Electrochemical Degradation of Indigocarmine Dye at Ni/Graphite Modified Electrode in Aqueous Solution

Nickel Graphite modified electrode (Ni/GME) was prepared by electrochemical method and degradation of Indigocarmine (IC) dye was carried out. An investigation between the efficiency of degradation by graphite electrode and the Ni/graphite modified electrode has been carried out. The different effects of concentration, current density and temperature on the rate of degradation were studied. This study shows that the rate of the degradation is more for Ni doped modified graphite electrode. UV-Visible spectra before and after degradation of the dye solution were observed. The thin film formation of Ni or encapsulated in graphite rod is observed by scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM & EDAX). The instantaneous current effectiveness values of different experimental conditions are evaluated. The anodic oxidation by Ni/ graphite modified electrode showed the complete degradation of aqueous solution indigocarmine, which is confirmed by UV-Visible and chemical oxygen demand (COD) measurements. The dye is converted into CO2, H2O and simpler inorganic salts. The results observed for reuse of modified electrodes indicate that the Ni/graphite modified electrode would be a promising anode for electrochemical degradation of indigocarmine. This method can be applied for the remediation of waste water containing organics, cost-effective and simple.

MOSAiC现场观测期间海冰厚度季节变化模拟误差分析

北极气候研究多学科漂流观测计划(Multidisciplinary drifting Observatory for the Study of Arctic Climate, MOSAiC)于2019年10月至2020年9月开展,期间获得了变量完整的大气、海洋、海冰厚度及积雪厚度观测,为海冰模式的发展提供了新的契机。本研究利用两个完整观测时段(2019年11月1日至2020年5月7日、2020年6月26日至7月27日)的大气和海洋强迫场,驱动一维海冰柱模式ICEPACK,模拟了MOSAiC期间海冰厚度的季节演变,同海冰厚度观测进行了对比,并诊断分析了海冰厚度模拟误差的原因。结果表明,在冬春季节,模式可以再现海冰厚度增长过程,但由于模式在春季高估了积雪向海冰的转化及对海冰物质平衡的贡献,模拟的春季海冰厚度偏厚。在夏季期间,2种热力学方案及3种融池方案的组合都表明模式高估了海冰表层的消融过程,导致模拟结束阶段的海冰厚度偏薄。我们的研究表明,使用变量完整的MOSAiC大气和海洋强迫场可以诊断目前海冰模式中的问题,为海冰模式的改进奠定基础。

多光谱图像融合的IC器件表面缺陷检测

针对单可见光或单红外条件下的IC器件表面缺陷对比度不足,缺陷检测精度低的问题,提出多光谱图像融合的IC器件表面缺陷检测方法。针对IC器件可见光与红外图像配准中存在尺度不一致和对比度反转问题,引入拉普拉斯金字塔和特征描述符重组策略改进ORB(Oriented FAST and Rotated BRIEF)图像配准算法。在图像配准的基础上,提出NSST_VP图像融合方法,以非下采样剪切波变换(Non-Subsample Shearlet Transform, NSST)得到红外图像和已配准可见光图像的低频和高频子带,对低频子带采用视觉显著图(Visual Significance Map, VSM)加权融合规则,高频子带则采用自适应脉冲耦合神经网络(PA-Pulse Coupled Neural Network, PA-PCNN)决策融合规则,进而通过NSST逆变换得到高质量多光谱融合图像。最后,将融合图像输入YOLOv8s模型进行检测。实验结果表明,改进ORB的图像配准平均精度为87.8%,比ORB图像配准精度提高了62%,NSST_VP图像融合算法在主观视觉效果和客观评价指标上均有所提高。在缺陷检测实验中,NSST_VP融合方法的均值平均精度(mean Average Precision, mAP)达到83.15%,比单可见光、单红外缺陷图像检测的mAP分别提高了22.97%,28.31%,比双树复小波变换融合、曲线变换融合、非下采样轮廓波变换融合方法的mAP分别提高了13.14%,15.01%,20.35%。

带有左正则带的IC拟适当半群研究

文章利用富足半群上的L^(*)和R^(*)关系,得到了带有左正则带的IC拟适当半群的若干性质,特别是研究了幂等元导出的一类自同态映射。构造了半群上的一类好同余,将商半群简化为型-A半群,组建了一种具有半直积结构的AGC-系统,以此给出了带有左正则带的IC拟适当半群的一般刻画,改进了现有文献的部分结果。

