Experimental Sections Research Articles

Robot control system based on deep learning and RPA

Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot control system in perception and decision-making experiments was 94.62%. The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology.

The Astrochemistry Low-energy Electron Cross-Section (ALeCS) database

Context. Electron–molecule interaction is a fundamental process in radiation-driven chemistry in space, from the interstellar medium to comets. Therefore, knowledge of interaction cross sections is key. There have been a plethora of both theoretical and experimental studies of total ionization cross sections spanning from diatomics to complex organics. However, the data are often spread over many sources or are not public or readily available. Aims. We introduce the Astrochemistry Low-energy Electron cross-section (ALeCS) database. This is a public database for electron interaction cross sections and ionization rates for molecules of astrochemical interest. In particular, we present here the first data release, comprising total ionization cross sections and ionization rates for over 200 neutral molecules. Methods. We include optimized geometries and molecular orbital energies at various levels of quantum chemistry theory. Furthermore, for a subset of the molecules, we have calculated ionization potentials. We computed the total ionization cross sections using the binary-encounter Bethe model and screening-corrected additivity rule, and we computed ionization rates and reaction network coefficients for molecular cloud environments. Results. We present the cross sections and reaction rates for >200 neutral molecules ranging from diatomics to complex organics, with the largest being C14H10. We find that the screening-corrected additivity rule cross sections generally significantly overestimate experimental total ionization cross sections. We demonstrate that our binary-encounter Bethe cross sections agree well with experimental data. We show that the ionization rates scale roughly linearly with the number of constituent atoms in the molecule. Conclusions. We introduce and describe the public ALeCS database. For the initial release, we include total ionization cross sections for >200 neutral molecules and several cations and anions calculated with different levels of quantum chemistry theory, the chemical reaction rates for the ionization, and network files in the formats of the two most popular astrochemical networks: the Kinetic Database for Astrochemistry, and UMIST. The database will be continuously updated for more molecules and interactions.

Influence of various gases and water vapors on the processes of tritium release from two-phase lithium ceramics

The processes of tritium generation and release in lithium-ceramic breeder blankets are affected by many factors, for example, the presence of impurities in the purge gas. As was shown earlier, oxygen, water vapor and hydrogen have the greatest influence on gas release from lithium ceramics. Hydrogen is planned to be specifically added to the purge gas mixture in breeder blanket to facilitate tritium release.The paper presents data of reactor experiments on irradiation of two-phase lithium ceramics (65 mol% Li4SiO4 /35 mol% Li2TiO3) under conditions of supplying various gases and water vapor into the chamber with samples.We analyzed the sections of experiments in which sequential, step-by-step injection of water, oxygen, hydrogen and deuterium vapors into a continuously pumped chamber with samples at steady-state quasi-equilibrium release of tritium from ceramics was carried out. Using a mass spectrometer, the gas composition in the chamber and, in particular, the release features of tritium-containing molecules, were recorded. Based on the obtained data, the mechanisms of tritium interaction with the investigated gases and water vapor on the ceramic surface were determined, as well as their influence on the processes of tritium release from ceramics.

Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology

Machine learning and image processing are closely related fields that have undergone major development and application in recent years. Machine learning algorithms are being used to develop sophisticated techniques for analyzing and interpreting images, such as object detection, image classification, and image segmentation. One important aspect of image processing is the ability to compare and measure the similarity between different images by providing a way to quantify the similarity between images using various features such as contrast, luminance, and structure. Generally, the flexibility of similarity measures enables fine-tuning the comparison process to achieve the desired outcomes. This is while the existing similarity measures are not flexible enough to address diverse and comprehensive practical aspects. To this end, we utilize triangular norms (t-norms) to construct an inclusive class of similarity measures in this article. As is well-known, each t-norm possesses distinctive attributes that allow for novel interpretations of image similarities. The proposed class of t-norm-based structural similarity measures offers numerous options for decisionmakers to consider various issues and interpret results more broadly in line with their objectives. For more details, in the Experiments section, the proposed method is applied to grayscale and binarized images and a specific experiment related to meteorology. Eventually, the presented diverse case studies confirm the efficiency and key features of the t-norm-based structural similarity.

Molecular factors determining brightness in fluorescence-encoded infrared vibrational spectroscopy.

