图像处理技术在面包烘焙品质检验中的应用

阐速了数字图像处理技术在烘焙面包品质检验中的应用,依据灰度共现矩阵算法提取图像的纹理特征,并通过二值化后的烘焙面包切片计算出其体积。试验结果表明,该方法不仅为面包烘焙品质的快速自动检验提供了客观准确的科学依据,使感官评价的指标客观化,而且经济可行。     

A rationale for the TEI recommendations for feature-structure markup

In this paper, we concentrate on justifying the decisions we made in developing the TEI recommendations for feature structure markup. The first four sections of this paper present the justification for the recommended treatment of feature structures, of features and their values, and of combinations of features or values and of alternations and negations of features and their values. Section 5 departs briefly from the linguistic focus to argue that the markup scheme developed for feature structures is in fact a general-purpose mechanism that can be used for a wide range of applications. Section 6 describes an auxiliary document called a feature system declaration that is used to document and validate a system of feature-structure markup. The seventh and final section illustrates the use of the recommended markup scheme with two examples, lexical tagging and interlinear text analysis.Terry Langendoen is Professor and Head of the Department of Linguistics at The University of Arizona. He was Chair of the TEI Committee on Analysis and Interpretation. He received his PhD in Linguistics from the Massachusetts Institute of Technology in 1964, and held teaching positions at The Ohio State University and the City University of New York (Brooklyn College and the Graduate Center) before moving to Arizona in 1988. He is author, co-author, or co-editor of six books in linguistics, and of numerous articles.Gary Simons is Director of the Academic Computing Department of the Summer Institute of Linguistics, Dallas, TX. He served on the TEI Committee on Analysis and Interpretation. He received his PhD in Linguistics (with minor emphasis in Computer Science) from Cornell University in 1979. Before taking up his current position in 1984, he spent five years in the Solomon Islands doing field work with SIL. He is author, co-author, or co-editor of eight books in the fields of linguistics and linguistic computing.The initial feature-structure recommendations were formulated by the Analysis and Interpretation Committee at a meeting in Tucson, Arizona in March 1990, following suggestions by Mitch Marcus and Beatrice Santorini. The authors received valuable help in the further revision and refinement of the recommendations from Steven Zepp.     

考虑广义储能与碳捕集设备联合调峰的电力系统低碳经济调度

在运用碳捕集设备提高深度调峰机组负荷低谷时段出力的同时,采用广义储能提高负荷高峰时段的碳捕集水平,是实现碳达峰、碳中和的重要途径之一。提出考虑广义储能与火电深度调峰的低碳经济调度模型,通过引入碳捕集设备与广义储能解决机组深度调峰损耗大及负荷高峰时段碳排放量高的问题。将深度调峰机组改造为碳捕集机组,提高其深度调峰时段的出力水平;将由价格型需求响应与储能装置构成的广义储能用于调度模型中,提高负荷高峰时段的碳捕集水平;以系统总运行成本最优为目标,制定各主体出力方案。仿真结果表明,所提模型能够缓解机组深度调峰压力,提高碳捕集机组的捕碳水平,兼顾系统的经济效益与低碳性能。     

DRBR-HDR: Dual-Branch recursive band reconstruction network for HDR with large motions

Ghosting artifacts due to misaligned imaging and missing content of the moving regions are major challenges of synthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR) with different exposures in dynamic scenes. Therefore, it hopes the HDR reconstruction model can align the LDRs’ features and restore the missing content without artifacts. In the paper, a new dual-branch recursive band reconstruction network for high dynamic range (DRBR-HDR) is proposed to generate credible result in missing content regions, which not only uses global features as supplementary information to help local features from different receptive fields for efficient feature alignment but also designs a series of coarse-to-fine band representation to better repair missing areas in the process of recursion. In addition, we introduce an interactive attention mechanism for local branches to alleviate ghosting artifacts. The experimental results demonstrate that DRBR-HDR achieves state-of-the-art performance compared with that of the prevailing HDR reconstruction methods in various challenging scenes.Index Terms—inverse tone mapping, band reconstruction, global features, high dynamic range images.     

基于B2B-MMC的柔性互联变电站优化调度方法

针对传统变电站内主变之间采用单母线分段开关难以准确调控潮流问题,提出一种基于背靠背模块化多电平换流器（B2B-MMC）的柔性互联变电站优化调度方法。首先,提出含柔性互联变电站的配电网架构,实现潮流的连续性控制及多区域间互相协调;然后,设计柔性互联变电站架构下的一种潮流优化调度方法,采用遗传算法对换流器进行赋值,利用改进交替迭代法进行潮流计算,实现配电网潮流的主动调控;最后,通过仿真模型验证了本文所提控制方法的有效性和合理性。     

Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a texture analysis methods to find and select the texture features of the tumor region of each slice to be segmented by support vector machine (SVM). The images considered for this study belongs to 208 benign and malignant tumor slices. The features are extracted and selected using Student's t‐test. The reduced optimal features are used to model and train the probabilistic neural network (PNN) classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of quantitative measure of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features have important contribution in segmenting and classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed hybrid texture feature analysis method using Probabilistic Neural Network (PNN) based classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by Jaccard index, sensitivity, and specificity.     

传统秦绣图案在现代平面设计中的创意实践

目的进一步理解传统秦绣作为民间艺术的文化内涵,使用其造型元素进行合理化创新,实现民间元素在现代平面设计上的艺术探索。方法以传统秦绣图案纹式造型特点为研究基础,通过对影响其意义传递的3个要素进行分析,来提炼代表性的设计元素进行设计实践。结论对于传统秦绣图案艺术特色的总结和设计实践的尝试,有助于保护和传承这一民族文化遗产,同时也是为了让它更好地与现代社会和现代手段相融合,提高其表现力,运用新的形式进行符合时代审美情趣的艺术创造。     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

僵尸网络检测技术研究

由于全球网络都在遭受日益广泛的僵尸网络所带来的威胁,因此,必须加大对其防治.僵尸网络检测的方法分为三个部分:“僵尸网络静态特征检测技术”、“僵尸网络动态特征检测技术”、“僵尸网络混合特征检测技术”,分别指出了僵尸网络静态特征检测技术的缺陷就是误报率高、僵尸网络动态特征检测技术缺乏先验性和处理数据量大等缺点,最后指出,把...