一种新型的光伏模型参数辨识GORao-1算法

针对Rao-1算法在光伏模型参数辨识中存在求解精度低、收敛速度慢、易陷入局部最优等问题,提出了一种基于广义对立学习(generalized opposition-based learning,GOBL)的改进Rao-1算法(GORao-1算法)。首先,采用2种不同的更新策略,种群个体根据适应度函数值排名选择相对应的更新公式,充分利用不同类型个体的信息,提高了算法的收敛速度和全局搜索能力。其次,引入GOBL策略,避免了算法陷入局部最优。最后,将GORao-1算法应用到Photo Watt-PWP201光伏组件模型的参数辨识中,并将辨识结果与其他7种优化算法的辨识结果进行对比,对比结果表明该算法在准确性和收敛速度方面优于其他算法;利用不同辐照度和不同温度下S75多晶硅光伏组件的实测数据进行实验,实验结果进一步验证了该算法能在不同环境下准确有效地进行参数辨识。

基于BP神经网络模型优化Fe_(1-x)O基氨合成催化剂

运用BP神经网络建立了助催化剂含量与催化剂活性之间的预测模型,对Fe_(1-x)O基氨合成催化剂的助催化剂进行优化。首先将前期实验数据整理归纳为含有3、4、5、6和7个助催化剂等5类催化剂,以助催化剂含量(体积分数)为输入变量,以425℃反应器出口氨浓度(活性)为输出变量,对助催化剂进行优化。结果表明,BP神经网络预测模型拟合值均方误差最高为0.2784,预测值均方误差最高为0.1592,构建的BP神经网络模型准确度较高。在该模型的基础上,运用多种群遗传算法进行极值寻优,求解最优的催化剂配方,并进行实验验证。结果表明,根据优化结果制备5个样品的实验测定值与预测值的相对误差最高为2.88%,优化结果较为准确;含有7个助催化剂的催化剂活性最高为18.83%,比原样本的统计平均活性值(17.52%)高1.31%,相对提高7.48%,助催化剂含量优化取得满意的结果。

基于Sentinel-1/2数据的洪水淹没范围提取模型

遥感是监测洪水淹没范围、掌握洪涝灾情演变的重要手段,而光学影像在洪水发生时往往有较多缺失,全天候的SAR影像在提取水体时精度略低。为快速、精准提取洪水淹没范围,构建了一种综合利用Sentinel-2光学影像和Sentinel-1雷达影像数据的洪水淹没范围提取模型,采用一种自适应阈值分割算法即大津算法(OTSU)分别对两种数据以及该模型进行了水体范围提取试验,并以河北省保定市为例进行了应用分析。结果显示:云量较少的Sentinel-2影像水体提取效果最好,总体精度(OA)达到95.6%;所构建的模型在引入部分可用Sentinel-2数据后,OA达到95%,相比单独使用Sentinel-1数据OA和Kappa系数分别提升1.2%和2.4%。该模型搭载于Google Earth Engine平台,能实现快速、准确、低成本的地表水体空间范围连续输出,不受限于云雾且比单独使用Sentinel-1影像的提取精度更高,在云覆盖严重导致Sentinel-2数据缺少的情况下,该模型可作为洪水淹没范围提取方法的一种选择。

Multinucleated giant cells of bladder mucosa are modified telocytes:Diagnostic and immunohistochemistry algorithm and relation to PDL1 expression score

BACKGROUND Multinucleated giant cells(MGCs)in bladder carcinomas are poorly studied.AIM To describe the function,morphogenesis,and origin of mononuclear and MGCs in urothelial carcinoma(UC)of the bladder in Bulgarian and French patients.METHODS Urothelial bladder carcinomas(n=104)from 2016-2020 were analyzed retrospectively using immunohistochemical(IHC)and histochemical stain examination.Giant cells in the bladder stroma were found in 35.6%of cases,more often in highgrades.RESULTS We confirm that MGCs in the mucosa in UC of the bladder were positive for both mesenchymal and myofibroblast markers(vimentin,smooth muscle actin,Desmin,and CD34)and the macrophage marker CD68.Furthermore,IHC studies revealed the following profile of these cells:Positive for p16;negative for epithelial(CK AE1/AE3 and GATA-3),vascular(CD31),neural(PS100 and CKIT),cambial,blastic(CD34-blasts and C-KIT),and immune markers(IG G,immunoglobulin G4,and PD-L1);no proliferative activity,possess no specific immune function,and cannot be used to calculate the Combined Positive Score scale.CONCLUSION In conclusion,the giant stromal cells in non-tumor and tumor bladder can be used as a characteristic and relatively constant,although nonspecific,histological marker for chronic bladder damage,reflecting the chronic irritation or inflammation.Likewise,according to the morphological and IHC of the mono-and multinucleated giant cells in the bladder,they are most likely represent telocytes capable of adapting their morphology to the pathology of the organ.

