Using PID-BP Digital Virtual Reality for Non-Heritage Protection: Recognition and Assessment

This paper studies the digital protection and dissemination of digital information based on intangible cultural heritage. The “digital era” is an important milestone, and there is no problem with the development of martial arts heritage. First, this paper explains that the development of the current martial arts heritage is not without drawbacks, for example, ecological marginalization and imperfect development. Then, the author of this study determined the benefits of combining incomplete martial arts technology with digital technology, and took the digital protection of Xingyi boxing as an example to explore its practice and specialize in digital protection. Then, the problems and procedures in digital protection are studied. Finally, with Xingyi boxing as an experiment, this form leads to separation. The experimental results show that, according to the full understanding of the status quo of nondigital defense culture, the protection measures to improve digital protection are put forward: determining the digital distribution process and writing models, completing knowledge presentation view, document creation, multimedia interactive platform development, and digital technology protection of public heritage, without any problems.

Objectives: The study aims to identify the challenges facing the Saudi family in achieving the values of digital citizenship, which are represented by the values of digital respect, digital knowledge, and digital protection. This study focuses on the challenges faced by families in attaining these digital citizenship values, with the hope that specialists, experts, practitioners, and individuals interested in family issues will find guidance to develop plans and proposals for overcoming these challenges.
 Methods: The social survey method was used with a simple random sample, reaching 335 female employees at the Imam Mohammad Ibn Saud Islamic University in Riyadh. The data were collected using a questionnaire tool during the period from 10/4/1442 AH to 10/5/1442 AH.
 Results: Several findings have been reached, the most important of which is that the Saudi family faces many challenges in achieving the values of digital citizenship, which are represented by digital respect, digital knowledge, and digital protection, all of which have a moderate degree.
 Conclusions: The study reveals several recommendations, the most important of which is the development of an integrated national plan to provide community members with the necessary skills to achieve the ideals of digital citizenship.

Architectural heritage is a lively carrier of historical development, as well as providing a valuable resource for human society. Recently, however, the longevity of architectural heritage has been influenced and even threatened by natural and human factors. Furthermore, the future remains uncertain. Therefore, this study aimed to develop a comprehensive method, namely, digital protection and utilization based on knowledge visualization (DPUKV), to protect and utilize architectural heritage. The most important problem to solve involves transforming digital protection data into a digital inheritance culture. We constructed a technical framework of knowledge acquisition, knowledge reconstruction, knowledge innovation, and knowledge management using qualitative and vertical methods to form hierarchical digital resources of architectural heritage. In particular, the knowledge reconstruction converted tacit knowledge of architectural heritage into explicit knowledge via visualization technology, providing research materials for more scholars. In knowledge innovation, architectural heritage culture was excavated and inherited through knowledge graphs, digital displays, and cultural creation with virtual reality, augmented reality, etc. Furthermore, this method was validated over four years via architectural heritage in China as case studies. In this paper, the buildings of Qinglian Temple in Shanxi, China, are discussed in detail as a case study. The results indicated that this method was effective for preserving architectural heritage and is suitable for other cultural heritage. It is also helpful to improve the public’s protection of and interest in cultural heritage, especially architectural heritage.

Historical sites are incomplete relics left by human activities and are also valuable resources for human society, with most of them buried deep underground. Because the protection of original historical sites is difficult, very few sites choose this method. Many funerary objects that have been in historical sites are scattered in museums, losing their original context and complicating their utilization. Thus, using digital technology to protect and utilize historical sites and to explore their outstanding value from historical, aesthetic, and anthropological perspectives is a foremost concern. Therefore, this study aims to develop a comprehensive method for the protection and utilization of historical sites, that is, digital protection and utilization based on a digital twin. We constructed a historical site digital twin model using qualitative and vertical methods, including a physical entity, virtual entity, twin data center, digital twin service, and connection. We also established a technical framework of data acquisition and processing, digital protection, and digital utilization, forming a layered management and application of digital resources. In digital protection, information in the real world and the virtual world are connected to monitor risks, collect data, create simulations, and propose protection strategies, quickly and accurately. In digital utilization, the knowledge graph is constructed to associate seemingly unrelated information, explore potential knowledge, and improve information sharing. In addition, the method is validated by means of case studies of historical sites in China. In this paper, the historical sites of the Northern Qi Dynasty in Taiyuan, Shanxi, especially the Xuxianxiu Tomb and Lourui Tomb, are discussed in detail. The results indicate that this method is effective for the protection and utilization of historical sites.

Development of a theory-monitoring integrated structure joints assessment method of prefabricated underground stations

For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness.

