Search Results (53,749)

The microwave field is used for drying and disinfecting grains during the pre-sowing seed treatment. The use of a microwave field in these installations leads to an increase in their productivity and a decrease in the energy consumed by them. In grain dryers, where the grain moves in a dense layer without being loosened, one of the challenges in using microwave fields is ensuring sufficient uniformity of the field distribution. In this article, waveguide design options that introduce microwave radiation into the grain layer are discussed. The objective of this study was to use application software to find the optimum type of transmitter from the three options presented. A mathematical simulation of the electromagnetic field distribution was performed with the use of CST Microwave Studio software 2019 in order to evaluate and compare horn-type, rectangular, and semicircular waveguides. The data on the standing wave ratio and radiation efficiency of these types of waveguides have been reported. The specific features of the microwave electromagnetic field distribution and radiation power in the output of these waveguides have been described. The results of mathematical simulations revealed that semicircular waveguides with slot-type radiators are preferable for processing dense grain layers. Full article

Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD $\alpha$) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD $\alpha$ system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification. Full article

Mobile emergency services are better able to meet the needs of frequent public emergencies; however, their data quality problems seriously affect decision-making. In order to reduce the interference of low-quality data and solve the problem of data quality ambiguity, this paper first summarizes the five characteristics of mobile emergency big data. Second, based on the characteristics of mobile emergency big data, four data quality dimensions are defined with reference to existing research and national standards and combined with the measure of medium truth degree to give single-dimension and multi-dimension data quality truth degree measure models. Finally, a subjective-objective, qualitative-quantitative mobile emergency big data quality evaluation method based on the measure of medium truth degree is formed. The validity and practicality of the method are also verified by examples of algorithmic analysis of fire text datasets from Australian mountain fire data and the Chinese Emergency Incident Corpus. The experiments show that the method can realize quantitative mobile emergency big data quality assessment, solve the problem of data quality ambiguity, and reduce the interference of low-quality data, so as to save resources and improve the analysis and decision-making ability. Full article

Forest fires can have devastating effects on the environment, communities, individuals, economy, and climate change in many countries. During a forest fire crisis, massive amounts of data, such as weather patterns and soil conditions, become available. Efficient management, intelligent integration, and processing the available information in order to extract useful insights and knowledge to facilitate advanced whereas and support human operators and authorities in a real operational scenario is a challenge. In this work, we present ONTO-SAFE, an ontology-based framework for wildfire events, adopting Semantic Web technologies for data integration and infusion of domain and background knowledge. More specifically, the framework creates a unified representation of the available assets, taking into account data generated from different sources, such as sensors, weather forecasts, earth observations, etc. To this end, previously existing ontologies and standards are used, such as Empathi and EmergencyFire ontology, to provide the conceptual model and the necessary level of abstraction in the form of interconnected knowledge graphs to satisfy the modeling requirements. On top of the generated knowledge graphs, a declarative framework extracts facts and higher-level inferred knowledge from asserted data to support users in decision making. In addition, the framework supports the generation of recommendations, such as sharing important wildfire information with citizens and professionals, that can be adjusted based on user-defined factors and the current disaster risk management phase. Full article

Exploring resistance training methods is crucial for optimizing performance programs. Isometric muscle actions have gained popularity in athletic training, but their impact on dynamic performance is uncertain. Isolated isometric actions also lack ecological validity. We compared the effects of 8-week combined isometric and plyometric (COMB) training and contrast strength training (CST) programs on junior male handball players. Thirty-six male first national division players (17.6 ± 1.0 years) were enrolled and randomly assigned to COMB, CST, or control (CONT) groups (all n = 12). Sprinting, change of direction, ball throwing velocity, jumping, and strength were assessed pre- and post-intervention. A significant group × time interaction was observed between the COMB and CONT groups for 20 and 30 m sprints (p ≤ 0.002) and between the COMB and CST groups (p ≤ 0.042). The COMB group had the largest improvements in change of direction and the modified T-test, with significant group × time interactions between the COMB and CONT groups (p ≤ 0.021). Significant group × time interactions were observed between the COMB and CST groups and between the COMB and CONT groups for 3 step running throw (p = 0.003; p < 0.001), running throw (p = 0.02; p = 0.031), and jumping throw (p = 0.001; p < 0.001). Countermovement jump showed a significant group × time interaction (p = 0.014), with the COMB group outperforming the other groups. Generally, COMB yielded larger improvements than CST. Coaches should consider incorporating a combination of isometric and plyometric exercises for in-season strength training. Full article

