Glistenings are liquid-filled microvacuoles in intraocular lenses (IOLs) appear when the IOL is in an aquatic environment that affect the quality of vision. In our glistenings Detection method, the candidate glistenin...
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Glistenings are liquid-filled microvacuoles in intraocular lenses (IOLs) appear when the IOL is in an aquatic environment that affect the quality of vision. In our glistenings Detection method, the candidate glistenings are automatically detected by mathematic morphology methodology. Machine learning approaches, feature selection and classification are used in this paper. The 68 features are extracted and used as training data for fine segmented using the classifiers. The detected glistenings are validated by object-based with ophthalmologist's hand-drawn ground-truth. Our proposed method, Feature Selection using Fuzzy-based Firefly algorithm (FS-FFA) applied the concept of fuzzy entropy to calculating the membership of features data for in order to select good sets of the relevant features that maximize the classification performance in glistenings Detection. The proposed FS-FFA is compared with feature selection methods the standard firefly algorithm (FS-FA) and without feature selection using basic classifier k-nearest neighbor. The results have shown that the Matthews correlation coefficient (MCC) and the diagnostic odds ratio (DOR) value increase after feature selection using firefly algorithm and fuzzy entropy. Small size of features set also decreased that classification time in testing phase.
This study presents a novel data-driven approach designed to address the intricate link between climate change and agriculture, focusing on rice farming in Southeast Asia. By using advanced modelling and optimization ...
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This study presents a novel data-driven approach designed to address the intricate link between climate change and agriculture, focusing on rice farming in Southeast Asia. By using advanced modelling and optimization tools, namely ARX models and Model Predictive Control, it aims to control the temperature anomaly across fifteen world’s subregions. Using ARX models to downscale the global temperature anomaly, the approach allows the evaluation of local climate effects. The methodology is applied to evaluate the impact of climate change on rice production in Southeast Asia, projecting potential outcomes under different emission scenarios. By optimizing greenhouse gas emissions, particularly carbon dioxide and methane, the goal is to keep the temperature anomaly below critical thresholds, ensuring resilient rice production, supporting food security, and minimizing economic and social costs.
In order to better cope with the complicated maritime situation, conduct maritime missions under the premise of ensuring the safety of maritime scientific research personnel and face unknown risks. Surface Unmanned Sh...
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In order to better cope with the complicated maritime situation, conduct maritime missions under the premise of ensuring the safety of maritime scientific research personnel and face unknown risks. Surface Unmanned Ship (Surface Unmanned Ship) came into being. The continuous deepening of maritime missions by various countries has also put forward higher requirements for the intelligent of unmanned ships. In recent years, the Internet of Things, cloud computing, big data, artificial intelligence and other new concepts and the enrichment of new technologies have enabled unmanned ships to better complete complex waterborne tasks. Therefore, the realization of safe obstacle avoidance based on overall and local path planning of unmanned ships has become the research focus of unmanned ship technology at this stage. Scholars from various countries have conducted research on the path planning and safe obstacle avoidance of unmanned ships, and proposed many methods to realize path planning. In the face of the requirements of timeliness and accuracy of path planning, this article summarizes the proposed methods, as well as the improvement and optimization methods, and hopes to provide inspiration for the next research direction.
Our paper presents chosen computational algorithms for solution of finite element models with structural uncertainties. An application of the chosen approaches will be presented–the first one, a simple combination of...
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Our paper presents chosen computational algorithms for solution of finite element models with structural uncertainties. An application of the chosen approaches will be presented–the first one, a simple combination of only inf-values or only sup-values; the second one presents full combination of all inf-sup values; the third one uses the optimizing process as a tool for finding out an inf-sup solution and last one is the Monte Carlo method as a comparison tool.
Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. ...
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Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two-stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step-by-step calculation method that is not feasible under real-time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system.
Modern (micro) grids host inverter-based generation units for utilizing renewable and sustainable energy resources. Due to the lack of physical inertia and, thus, the low inertia level of inverter-interfaced energy re...
