Motion Planning of Humanoid Robot for Obstacle Negotiation

The motion planning for obstacle negotiation by humanoid robot BHR-2 through stepping over or stepping on/off the wide and flat obstacle at known locations is presented. In the trajectory generation method, first the constraints of the foot motion parameters which include obstacle dimensions and the distance of obstacle from the humanoid robot is formulated. By varying the values of the constraint parameters, different types of foot motion for different obstacles can be produced. In this method, first the foot trajectory is generated, and then the waist trajectory is computed by using cubic spline interpolation without first calculating the zero moment point (ZMP) trajectory . The dynamic stability during the execution of stepping over and stepping on/off trajectories are ensured by incorporating the ZMP criterion. The effectiveness of the proposed method is confirmed by simulations and experiments on humanoid robot BHR-2.     

Application of three unsupervised neural network models to singular nonlinear BVP of transformed 2D Bratu equation

In this paper, numerical techniques are developed for solving two-dimensional Bratu equations using different neural network models optimized with the sequential quadratic programming technique. The original two-dimensional problem is transformed into an equivalent singular, nonlinear boundary value problem of ordinary differential equations. Three neural network models are developed for the transformed problem based on unsupervised error using log-sigmoid, radial basis and tan-sigmoid functions. Optimal weights for each model are trained with the help of the sequential quadratic programming algorithm. Three test cases of the equation are solved using the proposed schemes. Statistical analysis based on a large number of independent runs is carried out to validate the models in terms of accuracy, convergence and computational complexity.     

Laser treatment of aluminum surface: Analysis of thermal stress field in the irradiated region

Laser surface treatment of aluminum is considered and the temperature as well as the stress fields developed in the laser irradiated region are predicted using the finite element method (FEM). The predictions are obtained for two laser pulses with different pulse lengths. In the simulations, the variable thermal properties of the substrate material are used. The experiment is conducted to treat the aluminum specimen surface with the laser beam. The laser output pulse intensity consists of repetitive pulses, which are used in the model study to examine the metallurgical changes in the irradiated region. SEM and XRD are carried out in this regard. It is found that the von-Mises stress reaches the maximum in the surface vicinity, particularly at the onset of cooling cycle starts. The von-Mises stress attains values less than the critical values for the crack formation, which is particularly true after the end of the cooling cycle. The residual stress formed in the surface region is in the order of a few MPa.     

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the average of the feature engineering approaches includes: Recursive Feature Eliminator (RFE), Extreme Gradient Boosting (XGboost), Random Forest (RF), and are upgraded to extract the most relevant and significant features. To this end, we have proposed the DensetNet-121 network and Support Vector Machine (SVM) ensemble with Aquila Optimizer (AO) to ensure adaptability and handle the complexity of data in the classification. Further, the AO method helps to tune the parameters of DensNet (121 layers) and SVM, which achieves less training loss, computational time, minimized overfitting problems and more training/test accuracy. Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes. Our proposed method has achieved a minimal value of the Mean Average Percentage Error (MAPE) rate i.e., 8% by DenseNet-AO and 6% by SVM-AO and the maximum accurateness rate of 92% and 95%, respectively.     

Multi Sensor-Based Implicit User Identification

Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner’s personal information and services against the stored passwords. As a result of this potential scenario, this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors. A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features, then further optimized using a non-linear unsupervised feature selection method. The selected features create an underlying gait biometric representation able to discriminate among individuals and identify them uniquely. Different classifiers are adopted to achieve accurate legitimate user identification. Extensive experiments on a group of 16 individuals in an indoor environment show the effectiveness of the proposed solution: with 5 to 70 samples per window, KNN and bagging classifiers achieve 87–99% accuracy, 82–98% for ELM, and 81–94% for SVM. The proposed pipeline achieves a 100% true positive and 0% false-negative rate for almost all classifiers.     

