RESEARCH ON THE PACKING ALGORITHM FOR ANTI-SEU OF FPGA BASED ON TRIPLE MODULAR REDUNDANCY AND THE NUMBERS OF FAN-OUTS OF THE NET

Static Random Access Memory(SRAM) based Field Programmable Gate Array(FPGA) is widely applied in the field of aerospace, whose anti-SEU(Single Event Upset) capability becomes more and more important. To improve anti-FPGA SEU capability, the registers of the circuit netlist are tripled and divided into three categories in this study. By the packing algorithm, the registers of triple modular redundancy are loaded into different configurable logic block. At the same time, the packing algorithm considers the effect of large fan-out nets. The experimental results show that the algorithm successfully realize the packing of the register of Triple Modular Redundancy(TMR). Comparing with Timing Versatile PACKing(TVPACK), the algorithm in this study is able to obtain a 11% reduction of the number of the nets in critical path, and a 12% reduction of the time delay in critical path on average when TMR is not considered. Especially, some critical path delay of circuit can be improved about 33%.

Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

GNSS spoofing detection based on uncultivated wolf pack algorithm

In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.

Array Antenna Pattern Synthesis Based on Selective Levy Flight Culture Wolf Pack Algorithm

Due to the shortcomings such as the premature convergence and the bad local optimal searching capability in traditional intelligence methods for pattern synthesis,a new type of wolf pack algorithm named Levy⁃Cultural Wolf Pack Algorithm(LCWPA)was designed on the basis of the Cultural Wolf Pack Algorithm(CWPA),which obeys the selective Levy flight.Because of the good overall management ability provided by the cultural algorithm in optimization process and the characteristics of excellent population diversity brought by Levy flight,the search efficiency of the new algorithm was greatly improved.When the algorithm was applied in the pattern synthesis of array antenna,the simulation results showed its high performance with multi⁃null and low side⁃lobe restrictions.In addition,the algorithm was superior to the Quantum Particle Swarm Optimization(QPSO),Particle Swarm Optimization(PSO),and Genetic Algorithm(GA)in optimization accuracy and operation speed,and is of very good generalization.

Heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes

A heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes(MUAVSs)for multi-objective optimization problem(MOP)is proposed,which is based on quantum wolf pack evolution algorithm(QWPEA)and power law entropy(PLE)theory.The method is composed of preset move and autonomous coordination stages for satisfying non-repeated coverage,connectedness,and energy balance of sink layer critical requirements,which is actualized to cover sensors layer in large-scale outside wireless sensor networks(WSNs).Simulation results show that the performance of the proposed technique is better than the existing related coverage technique.

Validation of Pore-Scale Simulations of Hydrodynamic Dispersion in Random Sphere Packings

We employ the lattice Boltzmann method and random walk particle tracking to simulate the time evolution of hydrodynamic dispersion in bulk,random,monodisperse,hard-sphere packings with bed porosities(interparticle void volume fractions)between the random-close and the random-loose packing limit.Using JodreyTory and Monte Carlo-based algorithms and a systematic variation of the packing protocols we generate a portfolio of packings,whose microstructures differ in their degree of heterogeneity(DoH).Because the DoH quantifies the heterogeneity of the void space distribution in a packing,the asymptotic longitudinal dispersion coefficient calculated for the packings increases with the packings’DoH.We investigate the influence of packing length(up to 150 d_(p),where d_(p) is the sphere diameter)and grid resolution(up to 90 nodes per d_(p))on the simulated hydrodynamic dispersion coefficient,and demonstrate that the chosen packing dimensions of 10 d_(p)×10 d_(p)×70 d_(p) and the employed grid resolution of 60 nodes per d_(p) are sufficient to observe asymptotic behavior of the dispersion coefficient and to minimize finite size effects.Asymptotic values of the dispersion coefficients calculated for the generated packings are compared with simulated as well as experimental data from the literature and yield good to excellent agreement.

Uncertain bilevel knapsack problem based on an improved binary wolf pack algorithm

To address indeterminism in the bilevel knapsack problem,an uncertain bilevel knapsack problem(UBKP)model is proposed.Then,an uncertain solution for UBKP is proposed by defining thePE Nash equilibrium andPE Stackelberg-Nash equilibrium.To improve the computational efficiency of the uncertain solution,an evolutionary algorithm,the improved binary wolf pack algorithm,is constructed with one rule(wolf leader regulation),two operators(invert operator and move operator),and three intelligent behaviors(scouting behavior,intelligent hunting behavior,and upgrading).The UBKP model and thePE uncertain solution are applied to an armament transportation problem as a case study.

A mesoscale model based on Monte-Carlo method for concrete fracture behavior study

An aggregate generation and packing algorithm based on Monte-Carlo method is developed to express the aggregate random distribution in cement concrete. A mesoscale model is proposed on the basis of the algorithm. In this model, the concrete con- sists of three parts, namely coarse aggregate, cement matrix and the interracial transition zone （ITZ） between them. To verify the proposed model, a three-point bending beam test was performed and a series of two-dimensional mesoscale concrete mod- els were generated for crack behavior investigation. The results indicate that the numerical model proposed in this study is helpful in modeling crack behavior of concrete, and that treating concrete as heterogeneous material is very important in frac- ture modeling.

Lattice approaches to packed column simulations

This work presents a review of the findings into the ability of a digitally based particle packing algorithm, called DigiPac, to predict bed structure in a variety of packed columns, for a range of generic pellet shapes frequently used in the chemical and process engineering industries. Resulting macroscopic properties are compared with experimental data derived from both invasive and non-destructive measurement techniques. Additionally, fluid velocity distributions, through samples of the resulting bed structures, are analysed using lattice Boltzmann method （LBM） simulations and are compared against experimental data from the literature.

Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network

The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network.In this paper,a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network(IGAN).Firstly,both real and predicted data are used to train the IGAN consisting of a discriminator and a generator.The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction.Then,a new improved wolf pack algorithm(IWPA)is presented to solve the formulated robust voltage control model,since the accuracy of the solutions obtained by traditional methods is limited.The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads,which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods.Furthermore,IWPA has a better performance than traditional methods in terms of convergence speed,accuracy,and stability for robust voltage control.

Multi-objective layout optimization of a satellite module using the Wang-Landau sampling method with local search

The layout design of satellite modules is considered to be NP-hard. It is not only a complex coupled system design problem but also a special multi-objective optimization problem. The greatest challenge in solving this problem is that the function to be optimized is characterized by a multitude of local minima separated by high-energy barriers. The Wang-Landau(WL) sampling method, which is an improved Monte Carlo method, has been successfully applied to solve many optimization problems. In this paper we use the WL sampling method to optimize the layout of a satellite module. To accelerate the search for a global optimal layout, local search(LS) based on the gradient method is executed once the Monte-Carlo sweep produces a new layout. By combining the WL sampling algorithm, the LS method, and heuristic layout update strategies, a hybrid method called WL-LS is proposed to obtain a final layout scheme. Furthermore, to improve significantly the efficiency of the algorithm, we propose an accurate and fast computational method for the overlapping depth between two objects(such as two rectangular objects, two circular objects, or a rectangular object and a circular object) embedding each other. The rectangular objects are placed orthogonally. We test two instances using first 51 and then 53 objects. For both instances, the proposed WL-LS algorithm outperforms methods in the literature. Numerical results show that the WL-LS algorithm is an effective method for layout optimization of satellite modules.