The biomass of vinification results in up to 20% by-products (seeds, skins, pulp, and/or stems) that can be used in the production of diverse functional food, nutraceutical, pharmaceutical, and cosmetic ingredients, mainly due to their high polyphenol content. Conventional polyphenol extraction techniques are based on the use of solvents that are harmful to health and to the environment, creating a demand for sustainable complementary initiatives that mitigate part of the environmental effects and offer consumer safety. Current advances in these technologies allow for the recovery of valuable antioxidants from winemaking by-products free of hazardous solvents, biocompatible, and in compliance with international sustainable development guidelines. Nanotechnology has gained prominence in the development of green technologies to reduce or eliminate toxic agents and improve the stability and bioavailability of waste polyphenols. These efforts have led to the application of bioactive compounds from wine by-products in the development of more efficacious sunscreens, as a skin protection approach, and improvements in the antioxidant effectiveness of nanocarriers with potential use in the promotion of cutaneous health. We aimed to present different extraction and encapsulation technologies for biologically active compounds from wine by-products (Vitis vinifera L.). We also focused on a particular application of such compounds towards the development of value-added skin protection products aligned with a sustainable circular economy. Full article

Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images. Full article

With the popularization of small batch production, the main cutting method for sheet metal parts has changed. Laser cutting has become the main production method for coil material cutting. Developing an irregular part nesting method for the continuous cutting of coil material is urgent. Based on the coil material cutting process, this paper proposes an intelligent approach for the unit nesting problem of coil material. Firstly, a unit nesting model of coil material was constructed. Secondly, an intelligent approach using an improved marine predator algorithm was used to solve this model. In solving the model, the minimum nesting unit was continuously updated by changing the position, angle, and quantity of the nesting parts. Thirdly, the geometric characteristics of this minimum nesting unit were extracted. Finally, the nesting units for production were obtained using a single row or opposite row of the minimum nesting unit. The computational results and comparison prove that the presented approach is feasible and effective in improving material utilization, reducing production costs, and meeting the requirements of the production site. Full article

Open-die forging holds a pivotal role in shaping machine parts within industrial applications. This study focuses on the assessment of stress–strain curves for C45 material at different elevated temperatures and strain rates through numerical simulations employing the finite element method (FEM). Specifically, the research investigates how the flow curve of materials at elevated temperatures and individual strain rates impacts the forming force during the flange forming of C45 billet tubes. By comparing the simulation results with experimental data on the flange-forming force, this study observes that optimal outcomes arise when considering both elevated temperature and strain rates in the flow curve of materials. The study then conducts simulations for C45 billet tubes with varying upsetting ratios (H0/D0), (S0/D0), and the punch’s pitch angle (α), aiming to address optimization challenges related to the flange-forming force. Consequently, a mathematical model is developed to represent the relationship between the flange-forming force and geometric parameters (H0/D0, S0/D0, and α). This model accurately predicts the forming force under various flange-forming conditions, demonstrating high precision with a maximum error of 4.26% compared with the experimental results. This study significantly contributes to the advancement of flange-forming technology in open-die forging through numerical simulation, enabling the optimization of the flange-forming force and the selection of appropriate equipment. These findings pave the way for more effective and efficient industrial processes, fostering innovation and progress in the field. Full article

Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes in load curve fluctuations. Current deep learning models generally train with historical load series and load-related meteorological data series as input features, which limits the model’s ability to recognize the load fluctuations caused by DPVS. In order to further improve the accuracy of load forecasting models, this paper proposes an input feature reconstruction method based on the maximum information coefficient (MIC). Firstly, the load curves with DPVS are classified by Gaussian mixture model (GMM) clustering. Then, considering the coupling relationship between the load and input features at different times, the load data and input features are reordered. Finally, the MIC between different features and loads at different times is calculated to select the relevant features at those different times and construct new input features. The case analysis shows that the feature reconstruction strategy proposed in this paper effectively improves the prediction performance of deep neural networks. Full article

