通过分析-建模-识别算法和上下文标签识别工业4.0场景中的人类活动

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Integrated Computer-Aided Engineering Pub Date : 2021-09-16 DOI:10.3233/ica-210667
Borja Bordel, R. Alcarria, T. Robles
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引用次数: 11

摘要

活动识别技术只有在受控条件下才能表现出良好的性能,在受控条件下,允许的动作数量有限。相反,工业应用是具有真实和不受控制的条件的场景,其中可能开发出数千种不同的活动(例如运输或制造工艺产品),具有令人难以置信的可变性。在这种情况下,需要新的和增强的人类活动识别技术。因此,本文提出了一种针对工业4.0场景的新的活动识别技术。提出的机制由不同的步骤组成,包括第一个分析阶段,其中使用移动平均线,滤波器和信号处理技术处理物理信号,以及原子识别步骤,其中集成了动态时间扭曲技术和k近邻解决方案;第二阶段,使用广义马尔可夫模型对活动进行建模,并使用多层感知器识别上下文标签;第三步,使用之前创建的马尔可夫模型和上下文信息(格式化为标签)来识别活动。该方法的最佳识别率为87%,证明了所述方法的有效性。据报道,与最先进的解决方案相比,改进幅度高达10%。
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Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels
Activity recognition technologies only present a good performance in controlled conditions, where a limited number of actions are allowed. On the contrary, industrial applications are scenarios with real and uncontrolled conditions where thousands of different activities (such as transporting or manufacturing craft products), with an incredible variability, may be developed. In this context, new and enhanced human activity recognition technologies are needed. Therefore, in this paper, a new activity recognition technology, focused on Industry 4.0 scenarios, is proposed. The proposed mechanism consists of different steps, including a first analysis phase where physical signals are processed using moving averages, filters and signal processing techniques, and an atomic recognition step where Dynamic Time Warping technologies and k-nearest neighbors solutions are integrated; a second phase where activities are modeled using generalized Markov models and context labels are recognized using a multi-layer perceptron; and a third step where activities are recognized using the previously created Markov models and context information, formatted as labels. The proposed solution achieves the best recognition rate of 87% which demonstrates the efficacy of the described method. Compared to the state-of-the-art solutions, an improvement up to 10% is reported.
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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