Accurate Method For Evaluation Research Articles

Fast and accurate evaluation method of commutation failure in line‐commutated converter‐based high‐voltage direct current systems considering probability characteristics

Line-commutated converter-based high-voltage direct current (LCC-HVDC) systems suffer from commutation failure (CF) when receiving-end AC grid faults occur, which is a major drawback and threatens the security and stability of power systems. This paper presents a fast and accurate evaluation method of CF for the LCC-HVDC systems, considering the CF probability characteristics. Firstly, based on the analysis of the CF probability characteristics from the view of short-circuit impedance, the CF tolerance index (CFTI) and CF vulnerability index (CFVI) are proposed to quantitatively evaluate the LCC-HVDC systems' risk of the possible- and inevitable-occurrence CF, respectively. Secondly, four factors, that is, commutation voltage, fault occurrence time, DC current and firing angle command, are considered to calculate CF evaluation indices (CFEIs) and identify the CF risk area boundaries based on the theoretical analysis. Moreover, the relationships among short-circuit impedance, fault position, and extinction angle are established based on the AC/DC decoupling method, which helps realize the decoupling of multi-factors affecting CF during fault. Compared to earlier methods, this evaluation method combines the advantages of the accuracy of the electromagnetic transient simulation method and the rapidity of theoretical derivation. Finally, the validity and accuracy of the evaluation method are verified in the CIGRE HVDC benchmark model and IEEE 39-bus system.

Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning

The rapid development of three-dimensional (3D) laser scanning technology has provided a new technical means for the geometric accuracy evaluation of subway stations. With high precision and high efficiency, laser scanning technology can present the construction site condition in a panoramic way, which is essential for achieving high precision and all-round geometric accuracy evaluation. However, when the survey coordinate system of the design building information modeling (BIM) predefined in the design stage is not applied during the laser scanning data acquisition or the BIM loses the survey coordinate system during the interaction, the objects will have different coordinate positions in the point cloud and BIM, which will limit the accuracy comparison between the two data sources. Meanwhile, the existing methods mainly focus on the above overground buildings, and the accuracy evaluation of underground structures mainly focuses on the overall deformation monitoring. So far, the existing methods do not constitute a hierarchical index system to assess the geometric accuracy of various objects in the subway station. This study proposes a method to evaluate the geometric accuracy of subway stations based on laser scanning technology. A coarse-to-fine coordinate registration from point cloud to the design BIM is used to unify coordinates in different reference systems; and geometric accuracy evaluation of different structures in subway stations is achieved by developing geometric accuracy evaluation indexes and technical systems. The method is applied to the geometric accuracy monitoring of the Hongqi Road subway station, and the experimental results verify the reliability of the method.

Dynamic Modeling and Accuracy Evaluation Method for Complex Special-Shaped Components of Aviation Transmission

Dynamic Modeling and Accuracy Evaluation Method for Complex Special-Shaped Components of Aviation Transmission

Molecular Dynamics Study on the Adsorption and Modification Mechanism of Polymeric Sand-Fixing Agent.

Chemical sand-fixing technology has shown good potential in preventing desertification, but the effect is determined by materials. In this paper, the adsorption behavior of quartz and six common polymer sand-fixing agents under dry conditions was studied by molecular dynamics method. The results show that all polymers could be adsorbed on the surface of quartz and their functional groups play an important role in the adsorption process. Compared with other materials, the binding energy and the number of hydrogen bonds of PAA-quartz composites were improved by 30.7–65.6% and 8.3–333.3%, respectively. It was found that the number of hydrogen bonds formed under the unit molecular was positively correlated with the mechanical properties of the improved sandy soil. This study provides an accurate, efficient and inexpensive qualitative evaluation method for the curing effect of sand fixers, which will assist in the screening and development of new high performance sand fixers.

