Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

Clustering Algorithms: Their Application to Gene Expression Data

Energy management strategy of Supercapacitor/Fuel Cell energy storage devices for vehicle applications

An Evolutionary Neuro-Fuzzy C-means Clustering Technique

The purpose of this work is to design a power allocation method (PAM) for multistack fuel cell system (MFCS). Due to the limitations of technology, large-scale application of proton exchange membrane fuel cell (PEMFC) in the field of rail transportation has not been implemented. Therefore, this study presents an MFCS to promote the use of PEMFCs in the high-power energy market. During the operation of the MFCS, since the output power of the FC varies dynamically with different operation conditions, the performance of each stack is usually inconsistent. A method for evaluating the degree of performance degradation (DOPD) of stacks is presented in this study. In addition, a fuel cell (FC) semiempirical model is also adopted to simulate the effects of aging on the stacks’ performance. In order to maintain the uniform performance of stacks and enhance the lifespan of the MFCS, a PAM that considers the DOPD of each stack is presented. Besides, in order to facilitate system expansion, this article proposes a virtual droop control method to realize power splitting among the stacks. Also, a voltage control strategy is presented to compensate for the bus voltage drop caused by the droop control. The effectiveness and practicability of the presented PAM and MFCS are verified on the hardware-in-the-loop test bench constructed by RT-LAB.

A Power Allocation Method for Multistack PEMFC System Considering Fuel Cell Performance Consistency

In the conditions of climate change and the scarcity of natural resources, the future of energy is increasingly associated with the development of the so-called green energy. Its development is reflected in the European Commission strategic vision to transition to a climate-neutral economy. This is a challenge that the Central and Eastern European (CEE) countries, members of the EU, are also trying to meet. In recent years, these countries have seen an increase in the share of renewable energy and a reduction in greenhouse gas emissions (GGE). On the other hand, basing the energy sector on unstable energy sources (photovoltaics and wind technologies) may imply new challenges on the way to sustainable development. These are old problems in a new version (ecology, diversification of supplies) and new ones related to the features of renewable energy sources (RES; instability, dispersion). The aim of the article was to classify, on the basis of taxonomic methods, the CEE countries from the point of view of green energy transformation (original indicator) and to predict new threats to Romania, Poland, and Bulgaria, the countries representing different groups according to the applied classification. The issues presented are part of a holistic view of RES and can be useful in energy policy.

https://www.mdpi.com/1996-1073/14/4/884/pdf

Green Energy in Central and Eastern European (CEE) Countries: New Challenges on the Path to Sustainable Development

http://www.nt.ntnu.no/users/skoge/prost/proceedings/dycops2013-and-cab2013/media/DYCOPS/files/0117.pdf

A New Cluster Validity Index for Fuzzy Clustering

Optimal power distribution control for a network of fuel cell stacks

In power networks where multiple fuel cell stacks are employed to deliver the required power, optimal sharing of the power demand between different stacks is an important problem. This is because the total current collectively produced by all the stacks is directly proportional to the fuel utilization, through stoichiometry. As a result, one would like to produce the required power while minimizing the total current produced. In this paper, an optimization formulation is proposed for this power distribution control problem. An algorithm that identifies the globally optimal solution for this problem is developed. Through an analysis of the KKT conditions, the solution to the optimization problem is decomposed into on-line and on-line computations. The on-line computations reduce to simple equation solving. For an application with a specific v-i function derived from data, we show that analytical solutions exist for on-line computations. We also discuss the wider applicability of the proposed approach for similar problems in other domains.

Optimal Power Distribution Control for a Network of Fuel Cell Stacks

Optimum use of available energy sources is essential for cost effective and sustainable growth. Fuel cells - due to their ability in efficiently extracting energy from fuels - have gained considerable attention among the various energy conversion alternatives. Systems researchers working in the field of fuel cells have been focusing on optimal stack design and the attendant modeling aspects. In a power network where multiple fuel cell stacks combine together to achieve the required power, it is not enough to focus only on the optimal design of the stacks. While operating the stacks, the problem of optimal sharing of power between the different stacks in a power network is another important problem that needs to be addressed. This optimal power sharing problem is the focus of this paper. We will describe a novel solution approach for this optimization problem, which through prior off-line computations reduces the on-line optimization task to one of solving simple equations. Another major significance of this new approach is that unlike the conventional optimizers, global optimum is guaranteed using this approach. The proposed algorithm uses a data-based model between the voltage and current for optimization. To account for changes in the system characteristics with time, a model updater algorithm that updates the data-based model using newly available data and the previous model is proposed.

Sreeram Joopudi

Papers

A New Cluster Validity Index for Fuzzy Clustering

Optimal power distribution control for a network of fuel cell stacks

Optimal Power Distribution Control for a Network of Fuel Cell Stacks

Optimal Power Sharing Control in Networked Fuel Cell Stacks