When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to con...Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to convenience, high accuracy, and cost-effectiveness. Manual counting from pre-recorded video footage can be prone to inconsistencies and errors, leading to inaccurate counts. Besides, there are no standard guidelines for collecting video data and conducting manual counts from the recorded videos. This paper aims to comprehensively assess the accuracy of manual counts from pre-recorded videos and introduces guidelines for efficiently collecting video data and conducting manual counts by trained individuals. The accuracy assessment of the manual counts was conducted based on repeated counts, and the guidelines were provided from the experience of conducting a traffic survey on forty strip mall access points in Baton Rouge, Louisiana, USA. The percentage of total error, classification error, and interval error were found to be 1.05 percent, 1.08 percent, and 1.29 percent, respectively. Besides, the percent root mean square errors (RMSE) were found to be 1.13 percent, 1.21 percent, and 1.48 percent, respectively. Guidelines were provided for selecting survey sites, instruments and timeframe, fieldwork, and manual counts for an efficient traffic data collection survey.展开更多
The main aim of this study was to investigate the prevalence of intramammary infection (IMI) in early-lactation of primiparous cows using milk recording cow composite somatic cell count (CSCC) categories (combining th...The main aim of this study was to investigate the prevalence of intramammary infection (IMI) in early-lactation of primiparous cows using milk recording cow composite somatic cell count (CSCC) categories (combining the first 2 milk recording results after calving). Another aim was to evaluate the milk urea (MU) content as a potential supplementary indicator to SCC or CSCC for the identification of IMI in primiparous cows after calving. This retrospective observational study was conducted on records of test-day of primiparous cows over a period of 6 years (January 2016 to December 2021. The SCC data for 158 Holstein Friesian primiparous cows, with their first milk recording 5 to 35 days after calving and their second milk recording 28 to 56 days in milk (DIM), were identified. Each primiparous cow was assigned a CSCC category (low-low, low-high, high-low or high-high) based on the CSCC at the first 2 milking recordings using the following cut-offs: ≤150,000 cells/ml (low), >150,000 cells/ml (high). The association between CSCC categories and MV content was analyzed using correlation models. At the first milk recording, a proportion of 63.29% was in the low SCC category, and the rest (36.71%) was in the high SCC category. At the second milk recording, a proportion of primiparous cows in CSCC categories was 59.49%, 3.80%, 27.85% and 8.86% in low-low, low-high, high-low and high-high, respectively. At the second milk recording, a proportion of 12.66% of primiparous cows was in the high CSCC category and a proportion of 87.34% of primiparous cows was in the low CSCC category, indicating a poor and a good udder health, respectively. The association of SCC with MU content in low and in high SCC categories at the first milk recording was positive and moderate (+0.49) and negative and strong (-0.97), respectively. The association of CSCC categories with MU contents at the second milk recording was inconclusive. We concluded that CSCC categories may be a useful tool for identifying success and problems regarding the udder health of primiparous cows in early lactation.展开更多
Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experim...Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the grayscale.Images were created by using grayscale,which trained the convolutional neural network to count the alpha tracks.The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range,which was unaffected by the overlapping effect.The alpha track counts were unaffected by the fading effect within 60 min,where the calibrated formula for the fading effect was analyzed for 132.7 min.The detection efficiency of the trained convolutional neural network for inhomogeneous ^(241)Am sources(2π emission)was 0.6050±0.0399,whereas the efficiency curve of the photo-stimulated luminescence method was lower than that of the trained convolutional neural network.展开更多
Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and...Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.展开更多
AIM:To assess the performance of a bespoke software for automated counting of intraocular lens(IOL)glistenings in slit-lamp images.METHODS:IOL glistenings from slit-lamp-derived digital images were counted manually an...AIM:To assess the performance of a bespoke software for automated counting of intraocular lens(IOL)glistenings in slit-lamp images.METHODS:IOL glistenings from slit-lamp-derived digital images were counted manually and automatically by the bespoke software.The images of one randomly selected eye from each of 34 participants were used as a training set to determine the threshold setting that gave the best agreement between manual and automatic grading.A second set of 63 images,selected using randomised stratified sampling from 290 images,were used for software validation.The images were obtained using a previously described protocol.Software-derived automated glistenings counts were compared to manual counts produced by three ophthalmologists.RESULTS:A threshold value of 140 was determined that minimised the total deviation in the number of glistenings for the 34 images in the training set.Using this threshold value,only slight agreement was found between automated software counts and manual expert counts for the validating set of 63 images(κ=0.104,95%CI,0.040-0.168).Ten images(15.9%)had glistenings counts that agreed between the software and manual counting.There were 49 images(77.8%)where the software overestimated the number of glistenings.CONCLUSION:The low levels of agreement show between an initial release of software used to automatically count glistenings in in vivo slit-lamp images and manual counting indicates that this is a non-trivial application.Iterative improvement involving a dialogue between software developers and experienced ophthalmologists is required to optimise agreement.The results suggest that validation of software is necessary for studies involving semi-automatic evaluation of glistenings.展开更多