增强型群论优化算法求解折扣{0-1}背包问题

群论优化算法(GTOA)是基于群论方法提出的一个离散演化算法,非常适于求解以整型向量为可行解的组合优化问题。为了进一步提高GTOA求解折扣{0-1}背包问题(D{0-1}KP)的性能,首先指出了它的随机线性组合算子(RLCO)未能充分考虑当前个体位置信息的不足,基于个体基因保留策略对其进行改进。然后,在随机反向变异算子(IRMO)中引入增强0分量变异策略,用于处理因个体0分量无法及时变异而导致的解的质量下降、种群多样性降低等问题。在改进上述两个算子的基础上,提出了增强型GTOA(EGTOA),并基于它给出求解D{0-1}KP的新方法。随后,将改进策略应用于二进制GTOA(GTOA-2),提出了增强型GTOA-2(EGTOA-2)及其求解D{0-1}KP的新方法。为了验证EGTOA和EGTOA-2的性能提高程度与优异性,分别利用它们求解四类大规模D{0-1}KP实例,通过与GTOA、GTOA-2以及求解D{0-1}KP的已有8个最先进算法的比较表明:EGTOA和EGTOA-2求得最优解的能力比GTOA和GTOA-2提高了至少1.14倍,比8个最先进算法提高了5%~60%,它们的平均性能比GTOA、GTOA-2以及8个最先进算法的性能更佳。因此,EGTOA和EGTOA-2是当前求解D{0-1}KP的最佳算法。

基于O_(2)分子1.27μm气辉反演临近空间温度廓线的新方法

1.27μm波段O_(2)(a^(1)Δg)气辉的辐射强度高、自吸收效应弱,是反演临近空间大气温度的理想目标源。基于O_(2)分子气辉光谱理论以及“剥洋葱”算法,利用扫描成像大气吸收光谱仪(SCIAMACHY)的近红外临边观测数据,成功反演50~100 km区域的大气温度廓线。与SABER、ACE-FTS及激光雷达的观测结果对比表明,在55~85 km的切线高度范围内温度测量误差优于±10 K,而在55 km以下与85 km以上空间区域,由于受到自吸收效应、大气散射以及OH气辉的光谱污染等干扰,温度反演结果出现显著偏差。

求解0-1背包问题的牵制平衡算法

为扩充对于经典NP-hard问题中的0-1背包问题的求解方法,模拟生态系统中各物种间相互依存、牵制,最终达到动态平衡的自然机制,提出一种新型仿生算法:牵制平衡算法。算法以种群规模描述设计变量,以牵制关系为优化驱动力,以系统达到稳态为优化目标,设计了自成长函数、牵制函数、成长函数用以描述设计变量的变化规律,促进解的寻优进程。将牵制平衡算法对于10个不同规模0-1背包问题的求解结果与近年来文献数据进行对比,结果显示算法在8个不同规模的问题中能获得当前已知最优解,验证了牵制平衡算法的收敛性与求解性能,表明算法对于0-1背包问题的求解具有有效性和竞争力。

GM(1,N)模型的病态性研究及其在生态创新中的应用

本文以中国工业企业为研究对象,深入探究适用于多因素、少数据的生态创新相关指标特征的灰色模型预测技术。针对传统灰色预测模型在进行参数估计时可能存在的病态性问题展开研究,通过引入L2正则项的最小二乘法,利用粒子群算法求解最优值。将该模型应用于生态创新,与其他模型进行结果对比。结果表明,引入L2正则项的最小二乘法解决了模型的病态性问题,具有良好的预测性能,验证了该模型的有效性。

Improvement of Mono-window Algorithm for Retrieving Land Surface Temperature from HJ-1B Satellite Data

The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.

A Superlinerly Convergent ODE-type Trust Region Algorithm for LC^1 Optimization Problems

In this paper, a new trust region algorithm for unconstrained LC1 optimization problems is given. Compare with those existing trust regiion methods, this algorithm has a different feature: it obtains a stepsize at each iteration not by soloving a quadratic subproblem with a trust region bound, but by solving a system of linear equations. Thus it reduces computational complexity and improves computation efficiency. It is proven that this algorithm is globally convergent and locally superlinear under some conditions.

Multi-variable grey model (MGM(1,n,q)) based on genetic algorithm and its application in urban water consumption

Urban water consumption has some characteristics of grey because it is influenced by economy, population, standard of living and so on. The multi-variable grey model (MGM(1,n)), as the expansion and complement of GM(1,1) model, reveals the relationship between restriction and stimulation among variables, and the genetic algorithm has the whole optimal and parallel characteristics. In this paper, the parameter q of MGM(1,n) model was optimized, and a multi-variable grey model (MGM(1,n,q)) was built by using the genetic algorithm. The model was validated by examining the urban water consumption from 1990 to 2003 in Dalian City. The result indicated that the multi-variable grey model (MGM(1,n,q)) based on genetic algorithm was better than MGM(1,n) model, and the MGM(1,n) model was better than MGM(1,1) model.

