Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits

In this paper an approach based on an evolutionary algorithm to design synchronous sequential logic circuits with minimum number of logic gates is suggested. The proposed method consists of four main stages. The first stage is concerned with the use of genetic algorithms (GA) for the state assignment problem to compute optimal binary codes for each symbolic state and construct the state transition table of finite state machine (FSM). The second stage defines the subcircuits required to achieve the desired functionality. The third stage evaluates the subcircuits using extrinsic Evolvable Hardware (EHW). During the fourth stage, the final circuit is assembled. The obtained results compare favourably against those produced by manual methods and other methods based on heuristic techniques.     

遗传算法在隧洞开挖爆破参数优化中的应用

介绍了利用遗传算法优化隧洞开挖爆破参数的理论和方法。以及该算法在阿荣旗-深圳高速公路隧洞爆破开挖工程中的应用。实例表明,利用遗传算法来调整爆破参数和支护参数,可以有效地降低隧道爆破成本。     

Gene Variants Related to Cardiovascular and Pulmonary Diseases May Correlate with Severe Outcome of COVID-19

Background: Severe outcomes of COVID-19 account for up to 15% of all cases. The study aims to check if any gene variants related to cardiovascular (CVD) and pulmonary diseases (PD) are correlated with a severe outcome of COVID-19 in a Polish cohort of COVID-19 patients. Methods: In this study, a subset of 747 samples from unrelated individuals collected across Poland in 2020 and 2021 was used and whole-genome sequencing was performed. Results: The GWAS analysis of SNPs and short indels located in genes related to CVD identified one variant significant in COVID-19 severe outcome in the HADHA gene, while for the PD gene panel, we found two significant variants in the DRC1 gene. In this study, both potentially protective and risk variants were identified, of which variants in the HADHA gene deserve the most attention. Conclusions: This is the first study reporting the association between the HADHA and DRC1 genetic variants and COVID-19 severe outcome based on the cohort WGS analysis. Although all the identified variants are localised in introns, they may be correlated and therefore inherited along with other risk variants, potentially causative to severe outcome of COVID-19 but not discovered yet.     

Heterologous Expression of Xylanase xAor from Aspergillus oryzae in Komagataella phaffii T07

Xylanases (EC 3.2.1.8) hydrolyze the hemicellulose of plant cell walls. Xylanases are used in the food and paper industries and for bioconversion of lignocellulose to biofuel. In this work, the producer-strain with four copies of the xAor xylanase gene was organized in two tandem copies for optimal expression in Komagataella phaffii T07 yeast. The secreted 35 kDa xylanase was purified from culture medium by gel filtration on Sephadex G-25 and anion exchange chromatography on DEAE-Sepharose 6HF. Tryptic peptides of the recombinant enzyme were analyzed by liquid chromatography-tandem mass spectrometry where the amino acid sequence corresponded to Protein Accession # {"type":"entrez-protein","attrs":{"text":"O94163","term_id":"74582795","term_text":"O94163"}}O94163 for Endo-1,4-beta-xylanase from Aspergillus oryzae RIB40. The recombinant xylanase was produced in a bioreactor where the secreted enzyme hydrolyzed oat xylane with an activity of 258240 IU/mL. High activity in the culture medium suggested xylanase could be used for industrial applications without being purified or concentrated. The pH optimum for xylanase xAor was 7.5, though the enzyme was active from pH 2.5 to pH 10. Xylanase was active at temperatures from 35 °C to 85 °C with a maximum at 60 °C. In conclusion, this protocol yields soluble, secreted xylanase suitable for industrial scale production.     

不确定环境下的单机成套订单鲁棒计划

讨论了不确定环境下订单数量可变的单机成套计划的优化问题,利用对偶变换给出了该问题的鲁棒整数规划模型,并设计了相应的遗传算法。算法对约束条件难点的处理采用了四种不同的方法即死亡惩罚、罚函数法、修补方法和解码方法,以检验算法的性能。最后进行了数值仿真实验,以比较不同算法的有效性。     

基于遗传算法和支持向量机的特征子集选择方法

在模式分类系统中,往往需要从大量的特征中选择最优的特征子集,人工选择特征的方法往往费时费力,本文采用遗传算法(GA)对支持向量机进行封装的方法选择特征子集。首先使用遗传算法随机产生若干特征子集,通过选择、交叉和变异操作产生新的特征子集,经过若干代之后,得到最优的特征子集。在遗传算法中最重要的是适应度的确定,本文用支持向量机(SVM)作为分类器,为了避免出现“过拟和”,把特征子集的5阶交叉验证分类准确率和特征数量的联合函数作为适应度函数。对UCI机器学习库中SONAR和LED数据集进行实验,结果表明本方法可以有效滤除无关特征并提高分类准确率。     

基于免疫否定选择算法的机场航班延误状态检测与实现

把免疫算法中的否定选择算法运用到航班延误状态的诊断检测与预测中,将生物免疫系统机制与机场航班运行机制联系起来,建立了相应的自体集合、检测细胞、抗原信息及它们之间的匹配模型,给出了检测方法。首先根据机场航班运行数据生成航班检测的成熟检测器和记忆检测器,然后用否定选择算法和航班数据对检测器进行训练和改进,最后利用检测器中的信息对机场航班状态分段进行预测,获得每个时段的航班延误状况,为机场有关部门对航班延误应急处理提供决策支持。仿真实验表明,该方法能较准确地预测航班的延误状态,且实时性好。     

数字图像三点中值去隔行技术研究

对传统三点中值去隔行算法进行改进,采用同步更新迭代的方法,提高了图像的画面质量.实验证明,该算法不仅降低了时间复杂度,保护图像边缘,而且明显改善了噪声滤波效果.     

基于目的层预约的双子电梯群优化控制方法

针对双子电梯群控系统多目标性、不确定性的特点,提出了一种基于目的层预约的双子电梯群优化控制方法。在分析双子电梯结构特点的基础上,构造了适合双子电梯群的目标函数;在不同交通模式下,运用改进的遗传算法进行目的层预约系统下双子电梯群的优化控制仿真。仿真结果表明,控制方法能够适应双子电梯的运行特点,并能适应不同交通流模式,与传统电梯控制系统比较,平均候梯时间、长候梯时间发生率和电梯停靠次数明显减小,可为双子电梯群控系统的工程应用提供理论依据。     

A new TDOA algorithm based on Taylor series expansion in cellular networks

Time difference of arrival (TDOA) is the positioning technique with the most potential in cellular mobile telecommunication systems. The Taylor series expansion method has been widely used in solving nonlinear equations for its high accuracy and good robustness. However, the performance of the Taylor’s method depends highly on the initial estimation. Therefore, one new algorithm, hybrid optimizing algorithm (HOA) was proposed, which combines the Taylor series expansion method with the steepest decent method. The steepest decent method features fast convergence at the initial iteration and small computation complexity. HOA takes great advantage of both methods. Simulation results show that HOA achieves better performance on positioning accuracy and efficiency. __________ Translated from Journal on Communications, 2007, 28(6): 7–11 [译自: 通信学报]