Experimental investigation on combustion efficiency of a partially premixed kerosene-air rotating detonation combustor

It is believed that detonation waves travel a radially stratified mixture in a non-premixed rotating detonation combustor (RDC), which can result in non-ideal detonations. In order to further improve the combustion performance of RDCs, this paper presents a kerosene-air rotating detonation combustor with a partial premixing strategy, in which part of the fuel is injected into the air plenum to improve the mixing effect. A more convenient and accurate combustion efficiency evaluation method based on the mass flow function (MFF) and equivalent available pressure (EAP) is proposed to help to quantitatively study the combustion performance. A marked improvement in the combustion efficiency and operation range is obtained with increasing premixed equivalence ratio compared to a non-premixed strategy. The highest combustion efficiency for 0.3 premixed equivalence ratio can reach 98.06% in the experiments. The experimental results confirm that this partial mixing strategy can effectively prevent the flashback and improve combustion efficiency within a reasonable equivalence ratio. However, a total equivalence ratio and a premixed equivalence ratio that are too high will lead to a decrease in combustion efficiency and even failed detonation due to flashback events.

Accurate reliability analysis methods for approximate computing circuits

In recent years, Approximate Computing Circuits (ACCs) have been widely used in applications with intrinsic tolerance to errors. With the increased availability of approximate computing circuit approaches, reliability analysis methods for assessing their fault vulnerability have become highly necessary. In this study, two accurate reliability evaluation methods for approximate computing circuits are proposed. The reliability of approximate computing circuits is calculated on the basis of the iterative Probabilistic Transfer Matrix (PTM) model. During the calculation, the correlation coefficients are derived and combined to deal with the correlation problem caused by fanout reconvergence. The accuracy and scalability of the two methods are verified using three sets of approximate computing circuit instances and more circuits in EvoApprox8b, which is an approximate computing circuit open source library. Experimental results show that relative to the Monte Carlo simulation, the two methods achieve average error rates of 0.46% and 1.29% and time overheads of 0.002% and 0.1%. Different from the existing approaches to reliability estimation for approximate computing circuits based on the original PTM model, the proposed methods reduce the space overheads by nearly 50% and achieve time overheads of 1.78% and 2.19%.

Incorporating Tenax into the in vitro method to improve the predictive capability of bioaccessibility of triazole fungicides in grape

In vitro bioaccessibility assays have been developed for high-throughput prediction of relative bioavailability (RBA). However, methods to reliably and efficiently assess pesticide residues remain limited, hindering the precise estimation of pesticide exposure risk. The inclusion of a sorption sink material to simulate intestinal sorption could be a promising approach to optimize in vitro bioaccessibility methods. The current study aimed to explore the feasibility of incorporating Tenax into the Rijksinstituut voor Volksgezondheid en Milieu (RIVM) method for accurate evaluation of the bioaccessibility of triazole fungicides. The use of 1.0 g of Tenax enabled the valid trapping of triazole fungicides released from grape, resulting in a significant increase of 23.59–38.51 % in the value of bioaccessibility. A strong in vivo–in vitro correlation was observed between pesticide RBA and bioaccessibility, suggesting that the Tenax-assisted RIVM method is a suitable replacement for time-consuming and laborious in vivo alternatives. In addition, the exposure assessment indicated that the hazard quotients for triazole fungicides in grape may be overestimated by 5.79–27.34 % without considering bioaccessibility based on the Tenax-assisted RIVM method. These results provide further insights into the assessment of bioaccessibility-based human exposure to pesticides as well as dietary exposure and related risk for human health.

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

The reliability of smart micro-grids (SMGs), as a cyber-physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)-based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber-physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS-based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS-based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber-physical interdependencies.

Correction of breast asymmetry by autologous fat grafting with the aid of 3D laser-scanning technology

