Bottlenecks and Countermeasures of High-Penetration Renewable Energy Development in China

China has become the world’s largest producer and consumer of energy,and ranks first in its wind and solar power installation capacity.However,serious wind and solar curtailment in China has significantly hindered the development and utilization of renewable energy.To address problems in the consumption of renewable energy,this paper analyzes four key factors affecting the capacity of power generated from renewable energy sources:power balance,power regulation performance,transmission capacity,and load level.Focusing on these bottlenecks,we propose seven solutions:centralized and distributed development of renewable energy,improving the peak-load regulation flexibility of thermal power,increasing the proportion of gas turbines and pumped-hydropower storage,construction of transmission channels and a flexible smart grid developing demand response and virtual power plants,adopting new energy active support and energy storage,and establishing appropriate policies and market mechanisms.The Chinese Government and energy authorities have issued a series of policies and measures,and in the past three years,China has had remarkable achievements in the adoption of renewable energy.The rate of idle wind capacity decreased from 17%in 2016 to 7%in 2018,and that of solar decreased from 10%in 2016 to 3%in 2018.

Landscape genetics reveals inbreeding and genetic bottlenecks in the extremely rare short-globose cacti Mammillaria pectinifera (Cactaceae) as a result of habitat fragmentation

Mammillaria pectinifera is an endemic, short-globose cactus species, included in the IUCN list as a threatened species with only 18 remaining populations in the Tehuacán-Cuicatlán Valley in central Mexico. We evaluated the population genetic diversity and structure, connectivity, recent bottlenecks and population size, using nuclear microsatellites. M. pectinifera showed high genetic diversity but some evidence of heterozygote deficiency(F_(IS)), recent bottlenecks in some populations and reductions in population size. Also, we found low population genetic differentiation and high values of connectivity for M. pectinifera, as the result of historical events of gene flow through pollen and seed dispersal.M. pectinifera occurs in sites with some degree of disturbance leading to the isolation of its populations and decreasing the levels of gene flow among them. Excessive deforestation also changes the original vegetation damaging the natural habitats. This species will become extinct if it is not properly preserved.Furthermore, this species has some ecological features that make them more vulnerable to disturbance such as a very low growth rates and long life cycles. We suggest in situ conservation to prevent the decrease of population sizes and loss of genetic diversity in the natural protected areas such as the Tehuacán-Cuicatlán Biosphere Reserve. In addition, a long-term ex situ conservation program is need to construct seed banks, and optimize seed germination and plant establishment protocols that restore disturbed habitats. Furthermore, creating a supply of living plants for trade is critical to avoid further extraction of plants from nature.

The effect of moving bottlenecks on a two-lane traffic flow

In this paper, we study the effect of moving bottlenecks on traffic flow. The full velocity difference （FVD） model is extended to the traffic flow on a two-lane highway, and new lane changing rule is proposed to reproduce the vehicular lane changing behavior. Using this model, we derive the fundamental current-density diagrams for the traffic flow with the effect of moving bottleneck. Moreover, typical time-space diagram for a two-lane highway shows the formation and dissipation of a moving bottleneck. Results demonstrate that the effect of moving bottleneck enlarges with the increase of traffic density, but the effect can be reduced by increasing the maximum velocity of heavy truck. The effects of multiple moving bottlenecks under different conditions are investigated. The effect becomes more remarkable when the coupling effect of multiple moving bottlenecks occurs.

Key bottlenecks and distinct contradictions in fast commercialization of perovskite solar cells

Despite significant improvements in photo-electricity conversion efficiency of perovskite solar cells(PSCs)over the past several years,this emerging photovoltaic technology is still years away from large-scale commercial application.In this review,important research progresses on PSCs’‘golden triangle’parameters of efficiency,stability,and cost in literatures were objectively analyzed.We focused on their key bottlenecks and distinct contradictions hindering their fast commercialization.We also proposed the most urgent directions requiring intensive research and development input in the coming years to speed up the commercialization process of PSCs.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Jinwang Bismuth Industrial Overcomes Resource Bottlenecks and Accelerates Transformation

