Boundaries Of Different Regions Research Articles

Dynamic modeling and stability analysis of parafoil traction cable for high altitude wind power generation

In high altitude wind parafoil power generation (HAWPG), parafoil traction cable plays an important role. The cable, which is stretched by the parafoil, is prone to excite vibration even under small disturbances, which affects the stability of the entire HAWPG system. In view of this problem, this paper establishes parafoil traction cable dynamic model and analyzes the unstable regions of the cable parametrically excited vibration. The cable dynamic model, which has axial time-varying velocity and tension, is established based on Hamilton principle. Based on the cable ordinary differential equation, a second-order cable Mathieu–Hill differential equation can be obtained. Then, solve the vibration instability regions caused by changing parameters, the boundaries of different regions on the parameter plane can be directly determined by constructing the periodic solution. Finally, numerical examples and phase portraits are provided to verify the proposed method. The parameters, which includes the cable material properties, length and axial velocity, determined the boundedness of cable vibration. Therefore, the parafoil traction force on the cable should be set appropriately when determining the cable material properties to avoid the unstable vibration.

Holographic QFTs on AdSd, wormholes and holographic interfaces

We consider three related topics: (a) Holographic quantum field theories on AdS spaces. (b) Holographic interfaces of flat space QFTs. (c) Wormholes connecting generically different QFTs. We investigate in a concrete example how the related classical solutions explore the space of QFTs and we construct the general solutions that interpolate between the same or different CFTs with arbitrary couplings. The solution space contains many exotic flow solutions that realize unusual asymptotics, as boundaries of different regions in the space of solutions. We find phenomena like “walking” flows and the generation of extra boundaries via flow fragmentation.

Fast Generation of High-Fidelity RGB-D Images by Deep Learning With Adaptive Convolution

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning-based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules—the completion module, the refinement module, and the super-resolution module. The completion module is based on an architecture of encoder–decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for upsampling. Benefited from the adaptive convolution operators proposed in this article, our results outperform the existing deep-learning-based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high-fidelity RGB-D images can be generated efficiently at the rate of 22 frames/s. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —With the development of consumer-level RGB-D cameras, industries have started to employ these low-cost sensors in many robotic and automation applications. However, images generated by consumer-level RGB-D cameras are generally in low resolution. Moreover, the depth images often have incomplete regions when the surface of an object is transparent, highly reflective, or beyond the distance of sensing. With the help of our method, engineers are able to “repair” the images captured by consumer-level RGB-D cameras in high efficiency. As the typical deep-learning networks are employed in this approach, the proposed approach fits well with the GPU-based hardware architecture of deep-learning computation—therefore, it potentially can be integrated into the hardware of RGB-D cameras.

A Resonated and Synchrophased Three Beams Neutron Cancer Therapy Installation

Abstract The technique of composite region coupling by a neutron source at a common boundary of different regions that has been introduced in 2019 has allowed for an additive separation of variables neutron-density 3D wave analytical solution to the posing four-regional boundary value problem (BVP) of neutron cancer therapy (NCT). The three employable mutually orthogonal neutron beams, which may have different pulse shapes, have distinct modulation frequencies ω,ϖ, and ŵ and distinct relative time delays ε and ε̂. By employing this solution, we demonstrate in this paper how the therapeutic utility index and the ballistic index for this kind of dynamical NCT form a nonlinear optimization problem. Both of these indices are demonstrated to be remarkably periodically discontinuous in ε or ε̂, even in the neighborhood of the respective ε∗ or ε̂∗. As an extension of a result obtained also in 2019, for a certain lower-dimensional setup, a Pareto optimal control vector ω∗=(ω∗,ϖ∗,ε∗,ŵ∗,ε̂∗) is identified for this 3D problem. The existence of this vector paves the way toward what we call a “resonated and synchrophased three beams neutron cancer therapy (RASP-3BNCT) installation.”