BackgroundCurrently, the lack of clinically accurate measurement and evaluation methods for breast asymmetry has considerably limited the use of autologous fat grafting in the correction of breast asymmetry. ObjectiveThis study calculated the volume difference in the bilateral breasts by three-dimensional (3D) laser scanning technology, established a bridge between digitalization and surgery to guide the correction of breast asymmetry by autologous fat grafting, and evaluated the surgical effect. MethodsIn the experimental group (3D group), the measurement range was defined by standardized methods, the algorithm to calculate the volume difference in the bilateral breasts was determined by the established software instructions, and the volume of intraoperative autologous fat grafting was guided by personalized data. In the control group, the volume of intraoperative autologous fat grafting was determined based on the surgeon's visual assessment and surgical experience. ResultsThe volume difference in the bilateral breasts was less at 12 months after surgery (P < 0.05), the satisfaction of patients was higher (P < 0.05), and the reoperation rate was lower (P < 0.05). The incidence of postoperative complications was low in both groups (P > 0.05). Conclusions3D laser scanning technology can be used as a bridge between digitalization and surgery to significantly improve the surgical effect of autologous fat grafting in the correction of breast asymmetry, with high patient satisfaction and high clinical application value.

The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model (MAM error model), we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model. The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation. The experimental results show that based on the weighted least squares criterion, the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation. The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods, which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

Modeling and evaluation method for resilience analysis of multi-state networks

Multi-state networks which not only be connected but also function at a certain performance level exist widely in real-world engineering systems. Resilience provides a new approach for the design and analysis of networks that enhance the ability of systems to withstand and bounce back from disruptive events. Efficient and accurate evaluation method is essential for analyzing and enhancing the resilience of the multi-state networks. This research focuses on the resilience modeling and evaluation method for multi-state networks, in which the states of the components and network follow certain probability distributions. First, a multi-state network performance metric associated with the state distribution of components was proposed. Then, we proposed a semi-Markov-based resilience model to describe the transition of the component state distribution. Third, we proposed a fast-repeated algorithm to enhance resilience evaluation efficiency for multi-state networks such that managers can make timely decisions when disruptive events occur. Finally, a case study is conducted, where the optimal recovery strategy was obtained based on the proposed method. Compared to the distance-based recovery strategy and reliability-based recovery strategy, resilience-based method offers more flexible and timely strategies considering different mission requirements and/or preferences for the design, operation and recovery of multistate networks.

Evolutionary neural architecture search based on evaluation correction and functional units

Neural architecture search (NAS) has been a great success in the automated design of deep neural networks. However, neural architecture search using evolutionary algorithms is challenging due to the diverse structure of neural networks and the difficulty in performance evaluation. To this end, this paper proposes an evolutionary neural architecture search algorithm (called EF-ENAS) based on evaluation corrections and functional units. First, a mating selection operation based on evaluation correction is developed, which can help EF-ENAS discriminate high-performance network architectures and reduce the harmful effects of low fidelity accuracy evaluation methods. Then, a functional unit-based network architecture crossover operation is designed, which divides the neural network into different functional units for crossover and protects valuable network architectures from destruction. Finally, the idea of species protection is introduced into the traditional environmental selection operation and a species protection-based environmental selection operation is designed, which can improve the diversity of network architectures in a population. The EF-ENAS is tested on ten benchmark datasets with varying complexities. In addition, the proposed algorithm is compared with 44 state-of-the-art algorithms, including DARTS, EvoCNN, CNN-GA, AE-CNN, etc. The experimental results show that the proposed algorithm11The code of EF-ENAS is available at https://github.com/codesl173/EF-ENAS. can automatically design neural networks and perform better.

Quantitative Assessment of the Restoration of Original Anatomy after 3D Virtual Reduction of Long Bone Fractures.

Background: The purpose of this study was to demonstrate the usefulness of 3D image-based virtual reduction by validating the evaluation criteria according to guidelines suggested by the AO Surgery Reference. Methods: For this experiment, 19 intact radial ORTHObones (ORTHObones radius, 3B Scientific, Germany, Hamburg) without any fractures were prepared. All ORTHObones with six cortical marking holes (three points on the distal part and three points on the proximal part) were scanned using a CT scanner twice (before/after intentional fracture of the ORTHObone). After the virtual reduction of all 19 ORTHObones, accuracy evaluations using the four criteria (length variation, apposition variation, alignment variation, Rotation Variation) suggested in the AO Surgery Reference were performed. Results: The mean (M) length variation was 0.42 mm, with 0.01 mm standard deviation (SD). The M apposition variation was 0.48 mm, with 0.40 mm SD. The M AP angulation variation (for alignment variation) was 3.24°, with 2.95° SD. The M lateral angulation variation (for alignment variation) was 0.09°, with 0.13° SD. The M angle of axial rotation was 1.27° with SD: 1.19°. Conclusions: The method of accuracy evaluation used in this study can be helpful in establishing a reliable plan.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

