Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research

Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginners,opensource codes are undoubtedly the best alternative to learning TO,which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method.In this paper,we present a summary of various open-source codes and related literature on TO methods,including solid isotropic material with penalization(SIMP),evolutionary method,level set method(LSM),moving morphable components/voids(MMC/MMV)methods,multiscale topology optimization method,etc.Simultaneously,we classify the codes into five levels,fromeasy to difficult,depending on their difficulty,so that beginners can get started and understand the form of code implementation more quickly.

An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development.Nevertheless,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of blockchain.This paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and storage.Primarily,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission efficiency.Secondly,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission reliability.Finally,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on nodes.To evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive assessments.Empirical results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

Gray code based gradient-free optimization algorithm for parameterized quantum circuit

A Gray code based gradient-free optimization(GCO)algorithm is proposed to update the parameters of parameterized quantum circuits(PQCs)in this work.Each parameter of PQCs is encoded as a binary string,named as a gene,and a genetic-based method is adopted to select the offsprings.The individuals in the offspring are decoded in Gray code way to keep Hamming distance,and then are evaluated to obtain the best one with the lowest cost value in each iteration.The algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is reached.The GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets,and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization algorithm.The simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification tasks.Importantly,the GCO algorithm has a robust performance in the noise environment.

On Construction of Optimal A^2-Codes

Two authentication codes with arbitration (A 2 codes) are constructed from finite affine spaces to illustrate for the first time that the information theoretic lower bounds for A 2 codes can be strictly tighter than the combinatorial ones. The codes also illustrate that the conditional combinatorial lower bounds on numbers of encodingdecoding rules are not genuine ones. As an analogue of 3 dimensional case, an A 2 code from 4 dimensional finite projective spaces is constructed, which meets both the information theoretic and combinatorial lower bounds.

ERROR-DETECTING CODES AND UNDETECTED ERROR PROBABILITIES

The definition of good codes for error-detection is given. It is proved that a (n, k) linear block code in GF(q) are the good code for error-detection, if and only if its dual code is also. A series of new results about the good codes for error-detection are derived. New lower bounds for undetected error probabilities are obtained, which are relative to n and k only, and not the weight structure of the codes.

Optimal Asymmetric Quantum Codes from the Euclidean Sums of Linear Codes

In this paper,we first give the definition of the Euclidean sums of linear codes,and prove that the Euclidean sums of linear codes are Euclidean dual-containing.Then we construct two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of the Reed-Solomon codes,and two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of linear codes generated by Vandermonde matrices over finite fields.Moreover,these optimal asymmetric quantum errorcorrecting codes constructed in this paper are different from the ones in the literature.

Real-coded genetic algorithm for optimal vibration controlof flexible structure

Presents the study on the optimum location of actuators/sensors for active vibration control in aerospace flexible structures with the performance function first built by maximization of dissipation energy due to control action and a real coded genetic algorithm then proposed to produce a global optimum solution, and proves the feasibility and advantages of this algorithm with the example of a standard test function and a two collocated actuators/sensors cantilever, and comparing the results with those given in the literatures.

Low-Complexity Optimization Algorithm for Irregular Low-Density Parity-Check Codes

A low-complexity algorithm is proposed in this paper in order to optimize irregular low-density parity-check （LDPC） codes.The algorithm proposed can calculate the noise threshold by means of a one-dimensional density evolution and search the optimal degree profiles with fast-convergence differential evolution,so that it has a lower complexity and a faster convergence speed.Simulation resuits show that the irregular LDPC codes optimized by the presented algorithm can also perform better than Turbo codes at moderate block length even with less computation cost.

