Grid Cloud Research Articles

Holographic voice-interactive system with Taylor Rayleigh-Sommerfeld based point cloud gridding

In recent years, intelligent voice interaction technology and holographic display technology have garnered significant attention. We propose a holographic voice-interactive system. The holographic voice interaction system can provide users with a virtual assistant proficient in accurately handling user voice queries, facilitating smoother interactions. Additionally, users can interact with virtual objects via holographic projection, enhancing realism and engagement. In this research, we introduce the U-blocks and U-net based reverse attention residual algorithm to generate more effective and accurate point cloud datasets during the acquisition phase. Furthermore, we integrate the ChatGLM pre-trained language model as the core module for voice interaction in the holographic display system, covering aspects such as text interaction and output control. Additionally, we propose a Taylor Rayleigh-Sommerfeld point cloud gridding method aimed at improving the computational speed of hologram generation. We validate the feasibility of our proposed methods through numerical simulations and optical experiments.

Virtual trial assembly of large steel members with bolted connections based on multiscale point cloud fusion

AbstractVirtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time‐consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and hand‐held scanner (HHS). This approach considers both the global data and the local details of the steel members. Additionally, a precise registration method based on magnetic 3D targets is proposed for multiscale point clouds, which enables the registration accuracy of multisource point clouds to reach submillimeter precision. Subsequently, a novel algorithm for feature point screening is introduced, which utilizes a dichotomous point cloud grid approach to identify and extract a significant quantity of bolt holes effectively. This approach enables fully automated and fast extraction of the point cloud on the cylindrical inner surface of the holes. Finally, the bounding box and Procrustes analysis approach are employed to perform VTA using the point cloud of the cylindrical bolt holes as the assembled features. The accuracy and feasibility of the above method are verified by a down‐scale modeling experiment and project test, which provide technical support for the VTA of large steel truss structures.

Point cloud holographic encryption display system involving 3D face recognition and air-writing.

In this study, we proposed a holographic identity verification encryption system that integrates face recognition, air-writing, and the multiple point cloud gridding encryption (M-PCGE) method to ensure multi-level security for objects. The experimental results show that the M-PCGE algorithm proposed in this paper achieves image encryption and decryption quickly with a high degree of restoration, and the security is verified.

Spatial-Temporal resolution implementation of cloud-aerosols data through satellite cross-correlation

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard Terra and Aqua satellites provides measurements of several atmospheric parameters.This paper focuses on the cloud fraction data representing the number of cloudy pixels divided by the total number of pixels, and available through 1° x 1° grids spatial resolution with daily or monthly temporal resolution. The aim of the study is to propose a novel method called The Spatial-Temporal Implementation Algorithm (STIA) for analysing satellite daily 1° x 1°grid cloud fraction average values for•Comparing two datasets retrieved by MODIS aboard Aqua and Terra satellites to obtain information on the cloud formation in the afternoon and morning, respectively, thus enhancing the temporal resolution.•Comparing the actual parameter with others retrieved by instruments aboard of different satellites characterized by a better resolution. As an example of STIA application, this study uses the Aerosol Optical Depth (AOD) collected by the Ozone Monitoring Instrument (OMI) on board of Aura satellite for comparison with MODIS instrument to achieve and enhanced spatial resolution of the grid-cell.

Surface unequal charging effect of GEO satellite

When a satellite is in orbit, the plasma environment interacts with the surface material, and the surface charge accumulation of different structures is inconsistent,resulting in an unequal charging effect. In serious cases, this leads to electrostatic discharge and affects the normal operation of the satellite. In this study, a three-dimensional model of a geosynchronous orbit (GEO) satellite is constructed, and the particle cloud grid algorithm (Particle-In-Cell,PIC) was used to simulate the charging process and the unequal charged state between the satellite panels in the solar wind plasma environment and the hot plasma environment. The simulation results show that in the solar wind plasma environment, the charging balance potential of solar panels is low and the charging difference between panels is small, which does not affect the normal operation of the satellite. However, when the satellite is in an hot plasma environment, if some parts of the satellite suddenly encounter extremely strong interference, the charging balance of the satellite in the solar wind plasma will be destroyed, and there will be a large potential difference between the irradiated part and the adjacent structure, which can easily cause electrostatic discharge to harm the normal operation of the satellite.