An additive separation of variables 3D solution to a dynamical BVP for neutron cancer therapy

We study the boundary-value problem (BVP) for irradiation of a right parallelepipedal cancerous region in a (B/Gd) neutron cancer therapy (NCT) composite- region setup by three mutually orthogonal, time-modulated, one-speed neutron beams. The technique of composite region coupling by a neutron source at a common boundary of different regions is demonstrated to allow for an additive separation of variables (ASOV) regional neutron-density 3D wave solution to the posing four-regional BVP. The beams, which may have different pulse shapes, have different modulation frequencies and variable relative time delays. The obtained analytical solution is intended to facilitate future work on optimisation towards improving the therapeutic quality of the associated setup.

An additive separation of variables 3D solution to a dynamical BVP for neutron cancer therapy

We study the boundary-value problem (BVP) for irradiation of a right parallelepipedal cancerous region in a (B/Gd) neutron cancer therapy (NCT) composite- region setup by three mutually orthogonal, time-modulated, one-speed neutron beams. The technique of composite region coupling by a neutron source at a common boundary of different regions is demonstrated to allow for an additive separation of variables (ASOV) regional neutron-density 3D wave solution to the posing four-regional BVP. The beams, which may have different pulse shapes, have different modulation frequencies and variable relative time delays. The obtained analytical solution is intended to facilitate future work on optimisation towards improving the therapeutic quality of the associated setup.

A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation.

Image segmentation is considered as one of the most fundamental tasks in image processing applications. Segmentation of magnetic resonance (MR) brain images is also an important pre-processing step, since many neural disorders are associated with brain's volume changes. As a result, brain image segmentation can be considered as an essential measure toward automated diagnosis or interpretation of regions of interest, which can help surgical planning, analyzing changes of brain's volume in different tissue types, and identifying neural disorders. In many neural disorders such as Alzheimer and epilepsy, determining the volume of different brain tissues (i.e., white matter, gray matter, and cerebrospinal fluids) has been proven to be effective in quantifying diseases. A traditional way for segmenting brain images involves the use of a medical expert's experience in manually determining the boundary of different regions of interest in brain images. It may seem that manual segmentation of MR brain images by an expert is the first and the best choice. However, this method is proved to be time-consuming and challenging. Hence, numerous MR brain image segmentation methods with different degrees of complexity and accuracy have been introduced recently. Our work proposes an optimized thresholding method for segmentation of MR brain images using biologically inspired ant colony algorithm. In this proposed algorithm, textural features are adopted as heuristic information. Besides, post-processing image enhancement based on homogeneity is also performed to achieve a better performance. The empirical results on axial T1-weighted MR brain images have demonstrated competitive accuracy to traditional meta-heuristic methods, K-means, and expectation maximization.

Anderson localization in one-dimensional quasiperiodic lattice models with nearest- and next-nearest-neighbor hopping

We explore the reduced relative Shannon information entropies SR for a quasiperiodic lattice model with nearest- and next-nearest-neighbor hopping, where an irrational number is in the mathematical expression of incommensurate on-site potentials. Based on SR, we respectively unveil the phase diagrams for two irrationalities, i.e., the inverse bronze mean and the inverse golden mean. The corresponding phase diagrams include regions of purely localized phase, purely delocalized phase, pure critical phase, and regions with mobility edges. The boundaries of different regions depend on the values of irrational number. These studies present a more complete picture than existing works.