AbstractResearch into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive. To tackle these two issues, this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation (SAFE‐PSO) approach. Firstly, the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN. Secondly, the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates. Thirdly, a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost. The SAFE‐PSO algorithm is tested on two widely used datasets: The 10‐category (i.e., CIFAR10) and the 100−category (i.e., CIFAR100). The experimental results show that SAFE‐PSO is very effective and efficient, which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

An enhanced N-point interpolation method to eliminate average precision distortion

• Existing N -point interpolation method to calculate average precision raises three anomalies. • The average precision distortion problem is commonly present in the N -point interpolation method. • The proposed enhanced N -point interpolation method reduces more than 90% of the average precision distortion. Existing N -point interpolation methods generate large errors in the average precision calculation for object detection. These errors lead to average precision distortion, which makes it impossible to accurately evaluate the performance of the model. We investigate the reason for the average precision distortion and propose an enhanced N -point interpolation method. These improvements are based on the N -point interpolation method and can be summarized in two parts: (1) The interpolation point position is changed to the middle interpolation. (2) Dynamic selection of parameters for calculating the area of the interpolation interval. Experiments verify the existence of severe average precision distortion in the N -point interpolation method. Furthermore, the proposed enhanced N -point interpolation method reduces the average precision distortion by more than 90% to only 0.04%. In this way, the enhanced N -point interpolation method is able to replace the all-point interpolation method for fast and accurate evaluation of object detection model.

P‐133: Enhancement of Light‐Emitting Efficiency Through Impurity Controls by Using Prep‐NMR Technique

An electroluminescence (EL) material plays a key role in electrical and optical efficiency of an organic light emitting diode (OLED) display. High purity, over 99.9%, EL materials have been required to be deposited onto OLED panel, producing a long‐life time device. Even less than 0.1% of impurities are deposited on the panel, which can drop their efficiency and lifetimes very seriously. In this work, we have developed faster and more accurate evaluation methods to identify unknown impurities in emitting materials. We can separate each other by using semi‐prep liquid chromatography and define their chemical structures by combining mass spectrometry and nuclear magnetic resonance results. The impurities in the in the manufacturing process were identified, which causes the efficiency drop, down to 8%. We find out these were coming from other EL materials in the process by accident. Finally, we can guarantee highquality assurance of our panels and eliminate potential risk factors in the manufacturing process.

Maintenance accessibility evaluation method based on D-H model and comfort in a virtual maintenance environment

Maintainability, especially for complex products, is a very important general quality characteristic. Physical accessibility is the primary element in maintainability design. Up to now, VR (virtual reality) technology-assisted maintainability evaluation, analysis and design, called virtual maintenance, has become the mainstream and achieved some results. In virtual maintenance, the most widely used accessibility evaluation method is to use virtual human accessible envelope to judge and evaluate accessibility. However, this method can only give two kinds of evaluation results: accessible and inaccessible. Moreover, the envelope lacks sufficient data and theoretical support, so the precision and accuracy of evaluation method need to be improved. In this paper, a parameterized accessibility evaluation method is proposed and the corresponding evaluation models and tool are created. Firstly, to objectively describe the movement of human body, a 6-joint and 5-link D-H (Denavit–Hartenberg) link model is established from the waist to the fingertip of the human body, and the ranges of 10 degrees of freedom and angles related to accessibility are determined according to ergonomics. Then, comfort is introduced to refine the accessibility evaluation, and a multi-level accessibility evaluation system based on comfort is constructed according to RULA (the rapid upper limb assessment). Thirdly, in order to facilitate the application of this method in virtual environment, accessibility envelope models and accessibility evaluation tool are created based on the proposed method. Finally, compared with RULA and the accessible envelope provided by DELMIA, respectively, the results from virtual and real environment of the comparative experiment show that the proposed method and tool are accurate, more precise and more efficient.