Recent information about Jinwang Bismuth Industrial reveals that the company has developed the world’s first oxygen-rich sideblown redox duplex furnace technology,solving the long-standing problems of environmental protection and resource recycling in bismuth smelting.It’s learned that the company recovers up to100,000 tons of bismuth-containing lead-

Situation Awareness of Active Distribution Network:Roadmap,Technologies,and Bottlenecks

With the rapid development of local generation and demand response,the active distribution network(ADN),which aggregates and manages miscellaneous distributed resources,has moved from theory to practice.Secure and optimal operations now require an advanced situation awareness(SA)system so that operators are aware of operation states and potential risks.Current solutions in distribution supervisory control and data acquisition(DSCADA)as well as the distribution automation system(DAS)generally are not able to meet the technology requirements of SA.In this paper,the authors’participation in the project of developing an SA system as the basic component of a practical active distribution management system(ADMS)deployed in Beijing,China,is presented.This paper reviews the ADN’s development roadmap by illustrating the changes that are made in elements,topology,structure,and control scheme.Taking into consideration these hardware changes,a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN.The SA system’s implementation bottlenecks are also presented,including,but not limited to issues in big data platform,distribution forecasting,and security evaluation.Potential technology solutions are also provided.

BOTTLENECKS IN PRODUCTION NETWORKS: AN OVERVIEW

Bottlenecks, the key ingredients for improving the performances of the production networks, have been profoundly studied during the last decade. Yet, because of the complexity of the research results, there is still a significant gap between theory and practice. In this paper, we review various bottleneck definitions, detection methods and the asymptotic results and provide a practical guidance for recognizing and utilizing the bottlenecks in production networks. Queueing theory works as the mathematical foundation in our study. Various definitions of the bottlenecks are classified as either Performance in Processing （PIP） based or sensitivity based definitions, which reflect the preferences of the managers. Detection methods are surveyed closely based on the definitions. These methods are used to recognize the bottlenecks and to provide diagnosis results to managers. Comparisons show that different detection methods may lead to vastly different conclusions. The recognition of the bottlenecks has another advantage： the ultimate phenomena of the bottlenecks can greatly reduce the computation complexity in calculating the system performances. Bottlenecks based approximation and asymptotic results are studied to exhibit the contribution of bottlenecks in performance estimation and theoretical analysis.

A modified universal pedestrian motion model: Revisiting pedestrian simulation with bottlenecks

Pedestrian flow through narrow exits is one the most important features of crowd dynamics and evacuation.This is a particularly important aspect of pedestrian simulation models in that the accuracy is highly dependent on their ability to produce realistic exit flow rates.We firstly identified the four parameters that are most critical for physical interactions of the social force model and then calibrated them against two well-controlled pedestrian experiments.With these calibrated parameters,we discussed the reasonable settings of sensitive parameters for different levels of pedestrian competitiveness.Then,we revisited the basic questions about the effect of the exit location,the bottleneck length,and the effect of obstacles on pedestrian egress.Our simulation results indicated that:(1)The effect of the exit location on the pedestrian egress efficiency is uncertain,and the evacuation efficiency is also related to the exit width and the level of urgency.(2)The"pass-way"after the exit also named as the bottleneck length has a negative impact on the evacuation performance only in the scenarios that the bottleneck length is not more than 2.0 meters.When the bottleneck length exceeds 2.0 meters,pedestrian outflow efficiency reaches an asymptotic.(3)Setting an obstacle near an exit is not leading to a longer pedestrian evacuation time,instead,it is effectively improving pedestrian evacuation.

