Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design.

A Novel Objective Function DYNO for Automatic Multi-variable Calibration and Application to Assess Effects of Velocity versus Temperature Data for 3D Lake Models Calibration

This study introduced a novel Dynamically Normalized objective function (DYNO) for multi-variable (i.e., temperature and velocity) model calibration problems. DYNO combines the error metrics of multiple variables into a single objective function by dynamically normalizing each variable's error terms using information available during the search. DYNO is proposed to dynamically adjust the weight of the error of each variable hence balancing the calibration to each variable during optimization search. The DYNO is applied to calibrate a tropical hydrodynamic model where temperature and velocity observation data are used for model calibration simultaneously. We also investigated the efficiency of DYNO by comparing the result of using DYNO to results of calibrating to either temperature or velocity observation only. The result indicates that DYNO can balance the calibration in terms of water temperature and velocity and that calibrating to only one variable (e.g., temperature or velocity) cannot guarantee the goodness-of-fit of another variable (e.g., velocity or temperature). Our study suggested that both temperature and velocity measures should be used for hydrodynamic model calibration in real practice. Our example problems were computed with a parallel optimization method PODS but DYNO can also be easily used in serial applications.

Regulation Characteristics and Load Optimization of Pump-Turbine in Variable-Speed Operation

In order to improve the operating and regulation characteristics of the hydropower unit and to stabilize the load fluctuations, variable-speed pumped storage technology based on converters has been proposed and given more attention recently. However, different from the conventional units, due to the variability of operation conditions, variable-speed units need to develop a load optimization strategy in terms of operating parameter identification to ensure state matching for operation. Therefore, this paper proposes an optimization search step based on the model test curve, and the process of parameter optimization search is elaborated and calculated in the turbine operating condition and pump operating condition, respectively. A mathematical model of the turbine regulation system is established to analyze the influence of speed and guide vane related parameters on the regulation characteristics, and the achievable operating range and regulation capacity in the variable-speed condition is pointed out based on pump-turbine model test, as well as the advantages over the fixed-speed operation. The results show that by applying the load optimization method, the variable-speed unit can be significantly improved in terms of operating efficiency, especially at low head and low power conditions. Meanwhile, a certain range of active power regulation can be realized by the decoupling control of the converter and measuring the guide vane opening in both modes. The analysis of the model test verifies the effectiveness of the variable-speed regulation operation of pump-turbine and provides a reference for the design and operation of the variable-speed hydropower units.

Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

A REVIEW ON DIGITAL MARKETING

Digital marketing is the marketing of products or services using digital technologies, mainly on the Internet, but also including mobile phones, display advertising, and any other digital media. Digital marketing has gained full momentum due to technology revolution and sophisticated mobile technologies and as well as due to reasonable data prices. Marketers started different strategies like Search Engine Optimization, Search Engine Marketing, Content Marketing, Data analytics to reach the customers in a better and speedy way. The present study mainly highlights about different digital marketing components taken care of by the marketers for better customer reach and influence. The study is purely conducted with the help of secondary data. KEYWORDS: Digital Marketing, Search Engine Optimization, Search Engine Marketing, search results

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach supports the automation of deep learning (DL) based networks such as convolutional neural networks (CNN). Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. However, the engineering process of NAS is often limited by the potential solutions in search space and the search strategy. This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. This study proposes a NAS model with a novel search space initialization algorithm and a new search strategy. We designed a block-based stochastic categorical-to-binary (BSCB) algorithm for generating potential CNN solutions into the search space. Also, we applied and investigated the performance of a new bioinspired optimization algorithm, namely the Ebola optimization search algorithm (EOSA), for the search strategy. The evaluation strategy was achieved through computation of loss function, architectural latency and accuracy. The results obtained using images from the BACH and BreakHis databases showed that our approach obtained best performing architectures with the top-5 of the architectures yielding a significant detection rate. The top-1 CNN architecture demonstrated a state-of-the-art performance of base on classification accuracy. The NAS strategy applied in this study and the resulting candidate architecture provides researchers with the most appropriate or suitable network configuration for using digital histopathology.

Multiarea Economic Dispatch Using Evolutionary Algorithms

Multiarea economic dispatch (MAED) is a vital problem in the present power system to allocate the power generation through dispatch strategies to minimize fuel cost. In economic dispatch, this power generation distribution always needs to satisfy the following constraints: generating limit, transmission line, and power balance. MAED is a complex and nonlinear problem and cannot be solved with classical techniques. Many metaheuristic methods have been used to solve economic dispatch problems. In this study, the dynamic particle swarm optimization (DPSO) and grey wolf optimizer (GWO) have been used to solve the MAED problem for single-area 3 generation units, a two-area system with four generating units, and four areas with 40-unit system. The hunting and social behaviors of grey wolves are implemented to obtain optimal results. During the optimization search, this algorithm does not require any information regarding the objective function’s gradient. The tunable parameters of the original PSO that are three parameters are dynamically controlled in this work that provides the efficient cost values in less execution time although satisfying all the MAED problem’s diverse constraints. In this study, the authors also implemented the GWO algorithm with two tunable parameters, and its execution is straightforward to implement for the MAED problem.

