R. Subbu, P. Bonissone, N. Eklund, Weizhong Yan, N. Iyer, Feng Xue, R. Shah
{"title":"Management of Complex Dynamic Systems based on Model-Predictive Multi-objective Optimization","authors":"R. Subbu, P. Bonissone, N. Eklund, Weizhong Yan, N. Iyer, Feng Xue, R. Shah","doi":"10.1109/CIMSA.2006.250751","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250751","url":null,"abstract":"Over the past two decades, model predictive control and decision-making strategies have established themselves as powerful methods for optimally managing the behavior of complex dynamic industrial systems and processes. This paper presents a novel model-based multi-objective optimization and decision-making approach to model-predictive decision-making. The approach integrates predictive modeling based on neural networks, optimization based on multi-objective evolutionary algorithms, and decision-making based on Pareto frontier techniques. The predictive models are adaptive, and continually update themselves to reflect with high fidelity the gradually changing underlying system dynamics. The integrated approach, embedded in a real-time plant optimization and control software environment has been deployed to dynamically optimize emissions and efficiency while simultaneously meeting load demands and other operational constraints in a complex real-world power plant. While this approach is described in the context of power plants, the method is adaptable to a wide variety of industrial process control and management applications","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123682554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Soft Computing-Based Measurement System for Medical Applications in Diagnosis of Cardiac Arrhythmias by ECG Signals Analysis","authors":"Claudio De Capual, S. Falco, Rosario Morellol","doi":"10.1109/CIMSA.2006.250737","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250737","url":null,"abstract":"In this paper a telemedicine application in support of heart patients homecare is proposed. Diagnosis of cardiac arrhythmias typically requires heart monitoring for 24 or 48 consecutive hours, so it causes long waiting lists in hospital. In this sight the authors have projected an user-friendly virtual assistant able to acquire and analyze patient ECG signals by soft computing procedures. The system is based on a PDA equipped with a DAQ card acquiring, to a fixed sampling frequency, the heart electrical impulses by means of an ECG sensor. Then an embedded algorithm allows to depict the graph of the electrocardiogram on the PDA display. By build-in models the ECG waveform is analyzed in order to diagnose possible arrhythmias occurrences or the happening of a heart attack. In fact, the developed computational intelligence application enables the system to perform a patterns recognition taking into account information on the measurement uncertainty. In this way, according to some patient parameters like age, sex and physical constitution, a set of warning lights on display provides information on the current heart status of the patient. Consequently the heart report can be sent by a GPRS modem to a confidential Web page containing patient personal data. The M2M application allows information to be made available to expert medical staff of hospital or clinic for a further remote analysis. In presence of a potential emergency, an online doctor can decide the typology of intervention for the patient assistance","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126295727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}