Unmanned aircraft path planning for construction safety inspections

With its flexible and convenient features, the Unmanned Aircraft System (UAS) provides an efficient platform for safety inspections in hazardous areas. Yet sparse studies have explored path planning for construction safety inspections. This research developed a multi-precision unmanned aircraft path planning system (MP-UAS) by using MATLAB for safety inspections on partially known 3D exterior construction environments. Firstly, a detailed house-building checklist and monitoring levels are formulated. Subsequently, based on the principle of merging pixel points in four neighborhoods, the Four-neighborhood Growing algorithm extracts information from the point cloud grid model to divide inspection areas. Finally, the Ant Colony Optimization (ACO) combined with four-element optimization achieves a full coverage optimal design of UAS path in two-dimensional planes above the inspection area. The system greatly reduces the path length and minimizes energy consumption while meeting requirements of coverage accuracy in the validation case, and is expected to facilitate safety inspection and decision-making.

Experiment and Application of NATM Tunnel Deformation Monitoring Based on 3D Laser Scanning

In recent years, 3D laser scanning technology has been applied to tunnel engineering. Although more intelligent than traditional measurement technology, it is still challenging to estimate the real-time deformation of NATM tunnel excavation from laser detection and ranging point clouds. To further improve the measurement accuracy of 3D laser scanning technology in the tunnel construction process, this paper proposes an improved Kriging filtering algorithm. Considering the spatial correlation of the described object, the optimization method of point cloud grid filtering is studied. By analyzing the full-space deformation field of the tunnel lining, the deformation information of the measuring points on the surface of the tunnel lining is extracted. Based on the actual project, through the on-site monitoring comparison test, the three-dimensional laser point cloud data are grid processed and analyzed, and the deformation data obtained from the test are compared with the data measured by traditional methods. The experimental results show that the Kriging filtering algorithm can not only efficiently identify and extract the tunnel profile visualization data but also efficiently and accurately obtain the tunnel deformation. The measurement results obtained by using the proposed technology are in good agreement with those obtained by using traditional monitoring methods. Therefore, tunnel deformation monitoring based on 3D laser scanning technology can better reflect the evolution of the tunnel full-space deformation field under certain environmental conditions and can provide an effective safety warning for tunnel construction.

A Full-Color Holographic System Based on Taylor Rayleigh–Sommerfeld Diffraction Point Cloud Grid Algorithm

Real objects-based full-color holographic display systems usually collect data with a depth camera and then modulate the input light source to reconstruct the color three-dimensional scene of the real object. However, at present, the main problems of the real-time high quality full-color 3D display are slow speed, low reconstruction quality, and high consumption of hardware resources caused by excessive computing. Based on the hybrid Taylor Rayleigh–Sommerfeld diffraction algorithm and previous studies on full-color holographic systems, our paper proposes Taylor Rayleigh–Sommerfeld diffraction point cloud grid algorithm (TR-PCG), which is to perform Taylor expansion on the radial value of Rayleigh–Sommerfeld diffraction in the hologram generation stage and modify the data type to effectively accelerate the calculation speed and ensure the reconstruction quality. Compared with the wave-front recording plane, traditional point cloud gridding (PCG), C-PCG, and Rayleigh–Sommerfeld PCG without Taylor expansion, the computational complexity is significantly reduced. We demonstrate the feasibility of the proposed method through experiments.

Full-color holographic system featuring three-dimensional salient object detection based on a U2-RAS network.

In this study, a 3D salient object detection model is built at the acquisition step in the full-color holographic system, and a deep network architecture U 2-reverse attention and residual learning (RAS) algorithm is proposed for salient object detection to obtain more efficient and accurate point cloud information. In addition, we also use the point cloud gridding method to improve the hologram generation speed. Compared with the traditional region of interest method, RAS algorithm, and U 2-Net algorithm, the computational complexity is significantly reduced. Finally, the feasibility of this method is proved by experiments.

Implementation of a full-color holographic system using RGB-D salient object detection and divided point cloud gridding.

At present, a real objects-based full-color holographic system usually uses a digital single-lens reflex (DSLR) camera array or depth camera to collect data. It then relies on a spatial light modulator to modulate the input light source for the reconstruction of the 3-D scene of the real objects. However, the main challenges the high-quality holographic 3-D display faced were the limitation of generation speed and the low accuracy of the computer-generated holograms. This research generates more effective and accurate point cloud data by developing an RGB-D salient object detection model in the acquisition unit. In addition, a divided point cloud gridding method is proposed to enhance the computing speed of hologram generation. In the RGB channels, we categorized each object point into depth grids with identical depth values. The depth girds are divided into M × N parts, and only the effective parts will be calculated. Compared with traditional methods, the calculation time is dramatically reduced. The feasibility of our proposed approach is established through experiments.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically, smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks. These vulnerabilities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems. Thus, for this purpose, Intrusion detection system (IDS) plays a pivotal part in offering a reliable and secured range of services in the smart grid framework. Several existing approaches are there to detect the intrusions in smart grid framework, however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources. So as to overcome these limitations, the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anomalies in the smart grid network. In the grid side data acquisition, the power transmitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines. In this approach, power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC (Unified Power Quality Controller) and thereby storing the data in cloud storage. The data from smart grid cloud storage and KDD99 are pre-processed and are optimized using Improved Aquila Swarm Optimization (IASO) to extract optimal features. The probabilistic Recurrent Neural Network (PRNN) classifier is then employed for the prediction and classification of intrusions. At last, the performance is estimated and the outcomes are projected in terms of grid voltage, grid current, Total Harmonic Distortion (THD), voltage sag/swell, accuracy, precision, recall, F-score, false acceptance rate (FAR), and detection rate of the classifier. The analysis is compared with existing techniques to validate the proposed model efficiency.