A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.

Simple Insulin Dose Adjustment Using 3-3-1 Algorithm in Japanese Patients with Type 2 Diabetes: Start Kanazawa Study (Self-Titration Aggressive Algorithm with Glargine Trial)

We implemented a 3-3-1 algorithm in order to provide safe and simple self-titration in patients who newly initiated BOT as well as who were already on BOT and evaluated its utility in clinical setting. A total of 46 patients, 21 patients in the newly-initiated group and 25 patients in the existing BOT group performed dose adjustment using 3-3-1 algorithm. HbA1c was significantly improved 4 weeks after the initiation from 8.5% ± 1.2% at baseline to 7.3% ± 0.7% at the final evaluation (p 0.01, vs. Baseline). The average daily insulin units increased throughout the study period from 10.1 ± 6.7 at baseline to 14.6 ± 8.9 units at the final evaluation. Weight didn’t significantly change throughout the study (p = 0.12). The incidents of hypoglycemia were 0.8/month during the insulin dose self-adjustment period and 0.4/month during the follow-up period. The 3-3-1 algorithm using insulin glargine provided a safe and simple dose adjustment and demonstrated its utility in patients who were newly introduced to insulin treatment as well as who were already on BOT.

A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm

For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.

An Algorithm of 0-1 Knapsack Problem Based on Economic Model

In order to optimize the knapsack problem further, this paper proposes an innovative model based on dynamic expectation efficiency, and establishes a new optimization algorithm of 0-1 knapsack problem after analysis and research. Through analyzing the study of 30 groups of 0-1 knapsack problem from discrete coefficient of the data, we can find that dynamic expectation model can solve the following two types of knapsack problem. Compared to artificial glowworm swam algorithm, the convergence speed of this algorithm is ten times as fast as that of artificial glowworm swam algorithm, and the storage space of this algorithm is one quarter that of artificial glowworm swam algorithm. To sum up, it can be widely used in practical problems.

FAST DFT ALGORITHM WITH (N-1) / 2 MULTIPLICATIONS

A fast algorithm is proposed for recursively computing the DFTs of prime length. Only (N-1) / 2 real multiplications are required to compute all N frequency components in terms of permuting the input data. The multiplication in recursive computation is replaced by shifting. Complexity of the algorithm is studied. A factor η is introduced and presented. When the ratio of multiplier's period Tm to adder's period Ta is greater than the factor η (i.e.Tm / Ta >η), the new algorithm is faster than FFT. The necessary condition and error of the algorithm are studied. The signal-to-noise ratio for different length N is presented. A high accuracy scheme is proposed for improving the SNR about 20 -30dB.

Study on correlativity among capacity dimension D_0,informationd imension D_1,algorithmic complexityC

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Numerical Studies of the Generalized <i>l</i>₁Greedy Algorithm for Sparse Signals

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.

Simulating Particle Swarm Optimization Algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇obj.fun＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

基于新陈代谢算法优化的GM(1,N)动态网络灰分拟合

煤炭是我国能源资源安全的压舱石,“双碳”背景下实现煤炭清洁加工与高效利用意义重大,而灰分检测对煤炭清洁化和智能化发展尤为重要。针对现有灰分检测存在的检测精度有待提高的突出问题,以两淮矿区典型煤样为研究对象,通过慢灰和X射线荧光(X ray fluorescence,XRF)测试系统地探究了煤样的灰分和元素组成分布规律,并结合机器学习理论构建了灰分-元素特征数据集;结合灰色系统理论和新陈代谢算法,构建了自适应的GM(1,N)动态网络灰分拟合优化模型,并详细设计了动态网络算法流程;提出了GM(1,N)动态模型的关键超参数,并通过与常规拟合方法对比,全面地评价了模型拟合性能。结果表明:两淮矿区煤可视为由可燃元素和成灰元素共同构成,且成灰元素中质量分数占比最高为Si和Al,次之为S、Fe和Ca等,最少为P和Cl等,并且煤中成灰元素总含量与灰分呈正相关,而可燃元素与之相反;以灰分为标签值、以组成元素为特征值,形成了煤的灰分-元素特征数据集;以样本数据划分→动态网络灰分拟合→模型评价机制→动态拟合模型自适应优化→鲁棒性提升→多轮迭代优化为主线设计了GM(1,N)动态网络灰分拟合模型及其算法流程,有效提升了数据集稳定性和新鲜度,并且迭代收敛速度快,灰分误差阈值5%时其准确率达100%;对比经典GM(1,N)模型和常规多元线性回归模型,证明了新模型的灰分拟合性能得到显著提升,其相对误差为0.16%~4.96%、误差均值仅2.29%。