Arrow Research search

Author name cluster

Panos M. Pardalos

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers
2 author rows

Possible papers

6

EAAI Journal 2015 Journal Article

Intelligent virtual reference feedback tuning and its application to heat treatment electric furnace control

  • Ling Wang
  • Haoqi Ni
  • Ruixin Yang
  • Panos M. Pardalos
  • Li Jia
  • Minrui Fei

Virtual Reference Feedback Tuning (VRFT) is a data-driven one-shot control method which is very attractive for engineering applications. However, it cannot design controllers with the optimal control performance based on the standard VRFT approach as performance indices are not explicitly represented in its objective function. To deal with this problem, this paper presents a novel intelligent VRFT (IVRFT) based on adaptive binary ant system harmony search (ABASHS) where the reference model of VRFT, which potentially determines the control performance, is coordinately optimized with the controller by ABASHS to achieve the best control performance. Finally, the proposed ABASHS-based intelligent virtual reference feedback tuning (ABASHS-IVRFT) method is applied to the temperature control of the heat treatment electric furnace. The simulation results demonstrate that ABASHS-IVRFT is valid and can implement the optimal non-overshoot control easily and efficiently. Considering the characteristics such as ease of implementation and no need of the model information of controlled objects, ABASHS-IVRFT is a promising approach for engineering applications.

Details DOI

EAAI Journal 2014 Journal Article

MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system

  • Ling Wang
  • Haoqi Ni
  • Weifeng Zhou
  • Panos M. Pardalos
  • Jiating Fang
  • Minrui Fei

As the performance of Linear Quadratic Regulator (LQR) controllers greatly depends on its weighting matrices, i. e. Q and R, designing these two matrices is one of the most important components in the LQR problem which is a tedious and challenging work in the applications of LQR. Hence, a novel LQR approach based on the Pareto-based Multi-objective Binary Probability Optimization Algorithm (MBPOA) is proposed in this paper, in which MBPOA is utilized to search for the optimal weighting matrices to relieve the effort of parameter settings and improve the control performance according to the pre-defined objective functions. By combining LQR with MBPOA, the optimal controllers can be obtained easily and effortless. Moreover, the control performance can be adjusted further conveniently to meet the requirements of applications as a set of Pareto-optimal LQR controllers is offered. The simulation and experiment results on the double-parallel inverted pendulum system demonstrate the effectiveness and efficiency of the developed MBPOA-based LQR method. Considering the characteristics such as robustness, the optimal dynamic performance and easy implementation without prior knowledge, the MBPOA-based LQR is a promising control approach for engineering applications.

Details DOI

TCS Journal 2012 Journal Article

Robust optimization of graph partitioning involving interval uncertainty

  • Neng Fan
  • Qipeng P. Zheng
  • Panos M. Pardalos

The graph partitioning problem consists of partitioning the vertex set of a graph into several disjoint subsets so that the sum of weights of the edges between the disjoint subsets is minimized. In this paper, robust optimization models with two decomposition algorithms are introduced to solve the graph partitioning problem with interval uncertain weights of edges. The bipartite graph partitioning problem with edge uncertainty is also presented. Throughout this paper, we make no assumption regarding the probability of the uncertain weights.

Details DOI

AIIM Journal 2011 Journal Article

Classification of cancer cell death with spectral dimensionality reduction and generalized eigenvalues

  • Mario R. Guarracino
  • Petros Xanthopoulos
  • Georgios Pyrgiotakis
  • Vera Tomaino
  • Brij M. Moudgil
  • Panos M. Pardalos

Objective Accurate cell death discrimination is a time consuming and expensive process that can only be performed in biological laboratories. Nevertheless, it is very useful and arises in many biological and medical applications. Methods and material Raman spectra are collected for 84 samples of A549 cell line (human lung cancer epithelia cells) that has been exposed to toxins to simulate the necrotic and apoptotic death. The proposed data mining approach for the multiclass cell death discrimination problem uses a multiclass regularized generalized eigenvalue algorithm for classification (multiReGEC), together with a dimensionality reduction algorithm based on spectral clustering. Results The proposed algorithmic scheme can classify A549 lung cancer cells from three different classes (apoptotic death, necrotic death and control cells) with 97. 78%±0. 047 accuracy versus 92. 22±0. 095 without the proposed feature selection preprocessing. The spectrum areas depicted by the algorithm corresponds to the 〉C O bond from the lipids and the lipid bilayer. This chemical structure undergoes different change of state based on cell death type. Further evidence of the validity of the technique is obtained through the successful classification of 7 cell spectra that undergo hyperthermic treatment. Conclusions In this study we propose a fast and automated way of processing Raman spectra for cell death discrimination, using a feature selection algorithm that not only enhances the classification accuracy, but also gives more insight in the undergoing cell death process.

Details DOI
v2026.09.13