• Computers and Concrete
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• 제2권4호
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• pp.267-291
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• 2005

To date, many studies have been conducted for the analysis and design of reinforced concrete members with disturbed regions. However, prestressed concrete deep beams have not been the subject of many investigations. This paper presents an evaluation of the behavior and strength of three pre-tensioned concrete deep beams failed by shear and bond slip of prestressing strands using a nonlinear strut-tie model approach. In this approach, effective prestressing forces represented by equivalent external loads are gradually introduced along strand's transfer length in the nearest strut-tie model joints, the friction at the interface of main diagonal shear cracks is modeled by the aggregate interlock struts along the direction of the cracks in strut-tie model, and an algorithm considering the effect of bond slip of prestressing strands in the strut-tie model analysis and design of pre-tensioned concrete members is implemented. Through the strut-tie model analysis of pre-tensioned concrete deep beams, the nonlinear strut-tie model approach proved to present effective solutions for predicting the essential aspects of the behavior and strength of pre-tensioned concrete deep beams. The nonlinear strut-tie model approach is capable of predicting the strength and failure modes of pre-tensioned concrete deep beams including the anchorage failure of prestressing strands and, accordingly, can be employed in the practical and precise design of pre-tensioned concrete deep beams.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.599-614
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• 2021

This paper proposes a digital signage system based on an intelligent recommendation model. The proposed system consists of a server and an edge. The server manages the data, learns the advertisement recommendation model, and uses the trained advertisement recommendation model to determine the advertisements to be promoted in real time. The advertisement recommendation model provides predictions for various products and probabilities. The purchase index between the product and weather data was extracted and reflected using correlation analysis to improve the accuracy of predicting the probability of purchasing a product. First, the user information and product information are input to a deep neural network as a vector through an embedding process. With this information, the product candidate group generation model reduces the product candidates that can be purchased by a certain user. The advertisement recommendation model uses a wide and deep recommendation model to derive the recommendation list by predicting the probability of purchase for the selected products. Finally, the most suitable advertisements are selected using the predicted probability of purchase for all the users within the advertisement range. The proposed system does not communicate with the server. Therefore, it determines the advertisements using a model trained at the edge. It can also be applied to digital signage that requires immediate response from several users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• Safety and Health at Work
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• 제15권2호
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• pp.220-227
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• 2024

Background: Though the artificial neural network (ANN) technique has been used to predict noise-induced hearing loss (NIHL), the established prediction models have primarily relied on cross-sectional datasets, and hence, they may not comprehensively capture the chronic nature of NIHL as a disease linked to long-term noise exposure among workers. Methods: A comprehensive dataset was utilized, encompassing eight-year longitudinal personal hearing threshold levels (HTLs) as well as information on seven personal variables and two environmental variables to establish NIHL predicting models through the ANN technique. Three subdatasets were extracted from the afirementioned comprehensive dataset to assess the advantages of the present study in NIHL predictions. Results: The dataset was gathered from 170 workers employed in a steel-making industry, with a median cumulative noise exposure and HTL of 88.40 dBA-year and 19.58 dB, respectively. Utilizing the longitudinal dataset demonstrated superior prediction capabilities compared to cross-sectional datasets. Incorporating the more comprehensive dataset led to improved NIHL predictions, particularly when considering variables such as noise pattern and use of personal protective equipment. Despite fluctuations observed in the measured HTLs, the ANN predicting models consistently revealed a discernible trend. Conclusions: A consistent correlation was observed between the measured HTLs and the results obtained from the predicting models. However, it is essential to exercise caution when utilizing the model-predicted NIHLs for individual workers due to inherent personal fluctuations in HTLs. Nonetheless, these ANN models can serve as a valuable reference for the industry in effectively managing its hearing conservation program.

• 디지털융복합연구
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• 제11권6호
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• pp.103-112
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• 2013

전통적인 방식의 예측모형 구축에서 사용되는 데이터는 주로 데이터베이스에 잘 보관되어 있는 정형데이터를 사용하였다. 하지만 지금의 상황은 스마트 시대의 도래로 인한 통신수단의 획기적인 발달로 비정형 데이터가 전체데이터의 80%를 상회하는 현실이다. 이러한 현실에서 기존의 방법대로 정형데이터 만을 이용하여 예측모형을 개발하면 예측모형의 신뢰성에 문제가 있을 것이다. 즉 전체데이터의 80%에 해당하는 비정형(SNS, 페이스북, 트위트, 이미지, 동영상 등)과 반정형(로그데이터)데이터를 반드시 포함하여 모형을 구축해야 만이 신뢰성을 현실화 시킬 수 있을 것이다. 본 논문에서는 예측모형 개발시 빅데이타 방법론을 적용하여 예측 모형의 신뢰성을 높이고, 데이터를 이용하여 제안된 방법과 전통적인 방법의 예측모형의 신뢰도를 비교 분석 하였다.

