Search Results (220)

Optimizing heat generation capacity is crucial for geothermal system design and evaluation. Computer simulation is a valuable approach for determining the influence of various parameter combinations on a geothermal system’s ability to produce heat. However, computer simulation evaluations are often computationally demanding since all potential parameter combinations must be examined, posing significant hurdles for heat generation performance evaluation and optimization. This research proposes an adaptive Kriging-based heat generation performance optimization method. Firstly, a two-horizontal-well geothermal system with rectangular multi-parallel fractures is constructed. The heat production performance optimization problem is then established, and the temperature and enthalpy of the outlet water are calculated using computer simulation and Kriging. A parameterized lower confidence bounding sampling scheme (PLCB) is developed to adaptively update Kriging in order to strike a compromise between optimization accuracy and computation burden. The outcomes of the optimization are compared to those of the Kriging-based optimization approach and other common infill options to demonstrate the efficiency of the proposed method. The outlet temperature curve obtained with PLCB-AKO-1 rose for a longer time and the heat generation power curve reached a stable output without a downward trend. According to the Friedman and Wilcoxon signed ranks tests, the PLCB-1-AKO technique is statistically superior to alternative strategies. Full article

It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must be adequately managed. Research in fatigue is challenging because modeling can be quite complex. Furthermore, experimentation is time-consuming, which frequently yields limited data. Both of these exacerbate the magnitude of the potential error. The purpose of this paper is to demonstrate a procedure that combines modeling with independent experimental data to improve the estimation of the cumulative distribution function (cdf) for fatigue life. Subsequently, the effect of intrinsic error will be minimized. Herein, a simplified fatigue crack growth modeling is used. The data considered are a well-known collection of fatigue lives for an aluminum alloy. For lower applied stresses, the fatigue lives can range over an order of magnitude and up to 10⁷ cycles. For larger applied stresses, the scatter in the lives is considerably reduced. Consequently, modeling must encompass a variety of conditions. The primary conclusion of the effort is that merging independent experimental data with a reasonably acceptable model vastly improves the accuracy of the calibrated cdfs for fatigue life, given the loading conditions. This allows for improved life estimation and prediction. For the aluminum data, the calibrated cdfs are shown to be quite good by using statistical goodness-of-fit tests, stress-life (S-N) analysis, and confidence bounds estimated using the mean square error (MSE) method. A short investigation into the effect of sample size is also included. Thus, the proposed methodology is warranted. Full article

Orifice Hollow Cathodes are electric devices necessary for the functioning of common plasma thrusters for space applications. Their reliability mainly depends on the success of a spacecraft’s mission equipped with electric propulsion. The development of plasma models is crucial in the evaluation of plasma properties within the cathodes that are difficult to measure due to the small dimensions. Many models, based on non-linear systems of plasma equations, have been proposed in the openiterature. These are solved commonly by means of iterative procedures. This paper investigates the possibility of solving them by means of the Particle Swarm Optimization method. The results of the validation tests confirm the expected trends for all the unknowns; the confidence bound of the discharge current as a function of mass flow rate is very narrow (2 ÷ 5) V); moreover, the results match very well the experimental data except at theowest mass flow rate (0.08 mg/s) and discharge current (1A), where the computations underpredict the discharge current to the utmost by 40%. The highest data dispersion regards the plasma density in the emitter region (±20% of the average value) and the wall temperatures (±50 K with respect to the average values) of the orifice and insert; those of the others variables are very tiny. Full article

The achievable capacity index (ACI) is a simple and efficient approach for estimating the profitability of newsboy-type products, wherein profitability is defined as the probability of achieving the target profit by optimizing the order quantity. At present, the ACI is applicable to single retail stores (i.e., single demand) but not to multiple sales channels (i.e., multiple demand). This paper presents an integrated achievable capacity index (IACI) by which to measure the aggregate profitability of multiple mutually independent channels under normally distributed demand. An unbiased IACI estimator is also developed, to which is applied the Taylor expansion to approximate its sampling distribution, wherein the sizes, means, and variances of demand differ in each channel. Furthermore, overestimates due to sampling error are avoided by deriving the lower confidence bound for the IACI. This paper also provides generic tables to aid managers seeking conservative estimates of profitability. The applicability of the proposed scheme is demonstrated numerically using a real-world example involving an own-branding-and-manufacturing (OBM) enterprise with multiple owned channels. Full article

