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Application of digital media entertainment technology based on soft computing in immersive experience of remote piano teaching 基于软计算的数字媒体娱乐技术在远程钢琴教学沉浸式体验中的应用
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-17 DOI: 10.1016/j.entcom.2024.100822
Jun Wang

Digital media entertainment technology can enhance the interactive experience of the teaching process, allowing students to engage in immersive learning in a virtual reality environment. Traditional piano teaching places too much emphasis on the classroom, and new teaching methods should be optimized accordingly. Therefore, this article further optimizes and analyzes speech algorithms and applies them to soft computing technology. It also conducts multi-dimensional testing of the upgraded speech enhancement model, aiming to design a more suitable remote piano teaching platform for students, and attempts to directly apply the enhanced speech algorithms and soft computing technology in actual teaching. Relatively speaking, the effectiveness of traditional piano classroom teaching is not significant, and the teaching results of remote piano teaching have greatly improved compared to traditional classrooms. On this basis, students can also master most of the teaching content, which is conducive to enhancing their learning interest and thereby improving their academic performance. This platform can also provide piano learners with more efficient, convenient, and practical services.

数字媒体娱乐技术可以增强教学过程中的互动体验,让学生在虚拟现实环境中进行身临其境的学习。传统的钢琴教学过于强调课堂,新的教学方法也应相应优化。因此,本文进一步优化和分析了语音算法,并将其应用到软计算技术中。同时对升级后的语音增强模型进行多维度测试,旨在设计出更适合学生的远程钢琴教学平台,并尝试将增强后的语音算法和软计算技术直接应用于实际教学中。相对而言,传统钢琴课堂教学的效果并不显著,而远程钢琴教学的教学效果相比传统课堂有了很大的提升。在此基础上,学生还能掌握大部分教学内容,有利于提高学生的学习兴趣,进而提高学习成绩。该平台还能为钢琴学习者提供更加高效、便捷、实用的服务。
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引用次数: 0
Intelligent entertainment robots based on path navigation planning in tourism intelligent services and user entertainment experience analysis 基于路径导航规划的智能娱乐机器人在旅游智能服务中的应用及用户娱乐体验分析
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-15 DOI: 10.1016/j.entcom.2024.100829
Ai Rong , Song Jianwei , Xie Xiaowei

With the development of tourism industry, users have higher and higher demand for travel experience, while traditional tourism services can no longer meet the needs of users. Therefore, as an innovative tourism service mode, intelligent entertainment robots have broad application prospects. This paper proposes a design scheme of intelligent entertainment robot based on path navigation planning. By combining navigation technology and entertainment functions, intelligent entertainment robot can provide customized travel and entertainment services for users according to their needs and interests. By collecting the geographic information of the tourist scene and the user’s preference data, the path planning algorithm and machine learning technology are used to determine the robot’s cruise path and entertainment recommendation. At the same time, it also uses computer vision technology and emotion recognition technology to perceive and analyze the user’s emotional state, so as to provide a more personalized entertainment experience. The experimental results show that under the guidance and recommendation of intelligent entertainment robots, users’ travel experience has been significantly improved, and users’ satisfaction with tourism services and pleasure of entertainment experience have been improved.

随着旅游业的发展,用户对旅游体验的要求越来越高,而传统的旅游服务已不能满足用户的需求。因此,智能娱乐机器人作为一种创新的旅游服务模式,具有广阔的应用前景。本文提出了一种基于路径导航规划的智能娱乐机器人设计方案。通过将导航技术与娱乐功能相结合,智能娱乐机器人可以根据用户的需求和兴趣为其提供定制化的旅游娱乐服务。通过收集旅游场景的地理信息和用户的偏好数据,利用路径规划算法和机器学习技术确定机器人的巡游路径和娱乐推荐。同时,它还利用计算机视觉技术和情感识别技术感知和分析用户的情感状态,从而提供更加个性化的娱乐体验。实验结果表明,在智能娱乐机器人的引导和推荐下,用户的旅游体验得到了显著改善,用户对旅游服务的满意度和娱乐体验的愉悦度也得到了提高。
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引用次数: 0
Artificial intelligence based virtual gaming experience for sports training and simulation of human motion trajectory capture 基于人工智能的虚拟游戏体验,用于体育训练和人体运动轨迹捕捉模拟
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-15 DOI: 10.1016/j.entcom.2024.100828
Zhengli Li , Liantao Wang , Xueqing Wu

