Proposed Combination Technique Research Articles

Influences of heat treatment on mechanical properties of SCF/PEEK composites in FDM‐3D printing process with UV laser assistance

AbstractFused deposition modeling (FDM) process with laser assistance can effectively enhance the interfacial strength of short carbon fiber/poly‐ether‐ether‐ketone (SCF/PEEK) composites. However, excessive crystallization of the semi‐crystalline matrix generally results in irregular shrinkage, which seriously deteriorates dimensional accuracy of composite specimens. To overcome this issue, we establish a novel heat treatment technique by the combinations of laser assistance and post‐treatment to improve the interlayer properties and dimensional accuracy simultaneously. It is found that lateral cracks at the interlayer interfaces can be completely eliminated at laser pre‐heating temperature , due to the reduction of temperature gradient and residual stresses. The further increased evidently promotes molecular diffusion and crystallization behaviors, thus increasing the interlaminar shear strength of FDM‐printed specimens. The post‐treatment temperature visibly promotes mutual diffusions of the molecules and formations of the perfect crystals, strengthening the interlayer adhesion of specimens. The proposed combination technique can increase the interlayer strengths of composite specimens by approximately 22.5%, while their warpage deformations are suppressed simultaneously.Highlights Pre‐heated temperature was a threshold for eliminating lateral cracks at interlayer interfaces. Post‐treatment significantly promoted molecular diffusion and crystallization behaviors. A novel technique was established to improve interlayer strength while suppressing warpage. The combination technique could increase the interlayer shear strength by approximately 22.5%.

Study and Comparison of Seismic Behaviour of Isolator-Damper Hybrid Control System with Conventional Structural Systems

In this paper, in addition to introduce a hybrid structural system contained local isolators and dampers, its behavior and functional capabilities were studied on a conventional structure. For this purpose, an RC frame building with six-story was designed based on valid codes and then, in four cases based on the number of spans, it was split into two separate adjacent frames. Base isolation was done underneath the columns of one frame, while the bottom connections of the other frame’s columns were remained fixed and viscous dampers provided the connection of two adjacent frames on the same floors. Nonlinear time history analysis (NTHA) under three near-fault and three far-fault earthquakes and frequency-domain analysis are performed. Displacement, drift, acceleration and shear forces of the stories in the four proposed hybrid cases with two limited cases, full base-isolated and full base-fixed frames, as well as nonlinear hysteresis behavior of a damper and an isolator are assessed. The results showed that using the novel hybrid control method in most cases can mitigate deteriorating effects of all types of seismic motions observed in the conventional structural systems. However, among them, two cases (2 isolated columns -5 fixed columns and 3 isolated columns -4 fixed columns) had the best significant influence on seismic performance and structural response reduction. Furthermore, frequency response functions of displacement and acceleration with respect to ground acceleration demonstrated that the two proposed cases further suppress the responses of the limited cases, over a wide range of frequencies including all natural frequencies. Due to decrease about 50-70% in the number of base isolators (compared to full isolation) lead to considerable construction cost savings. In spite of the limitation of ASCE7-10 code on separately using base isolators and dampers on structure, applying the proposed combination technique of these two dissipating devices can overcome the limitation.

Bagging of credal decision trees for imprecise classification

The Credal Decision Trees (CDT) have been adapted for Imprecise Classification (ICDT). However, no ensembles of imprecise classifiers have been proposed so far. The reason might be that it is not a trivial question to combine the predictions made by multiple imprecise classifier. In fact, if the combination method used is not appropriate, the ensemble method could even worse the performance of one single classifier. On the other hand, the Bagging scheme has shown to provide satisfactory results in precise classification, specially when it is used with CDTs, which are known to be very weak and unstable classifiers. For these reasons, in this research, it is proposed a new Bagging scheme with ICDTs. It is presented a new technique for combining predictions made by imprecise classifiers that tries to maximize the precision of the bagging classifier. If the procedure for such a combination is too conservative it is easy to obtain few information and worse the results of a single classifier. Our proposal considers only the states with the minimum level of non-dominance. An exhaustive experimentation carried out in this work has shown that the Bagging of ICDTs, with our proposed combination technique, performs clearly better than a single ICDT.

