Determination Of Threshold Value Research Articles

A Low-Speed Position Sensorless Scheme From Standstill for Low-Cost SRM Drives Based on Triple Current Slope Difference Threshold

In this paper, a triple current slope difference threshold based low-speed sensorless scheme for low-cost SRM drives is proposed. Firstly, slope based methods, including the current slope difference method, are proposed and the advantages in terms of anti-interference ability over traditional instantaneous value based methods have been clarified. Secondly, the traditional single/double threshold methods and corresponding outgoing/incoming phase injection types are introduced and comprehensively compared. The limitations of conventional methods while adopted in low-cost SRM drives are stated. Accordingly, to solve the problem that the current cannot be obtained during the demagnetization stage in the resistor-based current sampling scheme, the triple current slope difference threshold method is proposed, whose 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> threshold indicates the zero-current position. Thirdly, a general threshold value determination method considering the three-phase unbalanced inductances is proposed, which only requires the unsaturated inductance vs. rotor position characteristics that is very easy to be obtained. The proposed method only needs to store six threshold values in the microprocessor and needs no complex calculations and additional hardware, which can be realized in low-cost drive systems. Finally, the experiments of both the steady-state and starting transient-state under over-load conditions are implemented to verify the feasibility and accuracy of the proposed method.

Micro-fault identification for lithium-ion batteries in electric vehicles based on similarity measurement of the charging sequence

Battery fault diagnosis is critical to safeguard the safety of electric vehicles. Since lithium-ion battery cells are usually inconsistent and difficult to distinguish from faults, existing micro-fault identification methods are difficult to detect faults as early as possible based on real-world vehicle data. In contrast to the operation process, the voltage and temperature changes are more regular since the current relatively remains constant and the charging strategy is fixed, which is conducive to accurate micro-fault identification. According to the charging strategy of electric vehicles, charging data extraction and selection methods are introduced. Considering the practicality and the difficulty of threshold value determination, the micro-fault identification approach based on DBSCAN is proposed. Normalized IAVs and LIP distances are utilized as features, which come from charging sequences. The results of the thermal runaway vehicle show that the method can effectively find fault cells in voltage signals 18 days before thermal runaway occurs.

Kriterij za objektivno određivanje vrijednosti praga osjetljivosti pri filtriranju signala hrapavosti strojno obrađene površine masivnog drva filtrom utemeljenim na brzoj Fourierovoj transformaciji (FFT)

The article presents the method for an objective determination of threshold value, needed for filtering out the anatomical roughness signal, with filter based on fast Fourier Transform (FFT), from surface roughness profile after machining. The method includes experimental preparation of solid wood surface by cutting in such a way to get a surface that can be considered to represent only anatomical roughness, with no other influence. Experiments were performed on radial cross section of solid oak wood (Quercus robur L.) so that the results could be compared with roughness profiles that were previously obtained in experiments after sawing with circular saw. From these samples and based on frequency analysis of anatomical roughness signals, the threshold value was determined to be 1.6 &amp;mu;m. The average value of Ra parameter of anatomical roughness for specimens of radial cross section of solid oak wood was 2.1 &amp;mu;m with standard deviation of 0.3 &amp;mu;m. The importance of choosing adequate sampling length in threshold determination, based on frequency analysis of anatomical roughness signal, was also established.

Determination of Air Stability Parameter Threshold Value for Cumulonimbus and Thunderstorm Cloud Events at Kualanamu Meteorological Station

Many studies have carried out calculations related to atmospheric lability as a reference in weather forecasts, especially cumulonimbus clouds, and thunderstorms. However, many air lability index values are found to be inappropriate in each region because conditions in each region are different from each other in the region. So it is necessary to use precise index thresholds to determine weather conditions. In the study, observational data and data from Showalter Index (SI), Lifted Index (LI), K Index (KI), Severe Weather Threat Index (SWEAT), and Convective data were used. Available Potential Energy (CAPE) for ten years (2013-2022), then statistical calculations and verification for one year (2022) are carried out. The results obtained are the atmospheric stability index with the best accuracy in predicting the presence of cumulonimbus clouds and thunderstorms at the Kualanamu Meteorological Station, Deli Serdang is the best LI index to predict TS 00 and TS 12, and the best KI index to predict CB 00 and CB 12.

