Experiments with a hybrid interior point/combinatorial approach for network flow problems

Interior point (IP) algorithms for Min Cost Flow (MCF) problems have been shown to be competitive with combinatorial approaches, at least on some problem classes and for very large instances. This is in part due to availability of specialized crossover routines for MCF; these allow early termination of the IP approach, sparing it with the final iterations where the Karush Kuhn-Tucker (KKT) systems become more difficult to solve. As the crossover procedures are nothing but combinatorial approaches to MCF that are only allowed to perform few iterations, the IP algorithm can be seen as a complex ‘multiple crash start’ routine for the combinatorial ones. We report our experiments of allowing one primal-dual combinatorial algorithm to MCF to perform as many iterations as required to solve the problem after being warm-started by an IP approach. Our results show that the efficiency of the combined approach critically depends on the accurate selection of a set of parameters among very many possible ones, for which designing accurate guidelines appears not to be an easy task; however, they also show that the combined approach can be competitive with the original combinatorial algorithm, at least on some ‘difficult’ instances.     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

溪源泄洪洞出口处闽江设计水位分析研究

溪源泄洪洞出口位于闽江之上,为分析泄洪洞出口处的设计水位,采用设计流量和水位流量关系推求法及实测水位资料推求法两种方法对工程设计水位进行计算,经过合理性分析,工程所在地近年水位变化剧烈,采用设计流量和水位流量关系推求法更符合现状情况,为工程建设提供可靠的设计成果。     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

基于图像引导滤波的人脸光照预处理算法

针对人脸识别中人脸图像光照预处理的问题,提出一种基于图像引导滤波的人脸光照预处理算法。根据自定义光照标准函数对引导图像和输入图像进行分类,经指数或对数非线性变换调整后进行直方图均衡化处理。采用图像引导滤波对图像细节进行增强,使变换后的图像更清晰。利用空域高通滤波来抑制局部锐化现象。在YaleB人脸数据库上进行验证,结果表明,该算法在识别性能上明显优于经典的主成分分析法,识别率可以提高2%~8%。     

爱因斯坦的高等教育观

爱因斯坦是二十世纪科学巨匠,和科学贡献一样,他的高等教育观是十分深刻的。爱因斯坦的高等教育观,本文梳理为六条:兴趣和爱好是最好的老师;想象力比知识更重要;发展独立思考和独立判断能力;讨论是学习的好形式;鼓励学生去实际行动;学校教育的目标是培养和谐的人。研究爱因斯坦的高等教育观,对于深化高等教育改革与培养高素质人才,具有积极意义。     

化学灌浆在输水隧洞渗漏处理中的运用

福清市闽江调水工程输水隧洞,原地下水较活跃,后经水泥灌浆,堵住大部分水,但渗漏现象还比较严重,难以靠单一的水泥灌浆彻底堵住渗漏,后经采用PU4水溶性聚氨酯对漏水点进行化学灌浆取得了较好的止水效果。PU4水溶性聚氨酯是一种单组份灌浆材料,其遇水后自行分散、乳化、发泡,立即进行聚合反应,聚合后的固结体有良好的延伸性、弹性及抗渗性,体现了弹性止水和以水止水的双重功能。可供类似工程参考。     

闽学大师朱熹与兵部侍郎福清林栗辨易探究

朱熹是闽学代表人物,其《周易本义》成为元明清三代开科取士的教科书。林栗是宋代福州福清的名宦,官至兵部侍郎。但二人在易学思想上意见分歧,朱熹为宋易突出代表,对宋易邵雍先天之学推崇备至;林栗重视易学重象之传统,反对邵雍先天之学,而又赞成宋代流行的河图洛书之说。朱熹林栗二人辨易,展现了宋代汉易、宋易交糅纷呈的状况,也反映了闽学在争鸣中不断前进的历史事实。朱熹林栗二人辨易发展为相互讦奏,为一大憾事。但平心而论,《宋史》对林栗论定有失偏颇,林栗仍不失＂为时直臣＂。     

GB/T 12706对低压电力电缆护套厚度的控制及应用

对电力电缆外护套厚度的检验若控制不当，就会增加操作难度和产品成本。正确控制电力电缆外护套的平均厚度和最小厚度，对产品质量控制、生产效率提高、降低成本增加效益都具有非常现实的意义。     

HE染色乳腺癌组织病理图像癌巢与间质分割研究

HE染色的乳腺癌组织病理图像是分析诊断乳腺癌常用的方法,病理学家普遍认为癌巢和间质的病理形态学特征对研究乳腺癌的生物学行为有着预示作用,所以准确分割癌巢和间质显得尤为重要。对于HE染色乳腺癌组织病理图像,视癌巢和间质的分割为图像中像素点的分类问题,提取并分析特征,选取最佳特征组合,然后分类为癌巢或间质,并结合间隔采样、归一化与阈值法。实验表明,该方法能较为准确地分割出癌巢和间质,保证较高准确率和精度,在时间效率上能达到较为满意的结果。