Due to the black and glossy appearance of the carbon fiber prepreg bundle surface, the accurate identification of surface defects in automated fiber placement (AFP) presents a high level of difficulty. Currently, the enhanced YOLOv7 algorithm demonstrates certain performance advantages in this detection task, yet issues with missed detections, false alarms, and low confidence levels persist. Therefore, this study proposes an improved YOLOv7 algorithm to further enhance the performance and generalization of surface defect detection in AFP. Firstly, to enhance the model’s feature extraction capability, the BiFormer attention mechanism is introduced to make the model pay more attention to small target defects, thereby improving feature discriminability. Next, the AFPN structure is used to replace the PAFPN at the neck layer to strengthen feature fusion, preserve semantic information to a greater extent, and finely integrate multi-scale features. Finally, WIoU is adopted to replace CIoU as the bounding box regression loss function, making it more sensitive to small targets, enabling more accurate prediction of object bounding boxes, and enhancing the model’s detection accuracy and generalization capability. Through a series of ablation experiments, the improved YOLOv7 shows a 10.5% increase in mAP and a 14 FPS increase in frame rate, with a maximum detection speed of 35 m/min during the AFP process, meeting the requirements of online detection and thus being able to be applied to surface defect detection in AFP operations. Full article

To improve the efficiency of the multidisciplinary design optimization of underwater vehicles, this paper proposes a combined proxy model with adaptive dynamic sampling. The radial basis function model (RBF), Kriging model, and polynomial response surface model (PRS) are used to construct the proxy model. Efficient sample points are collected based on the synthetic minority oversampling technique (SMOTE) algorithm and the lower confidence bound (LCB) criterion. The proxy model process is integrated after dynamic sampling. The collaborative optimization framework is used, which considers the coupling between the main system set and the subsystem set. The hierarchical analysis method is used to transform the multidisciplinary optimization problem into a single-objective optimization problem. Computational fluid dynamics (CFD) numerical simulation is utilized to simulate underwater submarine navigation. The optimization strategy is applied to the underwater vehicle SUBOFF to optimize resistance and energy consumption. Three dynamic proxy models and three static proxy models are compared. The results show that the optimization efficiency of the underwater vehicle has been improved. To prove the generalization performance of the proposed combined proxy model, a reducer example is investigated for comparison. The results show that the combined proxy model (CPM) is highly accurate and has excellent generalization performance. Full article

In the scenario of an integrated space–air–ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a Multi-Armed Bandit (MAB) model and proposes an optimization algorithm leveraging dynamic variance sampling (DVS). The algorithm posits that the prior distribution of each node’s network state conforms to a normal distribution, and by constructing the distribution’s expected value and variance, it maximizes the utilization of sample data, thereby maintaining an equilibrium between data exploitation and the exploration of the unknown. Theoretical substantiation is provided to illustrate that the Bayesian regret associated with the algorithm exhibits sublinear growth. Empirical simulations corroborate that the algorithm in question outperforms traditional ε-greedy, Upper Confidence Bound (UCB), and Thompson sampling algorithms in terms of higher cumulative rewards, diminished total regret, accelerated convergence rates, and enhanced system throughput. Full article

Stout beer was selected as the research object to screen angiotensin-converting enzyme (ACE) inhibitory peptides. The peptide sequences of stout beer were identified using ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry with de novo, and 41 peptides were identified with high confidence. Peptide Ranker was used to score the biological activity and six peptides with a score ≥ 0.5 were screened to predict their potential ACE inhibitory (ACEI) activity. The toxicity, hydrophilicity, absorption, and excretion of these peptides were predicted. In addition, molecular docking between the peptides and ACE revealed a significant property of the peptide DLGGFFGFQR. Furthermore, molecular docking conformation and molecular dynamics simulation revealed that DLGGFFGFQR could be tightly bound to ACE through hydrogen bonding and hydrophobic interaction. Lastly, the ACEI activity of DLGGFFGFQR was confirmed using in vitro evaluation and the IC₅₀ value was determined to be 24.45 μM. Full article

Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively. Full article

Semi-supervised training methods need reliable pseudo labels for unlabeled data. The current state-of-the-art methods based on pseudo labeling utilize only high-confidence predictions, whereas poor confidence predictions are discarded. This paper presents a novel approach to generate high-quality pseudo labels for unlabeled data. It utilizes predictions with high- and low-confidence levels to generate refined labels and then validates the accuracy of those predictions through bi-directional object tracking. The bi-directional object tracker leverages both past and future information to recover missing labels and increase the accuracy of the generated pseudo labels. This method can also substantially reduce the effort and time needed in label creation compared to the conventional manual labeling. The proposed method utilizes a buffer to accumulate detection labels (bounding boxes) predicted by the object detector. These labels are refined for accuracy though forward and backward tracking, ultimately constructing the final set of pseudo labels. The method is integrated in the YOLOv5 object detector and tested on the BDD100K dataset. Through the experiments, we demonstrate the effectiveness of the proposed scheme in automating the process of pseudo label generation with notably higher accuracy than the recent state-of-the-art pseudo label generation schemes. The results show that the proposed method outperforms previous methods in terms of mean average precision (mAP), label generation accuracy, and speed. Using the bi-directional recovery method, an increase in mAP@50 for the BDD100K dataset by 0.52% is achieved, and for the Waymo dataset, it provides an improvement of mAP@50 by 8.7% to 9.9% compared to 8.1% of the existing method when pre-training with 10% of the dataset. An improvement by 2.1% to 2.9% is achieved as compared to 1.7% of the existing method when pre-training with 20% of the dataset. Overall, the improved method leads to a significant enhancement in detection accuracy, achieving higher mAP scores across various datasets, thus demonstrating its robustness and effectiveness in diverse conditions. Full article

Inland capture fisheries play a critical role in supporting food security and livelihoods in Africa. Therefore, it is important to evaluate the genetic health of exploited fish populations. The African bonytongue, Heterotis niloticus, supports important commercial and subsistence fisheries in western Africa. However, sharp declines in stocks have been reported. Herein, we estimate contemporary effective population sizes (Ne) of four Heterotis populations in Nigeria, three in Benin, and five in Cameroon using Linkage Disequilibrium methods. Ne estimates were used to assess genetic short-term (i.e., inbreeding depression) and long-term (i.e., loss of evolutionary potential) risks. Ne point estimates obtained with the best estimator (out of 16), as determined by computer simulations, were <50 (range = 5.1–36.2) for nine of the twelve populations examined, which is below the minimum recommended for avoiding the potential deleterious effects of inbreeding depression (original criterion Ne ≥ 50, revised to Ne ≥ 100); and well below the minimum recommended for populations to retain evolutionary potential (original criterion Ne ≥ 500; revised to Ne ≥ 1000). The lower bound of the confidence interval for two of the remaining populations was below the minimum recommended to retain evolutionary potential (with the point estimate of one of them also below this threshold), and for some methods, values were lower than the minimum recommended to avoid inbreeding depression. Accordingly, our results suggest that urgent conservation and management plans are needed to guarantee the persistence and sustainability of the H. niloticus populations examined. Full article

The bounded confidence model assumes simple continuous opinion dynamics in which agents ignore opinions which are too far from their own. The two initial variants—Hegselmann–Krause (HK) and Deffuant–Weisbuch (DW)—of the model have attracted significant attention since the early 2000s. This paper revisits the version of the HK model applied to a probability distribution, earlier studied by Jan Lorenz. It shows that the bifurcation diagram depends on the parity of the size of the discretisation and that adding a small noise to the initial conditions leads to complex transitions involving several phases. Full article

RPL—Routing Protocol for Low-Power and Lossy Networks (usually pronounced “ripple”)—is the de facto standard for IoT networks. However, it neglects to exploit IoT devices’ full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent–child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%. Full article

We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection mitigates the impact of anomalous data on model updates. Data without concept drift are used for online model training and updating, enabling accurate predictions for the next processing cycle. Bayesian optimization is then employed for inverse optimization and process parameter design. Within OPPD, we introduce the online accelerated support vector regression (OASVR) algorithm for enhanced computational efficiency and model accuracy. OASVR simplifies support vector regression, boosting both speed and durability. Furthermore, we incorporate a dynamic window mechanism to regulate the training data volume for adapting to real-time demands posed by diverse online scenarios. Concept drift detection uses the EI-kMeans algorithm, and the Bayesian inverse design employs an upper confidence bound approach with an adaptive learning rate. Applied to single-crystal fabrication, the OPPD framework outperforms other models, with an RMSE of 0.12, meeting precision demands in production. Full article

This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form of its likelihood is known. The approach is tested in the non-parametric estimation of regression coefficients, where the least-square minimizing function is considered the maximum likelihood of a multivariate distribution. This approach has an advantage over traditional Markov Chain Monte Carlo methods because it is proven to converge and generate unbiased samples computationally efficiently. Full article