Information Acquisition Method Research Articles

Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method

The method for precursor information acquisition based on acoustic emission (AE) data for jointed rock masses is of significant importance for the early warning of dynamic disasters in underground engineering. A clustering-convolutional neural network (CNN) method is proposed, which comprises a clustering component and a CNN component. A series of uniaxial compression tests were conducted on granite specimens containing a persistent sawtooth joint, with different strain rates (10−5–10−2 s−1) and joint inclination angles (0°–50°). The results demonstrate that traditional precursory indicators based on full waveforms are effective for obtaining precursor information of the intact rock failure. However, these indicators are not universally applicable to the failure of rock masses with a single joint. The clustering-CNN method has the potential to be applied to obtain precursor information for all three failure modes (Modes I, II and III). Following the waveform clustering analysis, the effective waveforms exhibit a low main frequency, as well as high energy, ringing count, and rise time. Furthermore, the clustering method and the precursory indicators influence the acquisition of final precursor information. The Birch hierarchical clustering method and the S value precursory indicator can help to obtain more accurate results. The findings of this study may contribute to the development of warning methods for underground engineering across faults.

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.

Applicability of a new method for mesoscopic structure segmentation of asphalt mixture based on two-dimensional image

The mechanical properties of the asphalt mixture largely depend on its internal structure. Studies in mixture mesostructure are of great significance for perfecting relevant theoretical systems and improving mixture design methods. However, efficient segmentation of mesostructure is an important precondition for mesoscale research about asphalt mixtures. Therefore, this article proposes a new method for mesostructure information acquisition that mainly improves the function of image preprocessing and separation of touching objects. A visual and quantitative comparison between the proposed and common methods is adopted. The result shows that the proposed method performs better in identifying small particles and retaining the fidelity of the boundary. Besides, the mesostructure of four types of asphalt mixtures are compared based on the proposed method. The result shows that the optimum sample size, which makes the measured 2D information efficiently close to the actual 3D information, is around 20–30. Compared to SUP25, the coordination number of AC20 is about 0.15–0.23 higher. Compared with the samples compacted by Marshall, the samples compacted by gyratory have a larger coordination number, about 0.12–0.21 higher, a larger fractal dimension, about 0.059–0.062 higher, a smaller uniformity of major axis orientation, about 1.53–1.28 lower, and a smaller uniformity of area distribution, about 0.063–0.12 lower.

The Perceived Informational Needs of Family Caregivers of Children Hospitalized in a Burn Department: A Cross-sectional Study.

Children are most vulnerable to burn injuries, and their families are their most important source of support. Therefore, it is necessary to identify the information needs of such parents and support them to help children adapt to the new situation, recover to pre-accident conditions, and reintegrate into school and society. This study aimed to investigate the perceived information needs of family caregivers of children admitted to the burn wards of hospitals. This cross-sectional study was conducted on 200 family caregivers of children admitted to the burn ward of a hospital in Tabriz, Iran. Participants were selected through convenience sampling, and the required data were collected by using questionnaires on socio-demographic information, information needs, information resources, and information acquisition methods. The obtained data were analyzed statistically using descriptive statistics (mean, standard deviation, frequency, and percentage) and inferential statistics (Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests). Results indicated that the greatest informational need among family caregivers was related to the child's condition. The treatment team was identified as the most important source of information for them. It was found that information should be provided in a comprehensive and understandable manner, while maintaining honesty and human dignity. The study findings contribute to our understanding of the specific information needs of family caregivers in managing the medical care of children with burns. These findings can serve as a basis for interventions and support services aimed at meeting the needs of these families and improving the quality of care for children with burns.

The Impact of Social Media on Consumer Sentiment

With the wide use of network media, consumers’ information acquisition and communication methods have undergone significant changes. Network media not only provides them with a fast and efficient way to obtain information, but also provides them with a broader social space and communication platform. Therefore, the popularity of social media has brought some negative effects, such as low mood and depression among consumers. This paper will first study the impact of social media operation strategies on consumer sentiment. Secondly, the author will examine how social media self-control affects mood, which includes how users control their emotions and behaviors on social media, and the impact of this self-control on consumer mood. This paper will discuss and study through the research methods of psychology and sociology. Finally, this study aims to analyze the problem of information overload between social media and consumer sentiment by focusing on the comments made by consumers on social media. At the end of the study, the research results need to be summarized and analyzed in order to provide useful references for social media platforms to manage consumer emotions, so as to provide useful references for lawmakers, academics and practitioners.