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Modern (micro) grids host inverter-based generation units for utilizing renewable and sustainable energy resources. Due to the lack of physical inertia and, thus, the low inertia level of inverter-interfaced energy resources, the frequency dynamic is adversely affected, which critically impacts the stability of autonomous microgrids. The idea of virtual inertia control (VIC), assisted by battery energy storage systems (BESSs), has been presented to improve the frequency dynamic in islanded microgrids. This study presents the PD-FOPID cascaded controller for the BESS, a unique method for enhancing the performance of VIC in islanded microgrids. Using the firefly algorithm (FA), the settings of this controller are optimally tuned. This approach is robust to disruptions due to uncertainties in islanded microgrids. In several scenarios, the performance of the suggested approach is compared with those of other control techniques, such as VIC based on an MPC controller, VIC based on a robust H-infinite controller, adaptive VIC, and VIC based on an optimized PI controller. The simulation results in MATLAB show that the suggested methodology in the area of VIC is better than previous methods.
Wind power generation has strong volatility. Accurate wind speed forecasting can not only avoid the waste of power resources, but also facilitate the development of clean energy and promote the energy transition world...
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Wind power generation has strong volatility. Accurate wind speed forecasting can not only avoid the waste of power resources, but also facilitate the development of clean energy and promote the energy transition worldwide. However, previous research has predominantly focused on the accuracy of wind power prediction, while ignoring the reliability of wind speed prediction system. In this research, a hybrid forecasting system with both accuracy and reliability of wind power forecasting is proposed. Firstly, a hybrid adaptive decomposition denoising algorithm is proposed to solve the unreasonable decomposition and residual noise. To improve the search performance, the seagull algorithm is optimized by chaotic system and Cauchy operator, and then the parameters of long short-term memory model are adjusted. Finally, based on data enhancement theory, an interval prediction model combined with kernel density estimation is proposed. The model is verified by the historical data of Sotavento wind farm in Spain and Eman wind farm in China. The average absolute percentage error values of wind speed point prediction are 2.87% and 8.01%, respectively. At the same confidence level, the interval prediction model proposed has narrower widths compared to the comparative model, with higher average interval scores. The results indicate that the point prediction model proposed in this research exhibits higher accuracy, while the interval prediction model demonstrates greater stability and reliability. These findings provide technical support for wind power forecasting.
Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation...
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Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints;(2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%;(2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.
A highly sensitive dual-gas sensor based on a two-channel multipass cell (MPC) was designed and developed for simultaneous detection of atmospheric methane (CH4) and carbon dioxide (CO2) by using two distributed feedb...
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A highly sensitive dual-gas sensor based on a two-channel multipass cell (MPC) was designed and developed for simultaneous detection of atmospheric methane (CH4) and carbon dioxide (CO2) by using two distributed feedback lasers emitting at 1653 nm and 2004 nm. The nondominated sorting genetic algorithm was applied to intelligently optimize the MPC configuration and accelerate the dual-gas sensor design process. A compact and novel two-channel MPC was used to achieve two optical path lengths of 27.6 m and 2.1 m in a small volume of 233 cm3. Simultaneous measurements of CH4 and CO2 in the atmosphere were performed to demonstrate the stability and robustness of the gas sensor. According to the Allan deviation analysis, the optimal detection precision for CH4 and CO2 was 4.4 ppb at an integration time of 76 s and 437.8 ppb at an integration time of 271 s, respectively. The newly developed dual-gas sensor exhibits superior characteristics of high sensitivity and stability, cost-effectiveness and simple structure, which make it well-suited for multiple trace gas sensing in various applications, including environmental monitoring, safety inspections and clinical diagnosis.
In general, relative permeability data can be obtained from laboratory coreflooding experiments. Such experimental data can be interpreted analytically or numerically. Compared to analytical methods, when the numerica...
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In general, relative permeability data can be obtained from laboratory coreflooding experiments. Such experimental data can be interpreted analytically or numerically. Compared to analytical methods, when the numerical inversion methods are applied to interpret the coreflooding experimental data, the reservoir performance obtained prior to and after breakthrough can be utilized comprehensively, the capillary effects and the heterogeneity of core samples can also be taken into account, so the estimated result is not only accurate but also complete. Moreover, the numerical inversion methods can be applied to large-scale reservoirs. This article introduces systematically the methodology of numerical inversion methods, and then reviews the present research status. Finally, several proposals of implicitly estimating relative permeability data are put forward from aspects of optimization algorithms’ properties, estimation of endpoint saturations and treatment scale by automatic history matching.
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