Accelerated Reduced Gradient Algorithm with Constraint Relaxation in Differential Inverse Kinematics

The Moore-Penrose pseudoinverse-based solution of the differential inverse kinematic task of redundant robots corresponds to the result of a particular optimization underconstraints in which the implementation of Lagrange’s ReducedGradient Algorithm can be evaded simply by considering the zero partial derivatives of the ”Auxiliary Function” associated with this problem. This possibility arises because of the fact that the cost term is built up of quadratic functions of the variable of optimization while the constraint term is linear function of the same variables. Any modification in the cost and/or constraint structure makes it necessary the use of the numerical algorithm. Anyway, the penalty effect of the cost terms is always overridden by the hard constraints that makes practical problems in the vicinity of kinematic singularities where the possible solution stillexists but needs huge joint coordinate time-derivatives. While in the special case the pseudoinverse simply can be deformed, inthe more general one more sophisticated constraint relaxation can be applied. In this paper a formerly proposed acceleratedtreatment of the constraint terms is further developed by the introduction of a simple constraint relaxation. Furthermore, thenumerical results of the algorithm are smoothed by a third order tracking strategy to obtain dynamically implementable solution.The improved method’s operation is exemplified by computation results for a 7 degree of freedom open kinematic chain

An analysis of stochastic variance reduced gradient for linear inverse problems

Stochastic variance reduced gradient (SVRG) is a popular variance reduction technique for stochastic gradient descent (SGD). We provide a first analysis of the method for solving a class of linear inverse problems in the lens of the classical regularization theory. We prove that for a suitable constant step size schedule, the method can achieve an optimal convergence rate in terms of the noise level (under suitable regularity condition) and the variance of the SVRG iterate error is smaller than that by SGD. These theoretical findings are corroborated by a set of numerical experiments.

Couching the brain's multiple-demand and language-specific systems within a macroscale gradient of cortical connectivity

The cerebrum comprises a set of specialised systems that tile across the cortical sheet, forming a tapestry-like configuration. For example, the multiple-demand and language-specific systems occupy largely separate neural estates and exhibit disparate functional profiles. Although delimiting the boundary between systems informs where cortical sheet functionally fractionates, it remains unclear why different systems' topographical placements are spatially configured in typical manners and how a macroscale architecture arises from this topography. Novel approaches have tackled this challenge by condensing the topography into a principal gradient, which represents the workflow of information processing from sensory-motoric to abstract-cognitive. To understand how the multiple-demand and language-specific systems are accommodated in the gradient framework, here we used fMRI to probe cognitive operations in semantic vs. visuospatial domains and projected functional activities onto the principal gradient. We found that the two systems showed distinct trajectories of distribution along gradient tiers, suggesting different roles in the transition from sensation to cognition. Critically, when semantic processing became difficult, the brain recruited a specialised 'semantic-control' system that was a functional and anatomical 'hybrid' juxtaposed between the multi-demand and language systems. We discuss how the brain's modular division can be better understood through the lens of a dimensionality-reduced gradient-like architecture.

