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Under the background of intelligent construction of coal mine, this paper studies and discusses the construction of AI platform architecture system of the whole coal mine based on video perception, network and information technology as the media, artificial intelligence and big data as the technical support; according to the actual situation of coal mine, the real application scenario is mined and analyzed; the paper carries on research on intelligent sensing, deep learning and intelligent analysis for coal mine equipment status, personnel behavior and environmental changes to realize intelligent alarm and control; the paper researches and analyzes relevant key technologies such as artificial intelligence and deep learning.

With the vision “Innovation by experiment” the Bauhaus.MobilityLab started in July 2020 as a living lab in the district Brühl of the city Erfurt, Thuringia, Germany. As a unique project, it is coupling the sectors mobility, logistics and energy into a unified living lab. It allows to design, develop and evaluate innovative services to increase the quality of life in the city. Bauhaus.MobilityLab offers access to live smart city data of different domains and provides a set of powerful artificial intelligence (AI) algorithms for data processing, analytics and forecasting. In contrast to existing platforms, its uniqueness is the available and integrated living lab. It allows directly rolling out new smart city services and to evaluate the impact in the real world. This paper describes the implementation of the technical platform supporting the Bauhaus.MobilityLab, realized according to the DIN SPEC 91357 as an open urban platform. It focuses on data sharing based on the concepts of the International Data Spaces and the integration of AI algorithms. The concepts are presented based on examples in the energy domain.

Recently, damage accidents and damages during urban gas pipe excavation work have been increasing. Based on the Faster R-CNN AI model, an intelligent object recognition technique, excavators are detected in real-time images transmitted from drones and the excavation site is combined with GIS and Augmented Reality (AR) to monitor the excavator location after overlaying it on the map in real time. For intelligent architecture, Client Part is a drone, iPad app, and Server Part is designed as a GIS/AR and AI analysis model. Verification of accuracy was carried out by self-verification and on-site test bed verification. It has increased its ability to implement research by reflecting Real World's environment where regulatory sandboxes are applied. It has been confirmed that it is not unreasonable to apply to the site with an accuracy of about 94% and that the low detection rate, especially due to the nature of the industrial site, suggests that the research is successful. Approximately 58% of the time required for vehicle circuit inspection was reduced. This paper is expected to help develop safety management as the first case in Korea and abroad that combines drones with AI object recognition technology and GIS/AR technology into the urban gas safety management sector. [ABSTRACT FROM AUTHOR]

In this paper, we focus on presenting a novel AI-based service platform proposal called AIDI (Artificial Intelligence Distribution Interface for healthcare). AIDI proposal is based on our earlier research work in which we evaluated AI-based healthcare services which have been used successfully in practice among healthcare service providers. We have also used our systematic review about AI-based healthcare services benefits in various healthcare sectors. This novel AIDI proposal contains services for health assessment, healthcare evaluation, and cognitive assistant which can be used by researchers, healthcare service provides, clinicians, and consumers. AIDI integrates multiple health databases and data lakes with AI service providers and open access AI algorithms. It also gives healthcare service providers open access to state-of-the-art AI-based diagnosis and analysis services. This paper provides a description of AIDI platform, how it could be developed, what can become obstacles in the development, and how the platform can provide benefits to healthcare when it will be operational in the future.