ASSOCIATION OF EVERYDAY LIFE INFORMATION ACQUISITION METHODS AND DEPRESSION AMONG US OLDER ADULTS

Abstract Everyday life information acquisition refers to the incidental ways people obtain information through daily activities such as reading, watching television, or communicating with friends rather than through intentional or purposive information seeking behaviors. It has been identified as a fundamental way to obtain informational resources and is a potential means to promote social connections and engagement among older adults. Research has shown that social engagement and connections are protective factors of depression for older adults. However, little research has explored how information seeking behaviors effect risks of depression among older adults. To fill this gap in the literature and provide insights into the mechanism of information access impacts the mental health of older adults, this study examines the relationships between different ways for obtaining daily information (reading, using computer, and watching television) and depression among older adults in United States. The data retrieved from 2018’s Health and Retirement Study (HRS). We multiple linear regression analyses on HRS data including adults age 50 years and older (n=3,179). After adjusting for age, gender, race, marital status, education, and chronic diseases, all three different ways to obtaining daily information (reading, using computer, and watching television) were negatively associated with depression (β=-0.19, p< 0.001; β=-0.15, p=0.002; β=-0.15, p< 0.001, respectively). Findings suggest that the association between everyday life information acquisition and depression among older adults varies depending on the specific information acquisition methods used. These findings highlight the importance of implementing targeted interventions to promote effective information access and enhance social inclusion among older adults.

RESEARCH ON METHODS OF DETERMINING CUSTOMER LOYALTY AND ASSESSING THEIR LEVEL OF SATISFACTION

The subject of research in the article is methods of data collection and processing to assess the level of customer satisfaction and loyalty to the company, as well as the possibility of evaluating the collected data. The purpose of the work is the analysis of methods for determining customer loyalty and assessing their level of satisfaction, the development of a unified assessment algorithm based on various types of data. The article deals with the following tasks: analysis of information acquisition methods – questionnaires and reviews, evaluation methods definition and comparison questionnaires with closed answers, reviews and open answers’ text tonality evaluation methods analysis using an artificial intelligence, development of an algorithm for determining a unified evaluation and conducting an experiment. The following methods are used: theoretical research methods for determining existing data collection methods, as well as methods for assessing the level of customer loyalty and satisfaction using CSI, CSAT indexes, NLP methods for determining the text tonality, bringing the calculated values to one scale, determining the method of a unified assessment; empirical research methods for conducting an experiment, determining and proving the feasibility of applying the method. The following results were obtained: a method of assessing customer loyalty and their level of satisfaction based on the analysis of various types of information with further results unification was proposed. Various types of date are responses to questionnaires and user reviews. The questionnaires are analyzed using KPI, and reviews – using artificial intelligence methods. After normalizing the results (bringing them to one scale), the additive convolution method is used to unify the overall result. A prototype of the software system has been developed, which allows you to carry out a full cycle from collecting information to calculating both KPI-metrics and a unified assessment. Conclusions: Experimentally, it was determined that the method of assessing customer loyalty and their level of satisfaction based on the unification of a comprehensive assessment of various types of data is efficient and can be used to optimize business processes by reducing time and efforts spent on analyzing the gathered data. The use of this method is fully justified, since the measurement error is low, and the margin of error is acceptable.

Target Information Acquisition Method of Penetration Fuze Based on Composite Detection

When the traditional high-g acceleration sensor penetrates the hard multi-layered target, it is easy to cause the overlap of the penetration overload. The rigid body signal is submerged in the vibration signal, resulting in the error of the layer-counting recognition algorithm, and it is difficult to judge the time of the penetration ammunition. Based on the principle of the capacitance sensor and the piezoelectric sensor, a composite sensor for penetration fuze is designed in this paper. On this basis, a layer-recognition algorithm based on capacitance and piezoelectric signal fusion is designed, and its feasibility is verified by software simulation.