In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextracti...In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.展开更多
Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materia...Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.展开更多
In the process of aquaculture,monitoring the number of fish bait particles is of great significance to improve the growth and welfare of fish.Although the counting method based on onvolutional neural network(CNN)achie...In the process of aquaculture,monitoring the number of fish bait particles is of great significance to improve the growth and welfare of fish.Although the counting method based on onvolutional neural network(CNN)achieve good accuracy and applicability,it has a high amount of parameters and computation,which limit the deployment on resource-constrained hardware devices.In order to solve the above problems,this paper proposes a lightweight bait particle counting method based on shift quantization and model pruning strategies.Firstly,we take corresponding lightweight strategies for different layers to flexibly balance the counting accuracy and performance of the model.In order to deeply lighten the counting model,the redundant and less informative weights of the model are removed through the combination of model quantization and pruning.The experimental results show that the compression rate is nearly 9 times.Finally,the quantization candidate value is refined by introducing a power-of-two addition term,which improves the matches of the weight distribution.By analyzing the experimental results,the counting loss at 3 bit is reduced by 35.31%.In summary,the lightweight bait particle counting model proposed in this paper achieves lossless counting accuracy and reduces the storage and computational overhead required for running convolutional neural networks.展开更多
The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of con...The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.展开更多
[Objectives]This study was conducted to investigate the main factors affecting the aerobial plate count in raw milk.[Methods]Drinking water,medicated baths and raw milk under different storage and transportation condi...[Objectives]This study was conducted to investigate the main factors affecting the aerobial plate count in raw milk.[Methods]Drinking water,medicated baths and raw milk under different storage and transportation conditions were detected for the values of aerobial plate count to analyze their effects on the aerobial plate count in raw milk.[Results]Disinfection of drinking water tanks could significantly reduce the aerobial plate count in water.The use of medicated baths before and after milking could effectively reduce the aerobial plate count and had a significant bactericidal effect.The growth of microorganisms in raw milk stored below 4℃was relatively slow.Regularly disinfecting drinking water tanks and disinfecting nipples before and after milking could reduce the aerobial plate count in the tanks and nipples.After raw milk was extruded,the temperature should decrease to 0-4℃within 2 h,and the storage time should not exceed 48 h,which could effectively control the aerobial plate count in raw milk.[Conclusions]This study provides a reference for scientific control of the aerobial plate count in raw milk.展开更多
This paper presents vehicle localization and tracking methodology to utilize two-channel LiDAR data for turning movement counts. The proposed methodology uniquely integrates a K-means clustering technique, an inverse ...This paper presents vehicle localization and tracking methodology to utilize two-channel LiDAR data for turning movement counts. The proposed methodology uniquely integrates a K-means clustering technique, an inverse sensor model, and a Kalman filter to obtain the final trajectories of an individual vehicle. The objective of applying K-means clustering is to robustly differentiate LiDAR data generated by pedestrians and multiple vehicles to identify their presence in the LiDAR’s field of view (FOV). To localize the detected vehicle, an inverse sensor model was used to calculate the accurate location of the vehicles in the LiDAR’s FOV with a known LiDAR position. A constant velocity model based Kalman filter is defined to utilize the localized vehicle information to construct its trajectory by combining LiDAR data from the consecutive scanning cycles. To test the accuracy of the proposed methodology, the turning movement data was collected from busy intersections located in Newark, NJ. The results show that the proposed method can effectively develop the trajectories of the turning vehicles at the intersections and has an average accuracy of 83.8%. Obtained R-squared value for localizing the vehicles ranges from 0.87 to 0.89. To measure the accuracy of the proposed method, it is compared with previously developed methods that focused on the application of multiple-channel LiDARs. The comparison shows that the proposed methodology utilizes two-channel LiDAR data effectively which has a low resolution of data cluster and can achieve acceptable accuracy compared to multiple-channel LiDARs and therefore can be used as a cost-effective measure for large-scale data collection of smart cities.展开更多
