新时代背景下电力精准扶贫模式研究及建议

发挥电力行业优势特点,开展电力精准扶贫,对打赢脱贫攻坚战具有十分重要的作用,并能够助推脱贫攻坚与乡村振兴战略有效衔接.本文从电力扶贫在脱贫攻坚的重要作用出发,以“三聚焦”“三优先”“三模式”“三保障”为指引,详尽梳理并总结了电力精准扶贫主要模式和经验.在此基础上,深刻剖析了当前电力扶贫面临的困难及问题,提出电力精准扶贫的策略,以期为下一步开展电力精准扶贫提供参考与借鉴.     

基于多颜色分量CLBP提取的浮选泡沫状态识别

针对不同状态的浮选泡沫图像之间纹理结构相似、颜色差异不明显的问题，提出一种基于色调、饱和度和亮度（HSV）颜色空间的完全局部二进制模式（CLBP）纹理提取的浮选泡沫状态识别方法。首先使用双域去噪在保留纹理细节的同时滤除图像噪声，然后转换为HSV图像，在H、S和V颜色分量上分别提取三个尺度的CLBP纹理特征。将提取的纹理特征归一化后线性排列，建立高维度的纹理分类模型。最后通过一对一模式的支持向量机分类器对四类泡沫状态的样本集进行纹理提取后的分类训练与测试。结果表明，该方法对不同浮选泡沫状态的分类正确率较高，优于其他纹理描述方法，适用于浮选泡沫状态的识别。     

An SOFC anode model using TPB-based kinetics

Fundamental studies focusing on the electrode kinetics are essential in understanding the fuel cell operation and optimizing the electrode designs. In this study, we determined the triple-phase boundary (TPB)-based kinetics of hydrogen electrochemical oxidation using nickel patterned electrode experimental data and the Butler-Volmer formalism of the oxidation process. The same kinetics are then incorporated in a cermet electrode electrochemical model to estimate the effective TPB density of the nickel/yittrium-stabilized zirconia cermet anode. The kinetics are found to be of the same order of magnitude as previously determined by the microstructure reconstruction of cermet anode. Simulation results further revealed that the effective TPB density is several orders of magnitude lower than the typically reported physical densities of the cermet anode that possibly suggests that only a minor fraction of the physical TPB is actually required or available to produce the cell current at given cell voltage. The effect of various operating conditions on the anode activation overpotential is also investigated and discussed in this study.     

Detection of occupancy profile based on carbon dioxide concentration pattern matching

Occupancy is one of major factors influencing indoor microclimate. The aim of this work was to determine the influence of this factor on indoor air quality (IAQ) on the basis of CO₂ concentration measurements and statistical analysis. We wanted to identify periods of time when IAQ was strongly affected by the occupancy described by the given profile. The proposed approach consisted of several stages. The CO₂ concentration was measured and recorded in the form of univariate time series. Then, the relationship between occupancy and internal structure of the time series was disclosed. There were distinguished segments based on sample periodogram. Each segment was associated with a particular occupancy profile. In order to detect how human factor represented by a given occupancy profile influences IAQ we proposed to use pattern matching. In this approach there was examined the similarity between segments of the time series and the pattern of CO₂ variability, which represented a selected occupancy profile. The analysis was performed in time domain using moving time window technique. The similarity was judged based on two types of indexes, namely correlation coefficients and distance measures. It was shown that our approach may be applied to successfully detect a particular occupancy profile. The best performance was achieved when using angular distance as the similarity index. In this case we reached 82% true positive and 22% false positive detections. The proposed method may be applied in diagnostics problems to reveal sources of indoor air quality problems.     

A quantitative analysis of pattern production and its relationship to expert performance

Abstract. The modelling and measurement of expertise is a relatively new research area in artificial intelligence and cognitive science. Many domains do not have a formal method for evaluating expertise. When formal methods exist, they are frequently inefficient. Using an extension to the IAM program, a pattern recognition and acquisition method for evaluating the level of expertise for the domain of chess is developed. Chess players, as well as experts in other domains, use cognitive chunks of perceptual patterns to gain a cognitive economy that enables them to evaluate complex domain situations faster and more accurately than novices. The IAM program acquires a representative collection of the perceptual patterns demonstrated by a domain expert and uses those patterns to analyse skill level. A longitudinal study of a developing player and a comparison of the developing player to an established expert demonstrates the utility of the developed method for evaluating expertise.     

The ECHORD project proposals analysis – Research profiles,collaboration patterns and research topic trends

This paper investigates the research profiles, collaboration patterns and research topic trends which can be identified in the proposals submitted to the ECHORD (European Clearing House for Open Robotics Development) FP7 project. On a country level, clusters were identified and characterized by patterns of proposal production per inhabitant, score and international cooperation. Belgium and Sweden constitute a cluster characterized by high proposal production, with very high scores and extensive international collaboration. Belgium also excels from another cluster analysis, being as the only country where 100% of proposals involve industry–academia cooperation and obtain scores above 10. Other findings show that single partner proposals have significantly lower quality than multi-partner proposals but, on the other hand, the number of countries involved shows no influence on the quality of the proposals. Despite the high number of industrial participants present on the proposals, it is observed that they play secondary roles in the proposals, with a very low number projects leaded by companies. Also, it is observed that partnerships between research institutions (non-universities) are the most successful. Concerning topics of the proposals, the technology human–robot interface and the product vision robot for small-scale manufacturing are the most significant. Finally, the paper shows clusters of institutions extracted from the giant network of relations obtained from the ECHORD set of proposals.     

Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.     

A tribe competition-based genetic algorithm for feature selection in pattern classification

Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.