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Macro‐Raman mapping is an approach that is well suited to study the distribution of molecules over a surface of typically several cm2, such as the pigment distribution on a painting. During such an experiment, thousands Raman spectra are recorded in a predefined array of points. As there can be a huge difference in Raman sensitivity between different molecules, an alternative approach is proposed, where measurement times are adapted according to the quality of the Raman scatterer. Therefore, an iterative process is implemented, involving recording short accumulations, followed by a quick evaluation. As soon as the accumulated spectrum reaches a quality criterium, measurement of the next point is started, while in the other case further accumulations are added. The influence of using multiple accumulations opposite to a single measurement for a prolonged period has been assessed by comparing the overall measurement time and the spectral intensity of the main Raman band (520.5 cm−1) of silicon. Another prerequisite for this algorithm to be successful is the need for a quick and reliable evaluation of the spectral quality. This was examined by applying this function on the accumulations of Raman spectra of 49 paint samples from a colour chart. Finally, the variable‐accumulation algorithm is applied during a macro‐Raman mapping experiment of a watercolour painting on paper. Despite a significant gain in overall measuring time, this novel approach yields a qualitative data set that can be used for creating clear Raman maps, using different data processing methods. [ABSTRACT FROM AUTHOR]

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained. [ABSTRACT FROM AUTHOR]

The online detection of partial discharge (PD) in gas-insulated switchgear (GIS) is a crucial and powerful tool for maintaining their reliability. However, extracting weak discharge signals from strong disturbances is a significant challenge. The presence of noise can hamper the identification and localization of PD types, making the extraction of pure PD signals the focus of current research. This paper proposes a PRPD-based PD filtering algorithm that analyzes interference using the output information from PRPD and sets threshold parameters for noise reduction processing. This method is mainly used for secondary noise reduction at a later stage, without analyzing the noise source, to achieve effective signal acquisition while retaining the characteristics of the PD signals, thereby improving the system's sensitivity and the signal's purity. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID-19) is a pandemic causing millions of deaths, devastating social and economic disruptions. Testing individuals for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of COVID-19, is critical for mitigating and containing COVID-19. Many countries are implementing group testing strategies against COVID-19 to improve testing capacity and efficiency while saving required workloads and consumables. A group of individuals’ nasopharyngeal/oropharyngeal (NP/OP) swab samples is mixed to conduct one test. However, existing group testing methods neglect the fact that mixing samples usually leads to substantial dilution of viral ribonucleic acid (RNA) of SARS-CoV-2, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. To achieve this goal, we propose an Adaptive Group Testing (AdaGT) method. By collecting information on the number of positive and negative samples that have been identified during the screening process, the AdaGT method can estimate the ratio of positive samples in real-time. Based on this ratio, the AdaGT algorithm adjusts its testing strategy adaptively between an individual testing strategy and a group testing strategy. The group size of the group testing strategy is carefully selected to guarantee that the sensitivity of each test is higher than a predetermined threshold and that this group contains at most one positive sample on average. Theoretical performance analysis on the AdaGT algorithm is provided and then validated in experiments. Experimental results also show that the AdaGT algorithm outperforms existing methods in terms of efficiency and sensitivity. Note to Practitioners—Real-time reverse transcription-polymerase chain reaction (rRT-PCR) tests provide scope for automation and are one of the most widely used laboratory methods for detecting the SARS-CoV-2 virus. This paper is motivated by the following challenges: (1) Many countries are experiencing an acute shortage of professionals and consumables for conducting rRT-PCR tests; (2) Group sizes of existing group testing methods against COVID-19 may not be optimal, which adversely impacts the efficiency of the screening of the SARS-CoV-2 virus; (3) Existing group testing methods do not consider the fact that the sensitivity of rRT-PCR tests usually decreases with the group size. The objective of this paper is to improve the efficiency and sensitivity of large-scale screening against COVID-19. For achieving this goal, we propose an Adaptive Group Testing (AdaGT) algorithm, which has the following advantages: (1) It can improve the efficiency for screening the SARS-CoV-2 virus, mainly by adaptively adjusting its testing strategy between an individual testing strategy and a group testing strategy based upon an estimated ratio of positive samples during the screening process; (2) It can guarantee a high sensitivity of the rRT-PCR tests by determining the group sizes of the group testing strategy based upon some constraints; (3) We derive an appropriate threshold for the estimated ratio of positive samples such that the AdaGT algorithm can achieve a minimum average number of rRT-PCR tests and can be directly employed in practical applications. [ABSTRACT FROM AUTHOR]

This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast Cancer Detection being an area of importance in detecting it in early stages and to assist the Oncologists in analyzing the stage and mode of further treatment, it gains equal importance to all types of cancer detection. Using the proposed optimization based machine learning model in detection process is supposed to help the patients and the oncologists in deciding further process. In recent years, breast cancer detection is an important area of concentration towards curative image dispensation and exploration. Detection of a disease at an early stage is an important factor in taking it to the next level of treatment. Accuracy plays an important role in the detection of disease. COA-T2FCM (Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering) is constructed for detection of such malignancy with the highest accuracy in this paper. The proposed detection process is designed with the combination of type-2 intuitionistic fuzzy c-means clustering in addition to oppositional function. In the type-2 intuitionistic fuzzy c-means clustering, the efficient cluster center can be preferred using the chimp optimization algorithm. Initially, the objective function of the type-2 intuitionistic fuzzy c-means clustering is considered. The chimp optimization algorithm is utilized to optimize the cluster center and fuzzifier in the clustering method. The projected technique is implemented, and in addition, performance metrics such as specificity, sensitivity, accuracy, Jaccard Similarity Index (JSI), and Dice Similarity Coefficient (DSC) are assessed. The projected technique is compared with the conventional technique such as fuzzy c means clustering and k mean clustering methods. The resulting method was also compared with existing methods to ensure the accuracy in the proposed method. The proposed algorithm is tested for its effectiveness on the mammogram images of the three different datasets collected from the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and Inbreast. The accuracy and Jaccard index score are generally used to measure the similarity between the proposed output and the actual cancer affected regions from the image considered. On an average the proposed method achieved an accuracy of 97.29% and JSI of 95% [ABSTRACT FROM AUTHOR]

