Reviewerly/exHarmony huggingface dataset:
A Mixed Reality Guidance System to Assist the Operator in the Assembly Process of Complex Products

The paper presents a Mixed Reality (MR) software system for assisting the operators during the assembly process of complex products. The proposed solution is composed of a Mixed Reality (MR) Graphical User Interface (GUI), developed on a holographic hardware device (Microsoft HoloLens 2). The software architecture of the MR tool and the data integration framework with a collaborative robot in the factory infrastructure are also presented. In this paper, the MR interface is also exploited for the safety of the operator in the workplace and for training an operator who is assembling complex products for the first time. The software system has been tested on the real case of a company operating in the automotive sector in which the operator carries out the assembly process of complex electromotive vehicles entirely manually. The experimental results, measured during a real assembly process cycle, show that the approach actively contributes to reduce the total assembly time by 30% and to achieve the reduction of the learning time of about 50%. Considering these preliminary results, the proposed solution can significantly improve the quality, the efficiency and enhance the safety of the operator during the whole assembly process.
An Evaluation Method of Insulator Aging Grade Based on Extreme Learning Machine

As an important part of the power grid, insulators play a role in isolating and protecting the power system. During the operation of the power grid, insulators on the line are prone to corona aging due to environmental factors such as electric field strength. Insulator aging will lead to grid failures, outages, and other problems. For the corona aging detection of insulators, the traditional methods are usually tedious, therefore, this paper proposes an approach for evaluating the aging grade of insulators, which involves the use of hyperspectral detection technology based on a limit learning machine. The primary objective of this approach is to address the corona aging problem associated with insulators. In this paper, firstly, corona aging of silicone rubber sheet was carried out from 0 to 300 hours, then the spectral data of silicone rubber sheet samples were obtained using a handheld hyperspectral camera, and finally, the limit learning machine model was established to realize the evaluation of different aging grades of the silicone rubber sheet with an accuracy of 94%. The hyperspectral testing technology can achieve non-contact and high-accuracy insulator performance assessment, which reduces the operational complexity and improves the economic efficiency of the power system compared with the traditional methods and helps to guarantee the normal and stable operation of the power system.
An Improved YOLOv5 for Transformer Nameplate Text Detection

Aiming at the characteristics of dense text and large font size span of transformer nameplate, a text detection algorithm of transformer nameplate improved YOLOv5 is proposed. Efficient Channel Attention is added to the C3 module of backbone to suppress the interference of a large amount of background information generated by multiple convolutions to the target information, and to enhance the focus on text targets during feature extraction; The feature map after Neck feature fusion uses the Coordinate Attention to update the weight. It obtains global information and is more sensitive to text targets; Introducing SIoU_Loss replaces CIoU_Loss to accelerate algorithm convergence. The feasibility was verified on the ICDAR 2013 dataset. The results show that YOLOv5 has better performance than some classical algorithms; The experiment is conducted on the self-made dataset. It shows that the improved algorithm mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> has reached 0.88,091, Precision has reached 0.97757, and mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> : <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.95</inf> has increased by 3.3 percentage points compared with the original algorithm, achieving excellent performance.
Bird Detection Algorithm for Transmission Line Based on Improved YOLOv7

With the increase in the number of birds, the life of birds will seriously endanger the safety of transmission lines or transmission towers. To ensure peaceful coexistence between transmission towers or lines and birds, this paper proposes to identify birds based on the improved YOLOv7 network structure, which lays the foundation for driving or guiding different birds. Replace part of the Conv layer in YOLOv7 with DOConv layer, improve the network performance through deeply over-parameterized convolution, and do not over-parameterize in reasoning; Add SPDConv layer in small target recognition to improve the recognition rate of small target; SimAM attention mechanism is added to feature extraction to enhance feature extraction. Some birds fly at high speed, resulting in blurred pictures taken by traditional cameras, which will greatly reduce the recognition rate of birds. In this paper, motion blur processing of data sets is beneficial to the recognition of blurred bird images. The model can accurately identify birds in the collected images of power transmission lines, providing a reference for the prevention and control of bird-related faults on power transmission lines.
Intelligent Monitoring System for Substation Environment Based on UAV Inspection Mode