With the rapid development of artificial intelligence technology, the field of sports training has also actively explored the use of artificial intelligence technology to improve training effects and experience, and virtual game experience has been widely concerned as a new training method. In order to achieve the goal of virtual game experience in sports training, this study adopts a series of methods to build a realistic virtual game platform and realize real-time interaction between athletes and virtual environment. When building a virtual game platform, the use of computer graphics technology and model modeling technology to reproduce the details of different sports scenes provides an interactive interface that enables athletes to interact with the virtual environment in a real way, such as through joysticks, motion-sensing devices or virtual reality headsets. To be able to accurately capture the athlete’s movement trajectory, the study used deep learning techniques. By embedding cameras or other sensor devices in the platform, the movement data of athletes can be obtained in real time. Then, with the help of deep learning algorithms, these data are analyzed quickly and accurately, so as to understand the athlete’s movement posture, speed, Angle and other information. The captured movement data of athletes are processed and optimized based on artificial intelligence algorithm to realize real-time interaction between athletes and virtual environment. When athletes participate in training, they receive immediate feedback and personalized training guidance, which helps to enhance the training results and experience.

随着人工智能技术的飞速发展,体育训练领域也积极探索利用人工智能技术提高训练效果和体验,虚拟游戏体验作为一种新的训练方法受到广泛关注。为了实现体育训练中虚拟游戏体验的目标,本研究采用一系列方法构建逼真的虚拟游戏平台,实现运动员与虚拟环境的实时交互。在构建虚拟游戏平台时,利用计算机图形技术和模型建模技术再现不同运动场景的细节,提供交互界面,使运动员能够通过操纵杆、体感设备或虚拟现实头盔等方式与虚拟环境进行真实的交互。为了能够准确捕捉运动员的运动轨迹,这项研究采用了深度学习技术。通过在平台中嵌入摄像头或其他传感设备,可以实时获取运动员的运动数据。然后,在深度学习算法的帮助下,对这些数据进行快速、准确的分析,从而了解运动员的运动姿势、速度、角度等信息。基于人工智能算法,对捕捉到的运动员运动数据进行处理和优化,实现运动员与虚拟环境的实时交互。当运动员参与训练时,他们会收到即时反馈和个性化的训练指导,有助于提升训练效果和体验。
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引用次数: 0
Optimization of image recognition and gamification training process for entertainment robots in basketball training games based on tracking technology 基于跟踪技术优化篮球训练游戏中娱乐机器人的图像识别和游戏化训练流程
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-15 DOI: 10.1016/j.entcom.2024.100830
Dewei Chen

With the rapid development of artificial intelligence technology, the application of interactive entertainment robots in the field of sports training has attracted wide attention. The aim of this study is to optimize the process of basketball training competition by using tracking technology through image recognition and gamification training. In this study, tracking technology is adopted to realize image recognition in basketball training competition scenes. By installing cameras or sensors and other devices, robots can capture and recognize the position, posture and movement of trainers, and can transmit these data to the robot system in real time. Through the design of interesting training games, stimulate the interest and enthusiasm of the trainers, so that they can carry out effective basketball training in entertainment. The robot system can give different rewards and feedback based on the trainer’s performance, increasing the trainer’s fun and engagement, stimulating their competitive desire and promoting the improvement of skills. The trainer can enjoy the entertainment and challenge while interacting with the robot, and maintain the enthusiasm and motivation while improving the technical level. This interactive training method can improve the monotony and boredom of traditional training, and provide the trainer with a more interesting and stimulating learning environment. The experimental results show that interactive entertainment robot combined with gamification training can effectively optimize the process of basketball training competition.