Statement and solution the problem of designing of water-tight bulkheads web frames for large container vessels by using DNV-GL rules requirements and optimization techniques

Поперечные переборки крупнотоннажных контейнерных судов представляют собой сложные конструкции, при проектировании которых обычно используется методология проверочного расчета на основе метода конечных элементов (МКЭ). Для создания конечно-элементной модели необходимо знать размеры всех элементов конструкций, входящих в состав переборки. Поэтому такой подход к проектированию является итерационным, что обуславливает высокую трудоемкость процесса проектирования. На ранних стадиях проектирования размеры конструкций поперечных переборок контейнеровоза могут быть быстро и достаточно точно оценены на основе аппарата нелинейного программирования, относительно простой модели составной (конструктивно-ортотропной) пластины и нормативных требований Правил классификационных обществ. Такой подход применяется в Российской практике при проектировании двойных конструкций типа двойное дно или понтон плавучего дока. В статье предложено решение задачи проектирования рамного набора поперечной переборки крупнотоннажного контейнеровоза на нагрузки от контейнеров, действующие на переборку при качке судна. Конструкция переборки приводится к условной модели «коффердамного» типа. Затем используется методика приведения составной «конструктивно-ортотропной» пластины к изотропной пластине с несколько иным соотношением сторон, но с такими же параметрами изгиба. Это позволяет применить существующие табличные данные для определения расчетных изгибающих моментов и перерезывающих сил, которые после аппроксимации представляются в виде полиномов – аналитических зависимостей. Показана постановка оптимизационно-поисковой задачи математического программирования. Целевая функция – характеристика массы рамного набора. Ограничения задачи формируются на основе нормативных требований DNV-GL и математических зависимостей модели составной пластины. Для решения задачи используется инструмент MS Excel «Поиск решения» Представлены результаты тестового проектного расчета применительно к конструкции крупнотоннажного контейнеровоз с контейнерной вместимостью 18 тыс. TEU. Сопоставление результатов проектирования с оригинальными расчетами фирмы – проектанта показали удовлетворительное соответствие. The transverse bulkheads of the large container vessels are complex structures that are commonly designed using the finite element method (FEM) verification methodology. To create a finite element model, it is necessary to know the dimensions of all structural elements of the bulkhead. Therefore, this approach to design is iterative, which leads to a high complexity of the design process. At the early stages of design, the dimensions of the structures of the transverse bulkheads of a container vessel can be quickly and accurately estimated based on the nonlinear programming technique, a relatively simple model of a composite (structural-orthotropic) plate, and the regulatory requirements of the Rules of Classification Societies. This approach is used in practice in Russia when designing double structures such as a double bottom or pontoon of a floating dock. The article proposes a solution to the problem of the transverse bulkhead web frames designing in application to a large-tonnage container vessel for loads of containers acting on the bulkhead when the vessel is moving on the waves. The bulkhead structure is reduced to the conditional "cofferdam" type model. The technique is used to reduce a composite "structurally-orthotropic" plate to an isotropic one with a slightly different aspect ratio, but with the same bending parameters. This allows applying the existing tabular data to determine the design bending moments and shear forces, which, after approximation, are represented as polynomial analytical dependencies. The statement the optimization-search problem of mathematical programming is shown. The goal function is the characteristic of the bulkhead's webs mass. The constraints of the problem are formed on the DNV-GL regulatory requirements and mathematical relationships of the composite plate model. MS Excel tool "Solver" is used to solve the problem. The results of a test calculation are presented as applied to a large-capacity container ship with container capacity of 18000 TEU. Comparison of the design results with the original calculations of the designer’s company showed satisfactory agreement.

2.5D multifocusing imaging of crooked-line seismic surveys

Two-dimensional seismic surveys often are conducted along crooked line traverses due to the inaccessibility of rugged terrains, logistical and environmental restrictions, and budget limitations. The crookedness of line traverses, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could adversely affect the signal-to-noise ratio of the data. The crooked-line geometry violates the assumption of a straight-line survey that is a basic principle behind the 2D multifocusing (MF) method and leads to crossline spread of midpoints. Additionally, the crooked-line geometry can give rise to potential pitfalls and artifacts, thus, leads to difficulties in imaging and velocity-depth model estimation. We develop a novel multifocusing algorithm for crooked-line seismic data and revise the traveltime equation accordingly to achieve better signal alignment before stacking. Specifically, we present a 2.5D multifocusing reflection traveltime equation, which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects for normal, inline, and crossline dip moveouts simultaneously, which is significantly more accurate than removing these effects sequentially. Applying NMO, DMO, and CDMO separately tends to result in significant errors, especially for large offsets. The 2.5D multifocusing method can perform automatically with a coherence-based global optimization search on data. We investigated the accuracy of the new formulation by testing it on different synthetic models and a real seismic data set. Applying the proposed approach to the real data led to a high-resolution seismic image with a significant quality improvement compared to the conventional method. Numerical tests show that the new formula can accurately focus the primary reflections at their correct location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation. The proposed method efficiently projects and extracts valuable 3D structural information when applied to crooked-line seismic surveys.