Data classification with binary response through the Boosting algorithm and logistic regression

The task of classifying is natural to humans, but there are situations in which a person is not best suited to perform this function, which creates the need for automatic methods of classification. Traditional methods, such as logistic regression, are commonly used in this type of situation, but they lack robustness and accuracy. These methods do not not work very well when the data or when there is noise in the data, situations that are common in expert and intelligent systems. Due to the importance and the increasing complexity of problems of this type, there is a need for methods that provide greater accuracy and interpretability of the results. Among these methods, is Boosting, which operates sequentially by applying a classification algorithm to reweighted versions of the training data set. It was recently shown that Boosting may also be viewed as a method for functional estimation. The purpose of the present study was to compare the logistic regressions estimated by the maximum likelihood model (LRMML) and the logistic regression model estimated using the Boosting algorithm, specifically the Binomial Boosting algorithm (LRMBB), and to select the model with the better fit and discrimination capacity in the situation of presence(absence) of a given property (in this case, binary classification). To illustrate this situation, the example used was to classify the presence (absence) of coronary heart disease (CHD) as a function of various biological variables collected from patients. It is shown in the simulations results based on the strength of the indications that the LRMBB model is more appropriate than the LRMML model for the adjustment of data sets with several covariables and noisy data. The following sections report lower values of the information criteria AIC and BIC for the LRMBB model and that the Hosmer–Lemeshow test exhibits no evidence of a bad fit for the LRMBB model. The LRMBB model also presented a higher AUC, sensitivity, specificity and accuracy and lower values of false positives rates and false negatives rates, making it a model with better discrimination power compared to the LRMML model. Based on these results, the logistic model adjusted via the Binomial Boosting algorithm (LRMBB model) is better suited to describe the problem of binary response, because it provides more accurate information regarding the problem considered.     

Robust control design of an activated sludge process

A H-inf control strategy is presented for a robustly performing activated sludge process. In operational terms, the objective is to conduct the process imposing that the biomass concentration in the recycle stream follows closely a time-varying, process-dependent, reference signal. The corresponding control objective is described as the development of a robust reference-tracking control structure with the best possible disturbance compensation, able to perform with noisy measurements and able to cope with variations in key process model parameters. The proposed algorithm embeds an estimation of states by solving the Riccati equation and avoids parameter estimation by assuming their variability within known bounds. Results are presented which show a favourable behaviour of the robust controller in comparison with a conventional PI control structure. Copyright © 1999 John Wiley & Sons, Ltd.     

Multivariable linearizing control of an industrial sugar crystallization process

This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach proposes a setpoint tracking for the crystal mass/concentration couple. In this purpose, a model dedicated to last stage crystallization is designed, without consideration of crystal size distribution. The controlled variables, unavailable, are obtained using an extended Luenberger observer. The observer is validated on industrial data and shows good performance in both convergence rate and accuracy. The performance of the proposed linearizing strategy, which application to cane sugar crystallization constitutes a real novelty, is tested via simulation. The good performance in setpoint tracking, even in presence of noise, disturbances and modeling error, allows to consider a significant improvement of the global productivity.     

Intelligent control system of an industrial lime kiln process

In the face of strong competition, the pulp and paper industry is aiming at higher profitability through increased productivity and the reduction of costs. In addition, on the global scale the industry is facing increasing market demands for higher product quality, more specialty products and improved production flexibility and environmental protection. Consequently, extensive research is being conducted to improve existing processes. One alternative, which is gaining increasing attention within the industry, is the improved control of existing processes by means of intelligent systems. In this paper, an intelligent kiln control system is presented. In the system, neural network models are used in conjunction with advanced high-level controllers based on fuzzy logic principles and a novel linguistic equation approach. Finally, the testing results of the system are presented and discussed.     

Multivariate statistical process control of an industrial fluidised-bed reactor

Multivariate statistical process control (MSPC) is a tool for the comprehensive monitoring of the performance of a manufacturing process. There is now a real need to demonstrate the applicability of MSPC to complex manufacturing processes and highlight the benefits that can be derived from its implementation. Alongside this, is the increasing interest in predicting quality or important chemical quality variables associated with product yield and production. This paper demonstrates the performance monitoring potential of MSPC and the predictive capability of canonical variates analysis and projection to latent structures by application to an industrial fluidised-bed reactor.     

