Supplier selection with support vector regression and twin support vector regression

Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon (DVR) ve ikiz destek vektör regresyon (İDVR) teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir.Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression (SVR) and twin support vector regression (TSVR) techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR

Destek Vektör Regresyon ve İkiz Destek Vektör Regresyon Yöntemi ile Tedarikçi Seçimi

Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression (SVR) and twin support vector regression (TSVR) techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR.Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon (DVR) ve ikiz destek vektör regresyon (İDVR) teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir

Destek Vektör Regresyon ve İkiz Destek Vektör Regresyon Yöntemi ile Tedarikçi Seçimi

Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression (SVR) and twin support vector regression (TSVR) techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR.Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon (DVR) ve ikiz destek vektör regresyon (İDVR) teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir

Development and implementation of fuzzy controller in biodiesel production

Orientador: Flavio Vasconcelos da SilvaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuimicaResumo: A crescente utilização do biodiesel como combustível renovável e, principalmente, as situações de operação transiente, de característica complexa e não-linear, motivaram a implementação de um controlador digital avançado neste processo. O controlador avançado foi implementado em computador através de um sistema de comunicação digital, visando manter a temperatura da mistura reacional em 50 °C. A temperatura é uma variável importante deste processo devido à influência direta na taxa de conversão do óleo em biodiesel. Óleo de soja foi utilizado como fonte de ácidos graxos, além do uso do álcool etílico anidro e ácido sulfúrico como reagentes. A reação química foi acompanhada durante 1 hora de batelada visando assegurar uma boa conversão do óleo (acima de 90%). Neste trabalho, além do controlador avançado também foi implementado o controlador PID. Os parâmetros de sintonia do controlador PID foram obtidos em malha aberta e auxiliaram a sintonia do controlador fuzzy. Durante a etapa de sintonia fina do controlador fuzzy foram alteradas as funções de pertinência, o universo de discurso e a base de regras. A implementação do controlador fuzzy apresentou-se como uma ferramenta apropriada para o controle da temperatura reacional devido às complexidades advindas das variações dos parâmetros deste processo. A análise comparativa do desempenho dos controladores fuzzy e PID aplicados à produção de biodiesel comprovaram isso. O uso do controlador fuzzy reduziu o consumo de energia elétrica durante a batelada em 10% quando comparado ao parâmetro obtido para o controlador PID e também reduziu o tempo necessário para atingir a máxima conversão do óleo em biodiesel em 15 minAbstract: The increasing use of biodiesel as a renewable fuel, and mainly because of their nonlinearity and time varying properties this work aimed the implementation and the development of an automation system based in fuzzy logic. In this work, the fuzzy controller is applied in the maintenance of the temperature of bulk at 50?C, using digital system. The control of the temperature in this process is important to guarantee the final quality of biodiesel. Soybean oil was used like source of fatty acids, ethanol and sulfuric acid were used like reagents. This reaction occured during an hour to can achieved high conversion (above 90%). The PID controller tuning parameters were obtained via open-loop experiments. The tuning the fuzzy controller can be achieved by modifying the rules, the discurse universe and the pertinence functions. Due the complexity and the nonlinearity of this reaction, the results of this study showed the effectiveness of fuzzy controller. The fuzzy controller reduced the energy consumption (10% smaller) and the batch time (15 min smaller) when compared to a PID controllerDoutoradoSistemas de Processos Quimicos e InformaticaDoutor em Engenharia Químic

Critical Asset and Portfolio Risk Analysis for Homeland Security

Providing a defensible basis for allocating resources for critical infrastructure and key resource protection is an important and challenging problem. Investments can be made in countermeasures that improve the security and hardness of a potential target exposed to a security hazard, deterrence measures to decrease the likeliness of a security event, and capabilities to mitigate human, economic, and other types of losses following an incident. Multiple threat types must be considered, spanning everything from natural hazards, industrial accidents, and human-caused security threats. In addition, investment decisions can be made at multiple levels of abstraction and leadership, from tactical decisions for real-time protection of assets to operational and strategic decisions affecting individual assets and assets comprising a regions or sector. The objective of this research is to develop a probabilistic risk analysis methodology for critical asset protection, called Critical Asset and Portfolio Risk Analysis, or CAPRA, that supports operational and strategic resource allocation decisions at any level of leadership or system abstraction. The CAPRA methodology consists of six analysis phases: scenario identification, consequence and severity assessment, overall vulnerability assessment, threat probability assessment, actionable risk assessment, and benefit-cost analysis. The results from the first four phases of CAPRA combine in the fifth phase to produce actionable risk information that informs decision makers on where to focus attention for cost-effective risk reduction. If the risk is determined to be unacceptable and potentially mitigable, the sixth phase offers methods for conducting a probabilistic benefit-cost analysis of alternative risk mitigation strategies. Several case studies are provided to demonstrate the methodology, including an asset-level analysis that leverages systems reliability analysis techniques and a regional-level portfolio analysis that leverages techniques from approximate reasoning. The main achievements of this research are three-fold. First, this research develops methods for security risk analysis that specifically accommodates the dynamic behavior of intelligent adversaries, to include their tendency to shift attention toward attractive targets and to seek opportunities to exploit defender ignorance of plausible targets and attack modes to achieve surprise. Second, this research develops and employs an expanded definition of vulnerability that takes into account all system weaknesses from initiating event to consequence. That is, this research formally extends the meaning of vulnerability beyond security weaknesses to include target fragility, the intrinsic resistance to loss of the systems comprising the asset, and weaknesses in response and recovery capabilities. Third, this research demonstrates that useful actionable risk information can be produced even with limited information supporting precise estimates of model parameters

