The statistic inversion algorithms of water constituents for the Huanghai Sea and the East China Sea

A group of statistical algorithms are proposed for the inversion of the three major components of CaseII waters inthe coastal area of the Huanghai Sea and the East China Sea. The algorithms are based on the in situ data collected inthe spring of 2003 with strict quality assurance according to NASA ocean bio-optic protocols. These algorithms arethe first ones with quantitative confidence that can be applied for the area. The average relative error of the inversedand in situ measured components' concentrations are: Chl-a about 37%, total suspended matter (TSM) about 25%,respectively. This preliminary result is quite satisfactory for CaseII waters, although some aspects in the modelneed further study. The sensitivity of the input error of 5% to remote sensing reflectance (Rrs) is also analyzed andit shows the algorithms are quite stable. The algorithms show a large difference with Tassans local SeaWiFSalgorithms for different waters, except for the Chl-a algorithm.

A research on statistical retrieval algorithms and spectral characteristics of the total absorption coefficients in the Yellow Sea and the East China Sea

This paper suggests a group of statistical algorithms for calculating the total absorption coefficients based on in situ data of apparent optical property and inherent optical property collected with strict quality assurance according to NASA ocean bio-optic protocols in the Yellow Sea and the East China Sea in spring 2003. The band-ratios ofRrs412/Rrs555, Rrs49o/Rrs555 are used in the algorithms to derive the total absorption coefficients （at） at 412, 440, 488, 510, 532 and 555nm bands, respectively. The average relative errors between inversed and measured values are less than 25.8%, with the correlative coefficients （R2） being 0.75-0.85. Error sensitivity analysis shows that the maximum retrieval error is less than 24.0% at ＋5% error in Rrs＇s. So the statistical algorithms of this paper are practicable. In this paper, the relations between the total absorption coefficients at 412, 488, 510, 532, 555 nm and that of 440nm are also studied. The results show that the relations between the total absorption coefficients of 400-600 nm and that of 440 nm are correlated well and all of their correlative coefficients R2 are greater than 0.99. Furthermore, a regression analysis is also done for the slope of the linear relations and wavelengths, and the R2 is also 0.99. Thus it is possible to retrieve other bands＇ total absorption coefficients with only one band absorption value, which significantly reduce the number of unknown parameters in studying other ocean color related problems.

Optimal Dynamic Voltage Restorer Using Water Cycle Optimization Algorithm

This paper proposes a low complexity control scheme for voltage control of a dynamic voltage restorer(DVR)in a three-phase system.The control scheme employs the fractional order,proportional-integral-derivative(FOPID)controller to improve on the DVR performance in order to enhance the power quality in terms of the response time,steady-state error and total harmonic distortion(THD).The result obtained was compared with fractional order,proportionalintegral(FOPI),proportional-integral-derivative(PID)and proportional-integral(PI)controllers in order to show the effectiveness of the proposed DVR control scheme.A water cycle optimization algorithm(WCA)was utilized to find the optimal set for all the controller gains.They were used to solve four power quality issues;balanced voltage sag,balanced voltage swell,unbalanced voltage sag,and unbalanced voltage swell.It showed that one set of controller gain obtained from the WCA could solve all the power quality issues while the others in the literature needed an individual set of optimal gain for each power quality problem.To prove the concept,the proposed DVR algorithm was simulated in the MATLAB/Simulink software and the results revealed that the four optimal controllers can compensate for all the power quality problems.A comparative analysis of the results in various aspects of their dynamic response and%THD was discussed and analyzed.It was found that PID controller yields the most rapid performance in terms of average response time while FOPID controller yields the best performance in term of average%steady-state error.FOPI controller was found to provide the lowest THD percentage in the average%THD.FOPID did not differ much in average response from the PID and average%THD from FOPI;however,FOPID provided the most outstanding average steady-state error.According to the CBMA curve,the dynamic responses of all controllers fall in the acceptable power quality area.The total harmonic distortion(THD)of the compensated load voltage from all the controllers were within the 8%limit in accordance to the IEEE std.519-2014.

The Numerical Scheme Development of a Simplified Frozen Soil Model

In almost all frozen soil models used currently, three variables of temperature, ice content and moisture content are used as prognostic variables and the rate term, accounting for the contribution of the phase change between water and ice, is shown explicitly in both the energy and mass balance equations. The models must be solved by a numerical method with an iterative process, and the rate term of the phase change needs to be pre-estimated at the beginning in each iteration step. Since the rate term of the phase change in the energy equation is closely related to the release or absorption of the great amount of fusion heat, a small error in the rate term estimation will introduce greater error in the energy balance, which will amplify the error in the temperature calculation and in turn, cause problems for the numerical solution convergence. In this work, in order to first reduce the trouble, the methodology of the variable transformation is applied to a simplified frozen soil model used currently, which leads to new frozen soil scheme used in this work. In the new scheme, the enthalpy and the total water equivalent are used as predictive variables in the governing equations to replace temperature, volumetric soil moisture and ice content used in many current models. By doing so, the rate terms of the phase change are not shown explicitly in both the mass and energy equations and its pre-estimation is avoided. Secondly, in order to solve this new scheme more functionally, the development of the numerical scheme to the new scheme is described and a numerical algorithm appropriate to the numerical scheme is developed. In order to evaluate the new scheme of the frozen soil model and its relevant algorithm, a series of model evaluations are conducted by comparing numerical results from the new model scheme with three observational data sets. The comparisons show that the results from the model are in good agreement with these data sets in both the change trend of variables and their magnitude values, and the new scheme, together with the algorithm, is more efficient and saves more computer time.

An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of optimization.AI has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations research.Indeed,metaheuristic-based algorithms are a sub-field of AI.This study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution.Since the parameters are stochastic,the corresponding constraints are probabilistic.They are converted into deterministic constraints using the stochastic programming approach.In this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems.WCA is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous system.It is concluded that WCA presents better results when compared with the neural network algorithm.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

Steel dome structures,with their striking structural forms,take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns.In this paper,the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider(WS),grey wolf(GW),and brain storm optimization(BSO).The structural elements of the domes are treated as design variables collected in member groups.The structural stress and stability limitations are enforced by ASD-AISC provisions.Also,the displacement restrictions are considered in design procedure.The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface(OAPI).The optimum spatial steel dome designs achieved by proposed WS,GW,and BSO algorithms are compared with respect to solution accuracy,convergence rates,and reliability,utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization(HBO)algorithm.

Extended Approach to Water Flow Algorithm for Text Line Segmentation

This paper proposes a new approach to the water flow algorithm for text line segmentation. In the basic method the hypothetical water flows under few specified angles which have been defined by water flow angle as parameter. It is applied to the document image frame from left to right and vice versa. As a result, the unwetted and wetted areas are established. These areas separate text from non-text elements in each text line, respectively. Hence, they represent the control areas that are of major importance for text line segmentation. Primarily, an extended approach means extraction of the connected-components by bounding boxes over text. By this way, each connected component is mutually separated. Hence, the water flow angle, which defines the unwetted areas, is determined adaptively. By choosing appropriate water flow angle, the unwetted areas are lengthening which leads to the better text line segmentation. Results of this approach are encouraging due to the text line segmentation improvement which is the most challenging step in document image processing.

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de- veloped. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration （LC50） of a pollutant. The data were used to develop a method to evaluate water quality, so as 6＋ 2＋ to give an early indication of toxicity. Four kinds of metal ions （Cu2~, Hg2~, Cr , and Cd ） were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm （GA） was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.