Fatigue life improvement of 12Cr1МoV steel by irradiation with Zr+ ion beam

Cyclic tension and bend tests were performed on heat-resistant 12Cr1MoV steel specimens in as-supplied condition as well as after Zr⁺ ion beam surface irradiation. Distinct differences in strain induced relief, as well in cracking pattern of modified surface layer were observed by optical microscopy and interference profilometry. Changes in subsurface layer are characterized by means of nano- and microindentation and fractography of fracture surfaces (with the help of scanning electron microscopy). It is shown that the main influence on mechanical properties is mostly induced by thermal treatment during irradiation rather than formation of a 2 μm thick layer doped with Zr. The differences in deformation behavior may be explained by physical mesomechanics concepts.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.     

An enhanced anti-disturbance attitude control law for flexible spacecrafts subject to multiple disturbances

As well-known disturbance rejection methods, active disturbance rejection control and disturbance observer-based control can effectively improve the control performances of complex systems in the presence of disturbances. However, the accurate rejection of multiple disturbances for control systems of practical engineering, for example, the attitude control system of flexible spacecraft, is still a bottleneck problem. In order to further improve the anti-disturbance capability and reduce the conservativeness, this paper proposes a novel enhanced anti-disturbance control law for the attitude control system of flexible spacecraft by combining active disturbance rejection control and disturbance observer-based control in a unified framework. More specifically, the disturbance from flexible vibration is described by an uncertain exogenous system based on the partially known information including elastic damping ratios and modal frequencies. The disturbance observer-based control is utilized to estimate and thereby reject this disturbance. On the other hand, the other disturbances such as external environmental disturbance and complex model nonlinearity are merged into a equivalent disturbance with bounded derivative, which is compensated by using the active disturbance rejection control law. Stability and robustness analysis are carried out for the disturbance observer and extended state observer. Finally, simulation results of low-earth-orbit flexible satellite are presented to verify the effectiveness of proposed methods.     

Anchoring single Pt atoms on hollow Ag3VO4 spheres for improved activity towards photocatalytic H2 evolution reaction

The high cost of noble metal catalysts has been a great bottleneck for the catalyst industry. Using the noble metal at a single-atom level for catalytic applications could dramatically decrease the cost. The impacts of single Pt atoms on the photocatalytic performance of Ag₃VO₄ have been investigated and reported. In this report, single Pt atoms were anchored on the surface of Ag₃VO₄ (AVO) as a cocatalyst, and the resultant composite photocatalyst has been studied for photocatalytic H₂ production from water driven by visible light. The as-prepared AVO particles are hollow nanospheres in the monoclinic phase with a bandgap of 2.20 eV. The light absorption edge of AVO/Pt is slightly red-shifted compared to that of the pristine AVO, indicating more visible light absorption of AVO/Pt. The XPS peaks of Ag, V, and Pt exhibit a significant shift after AVO and Pt get into contact, suggesting the strong interaction between the surface Ag and V atoms, and single Pt atoms. After 3-h illumination, the photocatalytic H₂ evolution amount from AVO/Pt is improved up to 1400 μmol, which is 2.8 times that on the bare AVO. Such efficient photocatalytic H₂ evolution on AVO/Pt is still maintained after five reaction cycles. The better photocatalytic performance of AVO/Pt has been attributed to the more efficient visible light utilization and the lower interfacial charge transfer resistance, as demonstrated in the DRS and EIS spectra. The presence of the surface Pt atoms also leads to a higher amount of reactive radicals, which could efficiently promote the surface redox reactions.     

Type II fuzzy set-based data analytics to explore amino acid associations in protein sequences of Swine Influenza Virus

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset.     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

An effective coding approach for multiobjective integrated resource selection and operation sequences problem

In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions. Received: May 2005/Accepted: December 2005     

DISPERSION COMPENSATION CFBG IN100km 10Gbit／s RECIRCULATING SYSTEMS

A 10Gbit/s recirculating system is configured with Chirped Fiber Bragg Grating (CFBG) for the dispersion compensation. For the first time, the transmission distance in the loop reaches 1000km with bit error rate of 10-9. The effect of the group delay ripple of the fiber grating is also investigated in the recirculating systems, and it is shown that the transmission distance is limited to 4 cycles (4×167.1km ) in the loop with the power penalty fluctuation below 1.0dB. Thus the group delay ripple should be reduced to allow for the wavelength drift of±5GHz. At the end of this letter, the principles are given for designing long haul recirculating systems with dispersion compensation CFBG.