Linear time algorithm for computing min‐max movement of sink‐based mobile sensors for line barrier coverage

Zou, Wenjie; Guo, Longkun; Huang, Peihuang; Lin, Geng; Mei, Hengquan

doi:10.1002/cpe.6175

Cited by 17 publications

(9 citation statements)

References 22 publications

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“…e system has also formulated many rules to make the level more perfect, and the principle of the path of clearance also allows the complete system to be better played [48].…”

Section: Discussionmentioning

confidence: 99%

Automatic Generation of Real-Time Animation Game Learning Levels Based on Artificial Intelligence Assistant

Zhang

2022

Scientific Programming

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With the development of computer animation games, now many animation games use the automatic generation of animation game levels method to generate animation game content. Using the automatic generation levels of the animation game method can quickly generate many different levels, saving labor costs in manual production. And with the combination of other fields, more accurate results can be obtained. In this paper, the goal is to automatically generate the level of the animation game. This research proposed animation game objects and victory conditions to derive the concept of the clearance path and uses artificial intelligence genetic algorithms to obtain the clearance path that best meets the needs of the developer to recombine new levels. In addition, since the generated levels are regenerated from the clearance path, there are still too many blank areas. In order to make full use of these blank areas, this research provides a level complete system. The system has formulated its placement rules for existing animation game objects. These rules fill in these blank areas and produce a more complete level. The contributions of this search proved that the genetic algorithm can converge quickly due to the correction effect in the mutation. In the complete system, it has successfully achieved a complete and playable level. In the system running time, this research adjusted the method of selecting elites and the use of permutations and combinations to mate, so that the system’s operation can be stabilized on an average value.

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“…e system has also formulated many rules to make the level more perfect, and the principle of the path of clearance also allows the complete system to be better played [48].…”

Section: Discussionmentioning

confidence: 99%

Automatic Generation of Real-Time Animation Game Learning Levels Based on Artificial Intelligence Assistant

Zhang

2022

Scientific Programming

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“…The factors affecting teachersʼ use of information systems and the effectiveness of IT in helping students learn will involve the teachersʼ willingness to introduce IT in the classroom. Furthermore, sufficient resources, assistance in operating the information system or using IT, sufficient computers, sufficient funds, easy access to the Internet, adequate hardware and Technological acceptance and teaching quality software facilities, appropriate education and training, supervisor support, system stability and so forth will affect teachers' willingness to use IT and information systems (Zou et al, 2022;Li et al, 2022;Wang et al, 2021;Dai et al, 2020;Yu et al, 2021).…”

Section: Digital Teachingmentioning

confidence: 99%

Examining the influence of the performance of mobile devices on teaching quality based on the technological acceptance mode

Chen

2022

LHT

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PurposeThis study aims to examine the effects of iPad distribution on all teachers in a university and its application in teaching and student learning at home via wireless network during the coronavirus (COVID-19) pandemic. The attitude towards the use of iPads, behavioural intentions and the impact on the quality of teaching were evaluated.Design/methodology/approachThis study used the technology acceptance model to explore the use of iPad smart mobile devices in multimedia teaching applications by university teachers. Furthermore, it used the structural equation modelling (SEM) for data analysis to explore the causal relationship between model variables, and it aimed to examine the causal relationship between variables to verify the theory. The SEM analysis included the following two stages: measurement model analysis and structural model analysis.FindingsThe “Internet information environment” had a significant positive impact on “perceived usefulness” and “perceived ease of use”. Amongst them, perceived usefulness had a significant positive effect on the use attitude, and use attitude had a significant positive effect on behaviour intention.Originality/valueThe findings confirmed that a good information network environment will directly and positively affect the perceived usefulness and the ease of use of iPad smart devices, of which the perceived usefulness will further positively affect teachers' perception of iPad smart devices. The attitude and behaviour of using such devices will in turn positively affect the quality of teaching. The results of the quality performance evaluation can be referenced further by manufacturers and scholars regarding the use of iPad smart devices for work at home during the COVID-19 pandemic.

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“…YOLOv1 was proposed by Redmon, and Figure 2 is the network architecture diagram. Output an image with an input size of 448 × 448 as S × S grids (grid; the default is 7 × 7), and then detect whether there is an object in each grid separately, and generate B (default is 2) bounding boxes and N (default is 20) category of conditional class probabilities, the five prediction parameters in bounding boxes are x, y, w, h and confidence scores, x and y are the coordinates of the bounding boxes, w and h are the width and height of the bounding boxes, confidence scores is the value of whether there is an object 0 or 1, conditional class probabilities is the probability of N categories, and finally, the bounding boxes with the object are left, and then use Non-Maximum Suppression (NMS) to select the most suitable frame (Dai et al, 2020 ; Wang et al, 2021 ; Yu et al, 2021 ; Li et al, 2022 ; Zou et al, 2022 ).…”

Section: Relative Workmentioning

confidence: 99%

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Liu

Chen²,

Yao³

2022

Front. Psychol.

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With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”

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Linear time algorithm for computing min‐max movement of sink‐based mobile sensors for line barrier coverage

Cited by 17 publications

References 22 publications

Automatic Generation of Real-Time Animation Game Learning Levels Based on Artificial Intelligence Assistant

Automatic Generation of Real-Time Animation Game Learning Levels Based on Artificial Intelligence Assistant

Examining the influence of the performance of mobile devices on teaching quality based on the technological acceptance mode

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

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