基于功效特征的专利聚类方法

当前专利是按照领域划分的,而基于功效特征可以实现跨领域的专利聚类,这在企业创新设计中具有重要意义,而精确提取专利功效特征和快速获得最优聚类结果是其中的关键任务。为此提出一种信息实体语义增强表示（ERNIE）和卷积神经网络（CNN）相结合的功效特征联合提取（FEI-Joint）模型来提取专利文献的功效特征,并且改进自组织神经网络（SOM）算法,从而提出具有早期拒绝策略与类合并思想的自组织神经网络（ERCM-SOM）来实现基于功效特征的专利聚类。对FEI-Joint模型与TF-IDF、狄利克雷分布（LDA）、CNN在特征提取后的聚类效果上进行比较和分析,结果表明其F-measure值比其他模型有明显提高。ERCM-SOM算法与K-Means算法、SOM算法相比,在F-measure值提高的同时,其时间较SOM算法有明显缩短。对比使用专利分类号（IPC）的专利分类,采用基于功效特征的聚类方法可实现跨领域的专利聚类效果,为设计者借鉴其他领域的设计方法奠定了基础。     

Design and control of a planar bipedal robot ERNIE with parallel knee compliance

This paper presents the development of the planar bipedal robot ERNIE as well as numerical and experimental studies of the influence of parallel knee joint compliance on the energetic efficiency of walking in ERNIE. ERNIE has 5 links—a torso, two femurs and two tibias—and is configured to walk on a treadmill so that it can walk indefinitely in a confined space. Springs can be attached across the knee joints in parallel with the knee actuators. The hybrid zero dynamics framework serves as the basis for control of ERNIE’s walking. In the investigation of the effects of compliance on the energetic efficiency of walking, four cases were studied: one without springs and three with springs of different stiffnesses and preloads. It was found that for low-speed walking, the addition of soft springs may be used to increase energetic efficiency, while stiffer springs decrease the energetic efficiency. For high-speed walking, the addition of either soft or stiff springs increases the energetic efficiency of walking, while stiffer springs improve the energetic efficiency more than do softer springs. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.

一种基于ERNIE的军事文本实体关系抽取模型

针对军事文本实体关系抽取过程中存在的"一句对应多个三元组","一个主语对应多个客体"等问题提出一种基于ERNIE的军事文本三元组抽取模型,在编码层引入ERNIE模型获取每个词的编码序列,参考seq-to-seq解码器的建模方法和BIO序列标注,采用先预测主体,再传入主体标注序列预测客体和二者之间关系的方法实现三元组的抽...     

A small sample data-driven method: User needs elicitation from online reviews in new product iteration

Eliciting user needs from mass online reviews is playing a significant role in the product iteration process. Efficient user needs elicitation does achieve considerable benefits for maintaining higher competitiveness and a speedier lifecycle. However, there is inevitably an online review scarcity about new products due to the short time on the market and low buyer recognition compared with commonly used products. This paper proposes a small sample data-driven method for user needs elicitation from online reviews in new product iteration. In the first stage, a scraped initial online review dataset is pre-processed roughly to improve the data quality. And then, reviews are classified into multiple categories according to different topics using ERNIE. In the second stage, each topic-based dataset is reprocessed in detail. Thereafter, the key user needs set is determined and facilitated by extracting key product information phrases from every single dataset using improved SIFRank. Moreover, the case study of a smart cat feeder is carried out to demonstrate the feasibility and potential of the ERNIE-ISIFRank methodology. Finally, comparative experiments are conducted to verify the advantages of the proposed method which is primarily based on the pre-trained language model to enhance the deep understanding of the semantics of online reviews. The experimental results confirm that the proposed method can assist in identifying key user needs with high efficiency.     

基于社会新闻数据集的伦理行为判别方法

人工智能(artificial intelligence,AI)应用的伦理风险和挑战引起了人们的普遍关注,如何从技术实现角度开发出遵守人类价值观和伦理规范的AI系统,即,符合伦理的AI设计,是亟需解决的重要问题之一基于机器学习的伦理与道德判别是此方面的有益探索社会新闻数据具有丰富的伦理和道德的内容及知识,为机器学习的训...