Experimental investigation of supercapacitor based regenerative energy storage for a fuel cell vehicle equipped with an alternator

Recently, researchers have devoted more attention to supercapacitors (SCs) to integrate with batteries in energy storage systems (ESSs) for vehicle applications. In this study, we attempted to characterize the use of SCs in the ESS for a PEM fuel cell vehicle equipped with an alternator to maximize the performance of regenerative braking. We applied lithium-ion batteries (LIBs) and SCs as energy storage devices to examine their effect on ESS. Then we used a hysteresis brake to apply controllable braking force on the flywheel to form hybrid braking (HB) and made efforts to study its behavior to suggest a braking control strategy. We also ran the whole system over the rotational speed to cover the range of driving speed. At last, we sized the SCs for the most commonly used fuel cell electric vehicle (FCEV) in Korea, i.e., Hyundai NEXO, based on the results obtained from the above study by alternator efficiencies.     

Complex study of protective Cr3C2–NiAl coatings deposited by vacuum electro-spark alloying,pulsed cathodic arc evaporation,magnetron sputtering,and hybrid technology

Coatings were obtained by vacuum electro-spark alloying (VESA), pulsed cathodic arc evaporation (PCAE), magnetron sputtering (MS) techniques and VESA-PCAE-MS hybrid technology using Cr₃C₂–NiAl electrodes. The structure of the coatings was analyzed using scanning and transmission electron microscopy, X-ray diffraction and energy-dispersive spectroscopy. Mechanical properties were determined by nanoindentation, while tribological properties were assessed using pin-on-disk tribometer. Corrosion resistance was estimated by voltammetry in 1 N H₂SO₄ and 3.5%NaCl solutions. Oxidation resistance tests were performed at 800°С in air. The VESA coating had the highest thickness, low friction coefficient and high wear resistance. PCAE coating demonstrated the highest hardness (24 GPa) and elastic recovery (59%), oxidation resistance and superior corrosion resistance both in 1 N H₂SO₄ (i_corr = 70 μА/cm²) and 3.5%NaCl (i_corr = 0.74 μА/cm²) solutions. The MS coating had average mechanical properties and low corrosion current density (71 μА/cm²) in 1 N H₂SO₄. Deposition of coatings using VESA-PCAE-MS hybrid technology led to an increase in corrosion and oxidation resistance at least by 1.5 times in comparison with the VESA coating.     

Iron(II) Sulfate(VI) from Titania Production as a Raw Material for Preparation of Hydrogen Sulfide Sorbents

A hybrid sorbent material for removal of hydrogen sulfide from air was developed. The material is based on activated carbon and iron compounds obtained from waste iron(II) sulfate(VI) heptahydrate. The iron salt is deposited on the carbonaceous support and subjected to oxidation (Fe²⁺ to Fe³⁺) using atmospheric oxygen under alkaline conditions. An effect of H₂O₂ addition to the process on the composition of the resultant material was also examined. X-ray diffraction (XRD) analyses confirmed easy conversion of waste FeSO₄·7H₂O to iron oxides Fe₃O₄ and FeOOH. The activated carbon supporting iron oxides revealed a higher efficiency in H₂S elimination from air compared to the commercial activated carbon, without any modification.     

Decomposition of first-order hybrid Petri nets for hierarchical control of manufacturing systems

Although hybrid Petri net (HPN) is a popular formalism in modelling hybrid production systems, the HPN model of large scale systems gets substantially complicated for analysis and control due to large dimensionality of such systems. To overcome this problem, a typical approach is to decompose the net into subnets and then control the plant through hierarchical or decentralized structures. Although this concept has been widely discussed in the literature for discrete PNs, there is a lack of research for HPNs. In this paper, a new method of decomposition of first-order hybrid Petri nets (FOHPNs) is proposed first and then the hierarchical control of the subnets through a coordinator is introduced. The advantage of using the proposed approach is validated by an existing example. A sugar milling case study is analysed by using a decomposed FOHPN model and the optimization results are compared against the results of the approaches presented in other papers. Simulation results show not only an improvement in production rate, but also show the ability to control the plant online. In addition, by using the hierarchical control structure for an FOHPN model, it is possible to reduce the cost of communication links, improve the reliability of the system, maintain the plant locally, and partially redesign the system.     

