Green’s Function Technique and Global Optimization in Reconstruction of Elliptic Objects in the Regular Triangle

The reconstruction problem for elliptic voids located in the regular (equilateral) triangle is studied. A known point source is applied to the boundary of the domain, and it is assumed that the input data is obtained from the free-surface input data over a certain finite-length interval of the outer boundary. In the case when the boundary contour of the internal object is unknown, we propose a new algorithm to reconstruct its position and size on the basis of the input data. The key specific character of the proposed method is the construction of a special explicit-form Green's function satisfying the boundary condition over the outer boundary of the triangular domain. Some numerical examples demonstrate good stability of the proposed algorithm.

Measures for the Optimization of Subgrade and Pavement Design in the Reconstruction and Expansion of Highways

In comparison to the construction of modern highway engineering,several of China’s early pavement construction concerns,such as pavement collapse,are rather clear.Limited by historical and technical factors,the subgrade and pavement design for highways lacks scientificity,thus inducing potential safety problems in the operation.In order to comprehensively improve the subgrade and pavement design as well as ensure the quality and safety of highway engineering projects,this paper takes the reconstructed and expanded highway projects as research subjects and focuses on proposing optimization measures for the subgrade and pavement design of reconstructed and expanded highways,so as to provide adequate reference.

PARALLEL IMPLEMENTATION AND OPTIMIZATION OF THE SEBVHOS ALGORITHM

In this paper, a parallel Surface Extraction from Binary Volumes with Higher-Order Smoothness (SEBVHOS) algorithm is proposed to accelerate the SEBVHOS execution. The original SEBVHOS algorithm is parallelized first, and then several performance optimization techniques which are loop optimization, cache optimization, false sharing optimization, synchronization overhead op-timization, and thread affinity optimization, are used to improve the implementation's performance on multi-core systems. The performance of the parallel SEBVHOS algorithm is analyzed on a dual-core system. The experimental results show that the parallel SEBVHOS algorithm achieves an average of 1.86x speedup. More importantly, our method does not come with additional aliasing artifacts, com-paring to the original SEBVHOS algorithm.

Application of Reconstruction and Optimization Algorithms in Optical Tomography

Optical tomography is a non-invasive technique that uses visible or near infrared radiation to analyze biological tissues. Researchers take immense attention towards advancement in optical tomography because of its low cost and an advantage of providing anatomical information. Based on the information of optical characteristics, forward and inverse problem of tomography are solved. In this research, finite element method is employed for forward problem and gradient-based optimization algorithm is developed for inverse problem of optical tomography. It is found from simulations that information about imaging is processed more distinctly and in less computational time. Normal and abnormal conditions in imaging are readily distinguished.?Simulations are carried out in Matlab. Different scenarios are developed and are simulated to validate the performance of reconstruction and optimization algorithms in optical tomography.

Impacts of sample size for stomach content analysis on the estimation of ecosystem indices

This study used Ecopath model of the Jiaozhou Bay as an example to evaluate the effect of stomach sample size of three fish species on the projection of this model. The derived ecosystem indices were classified into three categories:(1) direct indices, like the trophic level of species, influenced by stomach sample size directly;(2)indirect indices, like ecology efficiency(EE) of invertebrates, influenced by the multiple prey-predator relationships;and(3) systemic indices, like total system throughout(TST), describing the status of the whole ecosystem. The influences of different stomach sample sizes on these indices were evaluated. The results suggest that systemic indices of the ecosystem model were robust to stomach sample sizes, whereas specific indices related to species were indicated to be with low accuracy and precision when stomach samples were insufficient.The indices became more uncertain when the stomach sample sizes varied for more species. This study enhances the understanding of how the quality of diet composition data influences ecosystem modeling outputs. The results can also guide the design of stomach content analysis for developing ecosystem models.

Variation and optimization of acid-dissolved aluminum content in stainless steel

As a key step in secondary refining, the deoxidation process in clean stainless steel production is widely researched by many scholars. In this study, vacuum oxygen decarburization（VOD） deoxidation refining in a 40-t electric arc furnace ＋ VOD ＋ ingot casting process was analyzed and optimized on the basis of Al deoxidation of stainless steel and thermodynamic equilibrium reactions between the slag and steel. Under good stirring conditions in VOD, the deoxidation reaction reaches equilibrium rapidly, and the oxygen activity in the bulk steel is controlled by the slag composition and Al content. A basicity of 3–5 and an Al content greater than 0.015wt% in the melt resulted in an oxygen content less than 0.0006wt%. In addition, the dissolved oxygen content decreased slightly when the Al content in the steel was greater than 0.02wt%. Because of the equilibrium of the Si–O reaction between the slag and steel, the activity of SiO2 will increase while the Si content increases; thus, the Si content should be lowered to enable the formation of a high-basicity slag. A high-basicity, low-Al2O3 slag and an increased Si content will reduce the Al consumption caused by SiO2 reduction.