随着人工智能技术的飞速发展,互动娱乐机器人在体育训练领域的应用引起了广泛关注。本研究旨在通过图像识别和游戏化训练,利用跟踪技术优化篮球训练比赛过程。本研究采用跟踪技术实现篮球训练比赛场景中的图像识别。通过安装摄像头或传感器等设备,机器人可以捕捉和识别训练者的位置、姿势和动作,并将这些数据实时传输给机器人系统。通过设计有趣的训练游戏,激发训练者的兴趣和热情,让他们在娱乐中进行有效的篮球训练。机器人系统可以根据训练者的表现给予不同的奖励和反馈,增加训练者的趣味性和参与度,激发他们的竞争欲望,促进技能的提高。训练者可以在与机器人互动的过程中享受娱乐和挑战,在提高技术水平的同时保持热情和动力。这种互动式训练方法可以改善传统训练的单调和枯燥,为训练者提供更加有趣和刺激的学习环境。实验结果表明,互动娱乐机器人与游戏化训练相结合,能有效优化篮球训练比赛过程。
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引用次数: 0
Enhancing ballet posture Teaching: Evaluation of a scientific computing model with motion capture integration 加强芭蕾舞姿势教学:评估集成动作捕捉功能的科学计算模型
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-14 DOI: 10.1016/j.entcom.2024.100824
Ya Huang

To improve the effect of ballet teaching, this study used the scientific computing model integrated with motion capture to analyze and evaluate the teaching of ballet dance postures, so as to improve the intelligence of modern ballet teaching. Moreover, this study employed the coordinate transformation and D-H method to model and analyze the forward and inverse kinematics of the ballet posture teaching model, and used the Monte Carlo method to verify the correctness of the exoskeleton motion space analysis. In addition, this study established a dynamic model, and used the Lagrangian equation method for a dynamic solution to obtain the relationship between the position, velocity and torque of each component. The data analysis indicated that the ballet posture teaching system, which is based on the scientific computing model and integrated with motion capture, can play an important role in ballet teaching.

为提高芭蕾舞教学效果,本研究采用与动作捕捉相结合的科学计算模型,对芭蕾舞舞姿教学进行分析和评估,以提高现代芭蕾舞教学的智能化水平。此外,本研究采用坐标变换和 D-H 方法对芭蕾舞姿教学模型的正向和反向运动学进行建模和分析,并利用蒙特卡罗方法验证了外骨骼运动空间分析的正确性。此外,本研究还建立了动态模型,并采用拉格朗日方程法进行动态求解,得到了各部件的位置、速度和扭矩之间的关系。数据分析表明,基于科学计算模型并与运动捕捉相结合的芭蕾舞姿教学系统可在芭蕾舞教学中发挥重要作用。
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引用次数: 0
Entertainment robot simulation in interactive art process based on deep learning algorithms and gesture recognition 基于深度学习算法和手势识别的互动艺术过程中的娱乐机器人仿真
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-14 DOI: 10.1016/j.entcom.2024.100827
Hanlu Lyu

Entertainment robots, as a new type of entertainment device, have broad application prospects. Entertainment robots provide entertainment and entertainment experiences through interaction with users. This article designs a programming model to interpret and execute user gesture commands, and convert them into drawing actions that robots can process. By interacting with entertainment robots, users can guide robots to draw through gestures, making artistic creations more intuitive and interesting. We used deep learning algorithms for training and used existing art works as references to enable robots to learn and imitate the painting styles of different artists. Finally, by optimizing the algorithm, the optimal path for the entertainment robot to draw trajectories was determined, which improved the effectiveness and quality of the painting. Through the training of deep learning algorithms, entertainment robots can capture the characteristics and details of an artist’s painting style, and simulate it during the painting process. This provides users with a personalized artistic creation experience, allowing them to interact with entertainment robots, participate in artistic creation, and experience a creative process similar to that of real artists.

娱乐机器人作为一种新型娱乐设备,具有广阔的应用前景。娱乐机器人通过与用户互动,提供娱乐和娱乐体验。本文设计了一种编程模型,用于解释和执行用户的手势命令,并将其转换为机器人可以处理的绘画动作。通过与娱乐机器人互动,用户可以通过手势引导机器人进行绘画,使艺术创作更加直观有趣。我们使用深度学习算法进行训练,并以现有的艺术作品为参考,让机器人学习和模仿不同艺术家的绘画风格。最后,通过优化算法,确定了娱乐机器人绘画轨迹的最优路径,提高了绘画效果和质量。通过深度学习算法的训练,娱乐机器人可以捕捉艺术家绘画风格的特点和细节,并在绘画过程中进行模拟。这为用户提供了个性化的艺术创作体验,让他们能够与娱乐机器人互动,参与艺术创作,体验与真正艺术家相似的创作过程。
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引用次数: 0
Adaptive mixed reality robotic games for personalized consumer robot entertainment 面向个性化消费机器人娱乐的自适应混合现实机器人游戏
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-14 DOI: 10.1016/j.entcom.2024.100825
Ajmeera Kiran , J. Refonaa , Muhammad Nabeel , N. Navaprakash , Vuyyuru Lakshma Reddy , R.V.S. Lalitha

