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Copyright of Nankai Business Review is the property of Nankai Business Review Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is difficult to describe the complex relationship between spare parts consumption and influencing factors accurately in the prediction process. The resons are listed as the randomness, diversity, time-varying and insufficient information of spare parts demand. The theories has been developed rapidly when dealing with uncertainty based on the Intelligent computing and tools. It was summarized about relevant literature on handling the uncertainty of spare parts demand prediction, according to the four categories of uncertainty, namely, randomness, fuzziness, incompleteness and compound uncertainty. It was summarized about the limitations and development direction of relevant research. The research results can be used to serve as references for equipment spare parts management. [ABSTRACT FROM AUTHOR]

In order to ensure the safe production of mine threatened by water hazard, speed up the intelligent process of mine water hazard prediction and early warning technology, and improve the effect of mine water hazard prediction and early warning, based on the research status of water hazard mechanism and monitoring and early warning at home and abroad, four types of key technical issues for constructing water hazard monitoring and intelligent early warning systems are analyzed. The complexity of early warning requirements and data access standards, the classification and spatio-temporal matching of multi-source heterogeneous big data information, the intelligent processing and analysis of water hazard big data information, and the timeliness of early warning and intelligent decision information release are discussed in detail. From the perspective of early warning system resource integration and data drive, water hazard warning resources are divided into information collection resources and computing resources, water hazard warning big data information is divided into static source information and dynamic monitoring information, and data processing is divided into basic geological model data processing, numerical processing and Computational simulation and information fusion data processing divide coal mine disaster early warning into primary monitoring parameter early warning, intermediate index grading early warning, and advanced intelligent model early warning. The key technical architecture of an intelligent warning system for coal mine water hazards is proposed and analyzed. A software service architecture that meets the technical requirements is proposed, including infrastructure layer, data resource layer, application support layer, business application layer, and user presentation layer. Based on the water hazard warning construction process, a Gated Recurrent Unit algorithm warning model for water hazard monitoring data is proposed, and the network structure of the warning model is given. The forward calculation, backward propagation calculation, and weight gradient calculation methods of the warning model are studied. The classification of different types of perception data access, storage, encoding, models, construction and testing of intelligent deep learning models, and technical paths for warning information release are analyzed. It provides a reference for the intelligent construction of coal mine water hazard early warning.

The rapid development of key technologies for the intelligent research and development (R&D) of new materials has significantly promoted the R&D efficiency and industrialization of materials and attracted global attention. China’s development in this field lags behind the advanced international level in terms of key technologies and infrastructures, which has restricted the original innovation and industrial development of the material sector. This study summarizes the key technologies involving the intelligent R&D of new materials, explores the developing status of these key technologies in China and abroad, and analyzes the challenges faced by China. Moreover, the intelligent R&D technology system is summarized including intelligent computing and design technologies and software, autonomous/intelligent experiment technologies and equipment, artificial-intelligence-driven basic algorithms and technologies, digital twins, intelligent R&D platforms and collaborative innovation networks. Furthermore,countermeasures are proposed from the aspects of innovative ecology construction, industrial environment improvement, standards system establishment, talent training, and international cooperation.

The release of ChatGPT, sparked a wave of generative AI, representing the arrival of the singularity moment of general artificial intelligence and highly likely restructuring the information industry ecosystem.The domestic industry has strengthened the research in the field of intelligent computing represented by ChatGPT, and operators have become the main force in the construction of computing network infrastructure, ushering in new opportunities for the development of intelligent computing.A detailed analysis of the capabilities, development status, and application prospects of generative AI were provided, and the technical elements behind generative AI, the demands for computing network resources, the impact on the communication industry, the opportunities and challenges faced by operators in the development wave of generative AI were thought about.Finally, the positioning and response strategies of operators were discussed.