Reconstruction and stability of Fe_(3)O_(4)(001)surface:An investigation based on particle swarm optimization and machine learning

Magnetite nanoparticles show promising applications in drug delivery,catalysis,and spintronics.The surface of magnetite plays an important role in these applications.Therefore,it is critical to understand the surface structure of Fe_(3)O_(4)at atomic scale.Here,using a combination of first-principles calculations,particle swarm optimization(PSO)method and machine learning,we investigate the possible reconstruction and stability of Fe_(3)O_(4)(001)surface.The results show that besides the subsurface cation vacancy(SCV)reconstruction,an A layer with Fe vacancy(A-layer-V_(Fe))reconstruction of the(001)surface also shows very low surface energy especially at oxygen poor condition.Molecular dynamics simulation based on the iron–oxygen interaction potential function fitted by machine learning further confirms the thermodynamic stability of the A-layer-V_(Fe)reconstruction.Our results are also instructive for the study of surface reconstruction of other metal oxides.

Thermodynamic Analysis and Optimization of the C3/MRC Liquefaction Process

In the natural gas liquefaction process,the mixed refrigerant natural gas liquefaction process is widely used in LNG liquefaction plants because of its advantages of low energy consumption.This paper focuses on the influences of important parameters in the C3/MRC liquefaction process,that is,the comparison between propane precooling temperature and the number of moles of methane in mixed refrigerant,power consumption and loss.In addition,the total process was optimized with the optimizer and manual adjustment in HYSYS software to minimize the total power consumption.The results show that with increasing propane precooling temperature,the propane flow rate is almost unchanged,while the mixed refrigerant flow rate decreases significantly,and the loss of the heat exchanger increases significantly.The power consumption of the propane precooling cycle and hybrid refrigeration cycle increases with increasing methane content in the refrigerant,so the power consumption of the whole process increases accordingly.The effect of the methane content in the mixed refrigerant on the process evaluation index is more significant than that of the propane precooling temperature.

Optimization of Extraction Technology and Determination of Content of Total Phenols of Leaves in Acanthopanax giraldii Harms

[Objectives] To determine the optimum extraction technology for total phenols of leaves in Acanthopanax giraldii Harms.[Methods]The single factor test and orthogonal design were adopted to study the extraction technology for total phenols of leaves in Acanthopanax giraldii Harms. [Results]The optimum extraction conditions were determined as follows: extraction solvent( 70% methanol),solid-liquid ratio( 1∶ 35),and reflux extraction time( 60 min). Under these conditions,the yield of crude extract from total phenols of leaves in Acanthopanax giraldii Harms was 38. 67 mg/g.[Conclusions] The technology was simple,and of low cost and high extraction rate,suitable for the extraction of total phenols of leaves in Acanthopanax giraldii Harms.

Investigation and optimization of sampling characteristics of light field camera for flame temperature measurement

It is essential to investigate the light field camera parameters for the accurate flame temperature measurement because the sampling characteristics of the flame radiation can be varied with them. In this study, novel indices of the light field camera were proposed to investigate the directional and spatial sampling characteristics of the flame radiation. Effects of light field camera parameters such as focal length and magnification of the main lens, focal length and magnification of the microlens were investigated. It was observed that the sampling characteristics of the flame are varied with the different parameters of the light field camera. The optimized parameters of the light field camera were then proposed for the flame radiation sampling. The larger sampling angle(23 times larger) is achieved by the optimized parameters compared to the commercial light field camera parameters. A non-negative least square(NNLS) algorithm was used to reconstruct the flame temperature. The reconstruction accuracy was also evaluated by the optimized parameters. The results suggested that the optimized parameters can provide higher reconstruction accuracy for axisymmetric and non-symmetric flame conditions in comparison to the commercial light field camera.

Effect of Zr content on crack formation and mechanical properties of IN738LC processed by selective laser melting

Two batches of commercial IN738LC alloy powders with different Zr contents were printed under the same parameters.The influences of Zr content(0.024 wt.% and 0.12 wt.%,respectively) in powders on crack density,distribution,formation mechanism and mechanical properties of selective laser melting(SLM)-treated parts were systematically studied.It was found that the crack density(area ratio) increases from 0.15% to 0.87% in the XOY plane and from 0.21% to 1.81% in the XOZ plane along with the Zr content increase from 0.024 wt.% to 0.12 wt.% in the original powders.Solidification cracks are formed along the epitaxially grown <001>-oriented columnar grain boundaries in molten pool center.The ultimate tensile strength of Sample 1(0.024 wt.% Zr) is 1113 MPa,and there are dimples in tensile fracture.With an increase in the Zr content to 0.12 wt.%(Sample 2),the ultimate tensile strength of Sample 2 decreases to 610 MPa,and there are numerous original cracks and exposed columnar grain boundaries in tensile fracture.The optimization of printing parameters of Sample 2 considerably increases the ultimate tensile strength by 55.2% to 947 MPa,and the plasticity is greatly improved.