Dynamic Trust Continuous Evaluation-based Zero-Trust Access Control for Power Grid Cloud Service

The development of power grid cloud service puts forward higher requirements for remote secure access. To realize fine-grained access control of remote secure access of power grid cloud service, this paper proposes a zero-trust access control model for power grid cloud service based on dynamic trust continuous evaluation. The experimental results show that the model proposed in this paper can effectively implement continuous trust evaluation for remote access of PowerCloud services, and achieve zero trust access control, with an exception detection accuracy rate of 97.2% and the incremental learning accuracy rate of over 97% when the behavior model of remote access changes.

Internet of Vehicles Information Processing Method with Vehicle-Mounted Cloud Grid as the Underlying Data Fusion Structure

With the development of electronic information network technology, large car networking systems can produce all kinds of data such as text, images, and videos, a large number of heterogeneous data, different features of heterogeneous data, and different data structures. In the Internet of vehicles, the beacon message generation strategy needs to be researched and designed on the premise of meeting the requirements of vehicle location accuracy and wireless communication performance. According to the Kalman filter differential prediction equation, the message generation model of Kalman filter beacon is established. In the deep learning research on the underlying data fusion algorithm, the most effective way to solve the problem of insufficient integration degree between the underlying data is to improve the data quality and ensure data sharing and reuse between multisource heterogeneous data. Therefore, the D-S evidence theory fusion model and rough set underlying model are proposed in the vehicle-mounted cloud network. Among them, the D-S evidence theory fusion model ensures the improvement of underlying data quality, forms effective rule combination, and reduces conflicts between rules through filtering evidence theory. The rough set underlying data fusion model optimizes the underlying data of each device by improving the rough set attribute reduction method of particle swarm optimization algorithm.

Hybrid Big Bang-Big Crunch based resource scheduling to improve QoS in cloud infrastructure

The service-oriented computing paradigm changes the way of computing. Emerging technologies like grid computing, cloud computing, and smart health care application have changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside the cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. E-Health care services are one of the most computational intensive services in the cloud, they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This manuscript focuses on the task scheduling technique which enhances the performance in real-time with the least execution time, network cost, and execution cost. The presented model is inspired by Big Bang-Big Crunch algorithm in astronomy. The presented algorithm enhances the quality of service by reducing the scheduling delay, network delay with the least resource cost to complete the task in the least cost to the user with high quality of service.

Assessing the Traffic Noise Reduction Effect of Roadside Green Space Using LiDAR Point Cloud Data in Shenzhen, China

The characteristics of vegetation in urban road side green spaces affect their noise reduction capacity. How to objectively, extensively, and accurately evaluate the noise reduction effect of these complex structures is challenging. In this study, we take urban roadside green space quadrats as the research object, use knapsack LiDAR to collect point cloud data of vegetation in the quadrats, and then construct and extract factor indices that can reflect the different vegetation characteristics based on LiDAR point cloud data with LiDAR360 software. We then combine the actual collected and calculate attenuation of traffic noise using correlation analysis and ordinary least square regression analysis to clarify the characteristic factors and correlation of noise attenuation in order to explore the influence of vegetation characteristics on the effect of noise reduction. The results show that a variety of factors affect the noise reduction effect of complex vegetation structures, and the importance degree is the following: horizontal occlusion degree > width > percentage of point cloud grid > leaf area index > coverage degree. By comparing the vegetation characteristic factors at different heights, we found that coverage degree, leaf area index, horizontal occlusion degree, and the percentage of the point cloud grid have the most significant positive correlation with the actual attenuation at a height of 5 m, but the coverage degree and leaf area index at absolute height have no correlation with the actual attenuation. The amount of vegetation near the road has a greater effect on noise reduction than that on the far side. The actual noise attenuation and the vegetation characteristic factors of green space have a non-linear relationship, and the interaction has a comprehensive influence on the noise reduction effect. These findings can provide a scientific basis for the reduction of traffic noise through the structural optimization of urban green space.

