Optimal Scaling Factor Research Articles

Boosting Engineering Optimization With a Novel Recursive Transfer Bifidelity Surrogate Modeling

Abstract In the engineering optimization, there often exist the multiple sources of information with different fidelity levels. In general, low-fidelity (LF) information is usually more accessible than high-fidelity (HF) information, while the latter is usually more accurate than the former. Thus, to capitalize on the advantages of this information, this study proposes a novel recursive transfer bifidelity surrogate modeling to fuse information from HF and LF levels. First, the selection method of optimal scale factor is proposed for constructing bifidelity surrogate model. Then, a recursive method is developed to further improve its performance. The efficacy of the proposed model is comprehensively evaluated using numerical problems and an engineering example. Comparative analysis with some surrogate models (five multifidelity and a single-fidelity surrogate models) demonstrates the superior prediction accuracy and robustness of the proposed model. Additionally, the impact of varying cost ratios and combinations of HF and LF samples on the performance of the proposed model is also investigated, yielding consistent results. Overall, the proposed model has superior performance and holds potential for practical applications in engineering design optimization problems.

Adaptive spatial down-sampling method based on object occupancy distribution for video coding for machines

As the performance of machine vision continues to improve, it is being used in various industrial fields to analyze and generate massive amounts of video data. Although the demand for and consumption of video data by machines has increased significantly, video coding for machines needs to be improved. It is therefore necessary to consider a new codec that differs from conventional codecs based on the human visual system (HVS). Spatial down-sampling plays a critical role in video coding for machines because it reduces the volume of the video data to be processed while maintaining the shape of the data’s features that are important for the machine to reference when processing the video. An effective method of determining the intensity of spatial down-sampling as an efficient coding tool for machines is still in the early stages. Here, we propose a method of determining an optimal scale factor for spatial down-sampling by collecting and analyzing information on the number of objects and the ratio of the area occupied by the object within a picture. We compare the data reduction ratio to the machine accuracy error ratio (DRAER) to evaluate the performance of the proposed method. By applying the proposed method, the DRAER was found to be a maximum of 21.40 dB and a minimum of 11.94 dB. This shows that video coding gain for the machines could be achieved through the proposed method while maintaining the accuracy of machine vision tasks.

A systematic review of allometric scaling exponents for IgG mAbs

Increasing complexity of mAbs in development creates challenges in predicting human pharmacokinetic (PK) parameters from preclinical data. The aim of this analysis was to identify optimal allometric scaling exponents. Data were extracted from literature to create a central database (currently the largest available published database) of two-compartment model parameters for mAbs (n = 59) in cynomolgus monkey (CM) and human. Global allometric exponents were calculated and drug-dependent factors were investigated as potential variables in determining the optimal scaling factor. The global exponents for scaling CM mAb PK data were 0.74 (CL), 0.80 (CL with Fc-modified mAbs excluded), 0.44 (CL with Fc-modified mAbs only), 0.71 (Q), 1.12 (V1), and 0.99 (V2). These values are in line with previously published literature values.

Optimizing Convolutional Neural Networks for Image Classification on Resource-Constrained Microcontroller Units

Running machine learning algorithms for image classification locally on small, cheap, and low-power microcontroller units (MCUs) has advantages in terms of bandwidth, inference time, energy, reliability, and privacy for different applications. Therefore, TinyML focuses on deploying neural networks on MCUs with random access memory sizes between 2 KB and 512 KB and read-only memory storage capacities between 32 KB and 2 MB. Models designed for high-end devices are usually ported to MCUs using model scaling factors provided by the model architecture’s designers. However, our analysis shows that this naive approach of substantially scaling down convolutional neural networks (CNNs) for image classification using such default scaling factors results in suboptimal performance. Consequently, in this paper we present a systematic strategy for efficiently scaling down CNN model architectures to run on MCUs. Moreover, we present our CNN Analyzer, a dashboard-based tool for determining optimal CNN model architecture scaling factors for the downscaling strategy by gaining layer-wise insights into the model architecture scaling factors that drive model size, peak memory, and inference time. Using our strategy, we were able to introduce additional new model architecture scaling factors for MobileNet v1, MobileNet v2, MobileNet v3, and ShuffleNet v2 and to optimize these model architectures. Our best model variation outperforms the MobileNet v1 version provided in the MLPerf Tiny Benchmark on the Visual Wake Words image classification task, reducing the model size by 20.5% while increasing the accuracy by 4.0%.

