The project provides trained models of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for outdoor insulator detection and classification of the surface contamination, such as salt, snow, cement, soil and wet soil. The project is based on YOLOv3 implementation developed by Ultralytics/YOLOv3. The models were trained on custom insulator dataset consisting of 11816 images of different type insulators under various conditions.
Solar energy production has grown significantly in recent years in the European Union (EU), accounting for 12\% of the total in 2022. The growth can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources.
The liquid product H in stream 9 is considered as quality variable while 22 process variables XMEAS (1) – XMEAS (22) are selected as process variables. 960 samples are collected as training samples, and another 960 samples are collected as the test dataset in the same working condition.
The drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. Thus, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet).
Future 6G networks will consist of fully soft-warized networks that incorporate in-network intelligence for self-management. However, this intelligent management will require massive data mining, analytics, and processing. Therefore, we need resources like quantum technologies to help achieve 6G key performance indicators. We use Quantum Machine Learning (QML) to solve the controller placement problem for a multi-controller Software Defined Network (SDN). Network delay depends on the controller’s position.
Industrial cyber-physical systems (ICPS), which is the backbone of Industry 4.0, are the result of adapting emerging information communication technologies (ICT) to the industrial control systems (ICS). ICPS utilize autonomous robotic arms to accomplish manufacturing tasks. These arms follow a certain predetermined trajectory during the task.
In this dataset, we present four files generated from a setup that contains two Universal Robot UR3e collaborative robotic arms:
The data collection questionnaire consisted of two sections. One section involved the collection of data via Google Forms questionnaires, and the other involved the collection of WhatsApp voice samples. There were three subsections in the questionnaire section. The first consisted of the individual's basic information, such as email address, name, and identification number. The second was the personal health questionnaire depression scale (PHQ8), which included 8 groups of statements, and the third was the Beck Depression Inventory-II, which contained 21 groups of statements.
One of the most consequential creations in the human evolution phase is handwriting. Due to writing, today we are conveying our reflections, making business pacts, rendering an understandable world and making hitherto tasks austerer. Determining gender using offline handwriting is an applied research problem in forensics, psychology, and security applications, and with technological evolution, the need is growing. The general problem of gender detection from handwriting poses many difficulties resulting from interpersonal and intrapersonal differences.
The dataset included 640 patients' vital records, which ranged in age from 18 to 60.
The dataset contains performance values, Area Under the ROC Curve (AUC) and Average Precision (AP), of popular anomaly detection (AD) algorithms taken over a set of 9k AD benchmark datasets.
Datasets were initially published with the following paper:
Kandanaarachchi, S., Muñoz, M. A., Hyndman, R. J., & Smith-Miles, K. (2020). On normalization and algorithm selection for unsupervised outlier detection. Data Mining and Knowledge Discovery, 34(2), 309-354.
