Published: Dec 8, 2023
Converted to Gold OA:
DOI: 10.4018/IJGCMS.334700
Volume 16
Zhenjiang Cao, Zhenhai Cao
Aiming at the problem that it is difficult for art teachers to take into account each student in the art appreciation education in colleges and universities, this paper proposes a retrieval system...
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Aiming at the problem that it is difficult for art teachers to take into account each student in the art appreciation education in colleges and universities, this paper proposes a retrieval system for traditional cultural works of art. Dense connections are used to replace residual connections between bottlenecks in MobileNetV2 network and gradient transmission in the network. The dilution factor is used to control the size of the network to solve the problem of the rapid increase in the number of network channels. In addition, the non-local attention mechanism is effectively combined with the improved MobileNetV2 network structure, which effectively improves the classification accuracy of the network. Compared with VGG16, ResNet18, and ResNet34, the classification accuracy is increased by 21.3%, 9.2%, and 3%, respectively. The method in this paper has achieved good results in the classification of art works. According to the images of art works to be appreciated, it helps students understand the relevant cultural knowledge independently and reduce the burden of teachers.
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Cao, Zhenjiang, and Zhenhai Cao. "Design of a MobilNetV2-Based Retrieval System for Traditional Cultural Artworks." IJGCMS vol.16, no.1 2024: pp.1-17. http://doi.org/10.4018/IJGCMS.334700
APA
Cao, Z. & Cao, Z. (2024). Design of a MobilNetV2-Based Retrieval System for Traditional Cultural Artworks. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-17. http://doi.org/10.4018/IJGCMS.334700
Chicago
Cao, Zhenjiang, and Zhenhai Cao. "Design of a MobilNetV2-Based Retrieval System for Traditional Cultural Artworks," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-17. http://doi.org/10.4018/IJGCMS.334700
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Published: Jan 12, 2024
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DOI: 10.4018/IJGCMS.336288
Volume 16
Wei Guo, Shengbo Sun, Peng Tao, Fei Li, Jianyong Ding, Hongbo Li
Given that the current microgrid incorporates highly connected distributed energy sources, the conventional model control methods do not suffice to support complex and ever-changing operating...
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Given that the current microgrid incorporates highly connected distributed energy sources, the conventional model control methods do not suffice to support complex and ever-changing operating scenarios. This paper proposes a deep learning-based energy optimization method for microgrid energy management in the new power system scenarios. This article constructs a microgrid cloud edge collaboration architecture, which collects interactive network status data through terminal devices and network edge sides. A microgrid energy management model is constructed based on Bi-LSTM attention in the network cloud. And the model is sunk to provide real-time and efficient comprehensive load and power generation prediction output optimal scheduling decisions at the edge of the network, achieving collaborative control of microgrid light load storage. The simulation based on the actual available microgrid data shows that the proposed Bi-LSTM attention energy management model can achieve rapid analysis and optimize decision-making within 7.3 seconds for complex microgrid operation scenarios.
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Guo, Wei, et al. "A Deep Learning-Based Microgrid Energy Management Method Under the Internet of Things Architecture." IJGCMS vol.16, no.1 2024: pp.1-19. http://doi.org/10.4018/IJGCMS.336288
APA
Guo, W., Sun, S., Tao, P., Li, F., Ding, J., & Li, H. (2024). A Deep Learning-Based Microgrid Energy Management Method Under the Internet of Things Architecture. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-19. http://doi.org/10.4018/IJGCMS.336288
Chicago
Guo, Wei, et al. "A Deep Learning-Based Microgrid Energy Management Method Under the Internet of Things Architecture," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-19. http://doi.org/10.4018/IJGCMS.336288
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Published: Jan 23, 2024
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DOI: 10.4018/IJGCMS.336839
Volume 16
Zheqi Zhu, Kun Zhai
With the deepening of the Belt and Road Initiative (BRI), many Chinese have been dispatched to Southeast Asian countries to participate in technical support and management. However, there are no OCE...