Hybrid selection combining scheme for correlated cooperative communication over [formula omitted]-[formula omitted] fading channels

Due to poor (limited) scattering, a correlation among channels is inevitable, resulting in performance degradation of wireless communication systems. In this paper, we propose a hybrid selection combining (SC), also known as switched scaled – SC for correlated cooperative communication over κ-μ fading channel. By using paired error analysis, we derive the end-to-end symbol error probability of this system over bivariate κ-μ channel fading with M-ary phase shift keying (MPSK) modulation. In addition, we obtain significant parameters of hybrid SS-SC, such as the optimum scaling factor and optimum threshold by using the second derivative test. For every signal to noise ratio (SNR), we obtain different optimum thresholds and scaling factors. Our analytical results were found to overlap significantly with those of the simulation results. They were also compared with the variants of SC and switched diversity techniques. Our study demonstrates that the proposed hybrid technique performs better than the other SC and switched diversity schemes over correlated and uncorrelated fading channels. Furthermore, we derive an asymptotic symbol error probability for the proposed system, and the asymptotic results are in agreement with the analytical results at higher SNRs. Our study also shows that the diversity order of the system depends on the mutually exclusive events of the proposed technique.

Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications

Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.

Scaled Selection Combining Receiver for Cooperative NOMA-based 5G Cellular Systems

Non-Orthogonal Multiple Access (NOMA) technology can accommodate more subscribers concurrently and achieve higher spectrum efficiency than Orthogonal Multiple Access (OMA) technologies. This work investigates the performance of the two subscriber downlink cooperative NOMA system with Scaled Selection Combining (SSC) comprising a Base Station (BS), a cell edge subscriber and a near subscriber. Due to the channel conditions, the error performance of the cell edge subscriber is poorer compared to the near subscriber. To mitigate this and enhance the performance, the proposed SSC combines signals from BS and near subscriber and chooses the best signal between them. The Symbol Error Probability (SEP) of this cellular system with M−ary Phase Shift Keying (MPSK) modulation is derived under the Rayleigh fading environment. The optimum scaling factor to reduce the SEP is also obtained. The theoretical SEP expressions of SSC are validated with the Monte-Carlo simulations. The proposed system provides better SEP and outperforms the non-cooperation and traditional Selection Combining (SC) schemes. Additionally, the efficacy of the proposed system under various modulation schemes is studied.

Simulations of Functional Motions of Super Large Biomolecules with a Mixed-Resolution Model.

Many large protein machines function through an interplay between large-scale movements and intricate conformational changes. Understanding functional motions of these proteins through simulations becomes challenging for both all-atom and coarse-grained (CG) modeling techniques because neither approach alone can readily capture the full details of these motions. In this study, we develop a multiscale model by employing the popular MARTINI CG model to represent a heterogeneous environment and structurally stable proteins and using the united-atom (UA) model PACE to describe proteins undergoing subtle conformational changes. PACE was previously developed to be compatible with the MARTINI solvent and membrane. Here, we couple the protein descriptions of the two models by directly mixing UA and CG interaction parameters to greatly simplify parameter determination. Through extensive validations with diverse protein systems in solution or membrane, we demonstrate that only additional parameter rescaling is needed to enable the resulting model to recover the stability of native structures of proteins under mixed representation. Moreover, we identify the optimal scaling factors that can be applied to various protein systems, rendering the model potentially transferable. To further demonstrate its applicability for realistic systems, we apply the model to a mechanosensitive ion channel Piezo1 that has peripheral arms for sensing membrane tension and a central pore for ion conductance. The model can reproduce the coupling between Piezo1's large-scale arm movement and subtle pore opening in response to membrane stress while consuming much less computational costs than all-atom models. Therefore, our model shows promise for studying functional motions of large protein machines.

