의 효율성을 높이고자 하였다. 이를 위해 통합식품안전정보망에 수집된 총 303,272건의 해외제조가공업소 기본정보와 2019년도부터 2022년 4월까지의 현지실사 점검정보 데이터 1,689건을 수집하였다. 해외제조가공업소의 데이터 전처리 후 해외 제조업소_코드를 활용하여 현지실사 대상 데이터만 추출하였고, 총 1,689건의 데이터와 103개의 변수로 구성되었다. 103개의 변수를 테일유(Theil-U) 지표를 기준으로 '0'인 변수들을 제거하였고, 다중대응분석(Multiple Correspondence Analysis)을 적용해 축소 후 최종적으로 49개의 특성변수를 도출하였다. 서로 다른 8개의 모델을 생성하고, 모델 학습 과정에서는 5겹 교차검증으로 과적합을 방지하고, 하이퍼파라미터를 조정하여 비교 평가하였다. 현지실사 대상업체 선별의 연구목적은 부적합 업체를 부적합이라고 판정하는 확률인 검측률(recall)을 최대화하는 것이다. 머신러닝의 다양한 알고리즘을 적용한 결과 Recall_macro, AUROC, Average PR, F1-score, 균형정확도(Balanced Accuracy)가 가장 높은 랜덤포레스트(Random Forest)모델이 가장 우수한 모형으로 평가되었다. 마지막으로 모델에 의해서 평가된 개별 인스턴스의 부적합 업체 선정 근거를 제시하기 위해 SHAP(Shapley Additive exPlanations)을 적용하고 현지실사 업체 선정 시스템에의 적용 가능성을 제시하였다. 본 연구결과를 바탕으로 데이터에 기반한 과학적 위험관리 모델을 통해 수입식품 관리체계의 구축으로 인력·예산 등 한정된 자원의 효율적 운영방안 마련에 기여하길 기대한다.

• 대한영양사협회학술지
• /
• 제11권2호
• /
• pp.205-215
• /
• 2005

Health is easily overlooked because it doesn’t be changed good or bad due to sudden effort or indifference unexpectedly but kept in daily life. Especially, schoolchildren period, an important lifetime to develop both physically and mentally needs to be helpful to promote the growth of the body and keep well-balanced mind through balanced and nourishing diet. The purpose of this study was to develop nutrition education contents for discretional activities in elementary school. The present educational contents about food and nutrition was analysed in the curriculum of elementary school. The results showed the Korean language(20.8%) included an highest ratio in educational contents about food and nutrition, the next was the courses of physical education and wise life(18.1%, each). As the educational contents about food and nutrition in the textbook were dealt with food information (20.8%), Health․Disease(15.3%), and correct dietary habits by order. We could found more contents in the text for the higher classes than for the lower classes. But the most of the contents appeared lack of structure, profundity and continuity for the systematic nutrition education in its entirety. The developed nutrition education contents for discretional activities in this study consist of korean dinning cultures and foreign dinning cultures, correct dinning etiquette, how to choose healthy food, personal sanitary and health, nutrients and food tower, and problem for children’s nutrition as main subject. This six main subjects were composed of 23 subtitles. The teaching manual consisted of the educational goal, background, teaching plan and effect-evaluation plan, and the notice point for the effective lesson. The teaching plan was made for 30 hours and consisted of cooking course, singing/making lyrics, games in nutrition, debate on dietary habit, and role play etc which are oriented to practical learning. We intended to develop this program that attempts to improve in dietary habit of schoolchildren. It is because once formed an adults dietary habit is difficult to change. Schoolchildren’s period is the best adjustable stage. Therefore, nutrition education in elementary stage can change to dietary habit and build the awareness of health.

• 한국데이타베이스학회:학술대회논문집
• /
• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 정보교육학회논문지
• /
• 제19권1호
• /
• pp.149-158
• /
• 2015

프로그래밍 교육은 문제분석 능력, 논리적 사고력, 절차적 문제 해결방식과 상상적 문제 해결방식을 종합적으로 습득하는 데 매우 효과적이다. 그러나 아직까지 우리나라에서는 초 중등학교에서 정규시간에 프로그래밍 수업을 하지 않고 있어, 미국 등 IT 강국 중심으로 코딩수업이 활발히 진행되고 있는 것에 비해 대조적이다. 다행히 정부에서도 이러한 현실을 파악하고 2017년부터 초등학교에서 프로그래밍 수업을 정규 교과에서 실시하기로 결정하였다. 이러한 상황에서 많은 연구자들이 초 중등학교에서 학습할 수 있는 프로그래밍 교육모형 연구가 절실히 필요하다. 본 연구에서는 초등학교 5, 6학년들이 프로그래밍 수업에 활용할 수 있도록 스크래치언어와 센서보드를 연계한 프로그래밍 교육모형을 17차시 개발하여 제안하였다. 초등 프로그래밍 교육에 적합한지를 검증하기 위해 제안한 교육과정을 기초로 방과 후 시간에 5, 6학년 협동수업을 실시한 결과 만족할 만한 성취도를 얻었다. 향후, 제안한 프로그래밍 교육모형을 추가로 개선하여 초등학생들의 지적 능력에 맞는 최적 모형으로 개발하고자 한다.

• 한국과학교육학회지
• /
• 제18권3호
• /
• pp.383-398
• /
• 1998

본 연구는 96년도에 실시 한 우리 나라 중학교 과학 실험 실습 교육의 실태 조사 연구에 따른 계속 연구의 일환으로 고등학교 과학 실험 실습 교육의 실태를 조사하기 위한것이다. 연구내용은 고등학교 과학 교과서의 분석, 실험, 실습 실시 현황 분석, 현행 실험 실습 교육의 문제점 분석, 현행 학교구성원들의 실험 실습교육 운영 실태 조사 및 현행 실험 실습 교육의 개선을 위한 연구과제 도출을 주요 연구내용으로 다루었다. 연구방법은 전국의 고등학교 80개교를 대상으로 이들 학교의 학생 1,977명, 교사 165명, 학교장 80명에게 적용하였다. 본 연구에서 밝혀진 결과를 중심으로 우리 나라 고등학교 과학 실험 실습 교육의 정상화를 위한 실험 내적인 연구과제를 제시하면 다음과 같다.(1)교재 개발에 대한 연구, (2) 실험 킷트 개발에 대한 연구, (3)교사교육 및 재교육 프로그램 개발에 대한 연구, (4)교실밖 실험 실습 활동 프로그램 개발에 대한 연구, (5)과학 실험 실습 소재의 개발에 대한 연구.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.