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With the deepening of the Belt and Road Initiative (BRI), many Chinese have been dispatched to Southeast Asian countries to participate in technical support and management. However, there are no OCE schools locally that can provide educational resources for their children. The decision of whether to invest in overseas Chinese-OCE has become a challenge for Chinese education groups. In this paper, the authors put forward an evolutionary game analysis scheme to study the OCE investment decisions among host country governments, students, and Chinese enterprises. The simulation results show that (1) the government's subsidy is always helpful to Chinese enterprise, especially at the beginning stage, (2) applying a soft strength of positive execution and combining with other methods could encourage the enterprise at the beginning and regulate the market later on, and (3) high strength of high support of government for the educational infrastructure is a win-win method for both students and the enterprise. Some managerial insights and suggestions are proposed based on these results.
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Zhu, Zheqi, and Kun Zhai. "A Tripartite Evolutionary Game Model for Chinese-Style Education Investment in ASEAN Under Local Government Policy." IJGCMS vol.16, no.1 2024: pp.1-24. http://doi.org/10.4018/IJGCMS.336839
APA
Zhu, Z. & Zhai, K. (2024). A Tripartite Evolutionary Game Model for Chinese-Style Education Investment in ASEAN Under Local Government Policy. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-24. http://doi.org/10.4018/IJGCMS.336839
Chicago
Zhu, Zheqi, and Kun Zhai. "A Tripartite Evolutionary Game Model for Chinese-Style Education Investment in ASEAN Under Local Government Policy," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-24. http://doi.org/10.4018/IJGCMS.336839
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Published: Feb 7, 2024
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DOI: 10.4018/IJGCMS.336846
Volume 16
Wei Wang, Xiaotian Wang, Xiaotian Ma, Ruifeng Zhao, Heng Yang
Traditional residential electricity prediction methods have problems, such as difficulty in ensuring user privacy and poor convergence speed due to the influence of Different Residential Electricity...
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Traditional residential electricity prediction methods have problems, such as difficulty in ensuring user privacy and poor convergence speed due to the influence of Different Residential Electricity Consumption (REC) habits. A REC prediction method based on Deep Learning (D-L) and Fed-L under the Cloud Edge Collaboration (CEC) architecture is proposed to address the above issues. First, based on the CEC architecture, combining edge computing and cloud computing center server, the overall model of REC prediction is built. Then, Federated Learning (Fed-L) and D-L model Empirical Mode Decomposition - Long Short-Term Memory (EMD-LSTM) were introduced on the edge side, and the edge side Fed-L depth model was personalized by using EMD-LSTM. Finally, aggregation of edge side models was achieved in the cloud by receiving encrypted model parameters from the edge side and updating and optimizing all edge side models. The results show that the proposed method has the highest REC prediction accuracy, reaching 96.56%, and its performance is superior to the other three comparative algorithms.
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Wang, Wei, et al. "Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture." IJGCMS vol.16, no.1 2024: pp.1-19. http://doi.org/10.4018/IJGCMS.336846
APA
Wang, W., Wang, X., Ma, X., Zhao, R., & Yang, H. (2024). Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-19. http://doi.org/10.4018/IJGCMS.336846
Chicago
Wang, Wei, et al. "Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-19. http://doi.org/10.4018/IJGCMS.336846
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Published: Feb 14, 2024
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DOI: 10.4018/IJGCMS.337896
Volume 16
Poe Sriwatanathamma, Veerawat Sirivesmas, Sone Simatrang, Nobonita Himani Bhowmik
While there are numerous serious games that explore cognitive behavioral therapy (CBT) techniques through gamification on smartphones, the framework for developing interactions is not often...
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While there are numerous serious games that explore cognitive behavioral therapy (CBT) techniques through gamification on smartphones, the framework for developing interactions is not often thoroughly discussed. The objective of this study is to outline the process of combining CBT techniques, narrative setup, and game mechanics to create two types of interactions (verbal- and physical-based) using the player interaction framework (PIF). The PIF consists of three key sections: setup, aim, and execution. In the setup section, it utilizes the ABCDE (activating events, beliefs, consequences, disputation of beliefs, and effective new approaches) model to combine narrative and CBT techniques such as gratitude and self-monitoring. The aim section is used to break down the intended experience of both the player and their main character, at the same time clarifying the CBT goal of the interaction. Finally, the execution section includes the representation of how players will interact in the game through input and feedback. The efficacy of the framework in visual narrative serious games remains to be investigated through a randomized controlled trial after the completion of our serious game, BlueLine.