The optimal scaling factor based on Secant Method for image watermarking using the hybrid DTCWT-DCT domain

This paper proposes an implementation of the secant optimization method on the watermarkembedding in DTCWT domain. An objective function combining PSNR and NC (The Normalized Correlation)values calculated for a set of common attacks is used in the optimization process. The aim is to enhance the tradeoff between robustness and imperceptibility against the various image processing attacks via an automatic andcareful selection of the scaling factor using optimization. The watermark is inserted in the high energy bands ofDCT-transformed sub-vectors, which were generated from the DTCWT coefficient decomposition of the hostimage. Experimental results performed on a set of 512×512 greyscale images, show better results than theexisting schemes for most common image attacks such as compression, low-pass filtering and noise addition.

Adaptive Orthogonal Basis Function Detection Method for Unknown Magnetic Target Motion State

Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in practical detection. And, the non-ideal uniform motion of the magnetic target further complicates the process and adversely impacts the detector’s performance. To address this challenge, this paper introduces an adaptive scale factor method based on orthogonal basis function decomposition. This new method can be used to adjust the relative velocity between the detection platform and the magnetic target and to refine the characteristic time in the basis functions. In this paper, a mathematical relationship between the scale factor and the relative velocity is established, which is subsequently fitted into a Gauss function curve. The optimal scale factor, denoted as β, is adaptively chosen from the fitting curve when the magnetic target moves at a non-ideal uniform velocity with an unknown motion state. The results of simulations indicate that the scale factor improves the signal-to-noise ratio of the magnetic anomaly signals in a non-ideal state. And, this method can improve the energy value of OBF decomposition by 17.7%. Simultaneously, this method ensures that the moment the magnetic target passes the CPA coincides with the energy peak of the orthogonal basis detection, which improves the accuracy by 54.1% compared with the traditional method. The effectiveness and precision of the proposed method are verified using simulations and practical experiments.

An Optimized Medical Image Watermarking Approach for E-Health Applications

Background: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission. Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities. Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms. Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities.

Comparative assessment of structure–property relationships of new Cu(II) complex in selected density functionals

In order to evaluate the structure–property relationships of Cu(II) complex by using DFT methods, the structure of the newly synthesized Cu(II) complex, [Cu(6-Brpic)2(bpy)], was investigated by XRD, FTIR, UV–Vis, and fluorescence spectroscopic methods. In addition, Hirshfeld surface and NBO analyses were fulfilled to identify possible interactions in the intermolecular and coordination environment. The five different DFT methods (HCTH, M06L, TPSSTPSS, B3LYP, and CAM-B3LYP levels), having four different functionalities (the GGA, meta-GGA, hybrid-GGA, and range-separated hybrid), were carried out so as to investigate the structure–property relationship, considering the geometric parameters (bond lengths and angles), vibrational frequencies, electronic absorption wavelengths, electronic transitions, and linear and nonlinear optical parameters. The R2 for structural and vibrational parameters, as well as MPD%, MAD, an optimal scaling factor (λ) and overall root mean square (RMS) deviation, were considered only at vibration frequencies. While it was determined that M06-L and TPSSTPSS levels gave the best results for the bond lengths and angles of the Cu(II) complex, the best results for vibrational frequencies were obtained in the HCTH method along with these methods. In NLO parameters, the static and dynamic first-order hyperpolarizability (<β(0;0,0)> and β(−ω;ω,0)/<β(−2ω;ω,ω)>) values, the largest values were obtained in the HCTH method (38.817 × 10−30 and 437.86 × 10−30/201.55 × 10−30 esu), whereas the smallest values were found to be in the CAM–B3LYP/TPSSTPSS levels (6.118 × 10−30 esu, 8.270 × 10−30/11.730 × 10−30 esu). By regarding the static γ (<γ(0;0,0,0)>) and dynamic (<γ(−ω;ω,0,0)> parameters, the largest values were calculated in the M06L (232.101 × 10−36) and HCTH (1711.52 × 10−36) methods and the smallest values were obtained in the CAM–B3LYP (43.281 × 10−36 and 60.844 × 10−36) method. In fact, it is obviously seen that the β and γ values obtained by the aforementioned DFT levels are many times higher than that of the standard molecule of urea. These results indicate that the Cu(II) complex may be used as a potential NLO material to evolve optoelectronic devices.