The Adaptive Mixed Reality Robot Games (AMRRG) framework represents an innovative approach to integrating consumer robots into public spaces for personalized and engaging entertainment. AMRRG uses a combination of mixed reality headsets, robot movement, and interactive objects. These responsive entertainment environments need a new way to tell their story. Admixing game mechanics algorithm utilizes the world’s most advanced depth perception, computer vision, and simultaneous mapping and localization (SLAM) to enable effective distinguishing between player spaces, mixed reality areas, and robot paths for safe interaction. Reinforcement learning enables real-time adaptation of game difficulty and gameplay mechanics as players react to it. At 35 % higher than traditional video-game installations in a 5 m × 5 m environment accommodating up to 20 people, adaptive algorithms recorded efficiency values of 92 % and responsiveness of 98 %. The AMRRG framework brings home consumer robot platforms to deliver a happy gaming experience. Future research will explore the potential of AMRRG beyond simple adaptation, extending its use to therapy and education.

自适应混合现实机器人游戏(AMRRG)框架是将消费机器人整合到公共空间的一种创新方法,可提供个性化和吸引人的娱乐。AMRRG 结合使用了混合现实耳机、机器人运动和互动物体。这些响应式娱乐环境需要一种新的方式来讲述它们的故事。Admixing 游戏机制算法利用世界上最先进的深度感知、计算机视觉以及同步映射和定位(SLAM)技术,有效区分玩家空间、混合现实区域和机器人路径,实现安全互动。强化学习可根据玩家的反应实时调整游戏难度和游戏机制。在可容纳 20 人的 5 m × 5 m 环境中,自适应算法的效率值达到 92%,响应速度达到 98%,比传统视频游戏装置高出 35%。AMRRG 框架为家庭消费机器人平台带来了快乐的游戏体验。未来的研究将探索 AMRRG 在简单适应之外的潜力,并将其应用扩展到治疗和教育领域。
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引用次数: 0
User entertainment experience analysis of artificial intelligence entertainment robots based on convolutional neural networks in park plant landscape design 基于卷积神经网络的人工智能娱乐机器人在公园植物景观设计中的用户娱乐体验分析
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-11 DOI: 10.1016/j.entcom.2024.100817
Jingjing Zhao , Juan Yin , Yaqi Shi , Liang Qiao , Guihua Ma

Currently, the application of artificial intelligence entertainment robots in park plant landscape design has attracted increasing attention. This study aims to design an artificial intelligence entertainment robot that can provide a high-quality user experience. Through virtual reality and robotics technology, designers can be provided with visual and entertaining design solutions, and more interactive experiences can be provided for design clients. Convolutional neural networks can effectively extract features from images, and utilizing spectral feature extraction technology to further improve the accuracy of image recognition. Subsequently, this study designed a robot control system and calibrated the hand eye system. The robot control system can coordinate the various functions of the robot and ensure its smooth operation in the park plant landscape design. The calibration of the hand eye system is to ensure that the robot can accurately perceive the environment and locate its own position. Through real-time control strategies, robots can respond and adjust in a timely manner based on current environmental changes and user needs. By comparing with the actual position on the ground, the accuracy of robot positioning is obtained, and the system is further optimized and improved.