As one of the basic power supply links, there are many hidden safety hazards in the operating environment of substation, leading to frequent production accidents. However, traditional environmental monitoring methods cannot meet the needs of modern monitoring. Therefore, based on the drone inspection method, this article designs an intelligent monitoring system for the operation environment of the substation, which can monitor the hidden warning of workers' illegal dressing and operating environmental safety hazards in real time and make early warning analysis. Focus on image recognition and judgment function, environmental risk index early warning function, and design the optimization of drone inspection paths, which can meet the high efficiency and intelligent needs of the operation environment monitoring of the substation, improve the efficiency of safety emergency disposal, provide a strong guarantee for the safety assessment and monitoring of the power plant environment.
An Insulator Defect Detection Approach Based on Improved Feature Pyramid and Attention Mechanism

Insulators are crucial components of transmission lines for maintaining the normal operation of the power grid. With the improvement of UAV inspection equipment, the resolution of photos has significantly improved. However, these photos often contain multiple insulator strings of different sizes, making it difficult for traditional target detection algorithms to efficiently and accurately detect all defects such as Insulator self-explosion. To address this problem, we propose an improved target detection algorithm suitable for insulator defect detection. Our algorithm is based on the YOLOv5 detection network, which uses a bidirectional feature pyramid network to improve feature fusion capability. We also integrate feature extraction with ECANet attention mechanism and use Soft-NMS to prevent missing detections of close insulator strings, thus improving the comprehensive detection capability of insulator strings. We compared our proposed algorithm with the original YOLOv5 network using collected patrol images. Our results show that the improved algorithm increases mAP_0.5 and mAP_0.5:0.95 precision mean value indicators by 2.9% and 1.7% respectively.
Line Loss Assessment Method Based on Scene Clustering Method

In order to minimize unreasonable power loss and make effective and reasonable use of electric energy, it is essential to effectively evaluate the line loss state of the transformer area and provide guidance for the subsequent line loss control. This paper introduces a method of grid line loss assessment based on scene clustering, which evaluates the state of grid line loss through the probability density function after scene clustering. Firstly, the multi-dimensional monitoring data is obtained by preprocessing the data collected by the monitoring device. Then, in order to conduct scene clustering for the power grid, this paper uses 3D K-means clustering method to cluster the power data, and divide it to obtain multiple line-loss state scenes. The data with the highest correlation with the line loss rate is obtained as the label of maximum correlation of line loss in this situation by calculating the mutual information between the line loss rate data and other monitoring data in various circumstances. The multiplier approach is then used to solve the model after fitting several probability models to create a general line loss probability model. Ultimately, the power grid's general line loss probability model is developed under various situations, allowing for the evaluation of the line loss condition and serving as a guide for managing the line loss moving forward. A province's measured data are analyzed and evaluated in order to determine whether the proposed model is valid.
Electric Power Sensing Data Quality Classification Using Decision-Tree via IEC60870-5-101/104

The IEC60870-5-101 and IEC60870-5-104 standards have been widely adopted for use in substation automation systems. The quality of sensing data delivered via IEC60870-5-101/104 (IEC101/104) will determine the performance of systems. This paper presents an AI-based methodology to identify the 3-phase voltage or current sensed in the process level of substation, which is delivered from an IEC101/104-based substation automation system, to generate a data quality classification. The decision tree algorithm is used to classify the data quality such as value up-limitation, down-limitation, normal, phase sequence error as well as refresh failure. Considering that the Decision Tree algorithm requires a large amount of training data, an IEC101/104 simulator is used to generate messages that include not only good quality data but also bad quality data. Based on the decision tree algorithm, a specific decision tree model is built based on the training data to classify the data quality. In the final stage, the methodology is validated using some real data. In terms of sensing data quality, the mistakes from the primary devices to the gateway in substation are identified and improved in case of poor quality. In the future, the specific model will be used in edge computing to implement the sensing data quality assessment locally in the substation.
Construction of Disaster Prevention and Reduction Safety Barrier of Power Grid Based on Power Internet of Things Technology

This paper proposes to make the use of power Internet of Things technology to solve a series of technical problems faced by power grid companies in disaster prevention and reduction work. In this paper, the proposal of power Internet of Things architecture, technical path, security protection and other solutions, realize the timely dynamic perception of power grid equipment and surrounding environment, real-time aggregation, transmission and analysis of massive data, realize the expected effect of disaster prevention and reduction such as mountain fire prevention, and build a firm the security barrier of the power grid.
Object Detection Algorithm for Railway Scenes Based on Infrared and RGB Image Fusion