Rumor detection on social networks focusing on endogenous psychological motivation

Emerging information acquisition methods represented by social networks are increasingly popular. The freedom and concealment make social media become the main platform for rumor spreading. Rumor detection has attracted the attention of both academia and industry, and a series of rumor detection methods have emerged. However, existing methods focus on modeling exogenous features such as text content and user characteristics, while ignoring the endogenous psychological motivation of users. Sociological and psychological researches on rumor have demonstrated the correlation between the endogenous psychology of users and their behavior in social networks. Therefore, this study focuses on the intrinsic psychological motivation of users, probes the psychological changes of users after they are exposed to rumors from the perspectives of active sharing and passive response, analyzes and judges user behaviors to explore an efficient rumor detection method. Among them, active sharing behavior is based on the investigation of social users’ motivation, decision making and goal setting, and is related to users’ motivation to actively participate in content by creating posts and so on. Passive response, which is closely related to personal emotions originating from the influence of external factors, is a study of users’ cognitive processes and is associated with the way they handle information without active participation. Experiments demonstrate the superiority of our proposed method in rumor detection tasks, improving accuracy by 2.1% over the current baseline on the Twitter16 dataset and enabling early detection of rumors as they emerge.

A critical review of automated extraction of rock mass parameters using 3D point cloud data

Abstract This paper presents a comprehensive review aimed at gaining insights into the current state of research on quantification technology used for acquiring three-dimensional (3D) point cloud data of rock masses and extracting vital structural information. Addressing this significant challenge and problem within the field of rock engineering is the main objective of this study. The timely and accurate acquisition of rock mass data and fine characterization of rock mass parameters can avoid unnecessary personnel injury and property damage. First, the methods of point cloud information acquisition and structural information extraction are systematically summarized and classified. Then, various existing methods are analyzed for their advantages and disadvantages. Based on this analysis, the future development direction of relevant technologies is proposed to improve the level of acquisition of 3D information of rock mass and the level of extraction of key information of rock mass. The results indicate that rock mass point cloud information acquisition technology can be classified into two types: laser point cloud acquisition and image reconstruction based on structure from motion (SfM) algorithm. Rock mass structural information can be classified into rock mass structural planes and their attitudes, rock mass traces and their geometric parameters, and other rock mass parameters, including structural plane roughness, spacing, and block characteristics, etc. Different acquisition technologies and feature extraction methods have their own advantages, disadvantages, and applicable ranges. Therefore, a comprehensive selection of various evaluation methods should be made based on specific engineering characteristics and existing data situations in practice.

Information Acquisition Method of Tomato Plug Seedlings Based on Cycle-Consistent Adversarial Network

In order to solve the interference caused by the overlapping and extrusion of adjacent plug seedlings, accurately obtain the information of tomato plug seedlings, and improve the transplanting effect of automatic tomato transplanters, this study proposes a seedling information acquisition method based on Cycle-Consistent Adversarial Network (CycleGAN). CycleGAN is a generative unsupervised deep learning method, which can realize the free conversion of the source-domain plug seedling image and the target-domain plug label image. It collects more than 500 images of tomato plug seedlings in different growth stages as a collection image set; follows certain principles to label the plug seedling images to obtain a label image set, and uses two image sets to train the CycleGAN network model. Finally, the trained model is used to process the images of tomato plug seedlings to obtain their label images. According to the labeling principle, the correct rate of model recognition is between 91% and 97%. The recognition results show that the CycleGAN model can recognize and judge whether the seedlings affected by the adjacent seedling holes are suitable for transplanting, so the application of this method can greatly improve the intelligence level of the automatic tomato transplanters.

