Data Collection Scheme Research Articles

Research on Real Time Data Monitoring Method for Intelligent Factory Equipment Based on Ethernet Communication

<p>This article focuses on the production and processing of sleeve parts, using Ethernet as a data transmission medium to complete data collection and monitoring of typical intelligent factory processing workshops. Firstly, the layout design of the intelligent factory equipment layer, communication layer, and system application layer was completed. Then, unified management of standard and non-standard protocols was implemented for communication between different devices. Then, based on the sensors and sensor data used in each production process, a data collection scheme was designed, and finally, the data collection system was designed, Real time monitoring of data between various devices in the system can guide enterprise production.</p> <p> </p>

An AUV-Assisted Data Collection Scheme for UWSNs Based on Reinforcement Learning Under the Influence of Ocean Current

An AUV-Assisted Data Collection Scheme for UWSNs Based on Reinforcement Learning Under the Influence of Ocean Current

Multi-AUV Collaborative Data Collection Scheme to Clustering and Routing for Heterogeneous UWSNs

Multi-AUV Collaborative Data Collection Scheme to Clustering and Routing for Heterogeneous UWSNs

MLM-WR: A Swarm-Intelligence-Based Cloud–Edge–Terminal Collaboration Data Collection Scheme in the Era of AIoT

MLM-WR: A Swarm-Intelligence-Based Cloud–Edge–Terminal Collaboration Data Collection Scheme in the Era of AIoT

A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics

Abstract Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still in progress in the grand scheme of things, yet for the most part, such attempts have been centered around the models themselves, and their internal computational architecture, that is, the model-centric approach. These endeavors can certainly pave the way for more tailor-fitted models capable of producing accurate results. However, such a perspective often neglects a fundamental assumption of these models, which is the importance of reliability, correctness, and accessibility of the data used in constructing them. This challenge arises from the prevalent model-centric paradigm of thinking in the field. An alternative approach, however, would prioritize placing data at the focal point, focusing on systematically enhancing current datasets and devising frameworks to improve data collection schemes. This suggests a paradigm shift toward more data-centric thinking in water and environmental engineering. Practically, this shift is not without challenges and necessitates smarter data collection rather than an excessive one. Equally important is the ethical and accurate collection of data, making it available to everyone while safeguarding the rights of individuals and other legal entities involved in the process.

Collaborative relay for achieving long-term and low-AoI data collection in UAV-aided IoT systems

In Internet of Things (IoT) systems, sensor nodes are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of unmanned aerial vehicles (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy replenishment. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, we develop a UAV-aided IoT collaborative data collection mechanism and propose a matching games-based data collection (MGDC) scheme. In this scheme, we begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, we divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, we establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, we employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, we incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, we confirm the significant enhancement provided by our proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.

Data harmonization across eight publicly available longitudinal studies of aging, neurodegeneration and dementia

AbstractBackgroundLarge‐scale multi‐study analyses are required to ensure reproducibility, reliability and generalizability in mapping neurodegeneration and risk for ADRD. However, the heterogeneity in data collection paradigms can complicate and confound data pooling; data harmonization is essential. Longitudinal studies add to the complexity of harmonization as a variety of follow‐up time points and time encoding schemes are used. Here, we pool data from 8 publicly available longitudinal neuroimaging studies on neurodegeneration and dementia to highlight differences in: 1) diagnostic categorization of controls, and people with mild cognitive impairment, and dementia; 2) the extent of follow‐up visits across neuroimaging and clinical assessments; and 3) encodings of various meta‐data elements including scanner manufacturer, sex, and handedness. To allow a systematic approach to multi‐study dementia research, we propose an initial ontological framework for longitudinal data archival and retrieval, capable of capturing similar data elements within given themes while ensuring that unique differences across studies are retained.MethodAIBL, ADNI‐1, ADNI2/GO, ADNI‐3, OASIS‐2, OASIS‐3, PREVENT‐AD, and MIRIAD, were accessed. Study designs, inclusion and exclusion criteria, and downloaded data elements were used to describe diagnostic criteria, imaging data, demographic features, and longitudinal data collection schemes. We create common terms to map data elements across studies, and a consistent naming scheme for longitudinal data.ResultFigure 1 shows the breakdown of diagnostic labels per cohort, and highlights how different cognitive labels may be assigned to people with the same performance scores, for example, on the Mini Mental State Examination (MMSE). Figure 2 highlights the differences in longitudinal data collection schemes across cohorts, and in labels of commonly collected elements. Using common terms provided an easy way to simultaneously query data across all studies, while retaining a map back to the original terms. Figure 3 showcases our proposed naming scheme for capturing longitudinal data elements using a relational ontological framework. This framework successfully harmonized data across study demographics, timepoints, and imaging properties.ConclusionMulti‐study data analyses are becoming common practice thanks to large scale efforts such as ENIGMA and GAAIN. Harmonization of meta‐data across studies will allow for efficient pooling of data for tracking disease progression and related risk over time.