Wavelet transform coupled with Fischer plots for sequence stratigraphy: A case study in the Linxing area, Ordos Basin, China

Accurately dividing stratigraphic sequence using traditional data such as core and logging data is challenging due to the insufficient availability of paleontological fossils and rock data, particularly in Carboniferous-Permian strata of the Linxing area of Ordos Basin, dominated by lagoons, barrier islands, and tidal flat sedimentation. This drawback hinders the exploration and exploitation of hydrocarbon reservoirs. To address this issue, this study presents a combination of continuous wavelet transform (CWT) and Fischer plots, enabling the accurate identification of each level of sequence boundary and high-resolution sequence stratigraphic correlation. The optimal scale factors, 120, 60, and 20, are determined by applying different scale factors to gamma ray (GR) curves corresponding to long-term cycle, medium-term cycle, and short-term cycle divisions, respectively. The analysis of sequence characteristics of numerous core segments and the corresponding CWT results (i.e., wavelet coefficient curve and time-frequency energy spectrum) led to the identification of three main types of medium-term cycles (i.e., progradational, regressive, and aggradational) that were then applied to the entire well section of the study area. Moreover, the Fischer plots method was used to identify the long-term cycle based on the determination of medium-term cycles and the division of short-term cycles. This method provided intuitive identification of the relative changes in sea level position. Additionally, the characteristics of sea level changes were consistent with the results of the sea level analysis of rock and logging curves. Therefore, the combination of CWT and Fischer plots can effectively and comprehensively establish an isochronous stratigraphic framework for clastic rock hydrocarbon reservoirs, similar to this study.

Multi objectives optimization and transient analysis of an off-grid building with water desalination and waste heat recovery units

In this study, an off-grid apartment of a five-story building with a 150 m2 area, is considered. Building is considered in a remote area; therefore, all occupants' requirements are provided without using electricity and gas from the grid. For producing water, reverse osmosis (RO) desalination system is used. HVAC system is optimized for achieving the lowest CO2 emission and the best occupant’s thermal comfort simultaneously. Also, the use of two different sources, waste heat of diesel generator and solar collector, to provide domestic hot water for the building were investigated and compared. The roof-mounted PV panel is considered for supplying building's energy consumption. Due to the limited available area of the rooftop for each flat, the diesel generator is considered a backup power supplier for the building. Produced power is stored in the battery. Results show that the optimal scale factor of the heat pump is 0.9, and the cooling capacity of the heat pump should be 5.35 kW to supply thermal comfort. Regarding the results of optimization, the annual CO2 emission production of the diesel generator is 4.79 tons and PPD is 12.4%.

Study on a hybrid algorithm for accurate ripple detection in DC microgrids

AbstractWith the advantages of the low cost of transmission lines and high efficiency, the DC microgrid has become a rising star in the low‐voltage network. However, multi‐source and multi‐transformation characteristics in DC microgrids will lead to the existence of various forms of ripple generation sources in the system. The existence of a large amount of ripple not only directly cause the reduction of power quality of DC microgrids, but also decreases the accuracy of electricity billing. It is vital to achieving accurate ripple detection for the reliable power supply of the DC microgrid. A DC‐side ripple detection method, which combines the Whale Optimization Algorithm (WOA), Variational Modal Decomposition (VMD), and Hilbert Transform (HT), is therefore investigated and proposed here. Firstly, the sample entropy (SampEn) is used as the fitness function, applied the whale optimization algorithm, is to determine the optimal decomposition scale and penalty factor, of the variational modal decomposition; then, the ripple‐containing DC signal is decomposed into a series of eigenmodal components (imf) by the optimized variational modal decomposition algorithm. Finally, the Hilbert Transform is performed to obtain the amplitude and frequency of the ripple components. To prove the effectiveness, the proposed method is compared with the conventional Empirical Mode Decomposition (EMD) and the VMD algorithm, by testing simulated DC signals without and with noise. Results show that the proposed method has higher accuracy and noise robustness than the EMD and VMD algorithms.

