Conventional Methods
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Chapkit Charnsamorn ◽  
Suphongsa Khetkeeree

The existed interpolation method, based on the second-order tetration polynomial, has the asymmetric property. The interpolation results, for each considering region, give individual characteristics. Although the interpolation performance has been better than the conventional methods, the symmetric property for signal interpolation is also necessary. In this paper, we propose the symmetric interpolation formulas derived from the second-order tetration polynomial. The combination of the forward and backward operations was employed to construct two types of the symmetric interpolation. Several resolutions of the fundamental signals were used to evaluate the signal reconstruction performance. The results show that the proposed interpolations can be used to reconstruct the fundamental signal and its peak signal to noise ratio (PSNR) is superior to the conventional interpolation methods, except the cubic spline interpolation for the sine wave signal. However, the visual results show that it has a small difference. Moreover, our proposed interpolations converge to the steady-state faster than the cubic spline interpolation. In addition, the option number increasing will reinforce their sensitivity.

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 582
Holger Behrends ◽  
Dietmar Millinger ◽  
Werner Weihs-Sedivy ◽  
Anže Javornik ◽  
Gerold Roolfs ◽  

Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current.

2022 ◽  
Vol 12 (2) ◽  
pp. 837
Jian Xu ◽  
Kean Chen ◽  
Lei Wang ◽  
Jiangong Zhang

Low-frequency sound field reconstruction in an enclosed space has many applications where the plane wave approximation of acoustic modes plays a crucial role. However, the basis mismatch of the plane wave directions degrades the approximation accuracy. In this study, a two-stage method combining ℓ1-norm relaxation and parametric sparse Bayesian learning is proposed to address this problem. This method involves selecting sparse dominant plane wave directions from pre-discretized directions and constructing a parameterized dictionary of low dimensionality. This dictionary is used to re-estimate the plane wave complex amplitudes and directions based on the sparse Bayesian framework using the variational Bayesian expectation and maximization method. Numerical simulations show that the proposed method can efficiently optimize the plane wave directions to reduce the basis mismatch and improve acoustic mode approximation accuracy. The proposed method involves slightly increased computational cost but obtains a higher reconstruction accuracy at extrapolated field points and is more robust under low signal-to-noise ratios compared with conventional methods.

2022 ◽  
Vol 74 (1) ◽  
Shuhei Tsuji ◽  
Koshun Yamaoka ◽  
Ryoya Ikuta

AbstractWe developed a method to detect attenuation changes during seismic wave propagation excited by precisely controlled artificial seismic sources, namely Accurately Controlled Routinely Operated Signal System (ACROSS), and applied it to monitor the temporal changes for in situ data collected by previous studies. Our method, together with the use of the ACROSS sources, is less susceptible to noise level changes, from which conventional methods such as envelope calculation suffer. The method utilizes the noise level that is independently estimated in the frequency domain and eliminates the influence of the noise from the observed signal. For performance testing, we applied this method to a dataset that was obtained in an experiment at Awaji Island, Japan, from 2000 to 2001. We detected a change in amplitude caused by rainfall, variation in atmospheric temperature, and coseismic ground motions. Among them, coseismic changes are of particular interest because there are limited studies on coseismic attenuation change, in contrast to many studies on coseismic velocity decrease. At the 2000 Western Tottori earthquake (MW = 6.6, epicenter distance of 165 km), a sudden decrease in amplitude of up to 5% was observed. The coseismic amplitude reduction and its anisotropic characteristics, which showed a larger reduction in the direction of the major axis of velocity decrease, were consistent with the opening of fluid-filled cracks, as proposed by previous studies. The $$\Delta {Q}^{-1}$$ Δ Q - 1 corresponding to the amplitude change gives similar values to those reported in previous studies using natural earthquakes. Graphical Abstract

2022 ◽  
Vol 4 (1) ◽  
pp. 17-31
Atsushi Yamamoto ◽  
Tsumugu Kusudo ◽  
Masaomi Kimura ◽  
Yutaka Matsuno

Japanese agriculture is facing a decrease in agricultural workers. Mechanization, both to save time and reduce physical input, is essential to solving this issue. Recent worldwide progress in Internet-of-things technology has enabled the application of remote-controlled and unmanned machinery in agriculture. This study was conducted in the Gojo-Yoshino mountainous region in Nara, Japan, which is famous for its persimmon cultivation. The performance of newly introduced smart agricultural machinery was studied in the field by simulating cultivation work. The results showed that the remote-control weeder, speed sprayer, and remote-control mini crawler carrier saved 90%, 75%, and 5% of weeding, spraying, and harvesting times, respectively, when compared with conventional methods. Such time savings led to an 8% decrease in the total working time spent on persimmon cultivation. In addition, using the speed sprayer showed improvement in the fruit’s quality. Results of the power assist suits did not show a time-saving effect but showed a reduction of physical burden. These results suggest that the mechanization of persimmon cultivation is efficient and labor-saving, and satisfies the need for farmers. However, the high investment costs remain an issue in extending mechanization to the region.