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Sriwatanathamma, Poe, et al. "Developing a Framework for Interactions in CBT-Based Serious Games on Smartphones." IJGCMS vol.16, no.1 2024: pp.1-18. http://doi.org/10.4018/IJGCMS.337896
APA
Sriwatanathamma, P., Sirivesmas, V., Simatrang, S., & Bhowmik, N. H. (2024). Developing a Framework for Interactions in CBT-Based Serious Games on Smartphones. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-18. http://doi.org/10.4018/IJGCMS.337896
Chicago
Sriwatanathamma, Poe, et al. "Developing a Framework for Interactions in CBT-Based Serious Games on Smartphones," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-18. http://doi.org/10.4018/IJGCMS.337896
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Published: Mar 7, 2024
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DOI: 10.4018/IJGCMS.339198
Volume 16
Qiang Wang, Zhenwei Huang
In the evolving landscape of power grids, where green transportation and intermittent clean energy play a crucial role, ensuring the security and reliability of the urban network is of utmost...
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In the evolving landscape of power grids, where green transportation and intermittent clean energy play a crucial role, ensuring the security and reliability of the urban network is of utmost importance. However, the increasing volatility associated with these new energy sources poses a challenge to the traditional control methods. The large-scale integration of new energy in microgrids often leads to frequency instability and deviation in control performance standards. Addressing these issues, this paper introduces the SCQ(λ) algorithm, which accurately estimates the system's state to enhance controller capabilities. To evaluate the effectiveness of the proposed SCQ(λ) algorithm, the authors employ a load frequency control model in our simulation. In this model, they introduce various load change disturbances, including sine waves, square waves, and step disturbances to simulate realistic scenarios encountered in power systems. Throughout the simulation, they observe a significant reduction in frequency deviation in the case of step perturbation, with the deviation value decreasing by 0.0096.
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Wang, Qiang, and Zhenwei Huang. "Load Frequency Control Strategy for Islanded Microgrid Based on SCQ(λ) Algorithm." IJGCMS vol.16, no.1 2024: pp.1-16. http://doi.org/10.4018/IJGCMS.339198
APA
Wang, Q. & Huang, Z. (2024). Load Frequency Control Strategy for Islanded Microgrid Based on SCQ(λ) Algorithm. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-16. http://doi.org/10.4018/IJGCMS.339198
Chicago
Wang, Qiang, and Zhenwei Huang. "Load Frequency Control Strategy for Islanded Microgrid Based on SCQ(λ) Algorithm," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-16. http://doi.org/10.4018/IJGCMS.339198
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Published: May 16, 2024
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DOI: 10.4018/IJGCMS.343517
Volume 16
Yue Qi, Yiqin Wang, Yunyun Dong
This paper proposes an advanced approach for detecting faces with mask occlusion based on YOLOv5 to address various challenges encountered in face detection, including illumination blur and...
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This paper proposes an advanced approach for detecting faces with mask occlusion based on YOLOv5 to address various challenges encountered in face detection, including illumination blur and occlusion. The proposed methodology involves the integration of a convolutional block attention module into the backbone network and different network levels in the neck of the YOLOv5s. This approach enables the suppression of irrelevant features and emphasizes the identification of masked facial features. Replacing the conventional loss function with the Focal Loss function addresses the problem of sample imbalance. The enhanced YOLOv5s network was applied to detect mask-occluded faces. Empirical evaluations were conducted on the WIDER Face and AIZOO datasets. The simulation comparison results demonstrate that the proposed method achieves superior real-time detection performance, fulfilling the objective of developing a lightweight detection model.