Combined hardware–software multi-parallel prefiltering on the Convey HC-1 for fast homology detection

Protein databases used in research are huge and still grow at a fast pace. Many comparisons need to be done when searching similar (homologous) sequences for a given query sequence in these databases. Comparing a query sequence against all sequences of a huge database using the well-known Smith–Waterman algorithm is very time-consuming. Hidden Markov Models pose an opportunity for reducing the number of entries of a database and also enable to find distantly homologous sequences. Fewer entries are achieved by clustering similar sequences in a Hidden Markov Model. Such an approach is used by the bioinformatics tool HHblits. To further reduce the runtime, HHblits uses two-level prefiltering to reduce the number of time-consuming Viterbi comparisons. Still, prefiltering is very time-consuming. Highly parallel architectures and huge bandwidth are required for processing and transferring the massive amounts of data. In this article, we present an approach exploiting the reconfigurable, hybrid computer architecture Convey HC-1 for migrating the most time-consuming part. The Convey HC-1 with four FPGAs and high memory bandwidth of up to 76.8 GB/s serves as the platform of choice. Other bioinformatics applications have already been successfully supported by the HC-1. Limited by FPGA size only, we present a design that calculates four first-level prefiltering scores per FPGA concurrently, i.e. 16 calculations in total. This score calculation for the query profile against database sequences is done by a modified Smith–Waterman scheme that is internally parallelized 128 times in contrast to the original Streaming ‘Single Instruction Multiple Data (SIMD)’ Extensions (SSE)-supported implementation where only 16-fold parallelism can be exploited and where memory bandwidth poses the limiting factor. Preloading the query profile, we are able to transform the memory-bound implementation to a compute- and resource-bound FPGA design. We tightly integrated the FPGA-based coprocessor into the hybrid computing system by employing task-parallelism for the two-level prefiltering. Despite much lower clock rates, the FPGAs outperform SSE-based execution for the calculation of the prefiltering scores by a factor of 7.9.     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Using a Discrete-Event System Specifications (DEVS) for designing a Modelica compiler

We introduce a new architecture for the design of a tool for modeling and simulation of continuous and hybrid systems. The environment includes a compiler based on Modelica, a modular and a causal standard specification language for physical systems modeling (the tool supports models composed using certain component classes defined in the Modelica Standard Library, and the instantiation, parameterization and connection of these MSL components are described using a subset of Modelica). Models are defined in Modelica and are translated into DEVS models. DEVS theory (originally defined for modeling and simulation of discrete event systems) was extended in order to permit defining these of models. The different steps in the compiling process are show, including how to model these dynamic systems under the discrete event abstraction, including examples of model simulation with their execution results.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Hybrid make-to-stock and make-to-order systems: a taxonomic review

Production planning and control (PPC) systems that employ aspects from both make-to-order (MTO) and make-to-stock (MTS) production control are known as hybrid MTS/MTO systems. While both MTO and MTS separately have been studied extensively, their combined use has received less attention. However, the literature on this topic is growing and this paper shows that the review performed in this paper is an important addition to the field. We categorise relevant literature according to a novel taxonomy and show that hybrid MTS/MTO production control can be used in different contexts. In addition, an overview of the modelling techniques and methods used in these papers is provided. Based on the reviewed literature, relevant research questions and directions for future research are identified. Finally, it is shown that hybrid MTS/MTO production control is prevalent in practice by discussing research with industrial applications. The paper contains an overview of research on hybrid MTS/MTO production control to be used as reference for researchers active in the field, and provides managerial insights and directions for future research on this topic.     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.