IC厌氧反应器处理技术的应用研究及发展

IC厌氧反应器是第三代厌氧反应器,已广泛应用于高浓度有机废水处理。介绍了厌氧生物处理技术的特点及厌氧生物反应器的发展,重点介绍了IC厌氧反应器的构造、反应原理、特点、应用现状及发展趋势等,以期为IC厌氧反应器的应用提供参考。

Influence of Surface Ice Roughness on the Aerodynamic Performance of Wind Turbines

The focus of this research was on the equivalent particle roughness height correction required to account for the presence of ice when determining the performances of wind turbines.In particular,two icing processes(frost ice and clear ice)were examined by combining the FENSAP-ICE and FLUENT analysis tools.The ice type on the blade surfaces was predicted by using a multi-time step method.Accordingly,the influence of variations in icing shape and ice surface roughness on the aerodynamic performance of blades during frost ice formation or clear ice formation was investigated.The results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice.Clear ice has a greater impact on aerodynamics compared to frost ice,while frost ice is significantly influenced by the roughness of its icy surface.

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

IC卡数据驱动下的公交客流分布及线路优化策略研究

为提升公交车的运营效率,研究以某市公交IC数据为基础,对公交客流进行分析,并提出基于遗传算法改进的公交线路优化模型。研究发现,该市全天公交客流出现早晚两个峰值段。工作日到节假日,客流集中地从市中心附近转变为旅游景点、公园和购物中心等地区。公交线路优化模型将某公交线路的利用率提高了15%,将乘客的平均等待时间降低了21%,有效提高了公交系统的运营效率和乘客满意度。此次研究为提升公交运营效率、改善乘客体验、优化城市交通结构、促进城市可持续发展提供重要依据和方法。

基于ICS算法的列车运动模型参数辨识

列车运动模型参数辨识对其运行控制至关重要,文章采用单质点列车模型,对运行列车进行受力分析,建立列车运动模型,并提出利用改进的布谷鸟搜索(ICS)算法对列车基本阻力参数进行辨识;基于真实的列车运行数据,对基本阻力参数辨识进行仿真分析,并基于TD3算法对参数辨识的有效性进行仿真验证。仿真结果表明,基本阻力参数辨识对列车运行控制十分必要,相对于经验参数,辨识参数下的列车基本阻力更为准确,且辨识参数下的牵引能耗有一定的降低。

基于ICA的锂电池SOH估计曲线确定方法研究

针对如何提取容量增量(IC)曲线上更有效的特征参数进行锂电池健康状态(SOH)估计问题,提出了一种基于修正的洛伦兹电压容量(RL-VC)模型。首先使用传统滤波方法对锂电池进行容量增量分析(ICA)。然后使用RL-VC模型进行对比,获得相应的特征参数并计算容量建模误差。在基于自主搭建的试验平台上获得的试验数据与开源数据集NASA中的动态数据集NCM中分别进行试验。VC容量建模的误差分别在0.23%和0.16%以内。RL-VC模型拟合的IC曲线提取的特征参数与锂电池容量高度线性相关,为后续SOH工作奠定了基础。基于RL-VC模型的IC分析方法相较于传统滤波方法,不仅在电池老化方面具有更高的鲁棒性,同时在特征参数提取方面避免了主观性和不确定性。

Variations of Atmospheric ELF/VLF Radio Noise Due to Seismogenic Modifications in Tropospheric Conductivity

We suggest a possible explanation of the influence of pre-seismic activity on the registration rate of natural ELF(extremely low frequency)/VLF(very low frequency) pulses and the changes of their characteristics. The main idea is as follows. The distribution of the electric field around a thundercloud depends on the conductivity profile of the atmosphere. Quasi-static electric fields of a thundercloud decrease in those tropospheric regions where an increase of air conductivity is generated by pre-seismic activities due to emanation of radioactive gas and water into the lower atmosphere. The electric field becomes reduced in the lower troposphere, and the probability decreases of the cloud-to-ground (CG) strokes in such “contaminated” areas. Simultaneously, the electric field grows inside and above the thunderclouds, and hence, we anticipate a growth in the number of horizontal and tilted inter-cloud (or intra-cloud) (both termed as IC discharges) strokes. Spatial orientation of lightning strokes reduces vertical projection of their individual amplitudes, while the rate (median number strokes per a unit time) of discharges grows. We demonstrate that channel tilt of strokes modifies the spectral content of ELF/VLF radio noise and changes the rate of detected pulses during the earthquake preparation phase.