A novel Snap-FIT implant surgical guide design: a case report

To clinically evaluate the precision of a novel tooth-supported snap-FIT surgical guide (SF), inspired by the removable dentures clasp principle. Two patients with a single missing tooth underwent CBCT and optical scans, that were superimposed for implant virtual planning (BlueSkyBio, USA). A novel SF implant surgical guide for each case was designed and 3D printed (offset from the surface 0.07 mm, material thickness 2.3 mm, selective undercuts allowed). Under local anesthesia, a full-thickness flap was elevated and SF positioned. Drill sequence was partially guided, with profiling, taping of implant bed, and implant placement done free-hand. One narrow (NC) and one regular diameter (RC) bone-level implants were placed (Straumann, CH). Further, 5-0 non-resorbable sutures were used for wound closure. The duration of surgery was 24 min and 27 min, respectively. Healing was uneventful in both cases. After 4 months, implants were uncovered and implant position recorded with the intraoral scanner. Source files and the scanned data were aligned (error: 0.05 mm/0.02 mm, GOM inspect, Germany). A novel implant accuracy evaluation method was developed. The apical and neck point of the implant was determined as an intersection point of a plane and cylinder, generated from the scan body (Gaussian best-fit). Implant placement error at the implant neck was 0.67 mm/0.3 mm, at the apex 1.02 mm/0.32 mm and the angle deviation 3.28°/3.33° for NC and RC implant, respectively. The novel SF surgical guide is feasible with a simple modification of classical guide design, without requiring additional materials, devices, or procedures. It demonstrates high implant placement accuracy, similar to the fully guided protocol.

Influences of Microscopic Imaging Conditions on Accuracy of Cell Morphology Discrimination Using Convolutional Neural Network of Deep Learning.

Recently, automated cell culture devices have become necessary for cell therapy applications. The maintenance of cell functions is critical for cell expansion. However, there are risks of losing these functions, owing to disturbances in the surrounding environment and culturing procedures. Therefore, there is a need for a non-invasive and highly accurate evaluation method for cell phenotypes. In this study, we focused on an automated discrimination technique using image processing with a deep learning algorithm. This study aimed to clarify the effects of the optical magnification of the microscope and cell size in each image on the discrimination accuracy for cell phenotypes and morphologies. Myoblast cells (C2C12 cell line) were cultured and differentiated into myotubes. Microscopic images of the cultured cells were acquired at magnifications of 40× and 100×. A deep learning architecture was constructed to discriminate between undifferentiated and differentiated cells. The discrimination accuracy exceeded 90% even at a magnification of 40× for well-developed myogenic differentiation. For the cells under immature myogenic differentiation, a high optical magnification of 100× was required to maintain a discrimination accuracy over 90%. The microscopic optical magnification should be adjusted according to the cell differentiation to improve the efficiency of image-based cell discrimination.

Rotor Slot Design of Squirrel Cage Induction Motors With Improved Rated Efficiency and Starting Capability

Among the electro-mechanical devices transforming energy from electrical to mechanical, the squirrel cage induction motor can be surely considered a workhorse of the industry due to its robustness, low cost and good performance when directly fed by the a.c. grid. Being the most influencing motor topology in terms of energy consumption, optimizing the efficiency of squirrel cage induction motors could lead to a great impact towards the reduction of the human environmental footprint. The induction motor design aided by finite element analysis presents significant challenges because an accurate performance prediction requires a considerable computational burden. This paper makes use of an innovative fast and accurate performance evaluation method embedded into an automatic design procedure to optimize different rotor slot geometries. After introducing the performance estimation approach, its advantages and limits are discussed comparing its prediction with the experimental tests carried out on an off-the-shelf induction motor. Different rotor cage structures with increasing geometrical complexity are then optimized in terms of starting and rated performance adopting the same design optimization process, the same stator geometry and constituent materials. The analysis of the optimal solutions shows how it is possible to improve the rated efficiency without compromising other performance indexes. The presented results can be used as general design guidelines of squirrel cage induction motors for industrial applications.