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The release of ChatGPT, sparked a wave of generative AI, representing the arrival of the singularity moment of general artificial intelligence and highly likely restructuring the information industry ecosystem. The domestic industry has strengthened the research in the field of intelligent computing represented by ChatGPT, and operators have become the main force in the construction of computing network infrastructure, ushering in new opportunities for the development of intelligent computing. A detailed analysis of the capabilities, development status, and application prospects of generative AI were provided, and the technical elements behind generative AI, the demands for computing network resources, the impact on the communication industry, the opportunities and challenges faced by operators in the development wave of generative AI were thought about. Finally, the positioning and response strategies of operators were discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a cloud data center, one of the most important problems is using novel virtual machine placement strategy to promote the physical resource utilization.An approach of glowworm swarm optimization based virtual machine placement for cloud data centers called GSO-VMP was proposed.In the virtual placement, GSO algorithm was used to find a near-optimal solution.Each physical host had a luciferin value which represented the available CPU utilization.Whenever a VM was placed to a physical host, luciferin value of this physical host was updated.GSO-VMP algorithm could search the more available physical host within local range and thus the virtual migration numbers had been decreased and low energy consumption had been obtained.GSO-VMP had been evaluated using CloudSim with real-world workload data.The experimental results show that GSO-VMP has good performance in resource wastage and energy consumption.

To meet requirements of building intelligent avionics systems, and improve the intelligent combat level of manned/unmanned aerial vehicles, the software and hardware cooperative acceleration technology for CNN is designed and implemented to solve complex problems such as target recognition, auxiliary decision-making, and autonomous planning. Aiming at solving the conflicts between the huge amount of parameters and the limited storage resources for embedded environment, the neural network model is optimized with model structure optimization and quantization of parameters. Aiming at solving the conflicts between complex floating-point operations and the shortage of computing resources, the convolution accelerating operator and the pooling accelerating operator are designed based on Verilog HDL. The pipeline and full parallel method are used to achieve the purpose of acceleration. Through the synergy of software optimization and hardware accelerated, the inference process of convolutional neural network is accelerated. Two popular CNN networks, that are YOLOv3 and YOLOv3-Tiny, are used as examples to accelerate and verify on the Xilinx ZCU102 FPGA development board. The results show that compared with the original models, the parameters of the accelerated models can be compressed about 3/4. The inference speed of YOLOv3 is increased by nearly 65 times, and that of YOLOv3-Tiny is increased by about 23 times.

Aiming at the shortcomings of crow search algorithm ( CSA), this paper proposed a crow search algorithm using multi-mode flight ( MFCSA) . Based on the strength of foraging ability, the algorithm divided the population into two groups: strong and weak foraging ability. Those with strong foraging ability adopted the strategy of trailing and tracking the optimal target of the current group, and flied to the vicinity of the current optimal position of the group under the guidance of the group information to carry out search activities, which enhanced the local exploitation ability of the algorithm. Those with weaker foraging ability adopted the two strategies of observing and learning the foraging methods of the strong, and flying away quickly when encountering danger, the former could improve the global exploration ability of the algorithm, and the latter could maintain the diversity of the population. Through the numerical experiment simulation of 15 benchmark test functions and 2 engineering application problems, the results show that the MFCSA has better performance in optimization accuracy, convergence speed, etc., enhances the ability to avoid falling into the local optimum, and has better stability. [ABSTRACT FROM AUTHOR]

In a cloud data center, one of the most important problems is using novel virtual machine placement strategy to promote the physical resource utilization. An approach of glowworm swarm optimization based virtual machine placement for cloud data centers called GSO-VMP was proposed. In the virtual placement, GSO algorithm was used to find a near-optimal solution. Each physical host had a luciferin value which represented the available CPU utilization. Whenever a VM was placed to a physical host, luciferin value of this physical host was updated. GSO-VMP algorithm could search the more available physical host within local range and thus the virtual migration numbers had been decreased and low energy consumption had been obtained. GSO-VMP had been evaluated using CloudSim with real-world workload data. The experimental results show that GSO-VMP has good performance in resource wastage and energy consumption. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)