Active equalization for lithium-ion battery pack via data-driven residual charging capacity estimation

Considering the limitations in existing voltage-based and state-of-charge (SOC)-based active equalization strategies, including the difficulty in threshold value determination for equalization system on/off controlling, repeated estimation for equalization variable and the corresponding tremendous complexity, this paper designs a novel residual capacity-based active equalization strategy via data-driven residual charging capacity (RCC) estimation to fully charge all in-pack cells during constant current (CC) charging stage. Firstly, taking charge accumulation corresponding to specific voltage window as input, we build a data-driven RCC estimation model to online monitor each cell's RCC at specific voltage. Afterwards, considering the initial cell inconsistences, each cell's current RCC before equalization is further computed based on the estimated RCC and the subsequently measured charge accumulation. Finally, a parallel global search algorithm named particle swarm optimization is adopted to search the optimal combination of in-pack cells' equalization current considering equalization time and energy loss simultaneously. The verification results based on the Oxford battery degradation dataset demonstrate that the established data-driven model can realize accurate RCC estimation and all in-pack cells' totally charged capacity can roughly approach their maximum capacity at the end of CC charging stage using the proposed equalization strategy. Meanwhile, the maximum cell voltage difference and maximum cell SOC difference can be limited below 0.01V and 0.02, respectively. Moreover, by extending the charging time by only about 4min, the developed equalization strategy can further increase battery pack capacity by about 10%.

Performance analysis of cyclostationary spectrum sensing with dynamic thresholding using artificial neural network under varying signal to noise ratio and noise variance conditions

A key component of cognitive radio technology is spectrum sensing, which finds and accesses unused frequency bands to efficiently use the underutilized spectrum. A potential method for spectrum sensing called cyclostationary feature detection (CFD) uses the cyclostationary characteristics of signals to distinguish between the signal and noise. Artificial neural networks (ANNs) have been suggested in recent years as a method for CFD based spectrum detection, which increases detection accuracy and decreases complexity. However, the variable signal to noise ratio (SNR) and noise variance have an impact on the effectiveness of ANNs for CFD-based spectrum sensing. The effectiveness of ANNs for CFD based spectrum sensing under different SNR and noise variance conditions is evaluated in this work for the determination of threshold value in a dynamic way. We look into how SNR and noise variance affect the precision of probability of detection (Pd) and system complexity. Out analysis show how well ANNs work for CFD based spectrum detection with dynamic threshold value in the presence of changing SNR and noise variation. The findings demonstrate that ANNs may still obtain high Pd values with low SNR and large noise variance while maintaining a modest level of system complexity. According to our research, for a variety of SNR and noise variance situations, ANNs may be a viable option for CFD based spectrum detection in cognitive radio (CR) networks. The proposed approach can significantly improve the detection accuracy and reduce the complexity of the system, thereby enhancing the overall performance of cognitive radio networks. Based on the proposed work, it is determined that MPSK modulation function well with additive white Gaussian noise (AWGN), Rayleigh, and Rician channels up to a lower SNR value of – 30 dB and MQAM supports a lower SNR value of up to – 20 dB.

Genetic characterization of cotton varieties and genetic threshold value determination for similar variety selection in cotton DUS testing

Genetic characterization of cotton varieties and genetic threshold value determination for similar variety selection in cotton DUS testing

Whole-Genome Analysis of Influenza A(H3N2) and B/Victoria Viruses Detected in Myanmar during the COVID-19 Pandemic in 2021.

