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Functional model of traditional plant growing (a) and trimming model for hydroponic plant growing (b).
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![Figure 7. Hydroponic system with actuated above-plant platform [24].](publication/323755837/figure/fig2/AS:613934172209177@1523384721153/Hydroponic-system-with-actuated-above-plant-platform-24_Q320.jpg)
The new products are certainly decisive for achieving the business success of companies involved in the design and production of agricultural technology. Reducing the risk in the development and introduction of new technical products is the goal of analyzing the evolution of technical products. Effective innovation engineering procedures in the con...
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Trend of Engineering system evolution (TESE) is a popular tool in the theory of inventive problem-solving (TRIZ). Because it is applicable in various domains, it has been extensively used in practical projects to develop engineering products and forecast new technologies. However, although its generic pattern makes it a versatile tool for inventing and discovering the next generation of engineering systems, the scope of its practical use in product innovation has yet to be critically explored. This includes the applicability of its trends and sub-trends, efficacy and benefits in product development, and challenges and limitations. Therefore, this review uses reliable and noteworthy studies to overview the use of TESE in science and explore the benefits and challenges associated with its practical use. TESE-based studies have been conducted to prove its usability in technological development and innovation. We recommend that TESE should be integrated with various computer-aided aspects, such as scientific information, patents, social networking, disruptive technologies, manufacturing, and robotics, to address existing limitations. In addition, this review validates that these five aspects have yet to be demonstrated cohesively. This review provides TESE practitioners with insights into the application of the trends and sub-trends of the TESE toolkit.
This article presented a research work to enhance one of the TRIZ tools: Trends of Engineering System Evolution (TESE) which is useful to assess the evolution direction of technical systems in 4th industrial revolution (4IR) for forecasting technological trends. TESE has hierarchical levels of multiple trends and sub-trends for forecasting the technological evolution and was well-established in product innovation but has no link to the data in patent information. Patent data is growing exponentially annually and is Big Data that can be mined and integrated with TESE. In this paper, a novel model using Big Data technologies was proposed to extract semistructured data in U.S. Patents Data where the basis of classification and sorting of patents were done based on the trends and sub-trends of TESE for product innovation. Initial experiments were conducted to demonstrate the potential efficacy of the novel model.