Seismic control of adaptive variable stiffness intelligent structures using fuzzy control strategy combined with LSTM

A novel semi-active control algorithm for adaptive variable stiffness intelligent structures that combines the fuzzy control strategy with Long Short-Term Memory (LSTM) is proposed in this study, with the aim of mitigating structural responses under earthquake excitations. The combined Fuzzy-LSTM strategy utilizes an LSTM neural network to predict the displacement and velocity responses of the structure at the next moment, and uses the predicted results as input for fuzzy control to obtain active control forces. Due to the difference between the displacement-velocity response and the fuzzy control domain, the genetic algorithm is used to optimize scaling factors of displacement and velocity responses and the control force, which is implemented by exploiting stiffness variability and structural inter-shift. The Fuzzy-LSTM strategy can compensate for the time delay effect on active control caused by stiffness variations, algorithmic computations and sensor measurements, and therefore achieves a better control effect through prediction. The semi-active independently variable stiffness (SAIVS) device is used to implement the presented control algorithm, which is a diamond device consisting of four linear springs and an electromagnetic actuator. The SAIVS device can achieve a continuous and smooth stiffness variation and meet the application requirements of the developed algorithm. Therefore, active control forces can be obtained through structural stiffness variations and interlayer displacements under seismic excitation, to achieve an approximate active control effect through a semi-active method. A 10-story building is presented as a case study for numerical simulations. Results show that the Fuzzy-LSTM strategy can significantly improve the control effect compared to the conventional fuzzy control strategy, and therefore achieve a better seismic response mitigation performance.

Robust watermark based on Schur decomposition and dynamic weighting factors

Since multimedia data are now more widely distributed in digital form and easier to copy and change, copyright protection has become an essential need. One of the most common copyright protection techniques is watermarking. Invisibility and robustness are crucial aspects of watermarking; however, many of the currently used techniques simply focus more on invisibility than how robust they are. Moreover, the trade-off between invisibility and robustness is challenging. To this end, this paper proposes a novel watermark technique that efficiently overcomes the idea of a trade-off between robustness and invisibility, thereby increasing both under most attacks. Schur decomposition and a dynamic weighting factors matrix are added to the embedding process to improve the robustness of the proposed technique. Besides that, the embedding function is improved to simultaneously maximize imperceptibility and robustness. Another key contribution of the proposed approach is its use of a trajectory-based optimization algorithm rather than the more prevalent population-based algorithms to determine the optimal scaling factors. Consequently, the proposed technique rapidly identifies the best scaling factors for the embedding function. Statistical analysis is performed using the Friedman test. Experimental results show that the proposed technique outperforms other existing techniques for different sizes, shapes, and types of watermarks.

Hybrid Nature-Inspired Optimization and Encryption-Based Watermarking for E-Healthcare

With the growth and popularity of the utilization of medical images in smart healthcare, the security of these images using watermarks is one of the most recent research topics. This algorithm is based on the joint use of dual watermarking, nature-inspired optimization, and encryption schemes utilizing redundant-discrete wavelet transform (RDWT) and randomized-singular value decomposition (RSVD). The key idea of the proposed method is to embed system encoded media access control (MAC) address in patient's ID card image via discrete wavelet transform (DWT) to generate the final mark. Afterward, embed the generated watermark into computed tomography (CT) scan images of the COVID-19 patient and general images through employing the RDWT and RSVD. Further, we use a hybrid of particle swarm optimization (PSO) and Firefly optimization techniques to determine the optimal scaling factor for embedding purposes. After that, the watermarked CT scan image is encrypted using an encryption technique based on a nonlinear-chaotic map, random permutation, and singular value decomposition (SVD). Extensive evaluations establish the benefit of our proposed algorithm over the traditional schemes. The optimal robustness is more effective than the five traditional schemes at lower computational efficiency.

Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling

An accurate gas turbine performance model is important for system performance evaluation. To minimize the simulated performance error, an adaption method using coefficients of scaling factors is applied to tune the component characteristic maps and make the gas turbine model meet measurements of a few randomly sampled points. However, the field data are non-homogeneously distributed. In this situation, randomly selecting a few sampling may lead to the inappropriate correction of the component characteristic maps and lower the prediction accuracy of model. Firstly, the coefficients of scaling factors are introduced to construct the performance adaption optimization problem of the gas turbine model. Secondly, the k-means clustering algorithm is applied to divide the 146 field data points into 10 different categories, and then the points closest to the cluster centers are selected to form the sampling set. Thirdly, a particle swarm optimization algorithm is used to search the optimal scaling factors. As a result, the model error decreases from 2.947% to 0.610%. Finally, the proposed method is validated with the remaining field data of a real E-class gas turbine. The average predicted error is 0.466%. Compared with the performance results obtained by random sample, the model based on cluster sampling shows a better accuracy.

An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation.

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, , which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.