Fluorescence-encoded infrared (FEIR) spectroscopy is a recently developed technique for solution-phase vibrational spectroscopy with detection sensitivity at the single-molecule level. While its spectroscopic information content and important criteria for its practical experimental optimization have been identified, a general understanding of the electronic and nuclear properties required for highly sensitive detection, i.e., what makes a molecule a "good FEIR chromophore," is lacking. This work explores the molecular factors that determine FEIR vibrational activity and assesses computational approaches for its prediction. We employ density functional theory (DFT) and its time-dependent version (TD-DFT) to compute vibrational and electronic transition dipole moments, their relative orientation, and the Franck-Condon factors involved in FEIR activity. We apply these methods to compute the FEIR activities of normal modes of chromophores from the coumarin family and compare these predictions with experimental FEIR cross sections. We discuss the extent to which we can use computational models to predict the FEIR activity of individual vibrations in a candidate molecule. The results discussed in this work provide the groundwork for computational strategies for choosing FEIR vibrational probes or informing the structure of designer chromophores for single-molecule spectroscopic applications.

Research on the Simulation Method of HTTP Traffic Based on GAN

Due to the increasing severity of network security issues, training corresponding detection models requires large datasets. In this work, we propose a novel method based on generative adversarial networks to synthesize network data traffic. We introduced a network traffic data normalization method based on Gaussian mixture models (GMM), and for the first time, incorporated a generator based on the Swin Transformer structure into the field of network traffic generation. To further enhance the robustness of the model, we mapped real data through an AE (autoencoder) module and optimized the training results in the form of evolutionary algorithms. We validated the training results on four different datasets and introduced four additional models for comparative experiments in the experimental evaluation section. Our proposed SEGAN outperformed other state-of-the-art network traffic emulation methods.

Fabrication and Performance of a Perovskite Solar Cell: Effect of Acetylacetone on Compact TiO2 Layer

Solar energy has been the most emphasized issue in recent years, as it is sustainable and causes zero emissions. In the solar cell industry, new manufacturing protocols have led to the development of materials with enhanced properties. Over the past decades, perovskite solar cells (PSC) have obtained a power conversion efficiency (PCE) to be 25% due to the development of synthesis techniques, electrode materials, etc. There is an important relationship between the thickness of the transport layers (hole and electron) in the case of improving the yield of perovskite solar cells. We have investigated the influence of the acid-assisted and acetylacetone-assisted (AA) methods on TiO2 films and thus the effect on the PCE of perovskite solar cells. Perovskite (CH3NH3PbI3) layer and different compact TiO2 (c-TiO2) layers have been coated by the spin coating method, and the overall experimental section is made in the nitrogen medium at room temperature. Through an acid-assisted method, the cracked c-TiO2 film was formed. The planar solar cell structure of ITO/AA-TiO2/CH3NH3PbI3/P3HT/Ag resulted in 0.03% of PCE. However, the perovskite solar cells with a mesoporous solar cell structure of ITO/ AA-TiO2/m- TiO2 /CH3NH3PbI3/P3HT/Ag resulted in 0.1% of PCE.

A new empirical formula for calculation of (n,3n) cross sections of heavy mass nuclei in the energy region 22–27.5 MeV

In this paper we want to study (n,3n) reactions using an empirical formula derived on the basis of the statistical model considering reaction Q-value dependence. This formula was obtained by taking into account the exponential dependence on asymmetry parameter (N−Z)/A for neutron-induced reactions in Levkovskii's empirical formula. In addition, the present formula depends also on incident energy En, reaction Q-value and symmetry term (N−Z)2/A. Herein, a new analysis of experimental data of (n,3n) reactions in the energy region 22–27.5 MeV was carried out for quick estimation of cross sections of the heavy mass isotopes of odd Z-even N nuclides in the region of A = 151 to 209. We observed a good agreement of the experimental cross section data with the calculations performed using the present empirical formula.

Mixed Gaussian-impulse noise removal using non-convex high-order TV penalty

To restore images with clear edge details and no staircase artifacts from degraded versions, this paper incorporates the ℓ2 plus ℓ0 data fidelity and non-convex high-order total variation regularizer to establish an optimization model for eliminating mixed Gaussian-impulse noise. Among them, the ℓ2 fidelity is adopted to suppress Gaussian noise, while the ℓ0-norm is more suitable for detecting and removing impulse noise. In addition, the non-convex regularization displays excellent performance in overcoming the staircase effect and preserving edge details. Computationally, by using the iteratively reweighted ℓ1 algorithm and variable splitting method, this work designs a modified alternating minimization method to solve the optimization problem we construct. In theory, the convergence proof of our resulting algorithm is also presented. Finally, in the experimental section, we conduct extensive numerical experiments on degraded images and compare with other existing techniques. From the intuitive effects and restoration accuracy, it follows that our newly proposed method is effective and competitive for image deblurring and mixed noise removal.