By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting ...By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.展开更多
Objective: The main objective was to study the disturbances of the Blood Count of children hospitalized in the general pediatric of the Gabriel Touré teaching hospital. Methods: This was a prospective and descrip...Objective: The main objective was to study the disturbances of the Blood Count of children hospitalized in the general pediatric of the Gabriel Touré teaching hospital. Methods: This was a prospective and descriptive study conducted from September 1 to November 30, 2018 in the general pediatrics department of the Gabriel Touré teaching hospital in Bamako. Data were collected on patient records using a survey sheet. Results: We collected 512 files of children out of 1030 admissions during the study period;the rate of completion of the blood count is 50%. The male sex was predominant with a sex ratio of 1.3. The majority of patients were under 5 years old (58%). The majority of fathers (56%) and mothers (64%) of children had no education;they are mainly farmers (61%) and housewives (88%). Pallor was the reason for consultation in 29% of patients and present in 60%. On blood count, anemia was present in 92% of patients, half of whom (50%) had severe anemia with a hemoglobin level below 7 g/dL. The anemia was mainly microcytic (72%) and hypochromic (66%). Hyperleukocytosis (62%), eosinophilia (68%) and basophilia (58%) were the abnormalities observed in the white line. Thrombocytopenia accounted for 40%. Severe malaria (53%) was the main discharge diagnosis and almost all patients (99%) were alive at discharge. Conclusion: The characteristics of anemia require a study of the complete blood count in healthy children with dosage of serum iron and ferritin for a better understanding of the phenomenon.展开更多
Background: It is widely known that the human immune-deficiency virus (HIV) induces biochemical and physiological changes in affected persons. Consequently, the overall aim of this study was to evaluate the HIV-1 RNA ...Background: It is widely known that the human immune-deficiency virus (HIV) induces biochemical and physiological changes in affected persons. Consequently, the overall aim of this study was to evaluate the HIV-1 RNA viral load, CD4 count, and certain haematological parameters among HIV treatment-na?ve subjects in the Enugu metropolis of Nigeria. Materials and Methods: A total of 252 HIV-infected, ART-native subjects (≥18) attending the University of Nigeria Teaching Hospital (UNTH) in Ituku-Ozalla, Enugu were recruited for this study and were made up of 157 (62.3%) females and 95 (37.7%) males. A total of 250 HIV-negative subjects were used as control subjects (100 males and 150 females). Blood samples were collected from all the participants and their HIV-1 status was confirmed by an immunoblot confirmatory test. Their haematological parameters and CD4 count were evaluated, while the HIV-1 viral load was only assessed on confirmed HIV-positive subjects. Results: There was female predominance (62.3%) among these HIV-positive subjects. The mean age of HIV-positive subjects was 39.16 ± 10.08 years while the mean age of the control subjects was 34.8 ± 8.6 years. The age group of 31 - 40 years (102/252 (40.5%)) constituted most of the test subjects. The total white blood cells (TWBC) (6.05 ± 5.46), lymphocyte counts (36 ± 14), haemoglobin concentrations (Hb) (9.85 ± 7.36) and the CD4 counts (242 ± 228) of the HIV-infected subjects showed a significant difference when compared with their control counterpart values of TWBC (4.5 ± 0.568), lymphocytes (39.67 ± 8.2), Hb (13.48 ± 1.5), and CD4 counts (807 ± 249) (p 0.05). Anaemia, lymphocytopenia, and thrombocytopenia were the haematological abnormalities seen in the HIV-positive subjects. HIV viral load correlated with haemoglobin concentration, CD4 count, lymphocyte count, and neutrophil count (p Conclusion: Prognostic factors, such as haemoglobin concentrations, CD4 counts, lymphocyte counts, and neutrophil counts can be used to monitor patients’ viral loads since they correlate with the latter;furthermore, age is a factor that should be considered in the management of HIV-positive patients.展开更多
Background: Respiratory distress syndrome (RDS) is a major cause of neonatal morbidity and mortality, affecting approximately 1% of all live births and 10% of all preterm infants. Lamellar bodies represent a storage f...Background: Respiratory distress syndrome (RDS) is a major cause of neonatal morbidity and mortality, affecting approximately 1% of all live births and 10% of all preterm infants. Lamellar bodies represent a storage form of pulmonary surfactant within Type II pneumocytes, secretion of which increases with advancing gestational age, thus enabling prediction of the degree of FLM. Preterm premature rupture of membranes (PPROM) complicates approximately 1/3 of all preterm births. Birth within 1 week is the most likely outcome for any patient with PPROM in the absence of adjunctive treatments. Respiratory distress has been reported to be the most common complication of preterm birth. Sepsis, intraventricular haemorrhage, and necrotizing enterocolitis also are associated with prematurity, but these are less common near to term. Objective: To assess the efficacy of the amniotic fluid lamellar body counting from a vaginal pool in predicting fetal lung maturity in women with preterm premature rupture of membranes. Methods: This study was conducted at Ain Shams University Maternity Hospital in the emergency ward from January 2019 to September 2019. It included 106 women with singleton pregnancies, gestational age from 28 - 36 weeks with preterm premature rupture of membranes. This study is designed to assess the efficacy of the amniotic fluid