Search Results (10)

This study proposes a grading algorithm for Orah sorting lines based on machine vision and deep learning. The original ShuffleNet V2 network was modified by replacing the ReLU activation function with the Mish activation function to alleviate the neuron death problem. The ECA attention module was incorporated to enhance the extraction of Orah appearance features, and transfer learning was applied to improve model performance. As a result, the ShuffleNet_wogan model was developed. Based on the operational principles of the sorting line, a time-sequential grading algorithm was designed to improve grading accuracy, along with a multi-sampling diameter algorithm for simultaneous Orah diameter measurement. Experimental results show that the ShuffleNet_wogan model achieved an accuracy of 91.12%, a 3.92% improvement compared to the original ShuffleNet V2 network. The average prediction time for processing 10 input images was 51.44 ms. The sorting line achieved a grading speed of 10 Orahs per second, with an appearance grading accuracy of 92.5% and a diameter measurement compliance rate of 98.3%. The proposed algorithm is characterized by high speed and accuracy, enabling efficient Orah sorting. Full article

The Bayesian approach offers a flexible, interpretable and powerful framework for statistical analysis, making it a valuable tool to help in making optimal decisions under uncertainty. It incorporates prior knowledge or beliefs about the parameters, which can lead to more accurate and informative results. Also, it offers credible intervals as a measure of uncertainty, which are often more interpretable than confidence intervals. Hence, the Bayesian approach is utilized to estimate the parameters, reliability function, hazard rate function and reversed hazard rate function of a new competing risks model. A squared error loss function as a symmetric loss function and a linear exponential loss function as an asymmetric loss function are employed to derive the Bayesian estimators. Credible intervals of the parameters, reliability function, hazard rate function and reversed hazard rate function are obtained. Predicting future observations is important in many fields, from finance and weather forecasting to healthcare and engineering. Thus, two-sample prediction (as a special case of the multi-sample prediction) for future observation is considered. An adaptive Metropolis algorithm is applied to conduct a simulation study to evaluate the performance of the Bayes estimates and predictors. Moreover, two applications of medical and engineering data sets are used to test and validate the theoretical results, ensuring that they are accurate, applicable to real-world scenarios and contribute to the understanding of the world and inform decision-making. Full article

The multiple additional sampling point method has become popular for use in Efficient Global Optimization (EGO) to obtain aerodynamically shaped designs in recent years. It is a challenging task to study the influence of adding multi-sampling points, especially when multi-objective and multi-fidelity requirements are applied in the EGO process, because its factors have not been revealed yet in the research. In this study, the addition of two (multi-) sampling points (2-MAs) and four (multi-) sampling points (4-MAs) in each iteration are used to study the proposed techniques and compare them against results obtained from a single additional sampling point (1-SA); this is the approach that is conventionally used for updating the hybrid surrogate model. The multi-fidelity multi-objective method is included in EGO. The performance of the system, the computational convergence rate, and the model accuracy of the hybrid surrogate are the main elements for comparison. Each technique is verified by mathematical test functions and is applied to the airfoil design. Class Shape Function Transformation is used to create the airfoil shapes. The design objectives are to minimize drag and to maximize lift at designated conditions for a Reynolds number of one million. Computational Fluid Dynamics is used for ensuring high fidelity, whereas the panel method is employed when ensuring low fidelity. The Kriging method and the Radial Basis Function were utilized to construct high-fidelity and low-fidelity functions, respectively. The Genetic Algorithm was employed to maximize the Expected Hypervolume Improvement. Similar results were observed from the proposed techniques with a slight reduction in drag and a significant rise in lift compared to the initial design. Among the different techniques, the 4-MAs were found to converge at the greatest rate, with the best accuracy. Moreover, all multiple additional sampling point techniques are shown to improve the model accuracy of the hybrid surrogate and increase the diversity of the data compared to the single additional point technique. Hence, the addition of four sampling points can enhance the overall performance of multi-fidelity, multi-objective EGO and can be utilized in highly sophisticated aerodynamic design problems. Full article