Numerical Analysis of Novel Coronavirus (2019-nCov) Pandemic Model with Advection

Recently, the world is facing the terror of the novel corona-virus, termed as COVID-19. Various health institutes and researchers are continuously striving to control this pandemic. In this article, the SEIAR (susceptible, exposed, infected, symptomatically infected, asymptomatically infected and recovered) infection model of COVID-19 with a constant rate of advection is studied for the disease propagation. A simple model of the disease is extended to an advection model by accommodating the advection process and some appropriate parameters in the system. The continuous model is transposed into a discrete numerical model by discretizing the domains, finitely. To analyze the disease dynamics, a structure preserving non-standard finite difference scheme is designed. Two steady states of the continuous system are described i.e., virus free steady state and virus existing steady state. Graphical results show that both the steady states of the numerical design coincide with the fixed points of the continuous SEIAR model. Positivity of the state variables is ensured by applying the M-matrix theory. A result for the positivity property is established. For the proposed numerical design, two different types of the stability are investigated. Nonlinear stability and linear stability for the projected scheme is examined by applying some standard results. Von Neuman stability test is applied to ensure linear stability. The reproductive number is described and its pivotal role in stability analysis is also discussed. Consistency and convergence of the numerical model is also studied. Numerical graphs are presented via computer simulations to prove the worth and efficiency of the quarantine factor is explored graphically, which is helpful in controlling the disease dynamics. In the end, the conclusion of the study is also rendered.     

Probing the stabilizing role of C-terminal residues in trimethylamine dehydrogenase

In trimethylamine dehydrogenase, a homodimeric iron-sulfur flavoprotein,the C-terminal 17 residues of each subunit (residues 713- 729) embraceresidues on the other subunit. The role of this unusual mode of interactionat the subunit interface was probed by isolating three mutant forms oftrimethylamine dehydrogenase in which the C- terminus of the enzyme wasdeleted by five residues [delta(725-729], 10 residues [delta(720-729)] and17 residues [delta(713-729)]. The solution properties and conformationalstates of the three mutant enzymes were investigated using optical,fluorescence and circular dichroism spectroscopies, ANS binding and a noveland conformationally sensitive hydrodynamic method. The data reveal thatsequential deletion of the C-terminus of trimethylamine dehydrogenase doesnot affect significantly dimer stability or the overall structuralintegrity of the enzyme. However, deletion of the C-terminus severelycompromises, but does not abolish, the ability of the enzyme to becomecovalently coupled with the redox cofactor FMN in the active site, locatedover 20 A from the C-terminus. Hydrodynamic studies reveal minorconformational changes in the deletion mutants that lead to a more compactenzyme structure. These conformational changes are probably transmitted tothe active site via altering the interaction of the C-terminus with thesecond helix in the beta/alpha barrel of trimethylamine dehydrogenase,leading to poor flavinylation during the folding of the enzyme and assemblywith FMN.     

Controlled synthesis of bilayer graphene on nickel

ABSTRACT: We report uniform and low-defect synthesis of bilayer graphene on evaporated polycrystalline nickel films. We use atmospheric pressure chemical vapor deposition with ultra-fast substrate cooling after exposure to methane at 1000C. The optimized process parameters i.e. growth-time, annealing profile and flow rates of various gases are reported. By using Raman spectroscopy mapping, the ratio of 2D to G peak intensities (I2D/IG) is in the 0.9-1.6 range over 96 percent of 200umx200um area. Moreover, the average ratio of D to G peak intensities (ID/IG) is about 0.1.     

Effect of processing technique on the transport and mechanical properties of graphite nanoplatelet/rubbery epoxy composites for thermal interface applications

Graphite nanoplatelet (GNP)/rubbery epoxy composites were fabricated by mechanical mixer (MM) and dual asymmetric centrifuge speed mixer (SM). The properties of the GNP/rubbery epoxy were compared with GNP/glassy epoxy composites. The thermal conductivity of GNP/rubbery epoxy composite (25 wt.% GNP, particle size 15 μm) reached 2.35 W m⁻¹ K⁻¹ compared to 0.1795 W m⁻¹ K⁻¹ for rubbery epoxy. Compared with GNP/rubbery epoxy composite, at 20 wt.%, GNP/glassy epoxy composite has a slightly lower thermal conductivity but an electrical conductivity that is 3 orders of magnitude higher. The viscosity of rubbery epoxy is 4 times lower than that of glassy epoxy and thus allows higher loading. The thermal and electrical conductivities of composites produced by MM are slightly higher than those produced by SM due to greater shearing of GNPs in MM, which results in better dispersed GNPs. Compression and hardness testing showed that GNPs increase the compressive strength of rubbery epoxy ∼2 times without significantly affecting the compressive strain and hardness. The GNP/glassy epoxy composites are 40 times stiffer than the GNP/rubbery epoxy composites. GNP/rubbery epoxy composites with their high thermal conductivity, low electrical conductivity, low viscosity before curing and high conformability are promising thermal interface materials.