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Qi, Yue, et al. "An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model." IJGCMS vol.16, no.1 2024: pp.1-16. http://doi.org/10.4018/IJGCMS.343517
APA
Qi, Y., Wang, Y., & Dong, Y. (2024). An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-16. http://doi.org/10.4018/IJGCMS.343517
Chicago
Qi, Yue, Yiqin Wang, and Yunyun Dong. "An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-16. http://doi.org/10.4018/IJGCMS.343517
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Published: Jul 23, 2024
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DOI: 10.4018/IJGCMS.348052
Volume 16
Cheng Zhang, Jinming Yang, Mingxuan Li, Meng Deng
In this study, simulation and big data analytics are combined with machine learning techniques, specifically K-means clustering, Apriori algorithm, and a stacked integrated learning model, to...
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In this study, simulation and big data analytics are combined with machine learning techniques, specifically K-means clustering, Apriori algorithm, and a stacked integrated learning model, to predict academic performance of college students with a high accuracy of 95.5%. By analyzing behavioral data from over 1,000 undergraduates, we correlate various behaviors with academic success, focusing on the use of libraries, self-study habits, and internet usage. Our findings highlight the benefits of using big data and simulation in educational strategies, promoting effective resource allocation and teaching enhancements. The study acknowledges limitations due to its regional focus and proposes future research directions to enhance model generalization and technological integration for broader application.
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Zhang, Cheng, et al. "Simulation-Based Machine Learning for Predicting Academic Performance Using Big Data." IJGCMS vol.16, no.1 2024: pp.1-20. http://doi.org/10.4018/IJGCMS.348052
APA
Zhang, C., Yang, J., Li, M., & Deng, M. (2024). Simulation-Based Machine Learning for Predicting Academic Performance Using Big Data. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-20. http://doi.org/10.4018/IJGCMS.348052
Chicago
Zhang, Cheng, et al. "Simulation-Based Machine Learning for Predicting Academic Performance Using Big Data," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-20. http://doi.org/10.4018/IJGCMS.348052
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Published: Aug 16, 2024
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DOI: 10.4018/IJGCMS.353435
Volume 16
Maimaitirexiati Helili, Xiang Geng, Chao Zhang, Jiazhang Huang, Wenming Chen
To provide optimal cushioning capacity, an in-depth understanding of the biomechanical interactions between the foot and therapeutic insoles is mandatory. The goal of this study was to quantify the...
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To provide optimal cushioning capacity, an in-depth understanding of the biomechanical interactions between the foot and therapeutic insoles is mandatory. The goal of this study was to quantify the hardness of plantar soft tissues (PSTs) and investigate the potential association between the hardness of PSTs and the optimum hardness of insole materials. The authors tested the hardness of PSTs in eight plantar regions by using a durometer to examine 30 cadaveric feet. The effects of hardness and loading magnitude on peak plantar pressure at the heel–insole interface were investigated in simulated weight-bearing tests. Significant effects of insole hardness on the cushioning capacity were observed among different loading conditions (p<0.01). No significant association was found between the hardness of individual-specific PSTs and optimum cushioning materials (p<0.05). This study provides quantitative data on PST hardness, and this knowledge may be valuable for developing insole materials with similar hardness to the PST in order to achieve optimum pressure relief under the foot.
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Helili, Maimaitirexiati, et al. "Optimum Insole Hardness for Attenuating Peak Plantar Pressure Under Simulated Loading Scenarios." IJGCMS vol.16, no.1 2024: pp.1-12. http://doi.org/10.4018/IJGCMS.353435
APA
Helili, M., Geng, X., Zhang, C., Huang, J., & Chen, W. (2024). Optimum Insole Hardness for Attenuating Peak Plantar Pressure Under Simulated Loading Scenarios. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 16(1), 1-12. http://doi.org/10.4018/IJGCMS.353435
Chicago
Helili, Maimaitirexiati, et al. "Optimum Insole Hardness for Attenuating Peak Plantar Pressure Under Simulated Loading Scenarios," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 16, no.1: 1-12. http://doi.org/10.4018/IJGCMS.353435
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