An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection.

Predictions for sound events and soundscape impressions from environmental sound using deep neural networks

In this study, we investigate methods for quantifying soundscape impressions, meaning pleasantness and eventfulness, from environmental sounds. From a point of view of Machine Learning (ML) research areas, acoustic scene classification (ASC) tasks and sound event classification (SEC) tasks are intensively studied and their results are helpful to consider soundscape impressions. In general, while most of ASCs and SECs use only sound for the classifications, a soundscape impression should not be perceived just from a sound but perceived from a sound with a landscape by human beings. Therefore, to establish automatic quantification of soundscape impressions, it should use other information such as landscape in addition to sound. First, we tackle to predict of two soundscape impressions using sound data collected by the cloud sensing method. For this purpose, we have proposed prediction method of soundscape impressions using environmental sounds and aerial photographs. Second, we also tackle to establish environmental sound classification using feature extractor trained by Variational Autoencoder (VAE). The feature extractor by VAE can be trained as unsupervised learning, therefore, it could be promising approaches for the growth dataset like as our cloud-sensing data collection schemes. Finally, we discuss about an integration for these methods.

APLDP: Adaptive personalized local differential privacy data collection in mobile crowdsensing

Local differential privacy (LDP) enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In the majority of current works, they assumed that the privacy preservation parameter is totally determined by the data collector and then dispatched to all participants in mobile crowdsensing. However, in the real world, due to different privacy preferences of participants, it is inelegant and unpromising for all participants to accept the same privacy preservation level during data collection. To address such issue, an adaptive personalized local differential privacy (APLDP) data collection scheme is proposed to realize personalized privacy preservation while achieving higher data utility, in which two different LDP perturbation methods (basic RAPPOR and k-RR) are adaptively chosen by the participants according to their different privacy preferences, as well as the best perturbation probability is adaptively adopted by the participants to perturb their private data. In such case, the adaptive boundary based on the minimum mean square error (MSE) is theoretically derived to allow the participant to adaptively choose the best perturbation method, and meanwhile, it allows the participant to adaptively choose the best perturbation probability. Then, two estimation mergence methods named the direct combination (DC) and the weighted combination (WC) are demonstrated to do efficient data aggregation. Experiments on both synthetic and real data sets show that the proposed APLDP scheme performs better than previous non-personalized proposals in terms of the MSE and the average error rate (AER), especially using WC estimation method.

Research on the Comprehensive Evaluation Method of Driving Behavior of Mining Truck Drivers in an Open-Pit Mine

Trucking is an important production link in most open-pit mines, and its transportation cost accounts for more than 50% of the total production cost of open-pit mines. The quality of the driver’s driving behavior plays a crucial role in the fine control of the production cost of transportation. Different from the previous evaluation studies of drivers’ driving behavior in open-pit mines, which mainly took safety driving behavior index as a factor variable, this paper puts forward a comprehensive evaluation method of driving behavior of mining truck drivers, which takes both safety driving and transportation cost as factor variables. Taking the mining truck as the research object, firstly, a scientific and reasonable data collection scheme is established, and the data information characterizing the transport state of the mining truck is obtained through data collection and analysis. Secondly, the RKNN algorithm of time series prediction and the wavelet analysis method are used to achieve noise reduction and missing processing of the original data so as to obtain accurate sample data. Then, taking the principal component analysis method as the entry point, through constructing the principal component analysis theory model, the key index system representing safe driving behavior and transportation cost is established to realize the comprehensive evaluation of the driving behavior of mining truck drivers, and the evaluation system of “standard driving”, “prudent driving” and “aggressive driving” of mining truck drivers is formulated. The results show that after noise reduction, the accuracy of mining car operation data can be improved by 7~12%, and the transportation cost can be reduced by about 5% after the driver’s operation behavior is standardized.