lamellar body counting (LBC) from a vaginal pool in predicting fetal lung maturity in women with preterm premature rupture of membranes. Results: The current study revealed a highly significant increase in the lamellar body count in cases giving birth to neonates without RDS compared to that cases giving birth to neonates with RDS. Also, no statistically significant difference between LBC and age, parity and number of previous miscarriages in the mother was found. Gestational age at delivery was significantly lower among cases with respiratory distress. Steroid administration was significantly less frequent among cases with respiratory distress. However, lamellar bodies had high diagnostic performance in the prediction of respiratory distress. Conclusion: Lamellar body count (LBC) is an effective, safe, easy, and cost-effective method to assess fetal lung maturity (FLM). It does not need a highly equipped laboratory or specially trained personnel, it just needs the conventional blood count analyzer. Measurement of LBC is now replacing the conventional Lecithin/Sphyngomyelin L/S ratio. LBC cut-off value of ≤42.5 × 10<sup>3</sup>/μL can be used safely to decide fetal lung maturity with sensitivity of 95.7% and specificity of 97.6%.展开更多
Introduction: SARS-COV-2 infection is a real public health challenge for the World Health Organization and for our country. It is responsible for numerous hematological abnormalities in infected patients. Objectives: ...Introduction: SARS-COV-2 infection is a real public health challenge for the World Health Organization and for our country. It is responsible for numerous hematological abnormalities in infected patients. Objectives: To describe the haemogram abnormalities in patients infected with SARS-COV-2 and to determine which ones are associated with death. Material and Method: We conducted a retrospective, descriptive, analytical, cross-sectional study from March 2020 to September 2021. The study included all patients hospitalized with RT-PCR-confirmed COVID-19 who performed a blood count. We evaluated the blood count profile, the pathologies found and the associated blood count abnormalities. Results: A total of 263 patients were included. The mean age of the patients was 63.77 years (range 12 - 90 years). The male sex represented 54.75% (n = 144) while the female sex was 45.25% (n = 119) (sex ratio = 1.21). The most common pathologies were: diabetes: 30.03% (n = 79), high blood pressure: 41.04% (n = 108), and Chronic kidney disease: 7.98 (n = 21). The abnormalities of the haemogram found were essential: anaemia 28.13% (n = 121), hyperleukocytosis with neutrophilic predominance: 29.3% (126), lymphopenia: 34.41% (n = 148), thrombocytopenia: 8.16% (n = 35). The search for hematological factors associated with death in patients showed a significant difference between hyperleukocytosis (p = 0.000) and lymphopenia (p = 0.0001). Conclusion: SARS-COV-2 disease was a mortality factor when associated with lymphopenia and hyperleukocytosis in our series.展开更多
The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming...The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming at the problem of degradation of long-span continuous rigid frame bridges due to fatigue and environmental effects,this paper suggests a method to analyze the fatigue degradation mechanism of this type of bridge,which combines long-term in-site monitoring data collected by the health monitoring system(HMS)and fatigue theory.In the paper,the authors mainly carry out the research work in the following aspects:First of all,a long-span continuous rigid frame bridge installed with HMS is used as an example,and a large amount of health monitoring data have been acquired,which can provide efficient information for fatigue in terms of equivalent stress range and cumulative number of stress cycles;next,for calculating the cumulative fatigue damage of the bridge structure,fatigue stress spectrum got by rain flow counting method,S-N curves and damage criteria are used for fatigue damage analysis.Moreover,it was considered a linear accumulation damage through the Palmgren-Miner rule for the counting of stress cycles.The health monitoring data are adopted to obtain fatigue stress data and the rain flow counting method is used to count the amplitude varying fatigue stress.The proposed fatigue reliability approach in the paper can estimate the fatigue damage degree and its evolution law of bridge structures well,and also can help bridge engineers do the assessment of future service duration.展开更多
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
文摘Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to convenience, high accuracy, and cost-effectiveness. Manual counting from pre-recorded video footage can be prone to inconsistencies and errors, leading to inaccurate counts. Besides, there are no standard guidelines for collecting video data and conducting manual counts from the recorded videos. This paper aims to comprehensively assess the accuracy of manual counts from pre-recorded videos and introduces guidelines for efficiently collecting video data and conducting manual counts by trained individuals. The accuracy assessment of the manual counts was conducted based on repeated counts, and the guidelines were provided from the experience of conducting a traffic survey on forty strip mall access points in Baton Rouge, Louisiana, USA. The percentage of total error, classification error, and interval error were found to be 1.05 percent, 1.08 percent, and 1.29 percent, respectively. Besides, the percent root mean square errors (RMSE) were found to be 1.13 percent, 1.21 percent, and 1.48 percent, respectively. Guidelines were provided for selecting survey sites, instruments and timeframe, fieldwork, and manual counts for an efficient traffic data collection survey.
