Search Results (15)

This article considers a fluxgate magnetometer (FM) that operates based on a new physical principle. The authors analyze how the alternating electric charge potential of a cylindrical metal electrode impacts the structure of a cylindrical permanent magnet made of composite-conducting ferrite. They demonstrate that this impact and permanent magnet structure initiate the emergence of polarons with oscillating magnetism. This causes significant changes in the entropy of indirect exchange and the related sublattice magnetism fluctuations that ultimately result in the generation of circularly polarized spin waves at the spin wave resonance frequency that are channeled and evolve in dielectric ferrite waveguides of the FM. It is demonstrated that these moving spin waves have an electrodynamic impact on the measuring FM coils on the macro-level and perform parametric modulation of the magnetic permeability of the waveguide material. This results in the respective variations of the changeable magnetic field, which is also registered by the measuring FM coils. The authors considered a generalized flow of the physical processes in the FM to obtain a detailed representation of the operating functions of the FM. The presented experimental results for the proposed FM in the field meter mode confirm its operating parameters (±40 μT—measurement range, 0.5 nT—detection threshold). The usage of a cylindrical metal electrode as a source of exciting electrical change instead of a conventional multiturn excitation coil can significantly reduce temperature drift, simplify production technology, and reduce the unit weight and size. Full article

As one of the most commonly used and important data carriers, tables have the advantages of high structuring, strong readability and strong flexibility. However, in reality, tables usually present various forms, such as Excel, images, etc. Among them, the information in the table image cannot be read directly, let alone further applied. Therefore, the research related to image-based table recognition is crucial. It contains the table structure recognition and the table content recognition. Among them, table structure recognition is the most important and difficult task because the table structure is abstract and changeable. In order to address this problem, we propose an innovative table structure recognition method, named TSRDet (Table Structure Recognition based on object Detection). It includes a row-column detection method, named SACNet (StripAttention-CenterNet) and the corresponding post-processing. SACNet is an improved version of the original CenterNet. The specific improvements include the following: firstly, we introduce the Swin Transformer as the encoder to obtain the global feature map of the image. Then, we propose a plug-and-play row-column attention module, including a channel attention module and a row-column spatial attention module. It improves the detection accuracy of rows and columns by capturing long-range row-column feature maps in the image. After completing the row-column detection, this paper also designs a simple and fast post-processing to generate the table structure based on the row-column detection results. Experimental results show that for row-column detection, SACNet has high detection accuracy, even at a high IoU threshold. Specifically, when the threshold is 0.75, its mAP of row detection and column detection still exceeds 90%, which is 91.40% and 92.73% respectively. In addition, in the comparative experiment with the existing object detection methods, SACNet’s performance was significantly better than that of all others. For table structure recognition, the TEDS-Struct score of TSRDet is 95.7%, which shows competitive performance in table structure recognition, and verifies the rationality and superiority of the proposed method. Full article

In the field of construction engineering, the cracking of concrete structures is a common engineering problem, which has a great impact on the overall stability and service life of the engineered structure. During structural repair, crack detection is the most critical step. Automatic detection significantly reduces the engineering cost and human factor error compared with manual detection. However, due to the changeable environment of the project site and different image specifications, using a single algorithm makes it difficult to balance high efficiency and high accuracy. In this study, we designed a combined recognition method including the region growth algorithm and machine learning regression that can achieve a tradeoff between accuracy and efficiency. Firstly, the regression method learns the image features of the dataset and the specific region growth threshold, and the regression function is trained by using the open-source dataset to determine the region growth threshold using the characteristics of the images included in the tests. The region growth algorithm is used to expand the threshold from the seed points of the image to obtain the crack recognition results. The results show that this method improves the accuracy of SSIM by 7% compared with the traditional region growth algorithm, and does not significantly increase the computational cost, with an increase of 0.78 s per photo process. Compared with the deep learning method, the recognition accuracy of SSIM is decreased by 5.96%, but it takes less resources and has high efficiency. Full article

Due to the lack of timely protection measures against infectious diseases, or based on the particularity of the transmission of some infectious diseases and the time-varying connections between people, the transmission dynamics of infectious diseases in the information society are becoming more and more complex and changeable. A fractional-order epidemic mathematical model with network weighting and latency is proposed in this paper, and the stability near the disease-free equilibrium point and endemic equilibrium point are discussed separately. Subsequently, an event-triggered impulsive control strategy based on an infection rate threshold is put forward. By selecting the appropriate control gain, the Zeno phenomenon can be eliminated on the premise of ensuring the stability of the control error system. Finally, the theoretical results were validated numerically and some conclusions are presented. These findings contribute to future research on the limited-time event-triggered impulsive control of infectious diseases. Full article