ENSO Index Variations and Links with Solar and Volcanic Activity

In this paper, we investigated the Oceanic Niño Index (ONI), for simplicity called in this paper an El Nino Southern Oscillation (ENSO) index in 1950-2023 by applying the wavelet spectral transform and the IBM SPSS correlations analysis. ONI follows the three months’ current measurements of the average temperature of the sea surface in the East-Central tropical part of the Pacific Ocean nearby the international line of the date change over the average sea surface temperature over the past 30 years. The ENSO index is found to have a strong (>87%) correlation with the Global Land-Ocean Temperature (GLOT). The scatter plots of the ENSO-GLOT correlation with the linear and cubic fits have shown that the ENSO index is better fit by the cubic polynomial increasing proportionally to a cubic power of the GLOT variations. The wavelet analysis allowed us to detect the two key periods in the ENSO (ONI) index: 4 - 5 years and 12 years. The smaller period of 4.5 years can be linked to the motion of tectonic plates while the larger period of 12 years is shown to have a noticeable correlation of 25% with frequencies of the underwater (submarine) volcanic eruptions in the areas with ENSO occurrences. Not withholding any local terrestrial factors considered to contribute to the ENSO occurrences, we investigated the possibility of the volcanic eruptions causing ENSO to be also induced by the tidal forces of Jupiter and Sun showing the correlation of the underwater volcanic eruption frequency with the Jupiter-Earth distances to be 12% and with the Sun-Earth distances, induced by the solar inertial motion, in January, when the Earth is turned to the Sun with the southern hemisphere where the ENSO occurs, to become 15%. Hence, the underwater volcanic eruptions induced by tidal forces of Jupiter and Sun can be the essential additional factors imposing this 12 year period of the ENSO (ONI) index variations.

Optimizing the LSTM Deep Learning Model for Arctic Sea Ice Melting Prediction

The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that summer Arctic Sea Ice Extent (SIE) is shrinking by 12.2% per decade since 1979 due to warmer temperatures [2]. Given the rapidly changing Arctic conditions, accurate prediction models are crucial. Deep learning models developed for Arctic forecasts primarily focus on exploring convolutional neural networks (CNN) and convolutional Long Short-Term Memory (LSTM) networks, while the exploration of the power of LSTM networks is limited. In this research, we focus on enhancing the performance of an LSTM network for predicting monthly Arctic SIE. We leverage five climate and atmospheric variables, validated for their correlation with SIE in prior studies [3]. We utilize the Spearman’s rank correlation and ExtraTrees regressor to enhance our understanding of the importance of the five variables in predicting SIE. We further enhance our predictor variables with seasonal information, lagged time steps, and a linear regression simulated SIE that accounts for the influence of past SIE on current SIE. Statistical methods guide our selection of data scalers and best evaluation metrics for our model. By experimenting with hyperparameter optimization and advanced deep learning training techniques, such as batch sizes, number of neurons, early stopping, and model checkpoint, our model achieved a Mean Absolute Error (MAE) of 0.191 and R2 of 0.996, underscoring its ability to account for nearly all the variance in our data and holds great promise for the prediction of SIE.

数字图像相关方法中基于改进IC-GN算法高精度形变测量研究

在数字图像相关方法对物体形变测量中,FA-NR算法实现了高精度测量,IC-GN算法在此基础上提高了测量效率。为进一步提升测量精度,提出了一种基于IC-GN的改进算法(GIC-GN)。在已知整像素初始位置上,通过梯度法求得更准确的亚像素位移,减小Hessian矩阵计算过程中产生的误差,同时加快迭代的收敛速度,有效提高了测量精度和效率。仿真实验验证结果表明,GIC-GN算法误差能够稳定在10^(-4)~10^(-3)pixel之间,对比IC-GN算法精度提升了10%~60%,耗时是FA-NR算法的0.1倍、IC-GN算法的0.8倍,能够实现对物体形变信息的高精度、高效率测量。

超温及应力条件下IC10合金的组织演化及机理

针对燃气涡轮叶片超温运行可能引发的合金劣化问题,对燃气涡轮叶片用IC10合金超温(1070~1250℃)、拉/压应力(30~90 MPa)条件下的微观组演化进行系统分析。结果表明:无应力超温热暴露过程中IC10合金中二次γ′相的退化方式主要包括球化、粗化和回溶,随着热暴露温度的升高及热暴露时间的延长,在γ/γ′相界面能降低的驱动下,二次γ'相形状由花状向圆方状及球状演变。二次γ'相的面积分数随温度的升高及时间的延长逐渐降低。在高于1225℃下热暴露时间超过50 h后,二次γ'相完全回溶。随着温度的升高和时间的延长,二次γ'相尺寸逐渐增加,γ'相粗化符合由元素扩散控制的Lifshitz-Slozov-Wagner理论。在超温有应力热暴露过程中,随着温度的增加,γ'相筏化现象逐渐加剧。拉应力促进γ'相沿垂直于应力轴的方向出现N型筏化,压应力则促进γ'相沿平行于应力轴的方向出现P型筏化。相对于压应力,拉应力状态下γ'相筏化进程更加迅速。根据超温及应力条件下IC10合金的组织演化图谱可对服役后叶片的服役工况进行反向判定。