An influenza circulation was observed in Myanmar between October and November in 2021. Patients with symptoms of influenza-like illness were screened using rapid diagnostic test (RDT) kits, and 147/414 (35.5%) upper respiratory tract specimens presented positive results. All RDT-positive samples were screened by a commercial multiplex real-time polymerase chain reaction (RT-PCR) assay, and 30 samples positive for influenza A(H3N2) or B underwent further typing/subtyping for cycle threshold (Ct) value determination based on cycling probe RT-PCR. The majority of subtyped samples (n = 13) were influenza A(H3N2), while only three were B/Victoria. Clinical samples with low Ct values obtained by RT-PCR were used for whole-genome sequencing via next-generation sequencing technology. All collected viruses were distinct from the Southern Hemisphere vaccine strains of the corresponding season but matched with vaccines of the following season. Influenza A(H3N2) strains from Myanmar belonged to clade 2a.3 and shared the highest genetic proximity with Bahraini strains. B/Victoria viruses belonged to clade V1A.3a.2 and were genetically similar to Bangladeshi strains. This study highlights the importance of performing influenza virus surveillance with genetic characterization of the influenza virus in Myanmar, to contribute to global influenza surveillance during the COVID-19 pandemic.

Classifying thai news headlines using an artificial neural network

This research aimed to measure the effectiveness of Thai news headlines classification using an artificial neural network (ANN). The headlines consisted of i) political news, ii) sports news, iii) economic news, and iv) crime news, 1,200 headlines in total. The distribution of headlines was measured by using chi-square, information gain, and term frequency inverse class frequency (TFICF). Threshold default value was set in relation to terms of headlines before cross-validation was employed to categorize the data to examine the efficiency of the model using a neural network algorithm in classifying the headlines. The investigation of the news headline classification efficiency revealed that the 15-fold data division using TFICF was the most accurate in classifying headlines, with the accuracy rate of 99.60% and F-measure rate of 99.05%. Moreover, it was found that when more news headlines were provided as the learning data, the news headline classification became more accurate. Likewise, appropriate threshold value determination facilitated the selection of appropriate features in the headlines and resulted in more effective and accurate classification. Hence, it can be concluded that headline classification will be more accurate if the appropriate amount of learning data exists, and appropriate threshold value was set.

Some equations to identify the threshold value in the DEMATEL method

DEMATEL technique is a graphical representation method to deal with complex systems. The final analyzed cause and effect categorization would be fundamentally dependent on the threshold value setting. This research is intended to present some mathematical models for calculating the threshold value in the DEMATEL method. The min(max) operator has been intentionally used for considering three equations to identify the threshold value. Additionally, the proposed mathematical equations are gradually developed to gain more useful data to yield a threshold value as well. Particularly, the expert’s initial scoring for building the primary matrix would also be applied in one equation. Results show eliciting an expert’s opinions regarding the value of a threshold value determination leads to setting relatively high thresholds. But, there would be an equation which takes advantage of more data derived from the total influence matrix T. Moreover, a span of different threshold values is gained by making use of the Hamacher t-conorms operator which especially would cause better complexity management of the final total matrix T based on expert’s opinions. As a contribution to this research, threshold value determination is developed mathematically by making use of the direct data gained by the total matrix T. Besides combining data derived from total matrix T, the initial influence direct matrix given by experts, a simpler aggregating procedure and no need for statistical information compared to special Lenth’s method hints at this research’s novelty as well.

Voltage sensor fault detection, isolation and estimation for lithium-ion battery used in electric vehicles via a simple and practical method

Accurate measurement information, especially precise voltage, is essential for model-based multi-state estimation algorithms of lithium-ion battery. Regarding the shortcomings in existing diagnosis methods, such as the difficulty in threshold value determination, low voltage sensor fault detection efficiency and the assumption of no multiple faults occur simultaneously, a simple and practical voltage sensor fault detection, isolation and estimation method is proposed in this paper. Firstly, the widely used algorithm by fusion of recursive least square and extend Kalman filter is adopted to obtain the measurement innovation (MI) between measured and estimated terminal voltage during online state-of-charge estimation process. Subsequently, focusing on the MI generated under faultless and faulty voltage sensors, a simple feature point (FP) identification-based voltage sensor detection and isolation method is further designed. Finally, with the identified FP's y-coordinate, a three-step based voltage sensor fault estimation method is developed to determine the fault mode and the corresponding fault value. Moreover, the proposed voltage sensor fault diagnosis method is verified by Federal Urban Driving Schedule test, whose results demonstrate that it can still realize immediate voltage sensor fault detection (at the moment of sensor fault occurrence) and accurate voltage sensor fault estimation even though there exist certain current sensor fault.