A field study of the industrial modeling process

This paper analyzes the modeling process by means of a field study in the chemical industries. A diversified set of experienced modelers from two different countries have been interviewed using a case study approach. The interviews focussed on the modeling process. Other issues like life-cycle perspectives and the use of modeling in the process industries were treated as well. The information has been summarized and presented in a structured manner imposing the least possible bias from the authors. Based on the interview information the modeling process is discussed in detail. Further, fundamental research issues are identified, and a research agenda is proposed.     

Improved quality of continuous flow manufacturing through the combination of statistical process control and conventional computer process control

This paper represents a portion of the evaluation of the use of Statistical Process Control within the realm of Conventional Computer Process Control. Various control charts commonly utilized in continuous processes are evaluated when subjected to process disturbances typical of continuous processes. The control charts considered are the Moving Average and Moving Range Chart combination, the Individual and Moving Range Chart combination, and the Exponentially Weighted moving Average Chart. The types of process disturbances considered are unit step and linear trend disturbances to the process average. The evaluation is based on the determination of the Average Run Lengths (ARLs) resulting from computer simulations.     

On-line identification and nonlinear control of an industrial pH process

The control of pH for industrial processes is a highly nonlinear and challenging problem, especially when the nonlinearity is unknown and time-varying. In this work, a controller is developed and implemented for an industrial pH process with unknown chemical composition. The method used is an application of a general algorithm for pH processes, which is based on a representation of the nonlinearity that leads to on-line identification of a small number of parameters. The results show good performance of the pH control algorithm under normal operating conditions and satisfactory performance during several unusual hardware or process problems.     

Plant-wide hierarchical optimization and control of an industrial hydrocracking process

Hydrocracking is a crucial refinery process in which heavy hydrocarbons are converted to more valuable, low-molecular weight products. Hydrocracking plants operate with large throughputs and varying feedstocks. In addition the product specifications change due to varying economic and market conditions. In such a dynamic operating environment, the potential gains of real-time optimization (RTO) and control are quite high. At the same time, real-time optimization of hydrocracking plants is a challenging task. A complex network of reactions, which are difficult to characterize, takes place in the hydrocracker. The reactor effluent affects the operation of the fractionator downstream and the properties of the final products. In this paper, a lumped first-principles reactor model and an empirical fractionation model are used to predict the product distribution and properties on-line. Both models have been built and validated using industrial data. A cascaded model predictive control (MPC) structure is developed in order to operate both the reactor and fractionation column at maximum profit. In this cascade structure, reactor and fractionation units are controlled by local decentralized MPC controllers whose set-points are manipulated by a supervisory MPC controller. The coordinating action of the supervisory MPC controller accomplishes the transition between different optimum operating conditions and helps to reject disturbances without violating any constraints. Simulations illustrate the applicability of the proposed method on the industrial process.     

Quality inferential control of an industrial high density polyethylene process

The melt index (MI) is an important quality variable of control in polyolefin processes. However, it is difficult to measure this variable at frequent and regular sample intervals. A practical on-line inferential scheme is proposed in this paper for predicting the MI using secondary on-line measurements. This on-line MI inferential estimation scheme is combined with a quality control system that utilizes a two-degree of freedom cascaded model predictive controller. The proposed system has been successfully evaluated, under regulatory as well as tracking conditions, on an industrial polyolefin operation.     

Model predictive control of an activated sludge process: A case study

This paper details a case study application of model predictive control for a wastewater treatment process in Lancaster, North England. The control system was implemented in real time, together with a plant monitoring system for the purposes of process supervision. Following implementation, the model predictive control system provided significant benefits compared with the previously applied control system. These benefits included a reduction of over 25% in power usage and a similar increase in plant efficiency. The system therefore represents a useful tool in helping the water industry to reach its goal of significantly reducing its carbon footprint.     

l1-norm optimal control of N-removal in an activated sludge process

This paper presents an l₁-norm optimal state feedback controller for two-dimensional linear time invariant (LTI) systems with decoupled dynamics and a single control input. The controller is successfully applied to the problem of N-removal in activated sludge processes, both in simulation and on a pilot plant fed with real municipal wastewater. It optimises the moments at which the plant’s aerators are switched on/off. Improvement of operation strategies for the process of N-removal from wastewater is an important topic due to tightening government legislations with the objective to protect the aquatic environment.     