The actual driving conditions of electric vehicles (EVs) are complex and changeable. Limited by road adhesion conditions, it is necessary to give priority to ensuring safety, taking into account the energy recovery ratio of the vehicle during braking to obtain better braking quality. In this work, an electric vehicle with an EHB (electro-hydraulic braking) system whose braking force adaptive distribution control strategy is studied. Firstly, the vehicle dynamics model, including seven degrees of freedom, tire, drive motor, main reducer, battery pack, and braking system, was constructed, which is attributed to the vehicle configuration and braking system scheme. Second, based on curve I and ECE regulations, the adaptive braking force distribution control strategy was formulated by taking the maximum regenerative braking torque as the inflection point, the synchronous adhesion coefficient as the desired point, and the battery SOC, road adhesion coefficient, and braking strength as the threshold. Finally, the vehicle dynamics simulation model was built on the Matlab/Simulink platform, and the simulation results verified the feasibility of the proposed braking force adaptive allocation control strategy. The research shows that the adaptive distribution control strategy can better adapt to the complex and variable driving conditions of the vehicle by combining the inflection point and the desired point. The braking energy recovery ratios of the vehicle under the NEDC and NYCC cycle conditions on a high adhesion road are 52.62% and 47.45%. The braking force distribution curve is close to curve I under the low adhesion extreme road. Full article

Secret image sharing (SIS) is an important application of the traditional secret sharing scheme, which has become popular in recent years. In an SIS scheme, a confidential image is encrypted into a group of shadows. Any set of shadows that reaches the threshold can reconstruct the image; otherwise, nothing can be recovered at all. In most existing SIS schemes, the threshold on shadows for image reconstruction is fixed. However, in this work, we consider more complicated cases of SIS, such that the threshold is changeable according to the security environment. In this paper, we construct a $(k\leftrightarrow h,n)$ threshold-changeable SIS (TCSIS) scheme using a bivariate polynomial, which provides $h-k+1$ possible thresholds, $k,k+1,\dots ,h$. During image reconstruction, each participant can update their shadow according to the current threshold T based only on their initial shadow. Unlike previous TCSIS schemes, the proposed scheme achieves unconditional security and can overcome the information disclosure problem caused by homomorphism. Full article

Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware. Full article

In secret image sharing, the image is divided into several stego images, which are managed by corresponding participants. The secret image can be recovered only when the number of authorized participants is no less than the threshold. Thus, it is widely used to protect essential images, such as engineering drawings and product design drawings. In the traditional secret image sharing scheme, the threshold is fixed and unique. However, in practice, the security policy and the adversarial structure may change; therefore, the threshold must be adjusted dynamically. In this paper, we propose a novel secret image sharing scheme with a changeable threshold capability. Our scheme eliminates the limit of the changeable threshold and reduces the computation required. Also, our scheme is the first threshold changeable secret image sharing scheme that can recover an undistorted cover image. The theoretical analysis shows that our scheme is safe even if the threshold is changed. The experiments demonstrated that the stego image generated by our algorithm has better quality than other changeable-threshold, secret image sharing algorithms. Full article

Ga₂O₃ thin films were fabricated by the electron-beam evaporation technique at a varying oxygen partial pressure from 0 to 2.0 × 10⁻² Pa. The effect of oxygen partial pressure on the crystalline structure and optical properties of the Ga₂O₃ films was analyzed using sophisticated techniques including X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), Raman spectroscopy, spectroscopic ellipsometry, ultraviolet-visible spectroscopy and a laser-induced damage test system. The correlation between the oxygen partial pressure and the film’s properties in optics and materials were investigated. XRD and Raman revealed that all films were amorphous in spite of applying a varying oxygen partial pressure. With the change of oxygen partial pressure, XPS data indicated that the content of oxygen in the Ga₂O₃ films could be broadly modulable. As a result, a changeable refractive index of the Ga₂O₃ film is realizable and a variable blue-shift of absorption edges in transmittance spectra of the films is achievable. Moreover, the damage threshold value varied from 0.41 to 7.51 J/cm² according to the rise of oxygen partial pressure. These results demonstrated that the optical properties of Ga₂O₃ film can be broadly tunable by controlling the oxygen content in the film. Full article

Radar scientists typically define the radar beamwidth as a half-power beamwidth (HPBW) in the main lobe of the antenna pattern. However, the microwave radiations outside radar HPBW might also backscatter into the radar receiver and change the distribution of the received signal. To determine an actual and effective beamwidth illuminated on the measured targets, we first generate the simulated-waveforms derived from coincident lidar points and radar equation and then develop a waveform matching method to seek out an optimal beamwidth based on the 95% threshold of correlation coefficients between radar waveforms and the simulated-waveforms. The 8565 measurements of a Ku-band profiling radar named Tomoradar and coincident lidar data in a widespread heterogeneous forest area of southern Finland are employed for resolving the effective beamwidth. The results reveal that about 97% of the effective beamwidth are larger than Tomoradar HPBW, but the effective beamwidth could be changeable for each measurement due to variations in the scattering properties of vegetation. Thus, a fixed average effective beamwidth (AEBW) with 0.1-degree resolution is introduced to determine Tomoradar cone according to the effective beamwidth and corresponding proportions. We discover that Tomoradar AEBW is approximately approaching to 8°, which is larger than Tomoradar HPBW of 6°. If we regard AEBW as the actual Tomoradar beamwidth rather than HPBW, the simulated-waveforms have substantially stronger correlation strength with Tomoradar waveforms, and canopy tops derived from lidar data within Tomoradar AEBW are much closer to those extracted from Tomoradar waveforms. The results demonstrate that radar AEBW is a more appropriate reference for designing radar antenna and selecting the region size of validation data such as lidar points or the ground truth. However, considering that radar AEBW is variable for different radar antenna pattern, we suggest that actual radar beamwidth should be defined with a fraction of total radiation energy within radar AEBW, just like the definition of laser divergence of lidar based on the percentage of transmitted laser energy. In this paper, for a forest inventory research case, the fraction of total radiation energy within the AEBW for radar system is supposed to be 91%. Full article