文摘The main aim of this study was to investigate the prevalence of intramammary infection (IMI) in early-lactation of primiparous cows using milk recording cow composite somatic cell count (CSCC) categories (combining the first 2 milk recording results after calving). Another aim was to evaluate the milk urea (MU) content as a potential supplementary indicator to SCC or CSCC for the identification of IMI in primiparous cows after calving. This retrospective observational study was conducted on records of test-day of primiparous cows over a period of 6 years (January 2016 to December 2021. The SCC data for 158 Holstein Friesian primiparous cows, with their first milk recording 5 to 35 days after calving and their second milk recording 28 to 56 days in milk (DIM), were identified. Each primiparous cow was assigned a CSCC category (low-low, low-high, high-low or high-high) based on the CSCC at the first 2 milking recordings using the following cut-offs: ≤150,000 cells/ml (low), >150,000 cells/ml (high). The association between CSCC categories and MV content was analyzed using correlation models. At the first milk recording, a proportion of 63.29% was in the low SCC category, and the rest (36.71%) was in the high SCC category. At the second milk recording, a proportion of primiparous cows in CSCC categories was 59.49%, 3.80%, 27.85% and 8.86% in low-low, low-high, high-low and high-high, respectively. At the second milk recording, a proportion of 12.66% of primiparous cows was in the high CSCC category and a proportion of 87.34% of primiparous cows was in the low CSCC category, indicating a poor and a good udder health, respectively. The association of SCC with MU content in low and in high SCC categories at the first milk recording was positive and moderate (+0.49) and negative and strong (-0.97), respectively. The association of CSCC categories with MU contents at the second milk recording was inconclusive. We concluded that CSCC categories may be a useful tool for identifying success and problems regarding the udder health of primiparous cows in early lactation.
基金supported by the Hunan Provincial Innovation Foundation for Postgraduates (No.QL20210228)the National Natural Science Foundation of China (No.12075112)the National Natural Science Foundation of China (No.12175102).
文摘Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the grayscale.Images were created by using grayscale,which trained the convolutional neural network to count the alpha tracks.The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range,which was unaffected by the overlapping effect.The alpha track counts were unaffected by the fading effect within 60 min,where the calibrated formula for the fading effect was analyzed for 132.7 min.The detection efficiency of the trained convolutional neural network for inhomogeneous ^(241)Am sources(2π emission)was 0.6050±0.0399,whereas the efficiency curve of the photo-stimulated luminescence method was lower than that of the trained convolutional neural network.
基金supported by the National Natural Science Foundation of China (61701260 and 62271266)the Postgraduate Research&Practice Innovation Program of Jiangsu Province (SJCX21_0255)the Postdoctoral Research Program of Jiangsu Province(2019K287)。
文摘Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.
