Robot motion design using bunraku emotional expressions – focusing on Jo-Ha-Kyū in sounds and movements*

One of the UNESCO intangible cultural heritages Bunraku puppets can play one of the most beautiful puppet motions in the world. The Bunraku puppet motions can express emotions without the so-called ‘Uncanny Valley.’ We try to convert these emotional motions into robot affective motions so that robots can interact with human beings more comfortable. In so doing, in the present paper, we present a robot motion design framework using Bunraku affective motions that are based on the so-called ‘Jo-Ha-Kyū,’ and convert a few simple Bunraku motions into a robot motions using one of deep learning methods. Our primitive experiments show that Jo-Ha-Kyū can be incorporated into robot motion design smoothly, and some simple affective robot motions can be designed using our proposed framework.     

高性能计算分析技术在大型钠冷快堆屏蔽设计中的应用

钠冷快堆堆容器是一体化的池式结构，由众多堆内构件组成且结构复杂，堆芯到生物屏蔽外中子输运过程中各向异性明显且深穿透问题严重，大尺度范围下三维S_N方法计算是制约快堆屏蔽设计的瓶颈。通过将三维SN程序与高性能计算技术相结合，采用并行计算方法可解决快堆堆本体内各向异性的三维深穿透屏蔽问题。本文以中国示范快堆（CFR600）堆本体为研究对象，采用JSNT-CFR程序详细计算了堆本体内的中子注量率、光子注量率、剂量率，并将计算结果与已有的二维程序设计结果进行比较。结果表明，将传统屏蔽计算方法与高性能计算相结合，能满足CFR600堆本体屏蔽计算精度要求，获得更为全面的三维展示效果，在计算模型复杂、粒子穿透深度等复杂问题的屏蔽计算上具有较明显的优势，为大型钠冷快堆屏蔽设计提供有力支撑。     

黄沙坪铅锌多金属矿的成矿规律及深部找矿远景

众所周知,矿物质的成分是多种多样的,社会的发展和科学的进步需要运用到多重金属矿物质,在专业人员的勘探与挖掘中,发现了黄沙坪铅锌多金属矿,这个矿区中有丰富的有色金属,这个矿的发掘为研究成矿规律提供了物质基础,同时也为深部找矿提供了可靠的依据。本文主要分析黄沙坪铅锌多金属矿的成矿规律及深部找矿远景。     

300MW等级锅炉变工况低负荷稳燃能力数值模拟与预测

采用数值模拟软测量技术,在保证锅炉燃烧稳定和炉内温度较高水平的条件下,预测300 MW等级锅炉在实际运行中可降低到25%负荷稳定燃烧,并经过现场低负荷稳燃实验对结果进行验证。针对一次风速调整、磨煤机投运方式调整和配风方式及运行氧量调整三种方式对低负荷稳燃能力的影响进行分析,结果表明:在降负荷过程中调平一次风速偏差并适当降低一次风速,投运相邻层燃烧器,关小周界风,适当降低二次风量的同时适当降低运行氧量有利于提高低负荷稳燃特性。     

Hybrid fully convolutional networks-based skin lesion segmentation and melanoma detection using deep feature

Fully convolutional networks (FCNs) take the input of arbitrary size and produce correspondingly sized output with efficient inference and learning. The automatic diagnosis of melanoma is very essential for reducing the mortality rate by identifying the disease in earlier stages. A two-stage framework is used for implementing the melanoma detection, segmentation of skin lesion, and identification of melanoma lesions. Two FCNs based on VGG-16 and GoogLeNet are incorporated for improving the segmentation accuracy. A hybrid framework is used for incorporating these two FCNs. The classification is done by extracting the feature from segmented lesion by using deep residual network and a hand-crafted feature. Classification is done by support vector machine. The performance analysis of our framework gives a promising accuracy, that is, 0.8892 for classification in ISBI 2016 dataset and 0.853 for ISIC 2017 dataset.     

Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor

A semiconductor distributor that plays a third-party role in the supply chain will buy diverse components from different suppliers, warehouse and resell them to a number of electronics manufacturers with vendor-managed inventories, while suffering both risks of oversupply and shortage due to demand uncertainty. However, demand fluctuation and supply chain complexity are increasing due to shortening product life cycle in the consumer electronics era and long lead time for capacity expansion for high-tech manufacturing. Focusing realistic needs of a leading distributor for semiconductor components and modules, this study aims to construct a UNISON framework based on deep reinforcement learning (RL) for dynamically selecting the optimal demand forecast model for each of the products with the corresponding demand patterns to empower smart production for Industry 3.5. Deep RL that integrates deep learning architecture and RL algorithm can learn successful policies from the dynamic and complex real world. The reward function mechanism of deep RL can reduce negative impact of demand uncertainty. An empirical study was conducted for validation showing practical viability of the proposed approach. Indeed, the developed solution has been in real settings.     

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.