"S-E-T"是指:通过分析当前"社会、经济、技术"的状况,并预测未来发展趋势,进而找出现有产品和新趋势推动下所产生的需要或者改进可能性之间的缺口。以德国M U T E电动汽车为例,基于这三种因素对新产品机会进行了评..."S-E-T"是指:通过分析当前"社会、经济、技术"的状况,并预测未来发展趋势,进而找出现有产品和新趋势推动下所产生的需要或者改进可能性之间的缺口。以德国M U T E电动汽车为例,基于这三种因素对新产品机会进行了评估,并通过工业设计实现该机会。具体设计过程包括概念设计与造型设计,概念设计又包括车身工程设计、品牌规划两个阶段;造型设计部分又包括总体造型设计、标识性特征设计、细节设计三个阶段。最后对该方法进行了总结。展开更多
A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-...A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.展开更多
文摘"S-E-T"是指:通过分析当前"社会、经济、技术"的状况,并预测未来发展趋势,进而找出现有产品和新趋势推动下所产生的需要或者改进可能性之间的缺口。以德国M U T E电动汽车为例,基于这三种因素对新产品机会进行了评估,并通过工业设计实现该机会。具体设计过程包括概念设计与造型设计,概念设计又包括车身工程设计、品牌规划两个阶段;造型设计部分又包括总体造型设计、标识性特征设计、细节设计三个阶段。最后对该方法进行了总结。
文摘A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.