改进的基于YOLOv5s苹果树叶病害检测

针对目前在复杂环境下苹果树叶病害检测准确度低、鲁棒性差、计算量大等问题,提出一种改进的基于YOLOv5s苹果树叶病害的检测方法。首先,该方法在YOLOv5s网络基础上,选择考虑方向性的SIoU边框损失函数替代CIoU边框损失函数,使网络训练和推理过程更快,更准确。其次,在特征图转换成固定大小的特征向量的过程中,使用了简单化的快速金字塔池化(SimSPPF)替换快速金字塔池化(SPPF)模块,在不影响效率的情况下丢失的信息更少。最后在主干网络中使用BoTNet(bottleneck transformers)注意力机制,使网络准确的学习到每种病害的独有特征,并且使网络收敛更快。结果表明,相比于基准网络YOLOv5s,改进后的YOLOv5s网络mAP精度为86.5%,计算量为15.5GFLOPs,模型权重大小为13.1 MB,相对于基准YOLOv5s,平均精度提升了6.3百分点、计算量降低了0.3GFLOPs、模型权重压缩了1 MB。并适用于遮挡、阴影、强光、模糊的复杂环境。本研究所提出的方法,在降低了网络大小、权重、计算量的情况下提高了复杂环境下苹果树叶病害的检测精度,且对复杂环境具有一定的鲁棒性。在预防和治理苹果树叶病害上有较高的实际应用价值,在后续研究上,会扩充更多类别的病害数据集,部署到无人机等物联网设备,从而为实现智能果园种植提供技术参考。

基于改进YOLOv5的车辆与行人目标识别方法

为改进无人驾驶技术部署困难问题,该文提出一种基于YOLOv5的目标识别算法。算法引入Ghost Bottleneck模块,CBAM模块,将网络参数压缩,减少模型整体的计算量,同时提升模型推理速度;将原始模型CIoU损失函数替换为α-IoU损失函数,使得模型训练过程中更快收敛;用DWConv层替代原有网络中Neck部分的Conv层,可大大降低网络参数数量以及运算成本,同时可提取不同大小的特征信息,提升模型在物体识别方面的鲁棒性。实验证明,原始模型的参数量为7095906,权重文件为14.4 MB,mAP@0.5达到86.11%,检测速度为30.30 f/s;改进后模型参数量缩减为2546136,参数量减少了64.12%,权重文件为5.8 MB,权重文件大小减小了59.72%,mAP@0.5达到89.44%,检测速度提升至40.00 f/s,检测速度提升了32.01%。将改进后的模型部署至RK3399嵌入式移动端,算法识别速度较原始算法提升了约35.04%。

面向ADS-B信号辐射源个体识别的轻量化模型设计

针对辐射源个体识别高精确度、轻量化、实时性的现实应用需求,提出了面向广播式自动相关监测(ADS-B)信号辐射源个体识别的轻量化模型设计方法。根据信号数据特点进行解码处理,并对不均衡样本进行权重调节,改善样本质量;通过分组卷积获取不同维度的细微特征,与初始特征拼接,实现多维互补特征融合,并联同步进行提高识别效率。利用Ghost bottleneck结构实现网络模型压缩与跨层连接,在融合多维特征的同时节省计算资源。实验结果表明,本文算法结构精简,计算量低,识别率达到95.2%,并在不同容量的样本识别中效果稳定。本文算法较好地平衡了辐射源个体识别精确度、轻量化与高时效的需求。

基于改进YOLOv5n的轻量化光学遥感图像目标检测

针对光学遥感图像检测时存在背景复杂、目标密集、检测模型参数量和浮点计算量大的问题,提出了基于改进YOLOv5n的轻量化光学遥感图像目标检测方法。在主干网络中通过减少部分模块数量、引入无参数的SimAM注意力机制和Bottleneck Transformer结构,在参数量降低的情况下增强了网络的特征提取能力;引入Ghost卷积和使用同层跨越连接设计特征融合网络,降低模型复杂度和融合更多的特征信息;使用SIoU损失函数加快收敛速度和提升模型精度。通过在NWPU VHR-10和RSOD公开数据集上的实验结果表明,改进后的算法与原算法相比,平均精度均值(mAP0.5)分别提升了2.4%和1.5%,并且参数量减少25.4%,GFLOPs减少了21.4%。