Urban Mapping Accuracy Enhancement in High-Rise Built-Up Areas Deployed by 3D-Orthorectification Correction from WorldView-3 and LiDAR Imageries

Orthorectification is an important step in generating accurate land use/land cover (LULC) from satellite imagery, particularly in urban areas with high-rise buildings. Such buildings generally appear as oblique shapes on very-high-resolution (VHR) satellite images, which reflect a bigger area of coverage than the real built-up area on LULC mapping. This drawback can cause not only uncertainties in urban mapping and LULC classification, but can also result in inaccurate urban change detection. Overestimating volume or area of high-rise buildings has a negative impact on computing the exact amount of environmental heat and emission. Hence, in this study, we propose a method of orthorectfiying VHR WorldView-3 images by integrating light detection and ranging (LiDAR) data to overcome the aforementioned problems. A 3D rational polynomial coefficient (RPC) model was proposed with respect to high-accuracy ground control points collected from the LiDAR data derived from the digital surface model. Multiple probabilities for generating an orthrorectified image from WV-3 were assessed using 3D RCP model to achieve the optimal combination technique, with low vertical and horizontal errors. Ground control point (GCPs) collection is sensitive to variation in number and data collection pattern. These steps are important in orthorectification because they can cause the morbidity of a standard equation, thereby interrupting the stability of 3D RCP model by reducing the accuracy of the orthorectified image. Hence, we assessed the maximum possible scenarios of resampling and ground control point collection techniques to bridge the gap. Results show that the 3D RCP model accurately orthorectifies the VHR satellite image if 20 to 100 GCPs were collected by convenience pattern. In addition, cubic conventional resampling algorithm improved the precision and smoothness of the orthorectified image. According to the root mean square error, the proposed combination technique enhanced the vertical and horizontal accuracies of the geo-positioning process to up to 0.8 and 1.8 m, respectively. Such accuracy is considered very high in orthorectification. The proposed technique is easy to use and can be replicated for other VHR satellite and aerial photos.

A new consensus between the mean and median combination methods to improve forecasting accuracy

To improve the forecasting accuracies, researchers have long been using various combination techniques. In particular, the use of dissimilar methods for forecasting time series data is expected to provide superior results. Although numerous combination techniques have been proposed until date, the simple combination techniques - such as mean and median - maintain their strength, popularity, and utility. This paper proposes a new combination method based on the mean and median combination methods so as to combine the advantages of both these methods. The proposed combination technique attempts to utilize the strong aspects of each method and minimize the risk that arises from the selection of the combination method with poor performance. In order to depict the potential power of the proposed combining method, well-known six real-world time series data were used. Our results indicate that the proposed method presents with promising performances. In addition, a nonparametric statistical test was exploited to reveal the superiority of the proposed method over the single methods and other forecast combination methods from all of the investigated data sets.

Low sampling rate technique based frequency-domain random demodulation for broadband digital predistortion

This paper proposes a combination technique of the frequency-domain random demodulation (FRD) and the broadband digital predistorter (DPD). This technique can linearize the power amplifiers (PAs) at a low sampling rate in the feedback loop. Based on the theory of compressed sensing (CS), the FRD method preprocesses the original signal using the frequency domain sampling signal with different stages through multiple parallel channels. Then the FRD method is applied to the broadband DPD system to restrict the sampling process in the feedback loop. The proposed technique is assessed using a 30 W Class-F wideband PA driven by a 20 MHz orthogonal frequency division multiplexing (OFDM) signal, and a 40 W GaN Doherty PA driven by a 40 MHz 4-carrier long-term evolution (LTE) signal. The simulation and experimental results show that good linearization performance can be achieved at a lower sampling rate with about 24 dBc adjacent channel power ratio (ACPR) improvement by applying the proposed combination technique FRD-DPD. Furthermore, the performance of normalized mean square error (NMSE) and error vector magnitude (EVM) also has been much improved compared with the conventional technique.

Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid

In this paper, a modified backpropagation neural network is combined with a chaos-search genetic algorithm and simulated annealing algorithm for very short term electrical energy demand prediction in deregulated power industry. Multiple modifications are carried out on the conventional backpropagation algorithm such as improvements in the momentum factor and adaptive learning rate. In the hybrid scheme, the initial parameters of the modified neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. The solution set provided by the optimized genetic algorithm is further improved by using the strong local search ability of simulated annealing algorithm. The real data of New South Wales, Australian grid, is used in the experimentation for 1-h-ahead forecast with an emphasis on data analysis and preprocessing framework. The correlation analysis is used for the identification and selection of the most influential input variables. The simulation results reveal that the proposed combination technique effectively enhanced the prediction accuracy as compared to the available conventional methods. The prediction of 1-h-ahead load demand is critically important for decision-making response of the modern smart grid system. The acceptable precision of the proposed model concludes that it can be applied in the smart grid to enhance its demand responsiveness and other intelligent features.

Air-Gap Magnetic Field Analysis of Wind Generator With PM Embedded Salient Poles by Analytical and Finite Element Combination Technique

A combined analytical and FEM technique is proposed for predicting the air-gap magnetic field distribution of a PM embedded salient pole wind generator. The influence of slotting on both radial and circumferential components of air-gap flux density is taken into account. By employing the proposed combination technique, the magnetic field distributions when the stator and rotor are at different relative positions can be calculated much faster than that by FEM. Based on the magnetic field distributions, the cogging torques are predicted and verified by the FEM, while the relationship between cogging torque and design parameters are further investigated. The proposed combination technique provides an efficient and accurate prediction method for the air-gap magnetic field distribution of large electrical machines with a complex PM pole structure.

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.

Multistage SIMO-Model-Based Blind Source Separation Combining Frequency-Domain ICA and Time-Domain ICA

In this paper, single-input multiple-output (SIMO)-model-based blind source separation (BSS) is addressed, where unknown mixed source signals are detected at microphones, and can be separated, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. This technique is highly applicable to high-fidelity signal processing such as binaural signal processing. First, we provide an experimental comparison between two kinds of SIMO-model-based BSS methods, namely, conventional frequency-domain ICA with projection-back processing (FDICA-PB), and SIMO-ICA which was recently proposed by the authors. Secondly, we propose a new combination technique of the FDICA-PB and SIMO-ICA, which can achieve a higher separation performance than the two methods. The experimental results reveal that the accuracy of the separated SIMO signals in the simple SIMO-ICA is inferior to that of the signals obtained by FDICA-PB under low-quality initial value conditions, but the proposed combination technique can outperform both simple FDICA-PB and SIMO-ICA.

Rapid fabrication of large-area periodic structures containing well-defined defects by combining holography and mask techniques

We demonstrate a promising method to fabricate large-area periodic structures with desired defects by using the combination of multiple-exposure two-beam interference and mask-photolithography techniques. Multiple-exposure of two-beam interference pattern at 325 nm into a positive AZ-4620 (or a negative SU-8) photopolymerizable photoresist is used to form a square and hexagonal two-dimensional periodic structures. Desired defects are introduced in these structures by irradiating the sample with one beam of the same laser through a mask in which the design of defects is patterned. A 1cm x 1cm periodic structures with the lattice constant as small as 365nm embedding several kinds of defect, such as waveguide or Mach-Zehnder, was obtained by employing this combination technique. It shows that the proposed combination technique is useful for mass production of photonic crystal optoelectronics devices.