Handling routability in floorplan design with twin binary trees

As technology moves into the deep-submicron era, the complexities of VLSI circuits grow rapidly. Interconnect optimization has become an important concern. Most routability-driven floorplanners [H.M. Chen, H. Zhou, F.Y. Young, D.F. Wong, H.H. Yang, N. Sherwani, Integrated floorplanning and interconnect planning, in: Proceedings of IEEE International Conference on Computer-Aided Design, 1999, pp. 354–357; S. Krishnamoorthy, J. Lou, H.S. Sheng, Estimating routing congestion using probabilistic analysis, in: Proceedings of International Symposium on Physical Design, 2001, pp. 112–117; M. Wang, M. Sarrafzadeh, Modeling and minimization of routing congestion, in: IEEE Asia and South Pacific Design Automation Conference, 2000, pp. 185–190] use grid-based approach that divides a floorplan into grids as in global routing to estimate congestion by the expected number of nets passing through each grid. This approach is direct and accurate, but not efficient enough when dealing with complex circuits containing many nets. In this paper, an efficient and innovative interconnect-driven floorplanner using twin binary trees (TBT) representation [B. Yao, H. Chen, C.K. Cheng, R. Graham, Revisiting floorplan representations, in: Proceedings of International Symposium on Physical Design, 2001, pp. 138–143; E.F.Y. Young, C.C.N. Chu, Z.C. Shen, Twin binary sequences: a non-redundant representation for general non-slicing floorplan, in: Proceedings of International Symposium on Physical Design, 2002, pp. 196–201] is proposed. The estimations are based on the wire densities (number of wires passing through per unit length) on the half-perimeter boundaries of different regions in a floorplan. These regions are defined naturally by the TBT representation. Buffer planning is also considered by deciding if buffers can be inserted successfully for each net. In order to increase the efficiency of our floorplanner, a fast algorithm for the least common ancestor (LCA) problem in Bender and Farach-Colton [The LCA problem revisited, in: Latin American Theoretical INformatics, 2000, pp. 88–94] is used to compute wire density, and a table look-up approach is used to obtain the buffer insertion information. Experimental results show that our floorplanner can reduce the number of unroutable wires. The performance is comparable with other interconnect-driven floorplanners that perform global routing-like operations directly to estimate routability, but our estimation method is much faster and is scalable for large complex circuits.

Simplified Calculation of Mixing and Suspending Processes in Agitated Vessels – Part I: Basis of The Model

AbstractMixing processes with different stirring systems in agitated vessels are simulated by a multi‐region model. Because of discontinuities at the boundaries of different regions appearing for the already‐existing simple three‐region model, unrealistic values occur during the calculation of the concentration distribution at these locations. Therefore, the region model will be modified at first. The procedure is presented here for the example of a radial flow stirrer, considering the fluid velocity, the turbulence field, the particle diffusion, and the relative fluid‐to‐particle velocity. The modified model is discussed and validated. Thus, the behavior of single particles and particle swarms can be analyzed in the vessel considering different aspects.

Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images.

Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We present an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior information. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. Structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the maximum a posteriori probability estimation problem. We demonstrate the approach on 20 brain magnetic resonance images showing superior performance, particularly in cases where purely image based methods fail.

Recolouring digital textile printing design with high fidelity

An algorithm of recolouring of polychromatic digital textile printing images is proposed based on the investigation of texture and colour distribution in different channels and image segmentation. A polychromatic image is first separated into monochromatic regions based on watershed transformation in CIELAB colour space. The initial markers are selected by hierarchical histogram analysis to eliminate the inherent drawbacks of over‐segmentation in the watershed algorithm. Then the individual monochromatic regions can be mapped with different colours to obtain desirable designs. The artefacts in the boundaries of different regions are reduced by a technique of colour mixing through Gaussian blurring. The experimental simulation results indicated that the performance of the algorithm was quite good in both texture and colour fidelity.

Path Feasibility and Modification Based on the PUMA 560 Workspace Analysis

Robot paths are planned according to different industrial tasks. Their kinematic feasibility is restrained by the structure of the given robot manipulator. In order to design feasible paths under kinematic constraints and different task requirements, we propose first to utilize the geometry of the given robot to generate the geometric boundaries of different regions corresponding to kinematic constraints in the robot workspace. Geometric expressions are then derived to describe the relationship about the planned path and the robot workspace. Finally, by applying the developed modification strategies based on different task requirements, feasible paths can be obtained by modifying the infeasible portions of the paths. To demonstrate the proposed feasibility and modification schemes, the PUMA 560 robot manipulator is selected as a case study due to its complexity and practical application. The results are then extended to general wrist-partitioned types of industrial robot manipulators.