工业过程中液位的区间控制

液位的定位控制是造成物流频繁变化的主要因素之一。在工业过程中，充分利用液位所具有的容量特性对物流流量的缓冲作用，尽可能地对设备或容器的液位实行区间控制，能够大大提高生产装置的平稳运行水平和产品质量。模型预测控制是一种行之有效的方法，在吸收稳定装置的实际应用中取得了非常好的效果。     

Fuzzy control of the activated sludge wastewater treatment process

The activated sludge process is a commonly used method for treating sewage and waste waters. It is characterised by a lack of relevant instrumentation, control goals that are not always clearly stated, the use of qualitative information in decision making and poorly understood basic biological behaviour mechanisms. In this brief paper we examine the behaviour of an experimental fuzzy control algorithm constructed to reflect actual operational practice. We conclude that this algorithm does rather well and that a fuzzy controller would be a useful and practical way of regulating the activated sludge process.     

Accurate dynamic modeling and control parameters design of an industrial hybrid spray-painting robot

To obtain higher performance for hybrid robots subject to nonlinear dynamics and friction, feedforward compensations have been ubiquitously utilized in the industrial robotic field to attenuate these disturbances. However, due to the complex friction model and the coupling and time-varying dynamic of hybrid robots, there is no effective approach to realize accurate feedforward compensations in industrial control systems. This paper investigated an accurate dynamic modeling and control parameters design method to address these issues all at once. Taking the friction of each joint into account, the accurate dynamic model of the hybrid robot is developed and verified by experiments. With the accurate dynamic model, an exact control parameter design method is proposed based on the mapping relationship between the dynamic model and the feedforward compensations. Additionally, the control system designed by the method proposed in this paper is compared with the one tuned by an experienced engineer. Particularly, the robot's position and motion accuracy are also tested by a third-party inspection agency. The experimental and test results show that the position and velocity accuracy of the robot is improved significantly when the control system is designed by using the method proposed in this paper, which proves the effectiveness of the proposed method.     

Operating-system enhancements for a faulttolerant dual-processor structure for the control of an industrial process

A dual processor structure and operating system enhancements are described. These permit the construction, from available minicomputers, of a fualt-tolerant system for the control of an industrial process.     

先进控制技术在气相低密度聚乙烯装置中的应用

线型低密度聚乙烯(LLDPE)作为工业生产的重要原料,对其控制有着较高的要求,但是由于生产过程工况复杂,非线性特性明显、具有混沌特性、多变量耦合、缺乏质量指标的在线反馈、大时间滞后等特性,基于常规的控制策略已经不能满足对精细控制的要求.由此,开发了LLDPE装置的先进控制技术,对其质量指标及操作约束进行实时的在线估计及质量指标的闭环优化控制.实施结果表明,先进控制提高丁,装置的平稳性,实现了质量指标的在线闭环控制,提高了优级品率,降低了物耗.     

Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image

Logistic regression modeling was applied, as an alternative classification procedure, to a single post-fire Landsat-5 Thematic Mapper image for burned land mapping. The nature of the classification problem in this case allowed the structure and application of logistic regression models, since the dependent variable could be expressed in a dichotomous way. The two logistic regression models consisted of the TM 4, TM 7, TM 1 and TM 4, TM 7, TM 2 presented an overall accuracy of 97.37% and 97.30%, respectively and proved to be the most well performing three-channel color composites. The discriminator ability in respect to burned area mapping of each one of the six spectral channels of Thematic Mapper, which was achieved by applying six logistic regression models, agreed with the results taken from the separability indices Jeffries-Matusita and Transformed Divergence.     

基于静态二进制分析的工控协议逆向解析

针对传统的协议解析方法在工业控制系统中的局限性,提出了一种适用于工业控制系统通信协议的协议逆向分析方法。该方法的算法思想来源于动态污点算法,算法实现基于静态二进制分析平台IDA Pro及其提供的软件开发接口,解析结果可直接为模糊测试提供语义参考。该算法包括预处理、交叉引用分析、协议帧重构和语义提取等步骤,具有针对性强、通用性好的特点。将算法实现后应用于某组态软件,能够得到正确的分析结果,证明了该方法的正确性与有效性。     

A digital quality control system for an industrial dry process rotary cement kiln

A multivariate autoregressive moving average (ARMA) model for an industrial dry process rotary cement kiln is identified, from real process data, using the maximum likelihood method. The model obtained is then used in computing a controller for quality control of clinker production. It is shown that it is relevant to compute a minimum variance controller subject to restrictions both in the controller structure and the variances of the control signals. The resulting controller is finally implemented on the cement kiln process, together with a target adaptive controller for automatic adjustment of the clinker quality setpoint, in order to save energy.