• Design & Manufacturing
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• 제15권1호
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• pp.48-56
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• 2021

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.

• 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.

• Wind and Structures
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• 제29권5호
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• pp.313-321
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• 2019

The wind blowing at high velocity in an open storage yard leads to wind erosion and loss of material. Fence structures can be constructed around the periphery of the storage yard to reduce the erosion. The fence will cause turbulence and recirculation behind it which can be utilized to reduce the wind erosion and loss of material. A properly designed fence system will produce lesser turbulence and longer shelter effect. This paper aims to show the applicability of Support Vector Machine (SVM) to predict the recirculation length. A SVM model was built, trained and tested using the experimental data gathered from the literature. The newly developed model is compared with numerical turbulence model, in particular, modified $k-{\varepsilon}$ model along with the experimental results. From the results, it was observed that the SVM model has a better capability in predicting the recirculation length. The SVM model was able to predict the recirculation length at a lesser time as compared to modified $k-{\varepsilon}$ model. All the results are analyzed in terms of statistical measures, such as root mean square error, correlation coefficient, and scatter index. These examinations demonstrate that SVM has a strong potential as a feasible tool for predicting recirculation length.

• Nuclear Engineering and Technology
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• 제51권7호
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• pp.1749-1757
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• 2019

Predicting lower flammability limits (LFL) of hydrogen has become an ever-important task for safety of nuclear industry. While numerous experimental studies have been conducted, LFL results applicable for the harsh environment are still lack of information. Our aim is to develop a calculated non-adiabatic flame temperature (CNAFT) model to better predict LFL of hydrogen mixtures in nuclear power plant. The developed model is unique for incorporating radiative heat loss during flame propagation using the CNAFT coefficient derived through previous studies of flame propagation. Our new model is more consistent with the experimental results for various mixtures compared to the previous model, which relied on calculated adiabatic flame temperature (CAFT) to predict the LFL without any consideration of heat loss. Limitation of the previous model could be explained clearly based on the CNAFT coefficient magnitude. The prediction accuracy for hydrogen mixtures at elevated initial temperatures and high helium content was improved substantially. The model reliability was confirmed for $H_2-air$ mixtures up to $300^{\circ}C$ and $H_2-air-He$ mixtures up to 50 vol % helium concentration. Therefore, the CNAFT model developed based on radiation heat loss is expected as the practical method for predicting LFL in hydrogen risk analysis.

• Asian Pacific Journal of Cancer Prevention
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• 제17권5호
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• pp.2369-2374
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• 2016

Background: Differentiated thyroid cancer (DTC) is a cancer group that shares molecular and cellular origin but shows different clinical courses and prognoses. Several prognostic factors have been reported for predicting recurrence for individual patients. This literature review aimed to evaluate prognostic scores for predicting recurrence of DTC. Materials and Methods: A search of the MEDLINE database for articles published until December 2015 was carried out using the terms "thyroid neoplasms AND (recurrent OR persistent) AND (score OR model OR nomogram)". Studies were eligible for review if they indicated the development of prognostic scoring models, derived from a group of independent prognostic factors, in predicting disease recurrence in DTC patients. Results: Of the 308 articles obtained, five were eligible for evaluation. Two scoring models were developed for DTC including both papillary and follicular carcinoma, one for papillary carcinoma, and the other two for papillary microcarcinoma. The number of patients included in the score development cohort ranged from 59 to 1,669. The number of evaluated potential prognostic factors ranged from 4 to 25. Tumor-related factors were the most common factors included in the final scores, with cervical lymph node metastases being the most common. Only two studies showed internal validation of the derived score. Conclusions: There is a paucity of prognostic scores for predicting disease recurrence in patients with DTC, in particular for follicular thyroid carcinoma. Several limitations of the created scores were found. Performance of the scores has not been adequately studied. Comprehensive validation in multiple cohorts is recommended before widespread use.