Objective . Developing Machine Learning models (N Gorre et al 2023) for clinical applications from scratch can be a cumbersome task requiring varying levels of expertise. Seasoned developers and researchers may also often face incompatible frameworks and data preparation issues. This is further complicated in the context of diagnostic radiology and oncology applications, given the heterogenous nature of the input data and the specialized task requirements. Our goal is to provide clinicians, researchers, and early AI developers with a modular, flexible, and user-friendly software tool that can effectively meet their needs to explore, train, and test AI algorithms by allowing users to interpret their model results. This latter step involves the incorporation of interpretability and explainability methods that would allow visualizing performance as well as interpreting predictions across the different neural network layers of a deep learning algorithm. Approach . To demonstrate our proposed tool, we have developed the CRP10 AI Application Interface (CRP10AII) as part of the MIDRC consortium. CRP10AII is based on the web service Django framework in Python. CRP10AII/Django/Python in combination with another data manager tool/platform, data commons such as Gen3 can provide a comprehensive while easy to use machine/deep learning analytics tool. The tool allows to test, visualize, interpret how and why the deep learning model is performing. The major highlight of CRP10AII is its capability of visualization and interpretability of otherwise Blackbox AI algorithms. Results . CRP10AII provides many convenient features for model building and evaluation, including: (1) query and acquire data according to the specific application (e.g. classification, segmentation) from the data common platform (Gen3 here); (2) train the AI models from scratch or use pre-trained models (e.g. VGGNet, AlexNet, BERT) for transfer learning and test the model predictions, performance assessment, receiver operating characteristics curve evaluation; (3) interpret the AI model predictions using methods like SHAPLEY, LIME values; and (4) visualize the model learning through heatmaps and activation maps of individual layers of the neural network. Significance . Unexperienced users may have more time to swiftly pre-process, build/train their AI models on their own use-cases, and further visualize and explore these AI models as part of this pipeline, all in an end-to-end manner. CRP10AII will be provided as an open-source tool, and we expect to continue developing it based on users' feedback., (© 2023 Institute of Physics and Engineering in Medicine.)

• Deep learning techniques significantly improve results in microscopic image recognition. • A universal platform is developed to lower the implementation barrier for clinical users to apply deep learning technology. • ResNeXt-50-32 × 4d is a preferable model for blood cell classification. • MobileNet-V2 can well balance the classification performance and the computational cost for microscopic image recognition. • ShuffleNet-v2 × 1 has relatively low inference latency for microscopic image analysis. Background and Objective: Deep learning techniques are powerful tools for image analysis. However, the lack of programming experience makes it difficult for novice users to apply this technology. This project aims to lower the barrier for clinical users to implement deep learning methods in microscopic image classification. Methods: In this study, an out-of-the-box software, AIMIC (artificial intelligence-based microscopy image classifier), was developed for users to apply deep learning technology in a code-free manner. The platform was equipped with state-of-the-art deep learning techniques and data preprocessing approaches. Furthermore, we evaluated the built-in networks on four benchmark microscopy image datasets to assist entry-level practitioners in selecting a suitable algorithm. Results: The entire deep learning pipeline, from training a new network to inferring unseen samples using the trained model, could be implemented on the proposed platform without the need for programming. In the evaluation experiments, the ResNeXt-50-32 × 4d outperformed other competitor algorithms in terms of average accuracy (96.83%) and average F1-score (96.82%). In addition, the MobileNet-V2 achieved a good balance between the performance (accuracy of 95.72%) and computational cost (inference time of 0.109s for identifying one sample). Conclusions: The proposed AI platform allows people without programming experience to use artificial intelligence methods in microscopy image analysis. Besides, the ResNeXt-50-32 × 4d is a preferable solution for microscopic image classification, and MobileNet-V2 is most likely to be an alternative selection for the scenario when computing resources are limited. [ABSTRACT FROM AUTHOR]

Since the machine learning platform can provide one-stop artificial intelligence (AI) application solutions, it has been widely used in the industrial and commercial internet fields in recent years. Based on the heterogeneous accelerator cards, scientific discovery using large-scale computation and massive data is a significant tendency in the future. However, building a platform for scientific discovery remains challenging, including large-scale heterogeneous resource scheduling and support for massive multi-source data. To free researchers from tedious resource management and environmental configuration, we propose a VenusAI platform for large-scale computing scenarios in scientific research, based on heterogeneous resources scheduling framework. This paper firstly illustrates the VenusAI platform architecture design scheme based on the supercomputers and elaborates on the virtualization and containerization of the underlying hardware resources. Next, a technical framework for heterogeneous resource aggregation and scheduling is proposed. A unified resource interface in the application service layer is introduced. Considering the core three parts of the AI scenario: data, model, and computing power, modularized service decoupling is carried out. Furthermore, three types of experiments are evaluated on the supercomputers and show that the performance of the scheduling framework on virtual clusters is better than that on common clusters. Finally, three scientific discovery applications deployed on VenusAI, i.e., new energy forecasting, materials design, and unmanned aerial vehicle planning, demonstrate the advantages of the platform in solving practical scientific problems. [ABSTRACT FROM AUTHOR]