文摘AIM:To assess the performance of a bespoke software for automated counting of intraocular lens(IOL)glistenings in slit-lamp images.METHODS:IOL glistenings from slit-lamp-derived digital images were counted manually and automatically by the bespoke software.The images of one randomly selected eye from each of 34 participants were used as a training set to determine the threshold setting that gave the best agreement between manual and automatic grading.A second set of 63 images,selected using randomised stratified sampling from 290 images,were used for software validation.The images were obtained using a previously described protocol.Software-derived automated glistenings counts were compared to manual counts produced by three ophthalmologists.RESULTS:A threshold value of 140 was determined that minimised the total deviation in the number of glistenings for the 34 images in the training set.Using this threshold value,only slight agreement was found between automated software counts and manual expert counts for the validating set of 63 images(κ=0.104,95%CI,0.040-0.168).Ten images(15.9%)had glistenings counts that agreed between the software and manual counting.There were 49 images(77.8%)where the software overestimated the number of glistenings.CONCLUSION:The low levels of agreement show between an initial release of software used to automatically count glistenings in in vivo slit-lamp images and manual counting indicates that this is a non-trivial application.Iterative improvement involving a dialogue between software developers and experienced ophthalmologists is required to optimise agreement.The results suggest that validation of software is necessary for studies involving semi-automatic evaluation of glistenings.
文摘In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.
基金This work was jointly supported by the Excellent Research Graduate Scholarship-EreG Scholarship Program Under the Memorandum of Understanding between Thammasat University and National Science and Technology Development Agency(NSTDA),Thailand[No.MOU-CO-2562-8675]the Center of Excellence in Logistics and Supply Chain System Engineering and Technology(COE LogEn)+1 种基金Sirindhorn International Institute of Technology(SIIT)Thammasat University,Thailand.
文摘Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.
基金supported by the National Key Research and Development Program of China(No.2019YFD0901000)。
文摘In the process of aquaculture,monitoring the number of fish bait particles is of great significance to improve the growth and welfare of fish.Although the counting method based on onvolutional neural network(CNN)achieve good accuracy and applicability,it has a high amount of parameters and computation,which limit the deployment on resource-constrained hardware devices.In order to solve the above problems,this paper proposes a lightweight bait particle counting method based on shift quantization and model pruning strategies.Firstly,we take corresponding lightweight strategies for different layers to flexibly balance the counting accuracy and performance of the model.In order to deeply lighten the counting model,the redundant and less informative weights of the model are removed through the combination of model quantization and pruning.The experimental results show that the compression rate is nearly 9 times.Finally,the quantization candidate value is refined by introducing a power-of-two addition term,which improves the matches of the weight distribution.By analyzing the experimental results,the counting loss at 3 bit is reduced by 35.31%.In summary,the lightweight bait particle counting model proposed in this paper achieves lossless counting accuracy and reduces the storage and computational overhead required for running convolutional neural networks.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1I1A1A01055652).
文摘The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.
基金Supported by Hebei Province Phase III Modern Agricultural Industry Technology System Cow Innovation Team Building ProjectThe Fourth Batch of High-end Talent Project in Hebei ProvinceHebei Provincial Science and Technology Innovation Leading Talents(21130243A).
文摘[Objectives]This study was conducted to investigate the main factors affecting the aerobial plate count in raw milk.[Methods]Drinking water,medicated baths and raw milk under different storage and transportation conditions were detected for the values of aerobial plate count to analyze their effects on the aerobial plate count in raw milk.[Results]Disinfection of drinking water tanks could significantly reduce the aerobial plate count in water.The use of medicated baths before and after milking could effectively reduce the aerobial plate count and had a significant bactericidal effect.The growth of microorganisms in raw milk stored below 4℃was relatively slow.Regularly disinfecting drinking water tanks and disinfecting nipples before and after milking could reduce the aerobial plate count in the tanks and nipples.After raw milk was extruded,the temperature should decrease to 0-4℃within 2 h,and the storage time should not exceed 48 h,which could effectively control the aerobial plate count in raw milk.[Conclusions]This study provides a reference for scientific control of the aerobial plate count in raw milk.
文摘This paper presents vehicle localization and tracking methodology to utilize two-channel LiDAR data for turning movement counts. The proposed methodology uniquely integrates a K-means clustering technique, an inverse sensor model, and a Kalman filter to obtain the final trajectories of an individual vehicle. The objective of applying K-means clustering is to robustly differentiate LiDAR data generated by pedestrians and multiple vehicles to identify their presence in the LiDAR’s field of view (FOV). To localize the detected vehicle, an inverse sensor model was used to calculate the accurate location of the vehicles in the LiDAR’s FOV with a known LiDAR position. A constant velocity model based Kalman filter is defined to utilize the localized vehicle information to construct its trajectory by combining LiDAR data from the consecutive scanning cycles. To test the accuracy of the proposed methodology, the turning movement data was collected from busy intersections located in Newark, NJ. The results show that the proposed method can effectively develop the trajectories of the turning vehicles at the intersections and has an average accuracy of 83.8%. Obtained R-squared value for localizing the vehicles ranges from 0.87 to 0.89. To measure the accuracy of the proposed method, it is compared with previously developed methods that focused on the application of multiple-channel LiDARs. The comparison shows that the proposed methodology utilizes two-channel LiDAR data effectively which has a low resolution of data cluster and can achieve acceptable accuracy compared to multiple-channel LiDARs and therefore can be used as a cost-effective measure for large-scale data collection of smart cities.