An improved k-NN anomaly detection framework based on locality sensitive hashing for edge computing environment

Large deployment of wireless sensor networks in various fields bring great benefits. With the increasing volume of sensor data, traditional data collection and processing schemes gradually become unable to meet the requirements in actual scenarios. As data quality is vital to data mining and value extraction, this paper presents a distributed anomaly detection framework which combines cloud computing and edge computing. The framework consists of three major components: k-nearest neighbors, locality sensitive hashing, and cosine similarity. The traditional k-nearest neighbors algorithm is improved by locality sensitive hashing in terms of computation cost and processing time. An initial anomaly detection result is given by the combination of k-nearest neighbors and locality sensitive hashing. To further improve the accuracy of anomaly detection, a second test for anomaly is provided based on cosine similarity. Extensive experiments are conducted to evaluate the performance of our proposal. Six popular methods are used for comparison. Experimental results show that our model has advantages in the aspects of accuracy, delay, and energy consumption.

A novel approach for pavement distress detection and quantification using RGB-D camera and deep learning algorithm

This study presents an innovative and cost-effective approach for pavement distress detection and quantification using an RGB-D camera in conjunction with an instance segmentation algorithm. The proposed method employs the instance segmentation algorithm to acquire 2D coordinate information pertaining to the affected pixels and integrates the internal reference matrix and depth data from the depth camera to transform the 2D images of the distressed areas into 3D point cloud data, consequently enabling distress quantification. To assess the dependability of the proposed approach, high-density polystyrene foam was used to simulate potholes and cracks of the pavement. Data collection was conducted under varying experimental conditions to identify the optimal data collection scheme. Using the approach, potholes and cracks of varying severity were collected from both asphalt and cement concrete pavements. The outcomes of the study demonstrate that the proposed methodology is capable of accurately detecting and quantifying potholes on the pavement. Furthermore, the errors associated with both the calculated area and volume exhibit a gradual decrease with an increase in the severity of pavement distress. However, for cracks, the method yields a larger error in results, primarily due to the instance segmentation algorithm's imprecise segmentation of crack edge pixels, despite superior restoration of the planar contour of cracks.

Research on Data Collection Methods for Assembly Performance of Array Antennas in Digital Twin Workshops

Aimed at the characteristics of multi-source heterogeneity and the rapid generation of data in digital twin workshops, as well as the current situation where communication protocols between equipment within the workshop vary greatly and are difficult to interconnect, a data integration method based on OPC UA is designed. Firstly, combining the process flow and data source characteristics of array antenna assembly, a data collection and transmission scheme based on OPC UA was designed. Secondly, a process information model of array antenna assembly was established to realize data perception and transmission and solve the difficulties of complex data structure, high real-time requirements, and heterogeneous data in digital twin workshop. Finally, the proposed method and model were applied to the performance prediction platform for an array antenna assembly process based on digital twins, achieving perception of process data during the assembly process of array antennas, and achieving performance prediction and visualization for various stages of array antennas based on assembly process data.

CITE: A content based trust evaluation scheme for data collection with Internet of Everything

Sensor–cloud system, as one of the vital components of the Internet of Everything, has increasingly gained popularity since it bridges the physical and cyber worlds. In the smart city, sensing devices are deployed to monitor the environment and the data aggregate at the roadside Aggregation Devices (ADs). Then, Mobile Vehicles (MVs) submit the data to the data center in an opportunistic routing way when passing ADs. We propose a Content based Intelligent Trust Evaluation (CITE) scheme for trustworthy data collection over large-scale sensor-cloud systems. In CITE scheme, unmanned Mobile Electric Vehicles (MEVs) are employed to visit some sensing devices and collect their data along the designed routing path. Then, a delicate comparison algorithm is proposed to determine the credibility of devices' data operations, and trust evaluation is based on it. Similarly, the trust of MVs is obtained by comparing the data reported from ADs with the data submitted to the data center. Moreover, to reduce the cost of MEVs, we also design a path planning algorithm. The simulation results show that the CITE scheme can identify reliable sensing devices and MVs with a rate of 87.3% and 100% and increase the collection rate by 25.2% on average compared with the previous strategies.

Charging of Vitreous Samples in Cryogenic Electron Microscopy Mitigated by Graphene.

Cryogenic electron microscopy can provide high-resolution reconstructions of macromolecules embedded in a thin layer of ice from which atomic models can be built de novo. However, the interaction between the ionizing electron beam and the sample results in beam-induced motion and image distortion, which limit the attainable resolutions. Sample charging is one contributing factor of beam-induced motions and image distortions, which is normally alleviated by including part of the supporting conducting film within the beam-exposed region. However, routine data collection schemes avoid strategies whereby the beam is not in contact with the supporting film, whose rationale is not fully understood. Here we characterize electrostatic charging of vitreous samples, both in imaging and in diffraction mode. We mitigate sample charging by depositing a single layer of conductive graphene on top of regular EM grids. We obtained high-resolution single-particle analysis (SPA) reconstructions at 2 Å when the electron beam only irradiates the middle of the hole on graphene-coated grids, using data collection schemes that previously failed to produce sub 3 Å reconstructions without the graphene layer. We also observe that the SPA data obtained with the graphene-coated grids exhibit a higher b factor and reduced particle movement compared to data obtained without the graphene layer. This mitigation of charging could have broad implications for various EM techniques, including SPA and cryotomography, and for the study of radiation damage and the development of future sample carriers. Furthermore, it may facilitate the exploration of more dose-efficient, scanning transmission EM based SPA techniques.