时效工艺对IC10/GH4169异种高温合金电子束焊接接头性能及组织的影响

采用电子束焊接将IC10与GH4169异种高温合金进行连接,结合显微硬度计、光学显微镜和扫描电镜等分析手段,研究了不同热处理工艺对IC10/GH4169异种高温合金焊接接头硬度及微观组织的影响。结果表明,焊缝由柱状晶及胞状组织构成,焊态焊缝中存在Laves相和NbC两类第二相。时效处理并未对接头宏观形貌产生影响,两侧母材区晶粒也未发生明显粗化。在不同时效工艺下,GH4169母材区及焊缝区显微硬度均发生了显著变化,而IC10母材显微硬度并未发生明显变化。时效处理使得IC10母材区晶界处出现了链状分布的析出相,GH4169母材及焊缝区出现了γ′/γ″相以及δ/η相。凝固过程中的元素富集和偏析使得时效处理后的焊缝区γ′/γ″相的分布不均匀。

IC器件表面缺陷多光谱图像特征融合检测方法

针对IC器件表面轻微缺陷在传统的像素级融合检测中容易被产生的冗余噪声淹没,干扰缺陷特征的提取,且在光照不稳定的复杂检测场景中不能够自适应调节可见光图像与红外图像对缺陷检测任务贡献度的问题,本文提出基于多光谱图像特征融合的IC器件表面缺陷检测方法,采用中期融合策略,设计了多光谱图像特征融合模块(MIFF),在YOLO框架下建立双路特征提取通道,构建多光谱图像特征融合端对端的YOLO-MIFF缺陷检测模型。实验表明,YOLO-MIFF融合检测比单可见光和单红外图像检测的mAP分别提高了24.69%、35.65%,相比于YOLO-Multiply、YOLO-Concat、YOLO-Add模型的检测精度分别提高了9.85%、6.67%、3.44%。

沉积热解炭与树脂炭比例对C/C-SiC复合材料弯曲强度的影响

以针刺整体炭毡为坯体,采用树脂浸渍和化学气相沉积混合的方法制备得到相同密度的C/C多孔体,采用熔硅浸渗(RMI)制得C/C-SiC复合材料。研究了沉积热解炭与树脂炭比例对基体显微结构及弯曲性能的影响。结果表明:随着沉积得到的沉积热解炭含量增加,C/C-SiC复合材料基体内部孔隙率先降后升,而密度、弯曲强度先增后减。当沉积热解炭与树脂炭的比例为1∶1时,C/C-SiC复合材料密度与弯曲强度最高,分别达到2.03 g/cm^(3)和240 MPa。

不同电子束焊接参数对GH4169和IC10镍基高温合金组织和力学性能的影响

采用电子束焊接对GH4169和IC10异种镍基高温合金进行了焊接实验,利用金相显微镜、扫描电镜等对电子束焊接接头进行组织观察,并分析了镍基高温合金的力学性能。结果表明,通过电子束焊接可以得到无明显焊接缺陷的接头;焊缝区由枝状晶和等轴晶构成,且熔合区附近富集大量柱状晶,垂直于熔合线朝着焊缝中心生长;焊接后,GH4169和IC10母材中主要第二相为δ-Ni_(3)Nb相;100 kV加速电压焊接后,焊缝区平均显微硬度为235 HV,150 kV加速电压焊接后焊缝区平均显微硬度为225 HV,2个参数下焊缝区平均显微硬度均高于两端母材区,两端母材越靠近焊缝,由于热影响区的存在,硬度都有提高趋势。

基于项目学习的IC设计类专业实践教学体系优化探索

文章首先阐述了基于项目学习的IC设计类专业实践教学体系优化原则,然后从明确教学目标、优化实践教学内容、优化教学方法、优化教学评价方式四个方面论述了基于项目学习的IC设计类专业实践教学体系优化策略,最后总结了基于项目学习的IC设计类专业实践教学体系优化成效。