Optimal selection of tests for fault detection and isolation in multi-operating mode system

The complex systems are often in the structure of multi-operating modes, and the components implementing system functions are different under different operation modes, which results in the problems that components often fail in different operating modes, faults can be only detected in specified operating modes, tests can be available in specified operating modes,and the cost and efficiency of detecting and isolating faults are different under different operating modes and isolation levels. Aiming at these problems, an optimal test selection method for fault detection and isolation in the multi-operating mode system is proposed by using the fault pair coding and rollout algorithm. Firstly,the faults in fault-test correlation matrices under different operating modes are combined to fault-pairs, which is used to construct the fault pair-test correlation matrices under different operating modes.Secondly, the final fault pair-test correlation matrix of the multioperating mode system is obtained by operating the fault pair-test correlation matrices under different operating modes. Based on the final fault pair-test correlation matrix, the necessary tests are selected by the rollout algorithm orderly. Finally, the effectiveness of the proposed method is verified by examples of the optimal test selection in the multi-operating mode system with faults isolated to different levels. The result shows that the proposed method can effectively mine the fault detection and isolation ability of tests and it is suitable for the optimal test selection of the multi-operating mode system with faults isolated to the replacement unit and specific fault.

Adaptive Delay Optimization of Multicast-Enabled Coded Caching in Device-to-Device Networks

This letter investigates a delay optimization problem in device-to-device(D2D)networks where users have pre-downloaded subfiles following a coded caching rule.Due to acquiring rest subfiles,users may suffer both receiving and transmitting delays.To achieve the delay minimization,we first propose a delay-aware mode-selection strategy to adaptively choose multicast or D2D communications mode to reduce the receiving-caused delay.By matching these transmit modes with distinct subfile sizes,we further formulate a min-max optimization problem to minimize the delivery delay.Finally,numerical results prove that the proposed scheme outperforms existing ones in terms of both receiving and transmitting delays.

CCOC：An Optimizing Compiling System for Concurrent C Supporting Inter－process Code Analysis and Optimization

At present, there are some static code analyses and optimizations that can be applied to Concurrent C programs to improve their performance or verify their logical correctness. These analyses and optimizations are inter-process. In order to make their implementation easy, we propose a new method to construct an optimizing compiling system CCOC for Concurrent C. CCOC supports inter-process code analysis and optimization to Concurrent C programs and does not affect the system's portability and separate compilation of source programs. We also discuss some implementation details of CCOC briefly.

Blind Decorrelating Detection Based on Particle Swarm Optimization under Spreading Code Mismatch

A way of resolving spreading code mismatches in blind multiuser detection with a particle swarm optimization （PSO） approach is proposed. It has been shown that the PSO algorithm incorporating the linear system of the decorrelating detector, which is termed as decorrelating PSO （DPSO）, can significantly improve the bit error rate （BER） and the system capacity. As the code mismatch occurs, the output BER performance is vulnerable to degradation for DPSO. With a blind decorrelating scheme, the proposed blind DPSO （BDPSO） offers more robust capabilities over existing DPSO under code mismatch scenarios.

OPTIMAL WAVELET FILTER DESIGN FOR REMOTE SENSING IMAGE COMPRESSION

A new approach for designing the Biorthogonal Wavelet Filter Bank (BWFB) for the purpose of image compression is presented in this letter. The approach is decomposed into two steps. First, an optimal filter bank is designed in theoretical sense based on Vaidyanathan’s coding gain criterion in SubBand Coding (SBC) system. Then the above filter bank is optimized based on the criterion of Peak Signal-to-Noise Ratio (PSNR) in JPEG2000 image compression system, resulting in a BWFB in practical application sense. With the approach, a series of BWFB for a specific class of applications related to image compression, such as remote sensing images, can be fast designed. Here, new 5/3 BWFB and 9/7 BWFB are presented based on the above approach for the remote sensing image compression applications. Experiments show that the two filter banks are equally performed with respect to CDF 9/7 and LT 5/3 filter in JPEG2000 standard; at the same time, the coefficients and the lifting parameters of the lifting scheme are all rational, which bring the computational advantage, and the ease for VLSI implementation.

Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

Optimal Power Allocation Strategy for TBLAST Based 4G Systems

There is a big demand for increasing number of subscribers in the fourth generation mobile communication systems. However, the system performance is limited by multi-path propagations and lack of efficient power allocation algorithms in conventional wireless communication systems. Optimal resource allocation and interference cancellation issues are critical for the improvement of system performance such as throughput and transmission reliability. In this paper, a turbo coded bell lab space time system (TBLAST) with optimal power allocation techniques based on eigen mode, Newton and convex optimization method and carrier-interference-and-noise ratio (CINR) are proposed to improve link reliability and to increase throughput with reasonable computational complexity. The proposed scheme is evaluated by Monte-Carlo simulations and is shown to outperform the conventional power allocation scheme.

Several Series of Optimal（v,W ,1,Q）-OOCs for W = {3,6},{3,4,6}

Variable-weight optical orthogonal code （VWOOC） has many important applications in communica tions. In this paper, we obtain several series of optimal （v, W, 1, Q） OOCs for W -- {3,6}, {3,4,6}.

Fluid Flow Optimization on Semiautomatic Code Marker Base

This paper describes optimization process of nozzle cup based on a device for contactless code marking to rough and contaminated surfaces--semiautomatic code marker. During tests using the CFD （computational fluid dynamics） software different geometry of nozzle cups were compared by such parameters as pressure, velocity and direction of flow. Goal of digital experiments was to find sprayer nozzle geometry that can keep stream of paint in diameter of 10 mm on 10-20 mm distance. Main problem is to avoid low-pressure regions around the stream. The optimal geometry of nozzle cup is designed to get adjusted code dimensions on given distance

基于改进实数编码遗传算法的神经网络超参数优化

针对神经网络超参数优化效果差、容易陷入次优解和优化效率低的问题,提出一种基于改进实数编码遗传算法(IRCGA)的深度神经网络超参数优化算法——IRCGA-DNN(IRCGA for Deep Neural Network)。首先,采用实数编码方式表示超参数的取值,使超参数的搜索空间更灵活;然后,引入分层比例选择算子增加解集多样性;最后,分别设计了改进的单点交叉和变异算子,以更全面地探索超参数空间,提高优化算法的效率和质量。基于两个仿真数据集,验证IRCGA-DNN的毁伤效果预测性能和收敛效率。实验结果表明,在两个数据集上,与GA-DNN(Genetic Algorithm for Deep Neural Network)相比,所提算法的收敛迭代次数分别减少了8.7%和13.6%,均方误差(MSE)相差不大;与IGA-DNN(Improved GA-DNN)相比,IRCGA-DNN的收敛迭代次数分别减少了22.2%和13.6%。实验结果表明,所提算法收敛速度和预测性能均更优,能有效处理神经网络超参数优化问题。

二元广义反码与最优LCD码

基于二元线性码的定义向量理论,引入广义反码及其定义向量概念,确立广义反码、它的参数与二元最优线性码之间的联系。利用广义反码的性质和参数研究对应二元最优线性码的线性补对偶(LCD)性质,证明11类二元最优线性码不是LCD码。该方法突破现有方法的局限性,为研究高维二元LCD的参数确定与构造问题提供了可借鉴的新理论和新方法。

禁飞区约束的CSTBC无人机中继通信系统航迹优化

中继无人机的飞行路径对无人机中继通信系统的链路传输可靠性具有重要影响。为提高无人机中继通信系统链路传输的可靠性,提出协作空时分组编码(CSTBC)无人机中继通信方案,并以链路中断概率最小化准则为基础,提出中继无人机的航迹优化方法;理论分析给出系统遍历容量和分集增益;为保障中继无人机飞行安全,并获取信道的分集增益,提出无人机禁飞区规避方法。结果表明:协作空时分组编码无人机中继通信系统可充分获取信道的分集增益,改善无人机中继通信系统链路传输的可靠性。