Rule-ranking method based on item utility in adaptive rule model

BackgroundDecision-making is an important part of most human activities regardless of their daily activities, profession, or political inclination. Some decisions are relatively simple specifically when the consequences are insignificant while others can be very complex and have significant effects. Real-life decision problems generally involve several conflicting points of view (criteria) needed to be considered and this is the reason recent decision-making processes are usually supported by data as indicated by different data mining techniques. Data mining is the process of extracting data to obtain useful information and a promising and widely applied method is association rule mining which has the ability to identify interesting relationships between sets of items in a dataset and predict the associative behavior for new data. However, the number of rules generated in association rules can be very large, thereby making the exploitation process difficult. This means it is necessary to prioritize the selection of more valuable and relevant rules.MethodsTherefore, this study proposes a method to rank rules based on the lift ratio value calculated from the frequency and utility of the item. The three main functions in proposed method are mining of association rules from different databases (in terms of sources, characteristics, and attributes), automatic threshold value determination process, and prioritization of the rules produced.ResultsExperiments conducted on six datasets showed that the number of rules generated by the adaptive rule model is higher and sorted from the largest lift ratio value compared to the apriori algorithm.

Rapid identification and isolation of patients with COVID-19 reduces the odds of transmission to hospital roommates

Background: The Cleveland Clinic Main Campus is a multispecialty academic medical center with 1,200 adult patient beds, 58% of which are double occupancy. Our facility relies on double-occupancy rooms to provide needed care during the COVID-19 pandemic. Inherently, double occupancy poses a greater risk of exposure to SARS-CoV-2 despite mitigation efforts. We investigated the incidence of postexposure SARS-CoV-2 infection in double-occupancy rooms and evaluated risk factors for viral transmission. Methods: Early in the observation period patients were tested for SARS-CoV-2 based on clinical suspicion. By June 2020, all admitted patients were tested. Symptomatic patients were admitted with pending tests under transmission-based precautions. Asymptomatic patients were managed with standard precautions including patients admitted to double-occupancy rooms. A double-occupancy exposure event was defined as an uninfected patient sharing a room with a patient positive for SARS-CoV-2. All patient exposures were tracked and evaluated by the infection prevention (IP) team. The IP prospective review of source patients included determination of lowest cycle threshold (Ct) value of first COVID-19 test, and whether their infection was hospital or community onset. Review of exposed patients included sex, age, and exposure time (in hours) to the source patient. Postexposure infection was defined as a positive test for SARS-CoV-2 in the exposed population within 14 days of the defined exposure event. We fit a multivariable logistic regression model to estimate the effect of exposure time on the odds of postexposure infection in susceptible roommates. Results: From March 15 to December 15, 2020, 172 susceptible patients were exposed to a roommate with COVID-19. Also, 28 exposed patients met our definition for postexposure infection (attack rate, 16%). The frequency of postexposure infection was higher in patients for whom the source was hospital-onset versus community-onset disease (25% vs 10%; P = .01) and when the source patient’s Ct value was below the median value of 21.1 (26% vs 11% p Conclusions: We identified a postexposure infection attack rate of 16% for double-occupancy patients in the first 9 months of the pandemic. Time exposed to source patient was significantly associated with infection. Our experience demonstrates the potential benefit of asymptomatic admission testing with expedited turnaround time to mitigate viral transmission between patients in double-occupancy rooms.Funding: NoneDisclosures: None

Determination of threshold value of protective level of class G antibodies to SARS-COV‑2 in late phase of immune response in COVID‑19 convalescents

