Optimal supplementary frequency controller design using the wind farm frequency model and controller parameters stability region

In most of the existing studies, the frequency response in the variable speed wind turbines (VSWTs) is simply realized by changing the torque set-point via appropriate inputs such as frequency deviations signal. However, effective dynamics and systematic process design have not been comprehensively discussed yet. Accordingly, this paper proposes a proportional-derivative frequency controller and investigates its performance in a wind farm consisting of several VSWTs. A band-pass filter is deployed before the proposed controller to avoid responding to either steady state frequency deviations or high rate of change of frequency. To design the controller, the frequency model of the wind farm is first characterized. The proposed controller is then designed based on the obtained open loop system. The stability region associated with the controller parameters is analytically determined by decomposing the closed-loop system's characteristic polynomial into the odd and even parts. The performance of the proposed controller is evaluated through extensive simulations in MATLAB/Simulink environment in a power system comprising a high penetration of VSWTs equipped with the proposed controller. Finally, based on the obtained feasible area and appropriate objective function, the optimal values associated with the controller parameters are determined using the genetic algorithm (GA).     

基于深度学习的彩色以及近红外图像去马赛克

单传感器捕获的彩色-近红外（RGB-NIR）图像存在光谱干扰，从而导致重建出的标准彩色图像（RGB）图像与近红外（NIR）图像存在色彩失真以及细节信息模糊。针对这个问题提出一种基于深度学习的去马赛克方法，通过引入跳远连接与稠密连接解决了梯度消失和梯度弥散问题，使得网络更容易训练，并且提升了网络的拟合能力。首先，用浅层特征提取层提取了马赛克图像的像素相关性以及通道相关性等低级特征；然后，将得到的浅层特征图输入到连续多个的残差稠密块以提取专门针对去马赛克的高级语义特征；其次，为充分利用低级特征与高级特征，将多个残差稠密块提取到的特征进行组合；最后，通过全局跳远连接恢复最终的RGB-NIR图像。在深度学习框架Tensorflow上使用公共的图像与视觉表示组（IVRG）数据集、有植被的户外多光谱图像（OMSIV）数据集和森林（Forest）三个公开数据集进行实验。实验结果表明，所提方法优于基于多级自适应残差插值、基于卷积卷积和神经神经网络以及基于深度残差U型网络的主流的RGB-NIR图像去马赛克方法。     

松辽盆地徐家围子断陷沙河子组储层特征及控制因素

徐家围子断陷沙河子组是松辽盆地北部近期天然气勘探的主要目标，这类致密砂砾岩气层的储层特征及控制因素研究是首要的。通过分析其白垩系早期拉张构造背景条件下形成的一套断陷沉积，纵向上可划分为4个三级层序，各层序在断陷湖盆陡坡带和缓坡带发育扇三角洲和辫状河三角洲沉积。研究表明其储层主要为砂砾岩和含砾粗砂岩等粗碎屑岩。沙河子组储层孔隙包括原生粒间孔和次生孔隙，孔隙度主要分布在2%~8%，渗透率多小于0.1×10^-3 μm²，为致密储层。沙河子组储层发育的控制因素包括埋深、沉积相带、岩石类型和次生成岩作用，埋藏浅的扇三角洲（辫状河三角洲）前缘、平原相砂砾岩储层物性好，含火山碎屑物质的砂岩、砂砾岩易于形成溶蚀孔隙，改善储集性能。徐家围子断陷沙河子组有利储层主要位于埋深小于4 000 m的安达东、西两侧的扇三角洲前缘、平原区和徐东、徐西的构造高部位扇体发育区，埋藏较浅的安达中部滨浅湖相和埋藏较深的徐东、徐西斜坡扇体发育区也是较有利的储层发育区。     

基于多特定决策类的不完备决策系统正域约简

现有的属性约简方法大部分关注决策系统中的所有决策类，而在实际决策过程中决策者往往仅关注决策系统中的一种或几种决策类。针对上述问题，提出基于多特定决策类的不完备决策系统正域约简的理论框架。首先，给出不完备决策系统单特定决策类正域约简的概念；第二，将单特定决策类正域约简推广到多特定决策类，构造了相应的差别矩阵及区分函数；第三，分析并证明了相关定理，提出基于差别矩阵的不完备决策系统多特定决策类正域约简算法（PRMDM）；最后，选取4组UCI数据集进行实验。在数据集Teaching-assistant-evaluation、House、Connectionist-bench和Cardiotocography上，基于差别矩阵的不完备决策系正域约简算法（PRDM）的平均约简长度分别为4.00、13.00、9.00和20.00，PRMDM算法（多特定决策类中决策类数目为2）的平均约简长度分别为3.00、8.00、8.00和18.00。实验结果验证了PRMDM算法的有效性。     