Bladder urothelial carcinoma (BLCA) is the 10th most common cancer with a low survival rate and strong male bias. We studied the field cancerization in BLCA using multi-sample- and multi-tissue-per-patient protocol for sensitive detection of autosomal post-zygotic chromosomal alterations and loss of chromosome Y (LOY). We analysed 277 samples of histologically normal urothelium, 145 tumors and 63 blood samples from 52 males and 15 females, using the in-house adapted Mosaic Chromosomal Alterations (MoChA) pipeline. This approach allows identification of the early aberrations in urothelium from BLCA patients. Overall, 45% of patients exhibited at least one alteration in at least one normal urothelium sample. Recurrence analysis resulted in 16 hotspots composed of either gains and copy number neutral loss of heterozygosity (CN-LOH) or deletions and CN-LOH, encompassing well-known and new BLCA cancer driver genes. Conservative assessment of LOY showed 29%, 27% and 18% of LOY-cells in tumors, blood and normal urothelium, respectively. We provide a proof of principle that our approach can characterize the earliest alterations preconditioning normal urothelium to BLCA development. Frequent LOY in blood and urothelium-derived tissues suggest its involvement in BLCA. Full article

In order to reduce the sampling delay and improve bandwidth, sability margin, and the robustness of the active damping in LCL-filtered grid-connected inverters, real-time sampling provides a convenient method. However, aliasing is easily introduced in the control loop because of high-frequency switching harmonics, resulting in a rise in low-order harmonics. To address the challenge of aliasing under real-time sampling, a new method based on multisampling and mean filtering is proposed by combining the proposed harmonic detection and control methods in this paper. This method works by sampling and controlling the fundamental current and selective subharmonic currents separately. The fundamental current control loop without any additional filters maintains real-time sampling, while multisampling and mean filters are applied to the harmonic current control loop. The proposed control method can not only improve the dynamic response performance and control stability of the system but also effectively suppress the selective low-order current harmonics and the aliasing of sampling. Finally, the correctness of the proposed sampling scheme and control strategy is verified by the simulation based on R2018b, MathWorks, Natick, MA, USA and an experimental prototype of a three-phase grid-connected photovoltaic inverter rated at 230 V/50 Hz/40 kW. Full article

With the development of power switches and processor performance in recent years, the control frequency of inverters has been significantly improved. However, limited by technology and price, the sensor sampling frequency in large-scale industrial applications is much lower than the inverter control frequency that can be realized. This frequency mismatch limits the performance improvement of the inverter. In this article, the current and voltage at the non-sampling time are reconstructed using the current prediction control principle and the input observer theory, allowing a single-phase inverter to implement multi-sampling rate control with a low sampling frequency and high control frequency. In addition, an improved adaptive controller is designed to solve the effect of incorrect model parameters, which realizes adaptive control when the sampling frequency and control frequency are mismatched. Finally, the effectiveness of the method is verified through a simulation and experiments. The proposed method can solve the problem of high-speed switching for inverters under low-sampling-frequency conditions, improving the inverter’s adaptive performance and robustness. Full article

Polar marine environments host a complex assemblage of cold-adapted auto- and heterotrophic microorganisms that affect water biogeochemistry and ecosystem functions. However, due to logistical difficulties, remote regions like those in close proximity to glaciers have received little attention, resulting in a paucity of microbiological data. To fill these gaps and obtain novel insights into microbial structure and function in Arctic regions, a survey of microbial communities in an area close to the Blomstrandbreen glacier in Kongsfjorden (Svalbard Archipelago; Arctic Ocean) was carried out during an early summer period. An Unmanned Autonomous Vehicle designed to safely obtain seawater samples from offshore-glacier transects (PROTEUS, Portable RObotic Technology for Unmanned Surveys) was equipped with an automatic remotely-controlled water multi-sampler so that it could sample just beneath the glacier, where access from the sea is difficult and dangerous. The samples were analysed by image analysis for the abundance of total prokaryotes, viable and respiring cells, their morphological traits and biomass; by flow cytometry for autotrophic and prokaryotic cells (with high and low nucleic acid contents) as well as virus-like particle counts; by BIOLOG ECOPLATES for potential community metabolism; and by fluorimetry for potential enzymatic activity rates on organic polymers. Contextually, the main physical and chemical (temperature, salinity, pH, dissolved oxygen and nutrients) parameters were detected. Altogether, besides the PROTEUS vehicle’s suitability for collecting samples from otherwise inaccessible sites, the multivariate analysis of the overall dataset allowed the identification of three main sub-regions differently affected by the haline gradient (close to the glacier) or terrigenous inputs coming from the coast. A complex microbiological scenario was depicted by different patterns of microbial abundance and metabolism among the transects, suggesting that ice melting and Atlantic water inflow differently supported microbial growth. Full article