PERFORMANCE INDEX MODEL OF GROUNDWATER IRRIGATION SYSTEMS

This research will develop a groundwater irrigation system performance index model with the aim of identifying the groundwater irrigation system performance index; this information can be used by stakeholders to determine management steps. The research location is in Gunungkidul Regency and includes surrounding areas — acknowledging that the karst aquifer has complex characteristics and non-karst aquifer, namely high heterogeneity as a result of the formation of a groundwater flow system through fractures which eventually becomes completely underground runoff. Screening for the variables was carried out using the smart-PLS (Partial Least Square) tool, which was then analyzed using the GRG (Generalized Reduced Gradient) method which is useful for solving non-linear equations. In this research, it examines the physical aspects, social aspects and management aspects as variables. The groundwater irrigation system performance index model examines 3 (three) variables, namely physical aspects, social aspects, and management aspects, then 11 (eleven) dimensions and 42 (forty two) indicators. The analysis using PLS SEM using smart PLS tools determined that the 3 (three) variables, 11 (eleven) dimensions and 30 (thirty) indicators are interrelated and effective. Whereas by using GRG (Generalized Reduced Gradient) analysis with the solver tool in Microsoft Excel, the most influential weights were obtained from the physical aspects, namely physical infrastructure (0.5782), geological conditions (0.2311), water quality (0.1286) and recharge area conditions (0.0475); the social aspects that obtained the most influential weight are socio-cultural (0.7471) and economy (0.2529); the management aspects that obtained the most influential weights are budgeting (0.2534), plant productivity (0.2270), WUA organizational conditions (0.2090), JIAT management organizational conditions (0.1987) and spatial planning directives (0.0674). In general, the weight of the influence of groundwater irrigation performance for these three aspects is 0.6686 physical aspects, 0.0856 social aspects and 0.2458 management aspects which are formulated into a performance index model for groundwater irrigation systems "Kautsar", namely IL = 0.6686 physical IL + 0.0856 social IL + 0.2458 IL management. For development, further research is needed on the performance index model of the groundwater irrigation system using Geography Information System (GIS) and a software application on android, iOS, or windows operation systems. A groundwater irrigation system performance index that consists of these three aspects is unique and has never been assembled in previous studies; it conveniently allow the user to determine survey results immediately.

Penerapan Model Exponential Smoothing berbasis Metode Evolutionary pada Kasus COVID-19 dan DBD di Bojonegoro

Latar Belakang: COVID-19 menjadi perhatian utama di Bojonegoro karena kasus terinfeksi meningkat sampai akhir tahun 2020. Selain itu, wabah demam berdarah dengue (DBD) juga perlu diantisipasi di musim penghujan agar tidak meningkat bersamaan dengan wabah COVID-19.Tujuan: Mengembangkan model exponential smoothing berbasis metode evolutionary untuk meramalkan banyaknya kasus terinfeksi COVID-19 dan DBD di Bojonegoro.Metode: Penelitian diawali dengan pembuatan aplikasi peramalan model exponential smoothing dengan metode evolutionary dan pemrograman Visual Basic yang dikembangkan di Excel dan Solver. Koefisien-koefisien model dioptimasi secara iteratif dengan metode evolutionary dan metode generalized reduced gradient. Model tersebut dievaluasi kinerjanya dengan nilai mean absolute percentage error (MAPE), mean absolute deviation (MAD), dan mean squared error (MSE). Sumber data penelitian menggunakan data sekunder dari Dinas Kesehatan Bojonegoro yang berisi data harian kasus terinfeksi COVID-19 dan data bulanan kasus DBD.Hasil: Model double exponential smoothing berbasis metode generalized reduced gradientmenghasilkan kesalahan model peramalan yang lebih kecil untuk nilai MAPE, MAD, dan MSE. Hasil peramalan menunjukkan bahwapeningkatan terjadi pada periode ke depan untuk kasus terinfeksi COVID-19 yang lebih besar dibandingkan DBD.Kesimpulan: Aplikasi peramalan model exponential smoothing dapat menjadi altenatif dalam meramalkan banyaknya kasus terinfeksi COVID-19 dan DBD di Bojonegoro.

Optimisation of Offshore Structures Decommissioning – Cost Considerations

The petroleum industry is currently being faced with a growing number of ageing offshore platforms that are no longer in use and require to be decommissioned. Offshore decommissioning is a complex venture, and such projects are expected to cost the industry billions of dollars in the next two decades. Early knowledge of decommissioning cost is important to platform owners who bear the asset retirement obligation. However, obtaining the cost estimate for decommissioning an offshore platform is a challenging task that requires extensive structural and economic studies. This is further complicated by the existence of several decommissioning options such as complete and partial removal. In this paper, project costs for decommissioning 23 offshore platforms under three different scenarios are estimated using information from a publicly available source which only specified the costs of completely removing the platforms. A novel mathematical model for predicting the decommissioning cost for a platform based on its features is developed. The development included curve-fitting with the aid of generalised reduced gradient tool in Excel® Solver and a training dataset. The developed model predicted, with a very high degree of accuracy, platform decommissioning costs for four (4) different options under the Pacific Outer Continental Shelf conditions. Model performance was evaluated by calculating the Mean Absolute Percentage Error of predictions using a test dataset. This yielded a value of about 6%, implying a 94% chance of correctly predicting decommissioning cost.