Determining the effectiveness and duration of humoral immunity to SARS-CoV‑2 is of great importance for the management tactics and forecasting of the COVID‑19 pandemic. However, it is important to understand that the high concentration of antibodies, which is characteristic of an acute immune response (including after vaccination), will not persist in its late stage of “memory”. The level of antibodies at the end of the acute phase of the immune response inevitably decreases, and then, having reached a certain limit, it stabilizes in most people.The purpose of this study is to substantiate the threshold value of the “protective” level of antibodies to SARS-CoV‑2 when using the SARS-CoV‑2- IgG-ELISA-BEST reagent system of Vector-Best JSC, Novosibirsk, Russia.The results of our study confirmed that 6–10 and even 18 months after the COVID‑19 disease and 6 months after vaccination, the antibody level positivity index (IP) remained relatively constant and comparable. Taking into account the risks of adverse reactions and complications caused by excessive immunization with repeated administration of the vaccine against a background of high antibody levels, it is advisable to focus on the average value of the level of antibodies of the late phase of the immune response, taking this level as the threshold value of “protective” immunity. The data obtained by us together with the results of foreign authors suggest that both infection with SARS-CoV‑2 and vaccination, in most cases, lead to the formation of long-term humoral immunity, which corresponds to the range of concentration of SARS-CoV‑2-IgG equal to IP=7,77 ± 1,64 or 102,63 ± 29,31 BAU / ml.

Determination of germination threshold value of chickpea varieties with GGE biplot method under different irrigation water salinity conditions

For sustainable agricultural production, besides drought, plant resistance to irrigation water and soil salinity should be investigated. Researchers mostly focused on salinity and drought resistance of common species (wheat, barley, maize etc.). However, number of studies on chickpea with various uses is quite limited. In present study, 11 chickpea varieties were subjected to germination tests under different electrical conductivity of irrigation water salinity (ECi) conditions to identify irrigation water salinity resistant and sensitive varieties. In germination tests, besides control treatment (0 dS/m), five different ECi levels (6, 8 10, 12 and 16 dS/m) were used in germination solution. Sodium Absorption Ratio (SAR) of saline waters were adjusted to be below 3. GGE biplot method was used for visual assessment of genotype response to saline irrigation waters. Threshold salinity value for germination was identified as 12 dS/m based on germination ratios and seedling dry weights, 8 dS/m based on seedling stem lengths and 10 dS/m based on seedling root lengths. Based on germination ratios and seedling dry weights, Azkan cultivar was identified as the most resistant and Cagatay cultivar was identified as the most sensitive cultivar to irrigation water salinity.

Comparison of samples found positive by anti-HCV screening test with line immunoassay and determination of threshold value.

This study aimed to compare the serum samples found reactive (≥1-≤20 signal-to-cutoff ratio) with Elecsys antibodies to hepatitis C virus screening test with innogenetics-line immunassay hepatitis C Virus Score test and to determine the most appropriate threshold value for our country, since positive results close to the cutoff value cause serious problems in routine diagnostic laboratories. Antibodies to hepatitis C virus-positive samples from 687 different patients were included in the study. Antibodies to hepatitis C virus antibody detection was performed using Elecsys antibodies to hepatitis C virus II kits (Roche Diagnostics, Germany), an electrochemiluminescence method based on the double-antigen sandwich principle, on the Cobas e601 analyzer (Roche Diagnostics) in accordance with the recommendations of the manufacturer. Samples that were initially identified as reactive were studied again. Samples with ≥1-≤20 signal-to-cutoff ratio reagents as a result of retest were included in the study to be validated with the third-Generation Line immunassay kit (innogenetics-line immunassay hepatitis C Virus, Belgium). A total of 687 samples with antibodies to hepatitis C virus positive and levels between 1-20 S/Co were found to be 56.1% negative, 14.8% indeterminate, and 29.1% positive by innogenetics-line immunassay hepatitis C Virus confirmation test. When the cases with indeterminate innogenetics-line immunassay hepatitis C Virus test results were accepted as positive, the signal-to-cutoff ratio value for antibodies to hepatitis C virus was determined as 5.8 (95% confidence interval) in distinguishing the innogenetics-line immunassay hepatitis C Virus negative and positive groups. It was concluded that with further studies on this subject, each country should determine the most appropriate S/Co value for its population, and thus it would be beneficial to reduce the problems such as test repetition and cost increase.

Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest

Feature selection is a pre-processing technique used to remove unnecessary characteristics, and speed up the algorithm's work process. A part of the technique is carried out by calculating the information gain value of each dataset characteristic. Also, the determined threshold rate from the information gain value is used in feature selection. However, the threshold value is used freely or through a rate of 0.05. Therefore this study proposed the threshold rate determination using the information gain value’s standard deviation generated by each feature in the dataset. The threshold value determination was tested on 10 original datasets transformed by FFT and IFFT and classified using Random Forest. On processing the transformed dataset with the proposed threshold this study resulted in lower accuracy and longer execution time compared to the same process with Correlation-Base Feature Selection (CBF) and a standard 0.05 threshold method. Similarly, the required accuracy value is lower when using transformed features. The study showed that by processing the original dataset with a standard deviation threshold resulted in better feature selection accuracy of Random Forest classification. Furthermore, by using the transformed feature with the proposed threshold excluding the imaginary numbers leads to a faster average time than the three methods compared.

Camera Captured Document De-Warping and De-Skewing

De-warping is the elementary step in the analysis of document images which are camera based. Processing of warped image is a challenging task. Therefore, to make the document images OCR understandable de-warping has become a major task. In this paper, we have presented an effective pre-processing, de-warping and de-skewing techniques for camera captured document image processing. In pre-processing, we have divided input color document image into R, G and B-band, further convert R, G, B-band to C, M and Y-color space respectively, convert the average CMY grey scale image to binary by determining threshold value automatically. In de-warping and de-skewing, we have presented an effective techniques to remove geometrical and perspective distortion in camera captured document by combination of centroid and mid-point of bounding box height, bounding box is plotted on text blocks using connected component analysis technique. The introduced work is robust in correcting geometric distortion and skew correction for standard printed data set and also for Kannada handwritten documents.

Threshold value of DC array current and DC string voltage for fault detection in grid-connected photovoltaic system

The failure detection in a grid-connected photovoltaic (PV) system has become an important aspect of solving the issue of the reduced energy output in the PV system. One of the methods in detecting failure is by using the threshold-based method to compute the ratio of actual and predicted DC array current and DC string voltage value. This value will be applied in the failure detection algorithm by using power loss analysis and may reduce the time, cost and labour needed to measure the quality of the energy output of the PV system. This study presented the threshold value of DC array current and DC string voltage to be implemented in the algorithm of fault detection in grid-connected photovoltaic (PV) system under the Malaysian climate. Data from the PV system located at Green Energy Research Center (GERC) was recorded in 12 months interval using the monocrystalline PV modules. The actual data was recorded using five minutes interval for 30 consecutive days. The prediction of the data was calculated using the mathematical method. The threshold value was determined from the ratio between actual and predicted data. The results show that the DC array current threshold value, σ is 0.9816. While, DC string voltage threshold value, λ is 0.9261. The proposed value may be beneficial for the determination of threshold value for regions with the tropical climate.

A novel angle extremum maximum method for recognition of pulse wave feature points

Background and objectivesPulse wave is one of the biomedical signals that has been studied over the past years. Accurate recognition of feature points is the basis of verifying the connections between pulse waves and certain diseases. Therefore, the aim of the study is to discuss the use of angle mapping on feature points recognition. MethodsThe mathematical method is based on the application of angle curve with parameter " k " on pulse wave. The data used is collected by PVDF sensor. Approximate curve and mathematical model are used for the discussion of the influence of parameter k and pulse wave amplitude by numerical calculation. The conclusion drawn from the numerical solution is that when k changes to maximize the angle extremum value, the corresponding position of angle extremum point is the feature point position. For the sampling rate f = 455Hz in this paper, k can be taken from 5 to 15. ResultsWe present the recognition results of unobvious feature points based on the "angle extremum maximum method" and corresponding angle values. The results are compared with traditional methods and the determination of angle threshold value is discussed. ConclusionsThis method can be used for accurate and efficient feature points identification, and it can be better applied to pulse waves with noise or unobvious feature points.