Encrypted traffic accounts for 95% of the total traffic in the backbone network environment with Tbps bandwidth. As network traffic becomes more and more encrypted and link rates increase in modern networks, the measurement of encrypted traffic relies more on collecting and analyzing massive network traffic data that can be separated from the support of high-speed network traffic measurement technology. Finding top-k elephant flows is a critical task with many applications in congestion control, anomaly detection, and traffic engineering. Owing to this, designing accurate and fast algorithms for online identification of elephant flows becomes more and more challenging. Existing methods either use large-size counters, i.e., 20 bit, to prevent overflows when recording flow sizes or require significant space overhead to measure the sizes of all flows. Thus, we adopt a novel strategy, called count-with-uth-level-sampling, in this paper, to find top-k elephant flows in limited resource environments. Moreover, the proposed algorithm, called MSLCFinder, incurs lightweight counter and uth-level multi-sampling with small, constant processing for millions of flows. Experimental results show that MSLCFinder can achieve more than 97% precision with an extremely limited hardware resource. Compared to the state-of-the-art, our method realizes the statistics and filtering of millions of data streams with less memory. Full article

This study aimed to evaluate the survival rates of several external hexagon implants directly connected to zirconia crowns after thermomechanical fatigue. The deformation of the hexagons and the integrity of zirconia crowns were also evaluated. A monolithic zirconia crown (Y-TZP) and four different external hexagon dental implants (n = 10, N = 40) were mounted together and embedded in polyurethane. The specimens were subjected to thermomechanical cycling for 2.5 × 10⁶ cycles, at 3.0 Hz frequency, at 200 N loading. The interface of the implant/zirconia crown system, zirconia crowns integrity before and after cycling, and the implant hexagon surface were evaluated under stereomicroscopy and SEM. A nanohardness analysis was performed to verify the hardness of zirconia and implants. Statistical analysis was performed using the Kaplan-Meier test, Multi-Sample Survival Tests, Logrank Test, (p = 0.05). The data did not show significant differences in the survival rates of different implant groups. However, some crowns presented fractures (16.67%) and the external hexagon region of the implants presented plastic deformations (100%). During chewing simulation, the interface between titanium implant and zirconia abutment can promote plastic deformation in the metal and surface defects in the ceramic. In addition, the types of interface defects can be affected by the external hexagon design. Full article

Traditional outlier detection methods assume that the sampling time and interval are the same. However, for plant-wide processes, since the signal change rate of different devices may vary by several orders of magnitude, the measured data in real-world systems usually have different sampling rates, resulting in missing data. To achieve reliable outlier detection, a missing data probability estimation-based Bayesian outlier detection method is adopted. In this strategy, the expectation–maximization (EM) algorithm is first used to estimate the likelihood probability of different evidence under different process statuses by using the history dataset which contains complete and incomplete samplings. Secondly, the realization of unavailable parts in the monitoring point is estimated as a probability through historical data and online moving horizon data. Bayesian theory and likelihood probability are then used to calculate the outlier posterior probability of different realization. Finally, the outlier probability of the monitoring sampling is calculated by the probability of different realizations and the corresponding outlier probability. Using the Tennessee Eastman (TE) dataset, a simulation indicates that the proposed method exhibits a significant improvement over the complete data method. Full article