Speech Emotion Recognition (SER) plays a crucial role in applications involving human-machine interaction. However, the scarcity of suitable emotional speech datasets presents a major challenge for accurate SER systems. Deep Neural Network (DNN)-based solutions currently in use require substantial labelled data for successful training. Previous studies have proposed strategies to expand the training set in this framework by leveraging available emotion speech corpora. This paper assesses the impact of a cross-corpus training extension for a SER system using self-supervised (SS) representations, namely HuBERT and WavLM. The feasibility of training systems with just a few minutes of in-domain audio is also analyzed. The experimental results demonstrate that augmenting the training set with EmoDB (German), RAVDESS, and CREMA-D (English) datasets leads to improved SER accuracy on the IEMOCAP dataset. By combining a cross-corpus training extension and SS representations, state-of-the-art performance is achieved. These findings suggest that the cross-corpus strategy effectively addresses the scarcity of labelled data and enhances the performance of SER systems. Full article

Ultrasonic welding is a high-frequency method of welding that uses mechanical energy to generate heat. This is a clean welding method and very suitable for plastic welding. In this study, using the Taguchi experimental method, the control factors of an ultrasonic plastic welding machine were optimized to affect the durability of knots of trawl nets made from polyamide (PA) and polypropylene (PP) filaments as an alternative to the traditional mesh knitting method. After optimization, the PA knots had an amplitude of 32 µm (34%), a welding pressure of 2.5 kg/cm² (41%), a hold time of 0.35 s (24%), and a speed of 5.5 mm/s (1%). The knots made of PP filament had relatively stable strength after optimization, with an amplitude of 36 µm (25%), a welding pressure of 2.0 kg/cm² (22%), a hold time of 0.25 s (16%), and a speed of 6.0 mm/s (37%). Finally, validation experiments were conducted to verify the results obtained in this study. Full article

The refrigeration cycle of an air source heat pump system is an important information that reveals critical operating data, such as the cooling capacity, power consumption, and performance of a system during operation. Operating data, such as refrigerant pressure and enthalpy in situ, can be difficult to measure. Therefore, this study developed an in situ refrigeration cycle measurement method using the airside data of an air source heat pump. A method for measuring the refrigeration cycle is proposed using the characteristics of evaporation, compression, condensation, and expansion processes. The distance function was analyzed by normalizing the difference between the refrigerant pressure and enthalpy of the existing and proposed measurement methods. In addition, the distance function for the maximum error of the pressure and enthalpy measurement devices was analyzed and compared with the distance function of the entire data used in the evaluation. All the evaluation data had low distance function values with a maximum difference of 5%, confirming the reliability of the proposed refrigeration cycle measurement method. The power consumption and calculated COP were also evaluated using the proposed method. The Mean Bias Error (MBE) of power consumption and COP were 0.15% and 0.04%, and the coefficient of variation of root-mean-square error (CvRMSE) was 8.967% and 7.14%, respectively. Full article

Human Activity Recognition (HAR) has been proven to be effective in various healthcare and telemonitoring applications. Current HAR methods, especially deep learning, are extensively employed owing to their exceptional recognition capabilities. However, in pursuit of enhancing feature expression abilities, deep learning often introduces a trade-off by increasing Time complexity. Moreover, the intricate nature of human activity data poses a challenge as it can lead to a notable decrease in recognition accuracy when affected by additional noise. These aspects will significantly impair recognition performance. To advance this field further, we present a HAR method based on an edge-computing-assisted and GRU deep-learning network. We initially proposed a model for edge computing to optimize the energy consumption and processing time of wearable devices. This model transmits HAR data to edge-computable nodes, deploys analytical models on edge servers for remote training, and returns results to wearable devices for processing. Then, we introduced an initial convolution method to preprocess large amounts of training data more effectively. To this end, an attention mechanism was integrated into the network structure to enhance the analysis of confusing data and improve the accuracy of action classification. Our results demonstrated that the proposed approach achieved an average accuracy of 85.4% on the 200 difficult-to-identify HAR data, which outperforms the Recurrent Neural Network (RNN) method’s accuracy of 77.1%. The experimental results showcase the efficacy of the proposed method and offer valuable insights for the future application of HAR. Full article