文摘By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.
文摘Objective: The main objective was to study the disturbances of the Blood Count of children hospitalized in the general pediatric of the Gabriel Touré teaching hospital. Methods: This was a prospective and descriptive study conducted from September 1 to November 30, 2018 in the general pediatrics department of the Gabriel Touré teaching hospital in Bamako. Data were collected on patient records using a survey sheet. Results: We collected 512 files of children out of 1030 admissions during the study period;the rate of completion of the blood count is 50%. The male sex was predominant with a sex ratio of 1.3. The majority of patients were under 5 years old (58%). The majority of fathers (56%) and mothers (64%) of children had no education;they are mainly farmers (61%) and housewives (88%). Pallor was the reason for consultation in 29% of patients and present in 60%. On blood count, anemia was present in 92% of patients, half of whom (50%) had severe anemia with a hemoglobin level below 7 g/dL. The anemia was mainly microcytic (72%) and hypochromic (66%). Hyperleukocytosis (62%), eosinophilia (68%) and basophilia (58%) were the abnormalities observed in the white line. Thrombocytopenia accounted for 40%. Severe malaria (53%) was the main discharge diagnosis and almost all patients (99%) were alive at discharge. Conclusion: The characteristics of anemia require a study of the complete blood count in healthy children with dosage of serum iron and ferritin for a better understanding of the phenomenon.
文摘Background: It is widely known that the human immune-deficiency virus (HIV) induces biochemical and physiological changes in affected persons. Consequently, the overall aim of this study was to evaluate the HIV-1 RNA viral load, CD4 count, and certain haematological parameters among HIV treatment-na?ve subjects in the Enugu metropolis of Nigeria. Materials and Methods: A total of 252 HIV-infected, ART-native subjects (≥18) attending the University of Nigeria Teaching Hospital (UNTH) in Ituku-Ozalla, Enugu were recruited for this study and were made up of 157 (62.3%) females and 95 (37.7%) males. A total of 250 HIV-negative subjects were used as control subjects (100 males and 150 females). Blood samples were collected from all the participants and their HIV-1 status was confirmed by an immunoblot confirmatory test. Their haematological parameters and CD4 count were evaluated, while the HIV-1 viral load was only assessed on confirmed HIV-positive subjects. Results: There was female predominance (62.3%) among these HIV-positive subjects. The mean age of HIV-positive subjects was 39.16 ± 10.08 years while the mean age of the control subjects was 34.8 ± 8.6 years. The age group of 31 - 40 years (102/252 (40.5%)) constituted most of the test subjects. The total white blood cells (TWBC) (6.05 ± 5.46), lymphocyte counts (36 ± 14), haemoglobin concentrations (Hb) (9.85 ± 7.36) and the CD4 counts (242 ± 228) of the HIV-infected subjects showed a significant difference when compared with their control counterpart values of TWBC (4.5 ± 0.568), lymphocytes (39.67 ± 8.2), Hb (13.48 ± 1.5), and CD4 counts (807 ± 249) (p 0.05). Anaemia, lymphocytopenia, and thrombocytopenia were the haematological abnormalities seen in the HIV-positive subjects. HIV viral load correlated with haemoglobin concentration, CD4 count, lymphocyte count, and neutrophil count (p Conclusion: Prognostic factors, such as haemoglobin concentrations, CD4 counts, lymphocyte counts, and neutrophil counts can be used to monitor patients’ viral loads since they correlate with the latter;furthermore, age is a factor that should be considered in the management of HIV-positive patients.