APRE: Annotation-Aware Prompt-Tuning for Relation Extraction

AbstractPrompt-tuning has been successfully applied to support classification tasks in natural language processing and has achieved promising performance. The main characteristic of prompt-tuning based classification is to verbalize class labels and predict masked tokens like a cloze-like task. It has the advantage to make use of knowledge in pre-trained language models (PLMs). Because prompt templates are manually designed, they are more prone to overfitting. Furthermore, traditional prompt templates are appended in the tail of an original sentence. They are far from some semantic units in a sentence. It is weak to decode semantic information of an input relevant to PLMs. To aggregate more semantic information from PLMs for masked token prediction, we propose an annotation-aware prompt-tuning model for relation extraction. In our method, entity type representations are used as entity annotations. They are implanted near the site of entities in a sentence for decoding semantic information of PLMs. It is effective to make full use of knowledge in PLMs for relation extraction. In the experiment section, our method is validated on the Chinese literature text and SemEval 2010 Task datasets and achieves 89.3% and 90.6% in terms of F1-score, respectively. It achieves the state-of-the-art performance on two public datasets. The result further demonstrates the effectiveness of our model to decode semantic information in PLMs.

Investigation on wireless communication reliability planning in cloud computing mMTC scenarios

With the development of cloud computing technology, cloud computing has been applied in various fields, but there is a lack of research in the Massive Machine Type Communication (mMTC) scenario. This article analyzed wireless communication reliability solutions in cloud computing mMTC scenarios to address this issue. Using the method of cloud computing, this article analyzed the reliability of wireless communication schemes and verified the effectiveness of the algorithm through credibility judgment and algorithm convergence characteristics under the guidance of wireless communication schemes. By analyzing the application of cloud computing in wireless communication solutions, this article analyzed the effectiveness of wireless communication solutions in the experimental section by comparing the error rate, success rate, and convergence of various algorithms. Through experimental analysis, it was found that the implementation success rate of chaotic particle swarm optimization algorithm was 5% higher than that of adaptive particle swarm optimization. The results indicated that cloud computing was well applied in wireless communication networks, thus providing reference significance for the application of cloud computing in other communication fields.

An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network

The process of iris recognition can result in a decline in recognition performance when the resolution of the iris images is insufficient. In this study, a super-resolution model for iris images, namely SwinGIris, which combines the Swin Transformer and the Generative Adversarial Network (GAN), is introduced. SwinGIris performs quadruple super-resolution reconstruction for low-resolution iris images, aiming to improve the resolution of iris images and thereby improving the recognition accuracy of iris recognition systems. The model utilizes residual Swin Transformer blocks to extract depth global features, and the progressive upsampling method along with sub-pixel convolution is conducive to focusing on the high-frequency iris information in the presence of more non-iris information. In order to preserve high-frequency details, the discriminator employs a VGG-style relative classifier to guide the generator in generating super-resolution images. In experimental section, we enhance low-resolution (56 × 56) iris images to high-resolution (224 × 224) iris images. Experimental results indicate that the SwinGIris model achieves satisfactory outcomes in restoring low-resolution iris image textures while preserving identity information.

Evaluation on model-driven knowledge graph and platform for grid operation and maintenance

With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value.

A classification method of marine mammal calls based on two-channel fusion network

AbstractMarine mammals are an important part of marine ecosystems, and human intervention seriously threatens their living environments. Few studies exist on the marine mammal call recognition task, and the accuracy of current research needs to improve. In this paper, a novel MG-ResFormer two-channel fusion network architecture is proposed, which can extract local features and global timing information from sound signals almost perfectly. Second, in the input stage of the model, we propose an improved acoustic feature energy fingerprint, which is different from the traditional single feature approach. This feature also contains frequency, energy, time sequence and other speech information and has a strong identity. Additionally, to achieve more reliable accuracy in the multiclass call recognition task, we propose a multigranular joint layer to capture the family and genus relationships between classes. In the experimental section, the proposed method is compared with the existing feature extraction methods and recognition methods. In addition, this paper also compares with the latest research, and the proposed method is the most advanced algorithm thus far. Ultimately, our proposed method achieves an accuracy of 99.39% in the marine mammal call recognition task.