Earthquake-induced building damage recognition from unmanned aerial vehicle remote sensing using scale-invariant feature transform characteristics and support vector machine classification

Building damage is the main cause of casualties and economic losses from earthquakes. Therefore, understanding building damage is critical for emergency handling. Current information acquisition methods for assessing earthquake damage using unmanned aerial vehicle (UAV) remote sensing systems offer great flexibility and high efficiency with the capability to obtain high-resolution images, which can reflect actual damage to affected areas intuitively. Consequently, UAV remote sensing has become a convenient and important means to acquire earthquake-induced building damage information. Although manual visual interpretation can achieve high recognition accuracy, it is extremely time-consuming. In contrast, although current automatic recognition methods require less time, they have relatively poor recognition accuracy. Neither approach can satisfactorily simultaneously meet efficiency and accuracy requirements for earthquake emergency handling. This article applies image classification algorithms based on the support vector machine (SVM) to earthquake-induced building damage recognition, and proposes a recognition method based on scale-invariant feature transform (SIFT) characteristics and SVM classification. We use the magnitude 6.4 earthquake at Yangbi (2021) as an example to validate the proposed method. Results verify that the proposed method can recognize earthquake damage quickly and accurately, providing effective support for decision-making regarding rescue actions.

Avoiding the Worst Decisions: A Simulation and Experiment

Many practical decisions are more realistic concerning preventing bad decisions than seeking better ones. However, there has been no behavioral decision theory research on avoiding the worst decisions. This study is the first behavioral decision research on decision strategies from the perspective of avoiding the worst decisions. We conducted a computer simulation with the Mersenne Twister method and a psychological experiment using the monitoring information acquisition method for two-stage decision strategies of all combinations for different decision strategies: lexicographic, lexicographic semi-order, elimination by aspect, conjunctive, disjunctive, weighted additive, equally weighted additive, additive difference, and a majority of confirming dimensions. The rate of choosing the least expected utility value among the alternatives was computed as the rate of choosing the worst alternative in each condition. The results suggest that attention-based decision rules such as disjunctive strategy lead to a worse decision, and that striving to make the best choice can conversely often lead to the worst outcome. From the simulation and the experiment, we concluded that simple decision strategies such as considering what is most important can lead to avoiding the worst decisions. The findings of this study provide practical implications for decision support in emergency situations.

Interference source-based quality assessment method for postauricular photoplethysmography signals

Photoplethysmography (PPG), as a physiological information acquisition method, is susceptible to the influence of baseline drift, motion artifacts, impulse interference, and stochastic noise from different body postures. Improving the accuracy of human physiological information is always hard work as the interference of diverse sources in PPG signals should be processed according to the corresponding methods. Firstly, the PPG signals and accelerometer signals were collected from post-auricula. Secondly, 16-dimensional features were extracted for the quality description according to the characteristics of PPG signals, motion level, and obvious interference. Finally, Random Forest (RF) was used for quality assessment. The 14700 samples from 31 subjects in 7 experiments were collected to verify algorithm efficiency. The results showed that the method accuracy was 99.68%. The specificity, sensitivity, precision, F1-score, and area under the curve (AUC) were all greater than 0.99. Compared with other decision methods and works, our method obtains higher indexes. The multi-type features were essential for the quality assessment method when PPG signals contained complex interference. Therefore, the proposed method and features could provide a helpful basis for the quality assessment of home monitoring and wearable devices.

Monitoring the Rice Panicle Blast Control Period Based on UAV Multispectral Remote Sensing and Machine Learning

The heading stage of rice is a critical period for disease control, such as for panicle blast. The rapid and accurate monitoring of rice growth is of great significance for plant protection operations in large areas for mobilizing resources. For this paper, the canopy multispectral information acquired continuously by an unmanned aerial vehicle (UAV) was used to obtain the heading rate by inversion. The results indicated that the multi-vegetation index inversion model is more accurate than the single-band and single-vegetation index inversion models. Compared with traditional inversion algorithms such as neural network (NN) and support vector regression (SVR), the adaptive boosting algorithm based on ensemble learning has a higher inversion accuracy, with a correlation coefficient (R2) of 0.94 and root mean square error (RMSE) of 0.12 for the model. The study suggests that a more effective inversion model of UAV multispectral remote sensing and heading rate can be built using the AdaBoost algorithm based on the multi-vegetation index, which provides a crop growth information acquisition and processing method for determining the timing of rice tassel control.