文摘Background: Respiratory distress syndrome (RDS) is a major cause of neonatal morbidity and mortality, affecting approximately 1% of all live births and 10% of all preterm infants. Lamellar bodies represent a storage form of pulmonary surfactant within Type II pneumocytes, secretion of which increases with advancing gestational age, thus enabling prediction of the degree of FLM. Preterm premature rupture of membranes (PPROM) complicates approximately 1/3 of all preterm births. Birth within 1 week is the most likely outcome for any patient with PPROM in the absence of adjunctive treatments. Respiratory distress has been reported to be the most common complication of preterm birth. Sepsis, intraventricular haemorrhage, and necrotizing enterocolitis also are associated with prematurity, but these are less common near to term. Objective: To assess the efficacy of the amniotic fluid lamellar body counting from a vaginal pool in predicting fetal lung maturity in women with preterm premature rupture of membranes. Methods: This study was conducted at Ain Shams University Maternity Hospital in the emergency ward from January 2019 to September 2019. It included 106 women with singleton pregnancies, gestational age from 28 - 36 weeks with preterm premature rupture of membranes. This study is designed to assess the efficacy of the amniotic fluid lamellar body counting (LBC) from a vaginal pool in predicting fetal lung maturity in women with preterm premature rupture of membranes. Results: The current study revealed a highly significant increase in the lamellar body count in cases giving birth to neonates without RDS compared to that cases giving birth to neonates with RDS. Also, no statistically significant difference between LBC and age, parity and number of previous miscarriages in the mother was found. Gestational age at delivery was significantly lower among cases with respiratory distress. Steroid administration was significantly less frequent among cases with respiratory distress. However, lamellar bodies had high diagnostic performance in the prediction of respiratory distress. Conclusion: Lamellar body count (LBC) is an effective, safe, easy, and cost-effective method to assess fetal lung maturity (FLM). It does not need a highly equipped laboratory or specially trained personnel, it just needs the conventional blood count analyzer. Measurement of LBC is now replacing the conventional Lecithin/Sphyngomyelin L/S ratio. LBC cut-off value of ≤42.5 × 10<sup>3</sup>/μL can be used safely to decide fetal lung maturity with sensitivity of 95.7% and specificity of 97.6%.
文摘Introduction: SARS-COV-2 infection is a real public health challenge for the World Health Organization and for our country. It is responsible for numerous hematological abnormalities in infected patients. Objectives: To describe the haemogram abnormalities in patients infected with SARS-COV-2 and to determine which ones are associated with death. Material and Method: We conducted a retrospective, descriptive, analytical, cross-sectional study from March 2020 to September 2021. The study included all patients hospitalized with RT-PCR-confirmed COVID-19 who performed a blood count. We evaluated the blood count profile, the pathologies found and the associated blood count abnormalities. Results: A total of 263 patients were included. The mean age of the patients was 63.77 years (range 12 - 90 years). The male sex represented 54.75% (n = 144) while the female sex was 45.25% (n = 119) (sex ratio = 1.21). The most common pathologies were: diabetes: 30.03% (n = 79), high blood pressure: 41.04% (n = 108), and Chronic kidney disease: 7.98 (n = 21). The abnormalities of the haemogram found were essential: anaemia 28.13% (n = 121), hyperleukocytosis with neutrophilic predominance: 29.3% (126), lymphopenia: 34.41% (n = 148), thrombocytopenia: 8.16% (n = 35). The search for hematological factors associated with death in patients showed a significant difference between hyperleukocytosis (p = 0.000) and lymphopenia (p = 0.0001). Conclusion: SARS-COV-2 disease was a mortality factor when associated with lymphopenia and hyperleukocytosis in our series.
文摘The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming at the problem of degradation of long-span continuous rigid frame bridges due to fatigue and environmental effects,this paper suggests a method to analyze the fatigue degradation mechanism of this type of bridge,which combines long-term in-site monitoring data collected by the health monitoring system(HMS)and fatigue theory.In the paper,the authors mainly carry out the research work in the following aspects:First of all,a long-span continuous rigid frame bridge installed with HMS is used as an example,and a large amount of health monitoring data have been acquired,which can provide efficient information for fatigue in terms of equivalent stress range and cumulative number of stress cycles;next,for calculating the cumulative fatigue damage of the bridge structure,fatigue stress spectrum got by rain flow counting method,S-N curves and damage criteria are used for fatigue damage analysis.Moreover,it was considered a linear accumulation damage through the Palmgren-Miner rule for the counting of stress cycles.The health monitoring data are adopted to obtain fatigue stress data and the rain flow counting method is used to count the amplitude varying fatigue stress.The proposed fatigue reliability approach in the paper can estimate the fatigue damage degree and its evolution law of bridge structures well,and also can help bridge engineers do the assessment of future service duration.