Shock-Induced Separation and Control in a 4.32-Aspect-Ratio Test Section

Experimental test sections studying shock-wave–boundary-layer interactions are usually right-angled by design to replicate the supersonic engine inlets where these interactions are observed. To avoid flow separation and shock effects on the side walls, investigations are typically performed on the centerline of the test section. However, the centerlines of small-aspect-ratio test sections can be significantly affected by the flow structures generated in the corners. Results using a large-aspect-ratio test section at a freestream Mach number of 1.6 include centerline and corner flow separation topologies, schlieren images of shock structures, total pressure profiles, and surface static pressures throughout the interaction region. These results provide evidence that the large-aspect-ratio test section can produce a large area of centerline flow that is unaffected by the separation and shock structures generated in the corners, which is a significant improvement over most of the smaller-aspect-ratio test sections employed for these types of studies. Microvortex generators are also employed upstream of the interaction, and measurements indicate a five-vortex system that energizes the near-wall boundary layer enough to overcome the shock’s adverse pressure gradient while remaining attached.

A Mood Semantic Awareness Model for Emotional Interactive Robots.

The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human-machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users' degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user's interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model's performance in terms of dialogue quality and user experience.

An effective theoretical and experimental analysis method for the improved slime mould algorithm

Metaheuristic intelligent optimization algorithms are effective methods for solving high-dimensional nonlinear complex optimization problems. The slime mould algorithm is a novel intelligent optimization algorithm proposed in 2020. However, the basic slime mould algorithm still has some shortcomings, such as slow convergence rate, easy to fall into local extremum, and unbalanced exploration and development capability. To further improve and expand the optimization ability and application scope of the slime mould algorithm, and enhance its performance in solving large-scale complex optimization problems, this paper proposes a slime mould algorithm (PPMSMA) based on Gaussian perturbation and phased position update, positive variation, and multi-strategy greedy selection. Firstly, Gaussian perturbation and phased position update mechanism are introduced to avoid the difficulty of the algorithm to jump out of the local extrema and also to speed up the convergence of the algorithm. Then, a positive variation strategy based on the sine cosine mechanism is introduced to move the variation of the population towards a better direction. Finally, a multi-strategy greedy selection mechanism is introduced, which effectively improves the search ability of the algorithm. The analysis and research on the optimization ability and performance of metaheuristic algorithms mainly include two aspects: theoretical analysis and experimental testing. Theoretical analysis has always been a relatively weak link in the research of metaheuristic algorithms, and there is currently no clear and effective method formed. For experimental testing, although there are more methods, they often lack systematization and adequacy. In this paper, a more complete, fine-grained and systematic approach to theoretical and experimental analysis is proposed. In the theoretical analysis part, the time complexity and spatial complexity of the PPMSMA algorithm are analytically proved to be the same as the basic slime mould algorithm, and the probability measure method is used to prove that PPMSMA algorithm can converge to the global optimal solution. In the simulation experiment section, the PPMSMA algorithm is compared with multiple sets of 10 representative comparison algorithms on the CEC2017 complex test function set suite for optimization accuracy analysis, Friedman comprehensive ranking analysis, average optimization rate analysis of PPMSMA relative to other algorithms, convergence curve analysis, and Wilcoxon rank-sum test analysis. To further examine the scalability of the improved algorithm in solving large-scale optimization problems, PPMSMA is compared with the above 10 comparative algorithms under 1000 dimensional conditions in the large-scale global optimization test set CEC2010, and the solution stability of each algorithm is analyzed through violin plots. The results show that the PPMSMA algorithm has significantly improved convergence performance, optimization accuracy, and solution stability in both high-dimensional and large-scale complex problems, and has significant advantages compared to multiple sets of 10 representative comparative algorithms. Finally, PPMSMA and 10 other comparative algorithms are used to solve engineering design optimization problems with different complexities. The experimental results validate the universality, reliability, and superiority of PPMSMA in handling engineering design constraint optimization problems.