Segmented Point Cloud Gridding Method for a Full-Color Holographic System With Real Objects

The large amount of computing data from hologram calculations incurs a heavy computational load for realistic full-color holographic displays. In this research, we propose a segmented point-cloud gridding (S-PCG) method to enhance the computing ability of a full-color holographic system. A depth camera is used to collect the color and depth information from actual scenes, which are then reconstructed into the point-cloud model. Object points are categorized into depth grids with identical depth values in the red, green, and blue (RGB) channels. In each channel, the depth grids are segmented into M×N parts, and only the effective area of the depth grids will be calculated. Computer-generated holograms (CGHs) are generated from efficient depth grids by using a fast Fourier transform (FFT). Compared to the wavefront recording plane (WRP) and traditional PCG methods, the computational complexity is dramatically reduced. The feasibility of the S-PCG approach is established through numerical simulations and optical reconstructions.

A renewable energy-aware power allocation for cloud data centers: A game theory approach

With the rapid emerging of Internet of Things (IoT) devices and the proliferation of cloud-based applications, the cloud computing industry is becoming a vital element for ensuring our daily services. However, cloud computing uses large scale data centers equipped with energy-hungry servers and huge power facilities that massively consume power. This presents a real challenge which can negatively influence the power grid, while exposing the environment to global warming issues. Therefore, minimizing cloud data center power consumption is a challenging problem and has to be addressed. In this paper, we look at renewable energy in the context of a smart grid–cloud architecture and investigate the issue of grid power dispatching to cloud data centers. Since cloud data centers have a non-cooperative nature regarding power demand from the power stations, we model our power allocation problem as a non-cooperative game. Afterwards, we prove the existence and the uniqueness of Nash equilibrium. Moreover, we formulate the payoff function of our game as a non-linear optimization problem before resolving it using Lagrange multipliers and Karush–Kuhn–Tucker (KKT) conditions. Thus, we determine the assigned optimal quantity to each data center based on three main criteria : renewable energy usage, number of critical running applications and workload charge. Extensive simulations are performed by comparing our scheme with an existing work. Results show that our scheme outperforms the comparing approach with a percentage of 31.2% in terms of power load rate and significantly reduces emissions of carbon dioxide.

Power Grid Cloud Resource Status Data Compression Method Based on Deep-Learning

Background: As the "three-type two-net, world-class" strategy proposed, the number of cloud resources in power grid continues to grow, there is a large amount of data to be filed every day. Which are key issues to be addressed, the long-term preservation of data, using back-up data for the operation and maintenance, fault recovery, fault drill and tracking of cloud platform are essential. The traditional compression algorithm faces severe challenges. Method: In this case, this paper proposes the deep-learning method for data compression. First, a more accurate and complete grid cloud resource status data is gathered through data cleaning, correction, and standardization. The preprocessed data is then compressed by SaDE-MSAE. Result: Experiments show that the SaDE-MSAE method can compress data faster. The data compression ratio based on neural network is basically between 45% and 60%, which is relatively stable and stronger than the traditional compression algorithm. Conclusion:: The paper can complete the compressed data quickly and efficiently in a large amount of power data. Improve the speed and accuracy of the algorithm while ensuring that the data is correct and complete, and improve the compression time and efficiency through the neural network. It gives better compression schemes cloud resource data grid.

Automatic classification model of semi-structured HTML text data based on State Grid cloud architecture

Regarding the construction of the “State Grid Cloud” platform, various businesses of the power grid have their own Wed systems. The data between different websites is scattered and the coupling between resources is low, which can easily form the problem of information islands. This paper is oriented to the semi-structured HTML text data in web pages under the State Grid cloud architecture platform, and uses the Python-based Scrapy framework to collect semi-structured power data information from various power business websites. We propose a semi-structured text data classification model based on BIGRU neural network and Bayesian classifier. BIGRU neural network is used to extract text features, Bayesian classifier is used for classification, and the TF-TDF algorithm is used to assign weights to improve the traditional recurrent neural network model with many parameters and long training model time. We use this method to simulate the semi-structured HTML text data of the State Grid, and conduct a comparative experiment with the traditional neural network model. The experimental results show that the classification algorithm can effectively improve the efficiency and accuracy of power semi-structured text data classification.

Study of Cloud Computing Business Framework by Pre-cloud recognition

The current study strongly analyzes the factors that determine the acceptance of cloud companies (SASS model), and this strategy to do their job. The research model is designed to explore factors that affect the use of computers, including host technology (TAM) models and other external such as organizational size and technical complexity. Data compiled from 200 companies are used to test the ideas. The results of this study show what important factors need to be considered and how they relate to each other. They highlight the needs of companies, especially those related to cloud operations, for those who want to use a real digital governance model. These technologies and technology-related applications that use virtual technology and operate in a distributed network that is accessible to sites and social networking sites. Developed and upgraded to cloud computing systems, cloud grid and computers.