The driver-assistance system tends to fuse multi-modal sensor data, for instance, the infrared and RGB sensors, to detect intrusion objects to enhance driving safety. However, the semantic misalignment dilemma and the spectral imb-alance between infrared and RGB images make it hard to exp-loit the advantages of multi-sensors in the end-to-end learning system. To solve these problems, we employ the widely used affine transformation on our railway dataset to solve the se-mantic-misalignment issue, in addition, we propose a fusion module, DMF, to fuse the well-aligned features, which can bri-dge the domain gap among different sensors. To this end, we propose an efficient railway invasive object detection network, YOLOv5s-DMF. Compared with the state-of-the-art metho-ds, the YOLOv5s-DMF substantially reduces the MR by 14.23% by employing the well-established decouple head. And our YOLOv5s-DMF further increases the mAP@0.5 by 5.7% and the mAP@0.5:0.95by4.1%.
Multi-temporal process quality prediction based on graph neural network

For the complex dependencies of production data in time and space, a multi-temporal processing process quality prediction model GLSTM based on graph neural networks is proposed, which uses graph structure data to model the process relationships among production indicators, uses graph neural networks to aggregate spatial information among production indicators, and uses long and short term memory networks to model the complex dependencies of shop floor processing quality indicator sequences in time, and the experimental The results show that the model is able to achieve relative performance improvements of 5.40%, 15.04% and 0.30% compared to time series analysis methods.
Structure-Enhanced Translation from PET to CT Modality with Paired GANs

Computed Tomography (CT) images play a crucial role in medical diagnosis and treatment planning. However, acquiring CT images can be difficult in certain scenarios, such as patients inability to undergo radiation exposure or unavailability of CT scanner. An alternative solution can be generating CT images from other imaging modalities. In this work, we propose a medical image translation pipeline for generating high-quality CT images from Positron Emission Tomography (PET) images using a Pix2Pix Generative Adversarial Network (GAN), which are effective in image translation tasks. However, traditional GAN loss functions often fail to capture the structural similarity between generated and target image. To alleviate this issue, we introduce a Multi-Scale Structural Similarity Index Measure (MS-SSIM) loss in addition to the GAN loss to ensure that the generated images preserve the anatomical structures and patterns present in the real CT images. Experiments on the ‘QIN-Breast’ dataset demonstrate that our proposed architecture achieves a Peak Signal-to-Noise Ratio (PSNR) of 17.70 dB and a Structural Similarity Index Measure (SSIM) of 42.51% in the region of interest.
Few-Shot Semantic Segmentation Based on Dual-Branch Feature Extraction

Few-shot semantic segmentation (FSS) requires only few labeled samples to achieve good segmentation performance and thus has received extensive attention. However, existing FFS methods usually adopt a simple convolutional structure as the backbone, which suffers from poor feature extraction ability. In order to address this issue, a novel few-shot segmentation network based on dual-branch feature extraction (DFESN) is proposed. First, an attention-enhanced ResNet is used as the local feature extraction branch. Specifically, we in-corporate channel attention operations into each building block of ResNet to model the importance among channels, which enables DFESN to learn important class information for the segmentation task. Besides, we introduce a Vision Transformer as the global feature extraction branch. This branch leverages the multi-head self-attention mechanism in Vision Transformer to model the global dependencies of support and query image features, further enhancing the feature extraction capabilities of DFESN. We conduct experiments on the PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> dataset and demonstrate the superiority of our DFESN.
Data Science during COVID-19 CORONA VIRUS in Sustainable Teaching and Learning in Architecture Engineering

On 11 March, the World Health Organization (WHO) announced that the COVID-19 outbreak became a global pandemic. The governments have been implementing measures to limit the number of people congregating in public places. Therefore, the Ministry of Education stated that all educational institutes should complete the 2019-2020-2 semester using online video conferences and virtual classes. This research aims to study the effect of COVID-19 on teaching and learning during the last three months of lockdown after shifting to virtual classes. The research studies the procedures applied by the College of Architecture Engineering at Dar Al Uloom University. Adding value is improving the E-Learning process for the upcoming semesters and solving the negative points for a better education. To achieve this objective the researcher, distribute a survey to the students to scale their experience and record the positive points, and find a solution to the negative points to solve these problems. The outcome of the research showed a good experience and many recommendations to be applied in the coming future.
Swamis of Mobile Robots for Area Exploration

This study investigates the use of a random-walk search algorithm for a swarm of mobile robots for area exploration. A stochastic process was employed to define the robots’ route, and a duster-based distribution factor and nature-inspired algorithms were used to explore undiscovered areas. A distributed search technique was also evaluated for area exploration using a multi-robot team, with cost and performance of the swarm analyzed for determining the optimal size of the swarm. The results suggested that the random-walk search algorithm is a viable and cost-effective solution for area exploration by a swarm of robots, with the added benefit of improving performance. In addition, the optimal size of the swarm analysis revealed the optimum number of robots for a particular task.
Automatic Enlarged Lymph Node Detection from 3D Mediastinum CT Images