Content Addressable Storage Optimization for Desktop Virtualization Based Disaster Backup Storage System

This paper proposes a content addres sable storage optimization method, VDeskCAS, for desktop virtualization storage based disaster backup storage system. The method implements a blocklevel storage optimization, by employing the algorithms of chunking image file into blocks, the blockffmger calculation and the block dedup li cation. A File system in Use Space （FUSE） based storage process for VDeskCAS is also introduced which optimizes current direct storage to suit our content addressable storage. An interface level modification makes our system easy to extend. Experiments on virtual desktop image files and normal files verify the effectiveness of our method and above 60% storage volume decrease is a chieved for Red Hat Enterprise Linux image files. Key words： disaster backup; desktop virtualization; storage optimization; content addressable storage

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

The forward and inverse problem of cardiac magnetic fields based on concentric ellipsoid torso-heart model

This paper constructs a concentric ellipsoid torso-heart model by boundary element method and investigates the impacts of model structures on the cardiac magnetic fields generated by both equivalent primary source--a current dipole and volume currents. Then by using the simulated magnetic fields based on torso-heart model as input, the cardiac current sources--an array of current dipoles by optimal constrained linear inverse method are constructed. Next, the current dipole array reconstruction considering boundaries is compared with that in an unbounded homogeneous medium. Furthermore, the influence of random noise on reconstruction is also considered and the reconstructing effect is judged by several reconstructing parameters.

Optimized CUDA Implementation to Improve the Performance of Bundle Adjustment Algorithm on GPUs

The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.

Reconstruction of B-spline surface by interpolating boundary curves and approximating inner points

In this paper, we present an algorithm for reconstruction of B-spline surface such that it interpolates the four given bound- ary curves and simultaneously approximates some given inner points. The main idea of our method is： first, we construct an initial surface which interpolates the four given boundary curves; then, while keeping the boundary control points of the initial surface un- changed, we reposition the inner control points of the surface with energy optimization method. Examples show that our algorithm is practicable and effective.

Reconstruction of symmetric models composed of analytic curves and surfaces from point cloud

This paper presents a method to reconstruct symmetric geometric models from point cloud with inherent symmetric structure. Symmetry types commonly found in engineering parts, i.e., translational, reflectional and rotational symmetries are considered. The reconstruction problem is formulated as a constrained optimization, where the objective function is the sum of squared distances of points to the model, and constraints are enforced to keep geometric relationships in the model. First, the explicit representations of symmetric models are presented. Then, by using the concept of parameterized points (where the coor-dinate components are represented as functions rather than constants), the distances of points to symmetric models are deduced. With these distance functions, symmetry information, for both 2D and 3D models, is uniformly represented in the process of reconstruction. The constrained optimization problem is solved by a standard nonlinear optimization method. Owing to the explicit representation of symmetry information, the computational complexity of our method is reduced greatly. Finally, examples are given to demonstrate the application of the proposed method.

NDN Content Poisoning Mitigation Using Bird Swarm Optimization and Trust Value

Information-Centric Networking(ICN)is considered a viable strategy for regulating Internet consumption using the Internet’s underlying architecture.Although Named Data Networking(NDN)and its reference-based implementa-tion,the NDN Forwarding Daemon(NFD),are the most established ICN solu-tions,their vulnerability to the Content Poisoning Attack(CPA)is regarded as a severe threat that might dramatically impact this architecture.Content Poisoning can significantly minimize the impact of NDN’s universal data caching.Using verification signatures to protect against content poisoning attacks may be imprac-tical due to the associated costs and the volume of messages sent across the net-work,resulting in high computational costs.Therefore,in this research,we designed a method in NDN called Bird Swarm Optimization Algorithm-Based Content Poisoning Mitigation(BSO-Content Poisoning Mitigation Scheme).By aggregating the security information of entire routers along the full path,this sys-tem introduces the BSO to explore the secure transmission path and alter the con-tent retrieval procedure.Meanwhile,based on the determined trustworthy value of each node,the BSO-Content Poisoning Mitigation Scheme can bypass malicious routers,preventing them from disseminating illicit content in the future.Addition-ally,the suggested technique can minimize content poisoning utilizing removing erroneous Data packets from the cache-store during the pathfinding process.The proposed method has been subjected to extensive analysis compared with the ROM scheme and improved performance justified in several metrics.BSO-Con-tent Poisoning Mitigation Scheme is more efficient and faster than the ROM tech-nique in obtaining valid Data packets and resulting in a higher good cache hit ratio in a comparatively less amount of time.

On recommendation-aware content caching for 6G:An artificial intelligence and optimization empowered paradigm

Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.