Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

气管上段腺样囊性癌的临床病理

目的　探讨气管上段腺样囊性癌的临床病理特征及诊断和鉴别诊断要点。方法　收集2000年1月至2014年2月在北京协和医院确诊的4例气管上段腺样囊性癌病例，通过光镜、免疫组织化学及组织化学染色分析其临床病理特征、免疫组织化学特点、诊断及鉴别诊断要点。结果　4例气管上段腺样囊性癌患者中，男1例，女3例，平均年龄47岁(38~57岁）；既往均无腺样囊性癌病史，1例患者既往有结节性甲状腺肿手术史。镜下检查4例均为筛状/管状型腺样囊性癌，3例累及甲状腺组织，3例累及神经组织，未见淋巴结受累。免疫组织化学染色示4例P16、CD117、BCL2、P63、SMA均阳性，Ki67指数平均8%，TTF1和P53均阴性；4例中基底膜样物胶原Ⅳ阳性；组织化学染色示AB/PAS阳性。术后均接受总剂量为48~56 Gy的放疗，随访6~120个月，平均72.5个月，1例术后96个月复发，3例随访期间无复发及转移。结论　气管上段腺样囊性癌是罕见的原发于气管的低度恶性肿瘤，肿瘤生长缓慢，就诊时多数已侵及甲状腺组织，需要与原发于甲状腺的恶性肿瘤相鉴别，特别是在甲状腺穿刺及术中冰冻检查时。结合电子喉气管镜下表现、典型的形态学及免疫组织化学和组织化学染色有助于准确诊断。手术难以切除干净，术后放疗对延缓疾病复发有一定帮助。     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

A Study on Turbo—rotor Multi—fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine.As it has many excellent qualities,the neural network might be used to solve the problems of multi-fault diagnasis of a turbo-rotor system.First,the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing.Then.the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propaga-tion)neural network.According to the BP neural network problems,the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated.The results indicated that the parallel Bp neural network method could solve the turbo-rotor multi-fault diagnosis problems.     

韩江三角洲海岸变迁遥感调查研究

利用1:10万TM影像，对广东东部韩江三角洲地区的海岸变迁及河道演变进行了调查研究。划分了不同时期的海岸界线和古河道遗迹，提出韩江三角洲的形成是河流动力和海洋动力共同作用的结果，对研究历史海平面变化及环境演变具重要意义。     

基于PⅢ工控机VI测控系统远程控制研究

虚拟仪器（VI）将计算机采集测试分析引入到电子测量领域，用数字化和软件技术极大地提高了测量的灵活性。而VI远程测控系统是网络技术、通信技术一计算机虚拟技术结合的产物。远程控制技术能够使操作突破地域的限制，已在网络环境下PⅢ工控机上实现。     

平面近场测试系统在天线测试和诊断中的应用

介绍了天线近场测量的基本原理和HD-1型平面近场测试系统,并对近场测量在实际天线测试中的应用情况、发展方向和应用前景等作了简单的描述。     

Assessing continuous bivariate effects among different groups through nonparametric regression models: An application to breast cancer detection

In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer.     

广播发射集散控制系统

本文介绍了广播发射台利用计算机技术、网络技术、遥控遥测技术、现代测试手段，对设备进行自动化控制与管理；实现设备技术指标自动测试，发射台无人值班、有人留守，建立机器的计算机档案制度，确保安全优质播出的方法和经验。     

产量递减规律的诊断方法

传统的Arps产量递减分析方法要求事先选定递减规律的初始时间、初始产量及初始递减率,事实上这些参数是很难确定的。实际应用时不得不人为地加以臆断而使得此方法带有不确定性.为此提出了产量递减规律的诊断方法。应用诊断曲线很容易确定递减规律的模型参数,克服常规方法的不足。通过选用参考时间、参考产量和参考递减率,使得递减分析方法具有一定的灵活性和优越性、该方法简便、实用。