To address the issues of low detection accuracy and poor real-time performance in existing methods for detecting minor abnormalities such as cracks, oil leaks, and loose bolts in rotating industrial machinery under dynamic vibration conditions, this paper proposes a lightweight detection system based on YOLOv8 (You Only Look Once version 8) with adaptive feature enhancement. First, this paper employs a temporal motion compensation module based on optical flow to estimate and correct the vibration displacement between adjacent frames. Second, this paper designs a lightweight YOLOv8 network, using depthwise separable convolution instead of traditional convolution. Finally, this paper employs a weighted fusion strategy to improve the accuracy of small object detection in complex backgrounds. This model is deployed on the Jetson AGX Xavier edge computing platform, utilizing FP16 (half-precision floating-point) / INT8 (8-bit integer) quantization and asynchronous pipeline inference to ensure real-time processing capabilities on edge devices. The experimental results show that the method achieves an average detection accuracy of 97.8% (mAP@0.5) and 86.6% (mAP@0.5:0.95), with an average inference speed of 29.5 FPS (frames per second). This demonstrates that the method has reached industrial-grade performance in terms of detection accuracy, real-time performance, and deployment stability, making it highly valuable for practical applications.

Digital Communication is the future of High Voltage (HV) substation automation, protection and control. It not only offer ease of diagnostics but acts fast to isolate healthy feeders from faulty ones. The effect of this digital protection technology is being appreciated by many utilities and industries for the benefit it offers to them and looks to be the future. Some of the utilities are replacing their secondary copper wires with Fibre Optics (FO) and Ethernet wires as they want load the cable trays less and identify the FO better than the secondary copper wires. Conventional relays are being replaced with Intelligent Electronic Devices (IEDs) during asset management replacement program. Switchyard equipment such as instrument transformers filled with oil and SF6 gas are being replaced with Non-Conventional Instrument Transformer (NCITs). Ushering new devices into the network are not without challenges. These devices and peripherals cannot be put into use by the end user without proper assessment and needs to be validated with number of tests for its performances. To get end users acceptance, laboratory and field tests are carried out to study the propagation delays and performance issues as applicable to digital automation and protection scheme. This paper discusses an early detection of the loss of data packets in a star connected Ethernet Substation Automation Systems (SAS). Understanding a real model in a lab environment is an important step towards performance evaluation and identification of issues. With the involvement of multiple manufacturer in the digital substation, it is good to set a trial of the interoperability of equipment involving their products which could address critical protection issues. This paper focuses on a star connected architecture using an Optimized Network Engineering Tool (OPNET) lab-based software simulating the latency and delays of a digital protection scheme in a 132/22-kV zone substation. This paper shares a practical case study of measurement of latencies while exchanging Generic Object-Oriented Substation Event (GOOSE) messages amongst the IED’s passing through peripherals.

In the process of ultra-deepwater drilling, the safe density window is narrow, and there are issues with large temperature differences and alternating high and low temperatures, making wellbore pressure control challenging and highly prone to complex lost circulation incidents. To achieve safe and efficient drilling, accurate identification and timely monitoring of complex lost circulation incidents are essential. To address the current issues of insufficient real-time performance and accuracy in lost circulation monitoring for ultra-deepwater drilling, this paper proposes a multi-parameter lost circulation monitoring method based on support vector machine (SVM). Firstly, a transient hydraulic model for lost circulation in ultra-deepwater drilling was established to capture the variation patterns of wellbore flow parameters during loss incidents, thereby creating a standard model for lost circulation monitoring parameters. Then, by considering the average difference and slope characteristics of the time series of these parameters, an improvement was made to the classic symbolic aggregate approximation (SAX) method. This method was used to perform dimensionality reduction on real-time drilling data, calculating the similarity metric distance between the monitoring parameters and their corresponding standard model, thereby enhancing data processing efficiency. Finally, a multi-parameter lost circulation monitoring model based on Particle Swarm Optimization-Support Vector Machine (PSO-SVM) was constructed. This model uses the similarity metric distances of multiple monitoring parameters to comprehensively assess complex lost circulation conditions, thus improving the accuracy of lost circulation monitoring. A case study was conducted on a lost circulation well in the South China Sea, and the results demonstrated that this method effectively identifies complex lost circulation conditions with good real-time performance and accuracy, providing a new approach for monitoring complex lost circulation conditions in ultra-deepwater drilling.