The reliability of aero engine has a direct impact on the flight safety of the whole plane. With the continuous improvement of performance requirements of aero engines, the related fatigue and reliability problems also appear. For the fatigue failure characteristics of the typical component (compressor disk) in an aero engine, the fatigue reliability of its multi-site damage structure in service is analyzed by using probability cumulative damage criterion in this paper. The probability distribution definitions of life, damage and damage threshold are discussed and the relationship among them is also introduced by the new proposed criterion. Meanwhile, a method to determine the probability distribution of cumulative damage threshold and probability life prediction is carried out, based on which a hierarchical index system of statistical analysis and reliability modeling principle on the system level is further constructed for compressor disk. At the end of the paper, a certain cruise of fighter plane is analyzed to verify the validity of the new model. Emphasizing the difference between the compressor disk and traditional component, the new reliability analysis model developed in this study is basically reasonable for most of the load histories for the compressor disk, other than the traditional one, especially for the changeable and complex cruise missions. Full article

Infrared maritime target detection is the key technology of maritime target search systems. However, infrared images generally have the defects of low signal-to-noise ratio and low resolution. At the same time, the maritime environment is complicated and changeable. Under the interference of islands, waves and other disturbances, the brightness of small dim targets is easily obscured, which makes them difficult to distinguish. This is difficult for traditional target detection algorithms to deal with. In order to solve these problems, through the analysis of infrared maritime images under a variety of sea conditions including small dim targets, this paper concludes that in infrared maritime images, small targets occupy very few pixels, often do not have any edge contour information, and the gray value and contrast values are very low. The background such as island and strong sea wave occupies a large number of pixels, with obvious texture features, and often has a high gray value. By deeply analyzing the difference between the target and the background, this paper proposes a detection algorithm (SRGM) for infrared small dim targets under different maritime background. Firstly, this algorithm proposes an efficient maritime background filter for the common background in the infrared maritime image. Firstly, the median filter based on the sensitive region selection is used to extract the image background accurately, and then the background is eliminated by image difference with the original image. In addition, this article analyzes the differences in gradient features between strong interference caused by the background and targets, proposes a small dim target extraction operator with two analysis factors that fit the target features perfectly and combines the adaptive threshold segmentation to realize the accurate extraction of the small dim target. The experimental results show that compared with the current popular small dim target detection algorithms, this paper has better performance for target detection in various maritime environments. Full article

Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time. Full article

In order to address the problem of the uncertainty of existing noise models and of the complexity and changeability of the edges and textures of low-resolution document images, this paper presents a projection onto convex sets (POCS) algorithm based on text features. The current method preserves the edge details and smooths the noise in text images by adding text features as constraints to original POCS algorithms and converting the fixed threshold to an adaptive one. In this paper, the optimized scale invariant feature transform (SIFT) algorithm was used for the registration of continuous frames, and finally the image was reconstructed under the improved POCS theoretical framework. Experimental results showed that the algorithm can significantly smooth the noise and eliminate noise caused by the shadows of the lines. The lines of the reconstructed text are smoother and the stroke contours of the reconstructed text are clearer, and this largely eliminates the text edge vibration to enhance the resolution of the document image text. Full article

Frequent graph mining has been proposed to find interesting patterns (i.e., frequent sub-graphs) from databases composed of graph transaction data, which can effectively express complex and large data in the real world. In addition, various applications for graph mining have been suggested. Traditional graph pattern mining methods use a single minimum support threshold factor in order to check whether or not mined patterns are interesting. However, it is not a sufficient factor that can consider valuable characteristics of graphs such as graph sizes and features of graph elements. That is, previous methods cannot consider such important characteristics in their mining operations since they only use a fixed minimum support threshold in the mining process. For this reason, in this paper, we propose a novel graph mining algorithm that can consider various multiple, minimum support constraints according to the types of graph elements and changeable minimum support conditions, depending on lengths of graph patterns. In addition, the proposed algorithm performs in mining operations more efficiently because it can minimize duplicated operations and computational overheads by considering symmetry features of graphs. Experimental results provided in this paper demonstrate that the proposed algorithm outperforms previous mining approaches in terms of pattern generation, runtime and memory usage. Full article