Detecting and characterising shocks is challenging because they do not occur in isolation but are instead superimposed onto underlying vehicle vibrations which themselves are a result of the interaction with uneven road surfaces. Consequently, shocks are buried within vehicle vibration response measurements (usually acceleration). This paper presents the development and validation of an automated algorithm to detect shocks from vertical acceleration signals measured from road vehicles. To avoid inherent difficulties with experimentation, this initial paper is confined to numerical simulation whereby the response of two typical quarter-car truck models (one with air ride and the other with steel suspension) when travelling on artificially-generated random road elevation profiles laced with Hanning-shaped surface aberrations of known amplitude, lengths and location along the elevation profile. The shock detection algorithm was developed as a dual mode classifier to accommodate the two natural frequencies of each 2DoF quarter car models. Detection was implemented by first passing the vibration response signal through a band-pass filter around the two resonant modes in turn then calculating the filtered signals' instantaneous frequency (envelope) by means of the Hilbert transform. Shock detection was based on the local peak-to-mean ratio (LPTMR) of the instantaneous magnitude where 'local' was defined by the duration of the filtered signal's impulse response function. Sensitivity analysis and validation were undertaken on artificially-generated roads of varying roughness onto which aberrations of known shape were superimposed. The effectiveness and limitations (detection threshold) of the algorithm were evaluated by creating a range of aberrations with a broad range of lengths (effective frequencies) and diminishing amplitudes. Results show that shocks of 'significant' magnitudes are always detected with no detection of false positives. As the road roughness increases relative to the aberration amplitudes, the resulting shocks become increasingly drowned-out by the vibration response due to the underlying road roughness, especially for the quarter-car model with steel suspension. The main conclusion is that the signal analysis approach taken is ultimately effective and needs to be further validated using experimental response data. [ABSTRACT FROM AUTHOR]

Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-scale networks. Since complex operation characteristics and topology have emerged in recent power systems, the power grid partition requires higher efficiency and quality. Therefore, this paper proposes a fast network partition method with a balanced-depth-based community detection algorithm. Its aim is to significantly improve the efficiency of partition while maintaining high quality of partition, with which the inter-zone coupling is minimized while the intra-zone coupling is maximized. In the meantime, a surrogate-optimization-based selection algorithm is proposed to select the zonal pilot bus, based on which the secondary voltage control method is used to evaluate the quality of partition. Results from four case studies conducted in various power networks with different sizes, as compared to other partition methods, validate the high efficiency and high quality of the proposed power grid partition approach. [ABSTRACT FROM AUTHOR]

Modifying the metaheuristics as a striking alternative of basic algorithms is outstanding and efficient scientific approach in optimization of engineering problems to improve robustness and convergence rate. Firefly algorithm (FA) is one of the new metaheuristics inspired by the flashing behavior of fireflies, where the performance of each randomly generated solution on objective function is evaluated by the brightness. In the current paper, a modified firefly algorithm (MFA) was introduced using expectation value and generalized weighted average of a random brightness and then evaluated with different benchmark functions. Since brightness varies with movements of fireflies, the parameter settings can adaptively be tuned for different problems. The capability of the MFA then in hybridizing with a developed automated multi-objective radial-based function network (MORBF) was examined. In blasting engineering, multi-objective models covering the peak particle velocity (PPV) and the vibration frequency (Fvib) due to providing more insight on safety criteria significantly are essential and great of interested. The hybrid MORBF-MFA then was applied on 78 blasting data comprising stemming, burden, spacing, total charge, distance, and charge per delay to provide more accurate predictive model. Detailed executed analyses through different metrics showed 1.01% and 2.43% improvement in hybrid MORBF-MFA corresponding to PPV and Fvib over MORBF-FA. The observed results approved that the introduced MFA as a reliable and feasible tool with accurate enough response can effectively be applied to multi-objective problems. Implemented sensitivity analyses scored the distance and burden as the most and least influences factors on predicted outputs. [ABSTRACT FROM AUTHOR]

The purpose is to deal with the problems of excessive pruning of important convolutional layers caused by equalscale pruning, retain many redundant parameters and large loss of accuracy. This paper integrated sensitivity analysis on the basis of FPGM pruning strategy for network pruning. The algorithm used precision feedback to analyze the importance of each convolutional layer, control the single-layer pruning ratio, analyze the impact of different pruning ratios in each layer on accuracy loss, and obtained the sensitivity of each convolutional layer. It could be combined with FPGM to analyze the importance of the convolution kernel and cut the unimportant convolution kernel according to the pruning ratio of the sensitivity to complete the neural network pruning. The experimental results show that the accuracy of this method only drops by l. 56% and 0. 11 % when the pruning rate on MobileNet-v 1 and ResNet50 is 50%. This method has a higher pruning rate and lower calculation amount on ResNet50 under the same accuracy loss. [ABSTRACT FROM AUTHOR]

In rehabilitation, the Fugl–Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment. [ABSTRACT FROM AUTHOR]