Three-dimensional mapping in multi-samples with large-scale imaging and multiplexed post staining

Dissection of the anatomical information at the single-cell level is crucial for understanding the organization rule and pathological mechanism of biological tissues. Mapping the whole organ in numerous groups with multiple conditions brings the challenges in imaging and analysis. Here, we describe an approach, named array fluorescent micro-optical sectioning tomography (array-fMOST), to identify the three-dimensional information at single-cell resolution from multi-samples. The pipeline contains array embedding, large-scale imaging, post-imaging staining and data analysis, which could image over 24 mouse brains simultaneously and collect the slices for further analysis. With transgenic mice, we acquired the distribution information of neuropeptide somatostatin neurons during natural aging and compared the changes in the microenvironments by multi-component labeling of serial sections with precise co-registration of serial datasets quantitatively. With viral labeling, we also analyzed the input circuits of the medial prefrontal cortex in the whole brain of Alzheimer’s disease and autism model mice. This pipeline is highly scalable to be applied to anatomical alterations screening and identification. It provides new opportunities for combining multi-sample whole-organ imaging and molecular phenotypes identification analysis together. Such integrated high-dimensional information acquisition method may accelerate our understanding of pathogenesis and progression of disease in situ at multiple levels.

Research progress of correlation imaging under outdoor environment

Image, as a method of information acquisition, is indispensable for human beings, and it plays an irreplaceable role in military and civilian fields, such as detection and scouting, precision guidance, transportation, and industrial production. In the outdoor environment, the resolution, signal-to-noise ratio, and working distance of optical imaging are limited as result of the influence of background light, stray light, and atmospheric medium. In recent years, with the development of muti-discipline such as optics, physics, information theory, and computer science, the new optical imaging technologies continue to emerge, thus bringing new opportunities for outdoor optical imaging towards long-distance, large field of view and high information flux. As one of the new active imaging technologies, correlation imaging has the potential applications of robustness against turbulence and noise, and the possibility of beating the Rayleigh limit. It can deal with the problems better, such as sharp attenuation of optical power caused by long distances, detection of interference signals from environmental noise, and influence of turbulence. Based on the principle of optical imaging, this paper analyzes the factors affecting optical imaging, in terms of resolution, signal-to-noise ratio, spatial bandwidth product, and imaging distance under outdoor environment, focusing on the research progress of outdoor correlation imaging including imaging systems, signal-to-noise screening technology and imaging algorithm. In addition, we analyze the requirements of optical imaging for longer distances and broader field of view, and consider the fundamental problems and the key technologies.

Research on the aesthetic impact of new media technology communication means on visual communication design

The development of new media technology has expanded the way of people's life and increased the form of communication. Due to the change of early application media, the name of the specialty was changed from the original graphic design to visual communication design, which made the name of the specialty more appropriate to the actual situation. The application of a large number of new electronic products to the consumer market has changed the means of information dissemination. Because the aesthetic standards and aesthetic methods are not static, the development of multimedia technology can highly integrate the visual communication design with the current audience's information acquisition methods, making the application scope of visual communication design become broader.

A Fast Data Transmission Method of Vehicle-Road Cooperative Communication Based on the Clustering Algorithm.

In order to improve the security and stability of data transmission in vehicle-road cooperative communication and reduce the error rate of data transmission, a fast data transmission method based on the clustering algorithm is proposed. First, the multisensor information acquisition method of the vehicle networking is used to realize data acquisition and data structure analysis of the vehicle-road cooperative communication, and a data transmission channel model for vehicle-road cooperative communication is constructed. The interference suppression in the data transmission process of the vehicle-road cooperative communication is realized by the matched filter detection algorithm. Then, according to the symbol characteristic distribution of the vehicle-road cooperative communication channel, the baud interval equalization method is used to realize the piecewise equalization adjustment of data transmission for the vehicle-road cooperative communication. With the adaptive error compensation and channel fading suppression, the K-means clustering algorithm is used to carry out the coordinated adjustment to data packets during the rapid data transmission of the vehicle-road cooperative communication. Finally, the adaptive control of data transmission for the vehicle-road cooperative communication is carried out according to the dynamic distribution characteristics of the clustering center so as to reduce the influence of channel fading and intersymbol interference caused by channel spread. The simulation results show that the bit error rate of this method is kept at about 0.05, and the data transmission rate continues to increase, most of which remain above 0.95. This method has strong anti-interference ability for the rapid data transmission of vehicle-road cooperative communication, with lower communication bit error rate, less end-to-end time delay, and higher stability and accuracy of data transmission.