目前,人工智能娱乐机器人在公园植物景观设计中的应用日益受到关注。本研究旨在设计一种能够提供高质量用户体验的人工智能娱乐机器人。通过虚拟现实和机器人技术,可以为设计师提供可视化、娱乐化的设计方案,为设计客户提供更多的互动体验。卷积神经网络可以有效地从图像中提取特征,利用光谱特征提取技术可以进一步提高图像识别的准确性。随后,本研究设计了一个机器人控制系统,并校准了手眼系统。机器人控制系统可以协调机器人的各种功能,确保其在公园植物景观设计中顺利运行。校准手眼系统是为了确保机器人能够准确感知环境并定位自身位置。通过实时控制策略,机器人可以根据当前环境变化和用户需求及时做出响应和调整。通过与地面实际位置的对比,获得机器人定位的准确性,并进一步优化和改进系统。
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引用次数: 0
Analysis of facial recognition attendance technology based on artificial intelligence algorithms in political course e-learning teaching 基于人工智能算法的人脸识别考勤技术在政治课网络教学中的应用分析
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-11 DOI: 10.1016/j.entcom.2024.100821
Lu Chen

The lag in course construction has led to some teaching content and methods not being well adapted to the characteristics of online teaching. Collected student face data and advanced facial recognition algorithms to automatically recognize student avatars, ensuring the accuracy of each student’s identity. During the course, facial recognition attendance technology will automatically recognize students’ attendance status and record it, thereby obtaining attendance data at any time during the teaching process. The attendance records of students are generated in real-time and easily imported into the academic affairs system for management and statistics. By applying facial recognition attendance technology, teachers can understand students’ attendance in real-time and take timely measures to improve their learning enthusiasm. Students also use this technology to more conveniently sign in, reducing potential omissions and errors in the attendance process.

课程建设的滞后导致一些教学内容和方法不能很好地适应网络教学的特点。采集学生人脸数据,采用先进的人脸识别算法自动识别学生头像,确保每个学生身份的准确性。在课程进行过程中,人脸识别考勤技术会自动识别学生的考勤状态并进行记录,从而在教学过程中随时获取考勤数据。学生的考勤记录实时生成,便于导入教务系统进行管理和统计。通过应用人脸识别考勤技术,教师可以实时了解学生的考勤情况,及时采取措施提高学生的学习积极性。学生也可以利用这项技术更方便地签到,减少考勤过程中可能出现的遗漏和错误。
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引用次数: 0
Research on virtual entertainment robots based on machine learning algorithms providing psychological health services for college students 基于机器学习算法的虚拟娱乐机器人为大学生提供心理健康服务的研究
IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-10 DOI: 10.1016/j.entcom.2024.100819
Xiao Ma

In modern society, college students are facing increasing psychological pressure and mental health problems. In this context, virtual entertainment robots have become a promising form of mental health services, which can utilize machine learning algorithms to provide personalized psychological support and guidance by analyzing a large amount of psychological data and user information. Study the use of sample calculation and screening methods to determine the number of samples and perform feature selection to improve algorithm performance. Then analyze the detection effect and evaluate the effectiveness of the algorithm. By designing the architecture of a virtual entertainment robot and adopting anti-interference strategies to ensure that the robot can accurately recognize mental health information, text recognition technology was implemented, its effectiveness was evaluated, and further multi-source information recognition was carried out to improve recognition accuracy. Finally, a psychological health evaluation system for college students was constructed, and corresponding psychological health service strategies were proposed to meet the needs of college students. The results of this study indicate that virtual entertainment robots based on machine learning algorithms can effectively provide mental health services, providing support and guidance for the mental health problems of college students.

现代社会,大学生面临的心理压力和心理健康问题日益增多。在此背景下,虚拟娱乐机器人成为一种很有前景的心理健康服务形式,它可以利用机器学习算法,通过分析大量的心理数据和用户信息,提供个性化的心理支持和指导。研究使用样本计算和筛选方法来确定样本数量并进行特征选择,以提高算法性能。然后分析检测效果,评估算法的有效性。通过设计虚拟娱乐机器人的架构,采用抗干扰策略,确保机器人能够准确识别心理健康信息,实现了文本识别技术,并对其效果进行了评估,进一步开展了多源信息识别,提高了识别准确率。最后,构建了大学生心理健康测评系统,并提出了相应的心理健康服务策略,以满足大学生的需求。研究结果表明,基于机器学习算法的虚拟娱乐机器人可以有效提供心理健康服务,为大学生的心理健康问题提供支持和指导。
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引用次数: 0
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Entertainment Computing
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