Detection and recognition of traffic lights is important for intelligent assisted driving. Traditional color space based traffic lights detection algorithms could be easily affected by other objects (such as buildings, car taillights) in the surrounding environment, and the detection accuracy and real-time performance are not ideal enough. Generally, the deep learning based methods have better advantages of real-time and accuracy performance for the normal scene with obvious traffic lights targets. However, the small traffic lights targets detection rate and accuracy in night-time of these methods are still can't be satisfactory. To solve this problem, this paper proposed a novel traffic lights detection and recognition algorithm based on multi-feature fusion, which can be implemented in two steps (detection and recognition). For the first step, the SLIC (simple linear iterative clustering) super-pixel segmentation algorithm is used for purposes reducing the image data processing complexity and improving the real-time performance. The mean-shift algorithm was used to cluster the HSV (Hue, Saturation, Value) color space components respectively for enhancing the target data and reducing the interference from other targets. For the second step, the feature information extracted by CNN (Convolutional Neural Network) and HOG(Histogram of Oriented Gradient) feature are fused. The SVM (Support Vector Machine) classifier is trained on a data set of traffic lights established by our own. To verify the proposed algorithm in this paper, amount of experiments were carried out in real traffic scenes. Experimental results show that this algorithm almost has the same real-time performance with YOLO_V3 neural network and a better accuracy.

With the acceleration of urbanization, many ancient buildings face the inevitable situation of being destroyed or even demolished, making the digital protection of historical buildings more and more practical. At the present stage, the digital protection methods of buildings are mainly three-dimensional modeling and image data recording, whose results and data are archived by local institutions. The inter-regional building materials are poorly correlated and lack systematic management. This research takes the Ancient city of Taizhou in Zhejiang Province as the research object, conducts digital protection and analysis of historical buildings with high protection value, and regulates the protection results in the form of a building space database. This research not only provides information for the restoration of ancient buildings after Taizhou prefectural city, but also has reference significance for the development of its cultural industry. It also provides templates for the protection of other historical areas.

Activity Modeling of Preliminary Additive Manufacturing Process Planning

Digital Protection and Dissemination of Regional Culture Based on Digital Technology

This paper presents an approach to monitor, support and optimize reservoir performance in real time using an Intelligent Field. The approach is demonstrated with a case study based on the experience of implementing Intelligent Field solutions in one of Saudi Aramco's onshore fields. The field is equipped with surface and subsurface instrumentation to capture and deliver real time data. The case study highlights the critical process of utilizing Intelligent Field data to monitor compliance between actual well production/injection rates and the reservoir management guidelines. The case study shows a giant field with under-saturated, heterogeneous and peripheral water-flooded reservoirs. A fundamental part of reservoir management best practices is to balance the pressure distribution within the reservoir and to ensure uniform flood front movement in the field through maintaining a calculated voidage replacement ratio (VRR). Due to the heterogenous nature of the reservoirs, the voidage replacement level is different from one area to another, which adds complexity in designing and monitoring the optimal injection-production ratios and guidelines. Frequent violations of these guidelines could lead to unwanted consequences, such as early water breakthrough and undesired reservoir pressure. The demonstrated workflow starts by assigning monthly wells production/injection guidelines based on the field target rate, well status and reservoir/well performance. Then, the workflow captures the real time flowing wellhead pressure and temperature to convert them through IPR/VLP models to rates. Then the calculated rate per well is compared to the assigned target rate per the production/injection guidelines. Responsible engineers receive compliance reports twice a day to take remedial action as needed. Implementing this workflow helped both reservoir and production engineers to monitor changes in well performance and ensure the compliance to the target rates in real time.

Sichuan embroidery, as one of China's traditional intangible cultural heritages, is renowned worldwide for its exquisite craftsmanship, profound cultural connotations, and unique artistic value. However, in the face of the impacts of modernization and globalization, the inheritance and development of Sichuan embroidery are facing numerous challenges. This article focuses on the digital preservation and innovation of Shu embroidery patterns, exploring the development model of Shu embroidery, a cultural heritage of Sichuan, by introducing artificial intelligence knowledge graphs and AIGC (Generative AI) technology. This paper focuses on Shu embroidery patterns as the core of the study and constructs the ontology of the Shu embroidery knowledge graph. It systematized the multidimensional information of Shu embroidery patterns, including cultural connotations, historical background, thematic meanings, embroidery techniques, and color structures, forming a structured digital resource. On this basis, by combining knowledge graphs with AIGC technology, a text-to-image pattern creation method was developed, achieving innovative design and efficient generation of Shu embroidery patterns. Research results indicate that knowledge graphs and AIGC technology play a significant role in the digital protection and innovation of Shu embroidery patterns. They not only achieve the modernization and inheritance of traditional culture but also provide new ideas for the industrial development of Shu embroidery art. This study holds important theoretical value and practical significance for the digital protection, intelligent innovation, and global dissemination of intangible cultural heritage.