The potentional application of artificial intelligence in motor vehicles braking system performance

Osnovni zahtevi koji se postavljaju pred današnje kočne sisteme motornih i priključnih vozila, u pogledu bezbednosti vozila i saobraćaja, se odnose na njihovo dalje unapređenje kroz razvoj novih, inteligentnih, rešenja. Suština ovih zahteva jeste da se omogući pomoć vozaču kroz inteligentno upravljanje sistemima na vozilu, odnosno njihovim performansama u različitim, dinamički promenljivim, radnim uslovima. Pošto kočne performanse vozila zavise od performansi kočnica, koje funkcionišu na principima trenja i samim tim imaju vrlo nepredvidiv karakter, i od usklađenosti tih performansi sa trenutnim uslovima prijanjanja u kontaktu pneumatika sa tlom, koji se mogu intenzivno menjati tokom samo jednog ciklusa kočenja, realizacija ovih zahteva je izuzetno kompleksna. To je osnovni razlog za sprovođenje istraživanja u pogledu razvoja i implementacije inteligentnijih načina upravljanja performansama kočnog sistema na osnovu uslova prijanjanja u kontaktu pneumatik–tlo. U ovoj doktorskoj disertaciji su istraživane mogućnosti primene tehnika iz oblasti veštačke inteligencije u cilju modeliranja složenih dinamičkih uticaja radnih režima kočnica motornih vozila i uslova u kontaktu pneumatik–tlo, kao i predviđanja ovih uticaja u cilju upravljanja performansama kočnica, a time i performansama kočnog sistema, u toku ciklusa kočenja. Zbog nemogućnosti modeliranja složenih dinamičkih uticaja radnih režima kočnica motornih vozila na njihove izlazne performanse, odnosno na vrednosti klizanja u kontaktu pneumatika i puta pomoću klasičnih matematičkih metoda, uvedena je nova inteligentna metoda bazirana na dinamičkim veštačkim neuronskim mrežama i fazi logici. U skladu sa time, u ovoj disertaciji su istraživane mogućnosti primene dinamičkih veštačkih neuronskih mreža i fazi logike u cilju modeliranja, predviđanja i inteligentnog upravljanja performansama kočnica, odnosno performansama kočnog sistema. Predmetno istraživanje je usmereno ka razvoju sposobnosti kočnog sistema ka inteligentnom prilagođavanju sile kočenja dinamičkim promenama podužnog klizanja točka (pneumatika) u kontaktu sa putem u toku ciklusa kočenja. Ovakav koncept upravljanja performansama kočnog sistema, na osnovu prethodnih i trenutnih vrednosti posmatranih uticajnih veličina i identifikovanih uslova prijanjanja tokom kočenja, podrazumeva predviđanje potrebne vrednosti pritisaka aktiviranja kočnica, na prednjoj i zadnjoj osovini, za date uslove kočenja (vrednosti pritiska aktiviranja kočnice, vrednosti brzine točka na prednjoj/zadnjoj osovini, temperature u kontaktu frikcionog para kočnice na prednjoj/zadnjoj osovini i vrednosti klizanja u kontaktu pneumatik–tlo) kako bi se u kontaktu pneumatika i tla postiglo željeno (optimalno) klizanje u podužnom pravcu.In terms of vehicle and traffic safety, the main demands imposed to the braking systems of motor vehicles and trailers are related to their further improvement through development of new, intelligent, solutions. It could enable the driver assistance function through an intelligent control of the vehicle systems performance in different and dynamically changing operating conditions. Since the braking performance of vehicles depend on the performance of the brakes, which based their function on the friction, it is a difficult to control stochastically changed the brakes performance. Furthermore, harmonization of that performance with the actual conditions in the tire-road contact, which is also intensively changed during a braking cycle, the realization of demands towards an intelligent control the braking system performance is very complex. This is the main reason for conducting research regarding development and implementation of more intelligent ways for control of the braking system performance. In this doctoral thesis, possibilities for employing of an artificial intelligence have been investigated in order to model and predict the impact of the brakes operating regimes and the complex conditions in the tire-road contact in order to provide intelligent controlling of the braking system performance during a braking cycle. Due to the impossibility for modeling of complex dynamic influences of brakes’ operating conditions on their performance and consequently on the value of the longitudinal wheel slip using conventional mathematical methods, a new method has been introduced based on an integration of dynamic neural networks and fuzzy logic. Accordingly, this thesis investigated possibilities for the proper integration of dynamic artificial neural networks and fuzzy logic in modeling, prediction, and intelligent control of the brakes’ performance, i.e. performance of the braking system. It should provide inherent capabilities of the braking system towards an intelligent adaptation of the braking forces to the dynamic changes of the longitudinal slip ratio in the tire–road contact during a braking cycle. This concept for control of the braking system performance, based on previous and current values of observed influential factors, means predicting of the brake applied pressure values, on the front and rear axle, for the given braking conditions (brake applied pressure, wheel speed on the front/rear axle, brake interface temperature on the front/rear axle, and wheel slip) in order to achieve the desired and/or optimal slip level in the longitudinal direction. Furthermore, the braking system should continuously learn about the complex and stochastic influences between these factors during a braking cycle. Since this is especially important for commercial vehicles, the focus of research has been directed on possibilities for improving the performance of electronically controlled braking system. It is done not only to achieve the optimal value of the longitudinal wheel slip in the tire-road contact, but also enables later optimization of the lateral wheel slip