HSKDLR:同类自知识蒸馏的轻量化唇语识别方法

针对唇语识别模型的识别率较低和计算量较大的问题,提出一种同类自知识蒸馏的轻量化唇语识别模型(HSKDLR)。首先,提出关注唇部图像空间特征的S-SE注意力模块,用其构建提取唇部图像通道特征和空间特征的i-Ghost Bottleneck模块,以提升唇语识别模型的准确率;其次,基于i-Ghost Bottleneck构建唇语识别模型,该模型通过优化瓶颈结构的组合方式降低模型计算量;然后,为提升模型准确率,减少模型运行时间,提出同类自知识蒸馏(HSKD)的模型训练方法;最后,使用同类自知识蒸馏方法训练唇语识别模型,并检验其识别性能。实验结果表明:与其他方法相比,HSKDLR具有更高的识别准确率和更低的计算量,在LRW数据集上的准确率达87.3%,浮点数运算量低至2.564 GFLOPs,参数量低至3.8723×107;同类自知识蒸馏可被应用于大多数唇语识别模型,帮助其有效提升识别准确率,减少训练时间。

废旧棉纺织品回收过程中小尺寸异纤检测方法

针对废旧棉纺织品在回收过程中小尺寸异纤出现漏检和误检等问题,设计了一种基于改进的YOLOv7小尺寸异纤检测模型。该模型在原始YOLOv7模型的主干网络中嵌入Bottleneck Transformer结构,并对YOLOv7网络结构进行修改,增强模型在浅层网络对小尺寸异纤全局位置信息提取能力,提高小尺寸异纤在深层网络中颜色、纹理等语义信息权重。试验结果表明:改进后模型的mAP值和对小尺寸异纤的检测精度分别达到0.991、0.987。该模型不仅加快了模型的收敛、减小了检测误差,而且还提高了对小尺寸异纤的特征提取能力,能有效解决小尺寸异纤漏检和误检等问题。

Efficient control of connected and automated vehicles on a two-lane highway with a moving bottleneck

This paper investigates the traffic flow of connected and automated vehicles(CAVs)inducing by a moving bottleneck on a two-lane highway.A heuristic rules-based algorithm(HRA)has been used to control the traffic flow upstream of the moving bottleneck.In the HRA,some CAVs in the control zone are mapped onto the neighboring lane as virtual ones.To improve the driving comfort,the command acceleration caused by virtual vehicle is restricted.Comparing with the benchmark in which the CAVs change lane as soon as the lane changing condition is met,the HRA significantly improves the traffic flow:the overtaking throughput as well as the outflow rate increases,the travel delay and the fuel consumption decrease,the comfort level could also be improved.

基于通道注意力的人脸表情识别

目的:解决通道注意力提取过程中各个通道信息利用不充分、交互性不足的问题。方法:提出一种基于SENet改进的通道注意力模型。本模型利用Inverted Bottleneck提取更加完整的通道信息。将GELU函数引入表情识别,以改善网络升维操作带来的过度融合问题。同时利用信息熵判断不同卷积核生成特征图的重要程度,为网络引入更多的归纳偏置。结果:在CK+和Oulu-CASIA库人脸表情数据集上的正确率分别达到95.92%和91.21%。结论:本方法能够更加充分地利用各通道特征,在有效提升准确率的同时具有较好的泛化能力。

路段拥挤条件下的高速公路匝道协调控制方法

为了提高主线路段拥挤条件下匝道启发式控制方法的有效性,提出了一种改进的Bottle-neck控制方法,以获得平稳的控制过程和缩短的调节时间.首先,依据拥挤持续时间内交通流波阵面向上游移动的距离,应用Greenshields模型确定交通拥挤的影响范围.其次,根据高速公路匝道桩号及拥挤的影响范围,确定用于协调控制的匝道数;并将密度作为控制变量,准确描述路段拥挤下的交通状况.最后,通过对局部调节率和协调调节率进行优化,克服了Bottleneck控制方法超调量过大、调节时间过长的缺陷.基于宁杭高速实测数据的仿真结果表明,改进的Bottle-neck控制方法能够使调节时间减少70%,超调量减小75%.

分布式文件系统Hadoop HDFS与传统文件系统Linux FS的比较与分析

对现有HDFS的设计架构进行分析,并借与LinuxFS架构的对比凸显HDFS的分布式特性。通过分析得出:现有的HDFS架构使用Java的Map界面,不利于任务的分解和并行处理,因此HDFS仅在数据的存储上实现分布式,数据处理依然是集中式的,这就形成了对NameNode的依赖,随着集群的扩大,NameNode的性能成为系统瓶颈,并提出了解决方向。