Investigation on the Height of Fracture Zone in Goaf of Steep Coal Seam Based on Microseismic Monitoring

The height of the fracture zone in the mined-out area is the basic parameter for controlling mine strata, preventing and controlling mine water, and arranging drilling holes for high-level gas extraction. Aiming at the development law of fracture zone in the steep seam and the limitation of traditional drilling inspection method, microseismic monitoring technology is used to investigate the height of fracture zone in goaf. Taking the 3103 south working face of a mine in Sichuan as an example, the scheme of microseismic monitoring and data collection of the working face is worked out. Through data analysis and processing, the range of the fracture zone in the goaf is 6.8 m– 34.2 m, and compared with the numerical simulation analysis results, the relevant results are basically consistent. It provides a new technical means for the investigation of “three zones” of overlying strata in the goaf of a steep seam.

Integration of tuna fishery data collection in the Southern Waters of Java (FMA-573)

Fishing operational data is the key to making fisheries management policies. Without this data, policymakers could only speculate on current field conditions; hence their decisions are not solutions to issues and problems in fisheries management and are even difficult to implement. Currently, the government encourages data collection on fishing operations through three schemes, namely data collection on the results of fish unloading at fishing ports, independent data reported by fishermen (fishing logbook), and data on monitoring results on fishing vessels by observers on board. Integrating such data is one solution to obtain sufficient operational data for fishing in quantity and quality. This study uses data on tuna catches from the three data collection schemes to tuna longline in the Southern Waters of Java, FMA 573. This study aims to analyze the differences between the established fisheries data collection standards with the existing conditions and the parties’ involvement in data collection activities. The analysis results show differences in data collection activities in existing conditions with the existing standards, affecting the completeness and quality of the resulting data. The integration of the three data is complementary and significantly impacts the quality of fishery data produced.

VC-DCPS: Verifiable Cross-Domain Data Collection and Privacy-Persevering Sharing Scheme Based on Lattice in Blockchain-Enhanced Smart Grids

With the advent of the era of Industry 4.0, blockchain-enhanced Smart Grids have received widespread attention from academics and industry personnel, with its great advantages in high transparency, flexibility and decentralization. However, there are still problems of privacy leakage and inefficiency. In this paper, we propose a cross-domain verifiable data collection and privacy-persevering sharing scheme based on lattice in blockchain-enhanced Smart Grids, called VC-DCPS. Firstly, lattice-based homomorphic encryption and spatial-temporal aggregation technology are utilized to enable cross-domain collection of data, dynamic addition and deletion of users and improvement of efficiency. Secondly, a post-verification method is designed to verify whether the outsourcing cloud executes predetermined operations correctly. Using consensus and incentive mechanisms via smart contract will enable flexible access control and efficient transactions on the blockchain. In addition, formal security analysis shows that the proposed scheme can ensure privacy and enhance security. Finally, performance evaluation and experimental comparison are performed and turn out that the proposed scheme is strong security, high efficiency and low storage overhead.

إدارة الصف في تعليم اللغة العربية في المدرسة الثانوية الإسلامية الحكومية باسوروان

Classroom management is a skill that must be possessed by every teacher, because in class management there are positive interactions that must be built by teachers and students during the learning process, therefore class management is a very important one that must be mastered by teachers. Classroom management activities for learning Arabic intend to create a stable and comfortable atmosphere and class conditions so that the teaching and learning process can take place effectively and efficiently. The purpose of this research is to reveal and analyze the classroom management in learning Arabic in the MAN Kota Pasuruan both in face-to-face and online learning activities. This research uses descriptive qualitative research. The data collection techniques used were interviews and observations. Data analysis was carried out through data collection schemes, condensation, display and verification as well as conclusions. The results of this study are that the implementation of learning is carried out in the classroom and outside the classroom, the class conditions allow for the learning process, learning is carried out using the 2013 curriculum. And also online learning activities using e-learning, WhatsApp groups, Youtube, and Zoom Meating. The implementation of learning in the classroom is carried out by instilling class discipline and providing rules that invite students to learn actively and the learning process is fun in the form of changing sitting positions with a specific purpose, while learning outside the classroom is carried out by habituating language practices and games.