Rajeh M. Al-Sharif ◽  
Khaled A. Althaqafi ◽  
Hend S. Alkathiry ◽  
Abdulrahman A. Alzeer ◽  
Raiya M. Shareef ◽  

Many applications for these technologies have been reported in multiple fields, including dentistry, within the last three decades. It can be used in periodontology, endodontics, orthodontics, oral implantology, maxillofacial and oral surgery, and prosthodontics. In the present literature review, we have discussed the different clinical applications of various 3D printing technologies in dentistry. Evidence indicates that 3D printing approaches are usually associated with favorable outcomes based on the continuous development and production of novel approaches, enabling clinicians to develop complex equipment in different clinical and surgical aspects. Developing work models to facilitate diagnostic and surgical settings is the commonest application of these modalities in dentistry. Besides, they can also be used to manufacture various implantable devices. Accordingly, they significantly help enhance the treatment process, reducing costs and less invasive procedures with favorable outcomes. Finally, 3D printing technologies can design complex devices in a facilitated and more accurate way than conventional methods. Therefore, 3D printing should be encouraged in clinical settings for its various advantages over conventional maneuvers.

2022 ◽  
Vol 27 ◽  
pp. 70-93
John Patrick Fitzsimmons ◽  
Ruodan Lu ◽  
Ying Hong ◽  
Ioannis Brilakis

The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.

2022 ◽  
Vol 2022 ◽  
pp. 1-4
Lingling Zhang

After entering the information society, all kinds of risks, crises, and conflicts in society are more severe, more sudden, and uncertain than those in agricultural society and industrial society. Under the unexpected events in colleges and universities, college students’ psychological crisis, which cannot be dealt with and overcome by conventional methods, arises from their own experiences, psychological endurance, and weak self-awareness. In the face of emergencies, as a talent training base, how to collect information quickly and accurately and make prevention and control plans is directly related to the success or failure of event handling. This study attempts to analyze the characteristics and causes of students’ psychological changes in public health emergencies in colleges and universities and puts forward relevant countermeasures, so as to improve the management system of public health emergencies in colleges and universities, improve the ability to effectively deal with and properly handle public health emergencies, and promote the harmonious development of society. In the face of public health emergencies, colleges and universities should enhance the awareness of emergency management of public health emergencies, change the concept of emergency, build an efficient emergency management system, improve the ability and level of emergency management, and ensure the harmony and stability of the school.

Eri Hashimoto ◽  
Keigo Tamura ◽  
Hayato Yamaguchi ◽  
Takeshi Watanabe ◽  
Fumihiko Matsui ◽  

Abstract We characterized CVD-grown graphene with high single-crystallinity on Ir(111)/α-Al2O3(0001) by photoelectron momentum microscopy. A multi-functional photoelectron momentum microscope (PMM), which is installed with element-specific valence band photoelectron spectroscopy and X-ray absorption spectroscopy, is a complementary characterization tool to conventional methods, such as Raman spectroscopy and atomic force microscopy, for comprehensive and quantitative characterization of graphene/Ir(111). Using PMM, we characterized the properties of CVD-grown graphene including the single-crystallinity, number of layers, crystal orientation, and degree of interaction between graphene and Ir(111) and clarified the relationship between these properties and the CVD growth conditions.

2022 ◽  
Vol 8 ◽  
Ephraim Fass ◽  
Gal Zizelski Valenci ◽  
Mor Rubinstein ◽  
Paul J. Freidlin ◽  
Shira Rosencwaig ◽  

The changing nature of the SARS-CoV-2 pandemic poses unprecedented challenges to the world's health systems. Emerging spike gene variants jeopardize global efforts to produce immunity and reduce morbidity and mortality. These challenges require effective real-time genomic surveillance solutions that the medical community can quickly adopt. The SARS-CoV-2 spike protein mediates host receptor recognition and entry into the cell and is susceptible to generation of variants with increased transmissibility and pathogenicity. The spike protein is the primary target of neutralizing antibodies in COVID-19 patients and the most common antigen for induction of effective vaccine immunity. Tight monitoring of spike protein gene variants is key to mitigating COVID-19 spread and generation of vaccine escape mutants. Currently, SARS-CoV-2 sequencing methods are labor intensive and expensive. When sequence demands are high sequencing resources are quickly exhausted. Consequently, most SARS-CoV-2 strains are sequenced in only a few developed countries and rarely in developing regions. This poses the risk that undetected, dangerous variants will emerge. In this work, we present HiSpike, a method for high-throughput cost effective targeted next generation sequencing of the spike gene. This simple three-step method can be completed in < 30 h, can sequence 10-fold more samples compared to conventional methods and at a fraction of their cost. HiSpike has been validated in Israel, and has identified multiple spike variants from real-time field samples including Alpha, Beta, Delta and the emerging Omicron variants. HiSpike provides affordable sequencing options to help laboratories conserve resources for widespread high-throughput, near real-time monitoring of spike gene variants.

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