Experimental and numerical investigation on the crashworthiness performance of double hat‐section Al‐CFRP beam subjected to quasi‐static bending test

AbstractNowadays, hybrid structures, which combine low‐density composites with low‐cost thin‐walled metals, have shown great effect in enhancing the performance of automotive body structures, especially the energy absorber components. This study focuses on the experimental and numerical investigation of the bending energy absorption behavior of CFRP/aluminum hybrid double hat‐section beams used in the side part of the car body. In the experimental section, two types of double hat‐section beams were fabricated: aluminum and Al/CFRP. These beams were then subjected to quasi‐static three‐point bending tests. The results demonstrated that the hybrid beam exhibited superior energy absorption capabilities compared to the single beam. Subsequently, a finite element model was developed using LS‐Dyna software and was validated by comparing the experimental and numerical load–displacement results. In‐depth numerical analyses were conducted to investigate the influence of various design parameters, including CFRP‐reinforcement configuration, numbers of CFRP layers, CFRP ply‐angle, CFRP reinforced length, and aluminum/CFRP mass, on crashworthiness indicators. The numerical findings indicate that the hybrid beam with , ply‐angles exhibited better crash force efficiency (CFE) and specific energy absorption (SEA) compared to other ply‐angles, respectively. Furthermore, increasing the number of CFRP layers within the total beam's mass improved its energy absorption capacity. It was also revealed that the CFRP length on the upper and lower hats could be considered as 42.5% of the total aluminum beam length, as opposed to the full CFRP length, providing enhanced energy absorption capabilities.Highlights Bending behavior of the Al and Al/CFRP double hat section beams. Effects of variable thickness and identical mass on the double hat section beam. Effects of variable ply angle and number of CFRP layers on the hybrid beams. Discrete effect of CFRP reinforcement configurations and inner CFRP lengths.

Possibility of Monitoring the Dynamic Impact of the Rolling Stock on the Track Superstructure Using Strain Gauges and Vibrometry

One of the priority tasks in development of railway transport is to raise its efficiency by increasing the time between repairs for both the track superstructure and the rolling stock. Solving this problem is impossible without timely, complete, and reliable information about the dynamics of interaction of the rolling stock with the infrastructure. To organise monitoring of dynamic processes, completely different types of primary transducers are used: resistance strain gauges, fibre-optic sensors, dynamometers (force sensor pads), accelerometers, acoustic emission sensors. This implies relevance of the scientific and technical problem of comparative testing of sensors of different types to assess the information content of their signals and justify the criteria for choosing primary transducers when solving specific monitoring problems.The objective of the study is to comparatively test removable resistive strain sensors, optical polarisation strain sensors and accelerometers under a passing train and to evaluate their information content to control rail depression and detect defects on the running surface of wagon wheels.The study using finite element modelling and a physical model of a rail as of a beam on an elastic foundation, substantiates the relationship between longitudinal deformations and vertical accelerations of the rail foot. Comparative tests of removable strain-resistive and optical polarisation strain sensors and accelerometers were held on an experimental section of track under a passing train. A signal processing algorithm has been developed and the equivalence of strain gauges and vibrometers for determining the depression of a rail under a passing train has been proven. Acomparison has been made of the pulse components of the signals of deformation and vibration acceleration during movement of a wheel with a defect on the running surface, and the requirements for the frequency characteristics of the sensors and their mounting on the rail surface have been substantiated.

Assessment of In Situ Compactness and Air Void Content of New Asphalt Layers Using Ground-Penetrating Radar Measurements

This paper deals with the possibilities of ground-penetrating radar (GPR)-based quality control testing, which was demonstrated on an experimental road section of a ~220–240 m long Hungarian residential street. The measurements and their assessment aimed to control the prescribed compactness and air void content of newly built asphalt layers. Research has discussed the relationship between the air void content and the dielectric constant of asphalt layers, and provided empirical results for this relationship. We suggest a new logistic model with lower and upper asymptotes instead of the exponential formula often used in the literature. Contrary to this newly developed robust model, existing models are sensitive to extreme dielectric constant values due to the mathematical nature of their exponential function. The results of the new logistic model are compared to those of the Hoegh–Dai (HD) and Minnesota Department of Transportation (MnDOT) models on the basis of a few calibration data points. Through systematic data collection and analysis, the developed robust empirical model demonstrates a significant correlation between the relative permittivity and air void content in asphalt mixes, enabling accurate estimation of the air void content within a ±0.5% margin of error. The air void content can be applied to estimate the asphalt layer modulus. The developed model can be further exploited by utilizing a combination of GPR and drone technology. The “symbiosis” of these technologies can lead to a totally non-destructive imaging system, which can then be applied to environmental monitoring of roads and their surroundings in terms of quality control of asphalt compaction work and the hot asphalt mix behind the compaction roller during pavement construction.