结合位图切割和区域合并的彩色图像分割

就经典分水岭图像分割算法中存在的过分割问题，提出一种结合位图切割和区域合并的彩色图像分割算法。对原始彩色图像通过空域梯度算子求其梯度图像，并利用位图切割重建梯度图像；对新梯度图像进行分水岭预分割；对预分割图像基于异质性最小原则进行区域合并，并获得最终分割结果。相比于现有的同类方法，该算法引入位图切割，抑制噪声对分割结果的影响，在边缘模糊处分割准确，得到符合人类视觉的较小分割区域数目，同时在运行效率上提高。     

多线激光V型焊缝图像特征提取与轨迹识别

为了减少焊缝识别中传感器安装位置带来的前置误差，提高识别精度，设计了一款多线激光传感器，对多线激光在V型焊缝上的图像进行处理得到焊缝轨迹。提出一种基于动态不规则感兴趣区域的图像分割方法，将多条激光分割后并行处理。单条激光线图像预处理后以像素行灰度值之和的极大值确定激光条纹所在行，对激光条纹分段拟合后求交点，得到坡口边缘与焊缝位置的特征点，并将焊缝位置特征点拟合得到焊缝轨迹。根据所提出的算法编写了焊缝轨迹识别程序，并进行实验，结果表明，所提出的算法识别焊缝走向误差在0.5°之内，符合焊缝跟踪的要求。     

Economic geology value of oil shale deposits: Ethiopia (Tigray) and Jordan

Oil shale is an organic-rich, fine-grained sedimentary rock, containing kerogen, from which liquid hydrocarbons (called shale oil) can be produced. The oil shale deposits in the Tigray region are found in the northern parts of Ethiopia, Eastern Africa. They are of Upper Paleozoic in age, existing as remnants of the Cretaceous erosion period, underlain by tillites and overlain by sandstones. They were formed during the glacial retreat followed by marine deposition of shales in a basin created by the enormous load of the glaciers. The Ethiopian-Tigray oil shale deposits cover an area extending over approximately 30 km², with an average mineable bed-thickness of 55 m, showing on the upper part inter-beds and laminations of shaley limestones. The oil shale resources in this region are estimated to be approximately 4 billion tonnes. The exploitation of the Ethiopian-Tigray oil shale deposits is an excellent alternative to fulfill the fuel and other petroleum products’ demand of Ethiopia. This study sheds light on the oil shale resources in the Ethiopian region of Tigray, as they are fairly investigated, regarding their geological characterization, and future strategies for their exploration and exploitation potential. In addition, the oil shale deposits in Jordan are also moderately investigated, as Jordan is considered a promising country for shale oil, taking into account that Jordan has no other hydrocarbon resources (such as crude oil and natural gas), unlike many other countries in the MENA (Middle East and North Africa) region, as MENA sets on “seas” of oil and natural gas. Furthermore, oil shale in the USA is also briefly investigated, as the USA is being the world’s largest country of oil shale resources and reserves. Also, some other issues related to the oil shale industry are investigated, such as economics, extraction technologies of shale oil, and the environmental impacts.     

Validating the moderating role of age in multi-perspective acceptance model of wearable healthcare technology

The recent trend of aging population, not to mention the unprecedented pandemic, draws great attention from the general public about health concerns. Since healthcare information technology is different from non-healthcare information technology, additional contexts should be properly incorporated into technology acceptance research to accurately identify influential factors affecting the acceptance of wearable healthcare technology. Thus, we selectively reconfigured factors from health, privacy, and socio-demographic contexts to formulate a health-aware acceptance model. Then, it was empirically analyzed using structural equation modeling. Based on the results, whereas privacy concerns were directly associated with intention to use wearable healthcare technology, health concerns were not. Moreover, age had a moderating effect on social influence and facilitating conditions. These findings suggest valuable insights that the adoption rate of healthcare technology is increased by 1) keeping personal information securely, 2) facilitating social interaction among users, and 3) offering intuitive user experience for elderly people.