| 1. |
- Yang, Jae-Jin, et al.
(author)
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사회보장재원구성에 관한 기초연구
- 2019
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Reports (other academic/artistic)
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| 2. |
- Hyun Kim, Jin, et al.
(author)
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A Process Algebraic Approach to Resource-Parameterized Timing Analysis of Automotive Software Architectures
- 2016
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In: IEEE Transactions on Industrial Informatics. - : IEEE Press. - 1551-3203 .- 1941-0050. ; 12:2, s. 655-671
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Journal article (peer-reviewed)abstract
- Modern automotive software components are often first developed by different suppliers and then integrated under limited resources by a manufacturer. The integration of software components under various resource configurations is prone to timing errors because the components are resources independently designed by the supplier and viewed by the manufacturer as black boxes during the integration stage, so that imposing resource constraints/requirements on their behavior is a challenge. This paper introduces an engineering awareness environment for the analysis of automotive systems with respect to two perspectives: 1) time-aware design models that correspond to the supplier perspective; and 2) resource-aware design models imposed by the manufacturer during integration. To this end, first we propose two timed behavioral models, a time-constrained model (TcM) and a resource-constrained model (RcM) that are extended from a functional model (FM). A timing analysis of applications can hence be conducted incrementally by adopting the separation of concerns principle coming from the model-driven architectures (MDAs). Second, given a basic application component description of AUTomotive Open System Architecture with timing properties, we specify how to define the behavior of the basic components as process terms using a process algebra, algebra of communicating shared resources with value passing (ACSR-VP), in order to exploit the description capability of the language for both timing aspects and resource-constrained aspects of a system. As a result, a timed behavioral model of a system can be seamlessly refined by various resource configurations, and both platform-independent and platform-dependent timing properties of real-time systems can be analyzed in a consistent and efficient manner.
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| 3. |
- Joshi, Bhupendra, et al.
(author)
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A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration
- 2024
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In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Journal article (peer-reviewed)abstract
- Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.
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| 4. |
- Malik, Anurag, et al.
(author)
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Modeling monthly pan evaporation process over the Indian central Himalayas : application of multiple learning artificial intelligence model
- 2020
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In: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 323-338
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Journal article (peer-reviewed)abstract
- The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.
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| 5. |
- Singh, Sachin Kumar, et al.
(author)
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Soil erosion control from trash residues at varying land slopes under simulated rainfall conditions
- 2023
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In: Mathematical Biosciences and Engineering. - : American Institute of Mathematical Sciences. - 1551-0018. ; 20:6, s. 11403-11428
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Journal article (peer-reviewed)abstract
- Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching on soil loss reduction. The number of mulches was taken as 6, 8 and 10 t/ha, three rainfall intensities viz. 11, 13 and 14.65 cm/h at 0, 2 and 4% land slopes were selected. The rainfall duration was fixed (10 minutes) for every mulch treatment. The total runoff volume varied with mulch rates for constant rainfall input and land slope. The average sediment concentration (SC) and sediment outflow rate (SOR) increased with the increasing land slope. However, SC and outflow decreased with the increasing mulch rate for a fixed land slope and rainfall intensity. The SOR for no mulch-treated land was higher than trash mulch-treated lands. Mathematical relationships were developed for relating SOR, SC, land slope, and rainfall intensity for a particular mulch treatment. It was observed that SOR and average SC values correlated with rainfall intensity and land slope for each mulch treatment. The developed models' correlation coefficients were more than 90%.
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| 6. |
- Tao, Hai, et al.
(author)
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Groundwater level prediction using machine learning models: A comprehensive review
- 2022
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In: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308
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Research review (peer-reviewed)abstract
- Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
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| 7. |
- Yaseen, Zaher, et al.
(author)
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Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis
- 2019
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In: Water. - Basel : MDPI. - 2073-4441. ; 11:3
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Journal article (peer-reviewed)abstract
- In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% confidence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.
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