Lymph nodes (LNs), part of the lymphatic system, have important role of the immune system. also have an important function in current clinical workflow. LN routinely assessed by clinicians cause of related to all kinds of cancer, cancer affected the lymph nodes to be enlarged, lymph nodes nearby primary cancer regions, accurate lymph node detection very important for cancer staging and to monitor disease progress also to prediction surgical planning, and to follow-up effectiveness of treatment and therapy. The detection and assessment of lymph nodes is usually performed manually, since finding the lymph nodes can be time – consuming and depend on the highly observer experience during the low contrast of surrounding structures in computed tomography (CT), a system for lymph node detection desired, so i proposed a method for automatic lymph node detection and measurement from 3D Mediastinum CT images by employ’s a combination between the speeded up robust features (SURF) and the blob detection, to produce a system with high accuracy using feature extraction method, Also produce algorithm with low computational time by analyzing multi slices by converted to one projected image each time using maximum intensity projection, also to eliminating vessels, and add new calculation parameter to the system which is the volume of the LN, to add extra accuracy for the proposed method, which allow the system from verifying the candidate LN., the method is evaluated for Mediastinum LN detection on 50 volumes containing 182 LNs, yielding an 83.3%, detection rate with 1.1 false positive per volume, with an average running time of 12-35 seconds per volume.
Text Mining to Analyze Mammogram Screening Results for Breast Cancer Patients in Saudi Arabia

Breast cancer (BC) affects women of all ages after puberty around the world, but the rate spontaneously rises as the women get older. Saudi Arabia is home to a large number of BC patients. Many women are diagnosed with BC and are frequently admitted to the hospital, resulting in a massive amount of clinical data that can be analyzed and used for medical research. However, proper text mining algorithms must be implemented in order to extract valuable insights and knowledge discovery from these medical notes. In this study, 7 years of BC patients' medical notes data (between 2012-2018) were officially obtained from the hospital and experimented to diagnose patient issues using three machine learning (ML) algorithms. The dataset contained 62,845 clinical text records on "X-ray screening results" recorded by the doctors after seeing the mammogram for patients who had visited the hospital through emergency and regular appointments. Natural language processing (NLP) models were employed to exploit the text data and converted into symbols. Furthermore, the text clustering algorithms K-means, K-medoids, and agglomerative clustering were carefully experimented. The results indicated that K-means outperforms the other two algorithms, with seven identified clusters. K-means clustering also yielded 0.198 work saved over sampling (WSS) and 5.5 database performance (DB) values and was identified as the best unsupervised learning algorithm for medical text clustering.
Iterative Uni-modal and Cross-modal Clustered Contrastive Learning for Image-text Retrieval

Multimedia data has exploded both in quantity and form. Under such background, cross-modal retrieval has become a research hot spot in recent years. We address the image-to-text and text-to-image retrieval problems by proposing a symmetric two-stream pre-training framework. In this work, the architecture is based on the CLIP model and it consists of a BERT-pretrained text encoder and a Vision Transformer (ViT)-pretrained image encoder. We utilize not only a cross-modal contrastive loss, but also two symmetric uni-modal contrast losses to train the model in an unsupervised manner. In addition, we propose novel training strategies, including the multi-stage training scheme and iterative training strategy with clustered hard negative data. Experimental results show that our model achieves better performance via introducing the uni-modal self-supervised branch and losses compared to the sole CLIP model.
Autonomous docking trajectory planning of heavy AGV based on slip compensation model

The track planning research of heavy AGV is the key to achieve accurate docking/undocking. The existing algorithms assume that the wheels are pure rolling, and do not consider the tire slip caused by the large inertia of heavy AGV in the actual situation. In order to realize that the trajectory planning is more in line with the requirements of real vehicles and make the AGV docking accuracy higher, the tire model of vehicle's transverse and longitudinal slip is derived, and the vehicle's incomplete constraint formula with slip motion is calculated to compensate the path error caused by slip; The path search uses the improved geometric target to inspire RRT. Based on the vehicle non-holonomic constraints, the line speed and wheel angle required for the RRT optimal step length are derived, and the search tree path satisfying the vehicle kinematics requirements is generated; In order to obtain the final trajectory, taking the curvature and smoothness of curvature as the objective functions, the conjugate gradient descent is used to optimize the redundant path solution, and the speed planning is allocated to generate the smooth trajectory. Finally, the rationality of the trajectory under the sliding model, the search efficiency of the improved RRT and the improvement of the solution quality by the optimization algorithm are compared through experiments.