An improved iterative stochastic multi-objective acceptability analysis method for robust alternative selection in new product development

Alternative selection in new product development (NPD) is a multi-criteria decision-making (MCDM) problem. It usually starts with incomplete, imprecise or even partially missing information. Currently, most existing methods in dealing with this problem cannot work well if required information is incomplete or missing. It is acknowledged that stochastic multi-objective acceptability analysis (SMAA) can be applied to address MCDM problem with incomplete preference information and uncertain criteria measurements. In SMAA, alternatives are evaluated based on SMAA measurements (acceptability index, central weight vector and confidence factor). The discriminability of SMAA for the optimum alternative heavily depends on differences of SMAA measurements among different alternatives. Usually, a large number of alternatives and high level of uncertainty are involved in alternative selection in NPD. In this situation, the differences among SMAA measurements are not obvious, and therefore SMAA cannot deal with such problem very well. To this end, this paper proposes an improved SMAA method called Iterative-SMAA (I-SMAA) for alternative selection in NPD. In the I-SMAA, an iterative multi-step decision-making process is suggested to improve differences of SMAA measurements among different alternatives, and thus assist decision makers (DMs) to positively discern from the most preferred alternative. To enhance the decision-making efficiency, sensitive criteria are acquired in each iteration by ranking sensitivity analysis. DMs are guided to provide partial preference information and give more accurate criteria measurements for sensitive criteria rather than all criteria. Eventually, to verify the proposed method, a numerical example of the existing literature is solved with the method, and the results are compared. And then, a practical example of a preparation equipment for coal samples is further employed to verify the practicability of the proposed I-SMAA.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

“中国制造2025”背景下“班组长型”人才培养的挑战与应对

"班组长型"人才正是实现"中国制造2025"宏伟蓝图的基层驱动力量之一,"班组长型"人才的培养是当前相关职业院校重要任务,分析了"中国制造2025"对当前高职教育的新挑战以及对企业班组长的新要求,并提出了"中国制造2025"背景下"班组长型"人才培养的应对措施。     

基于深度网络迁移学习的致痫区脑电识别* .txt

摘要：致痫区脑电识别能够为癫痫外科手术提供重要的参考价值。提出了一种基于深度网络迁移学习的致痫区脑电识别算法。首先利用连续小波变换（CWT）对脑电信号进行时频分析，获得脑电信号时频图；然后迁移学习AlexNet网络模型，调整网络结构使之适应于致痫区脑电识别，将模型第7层全连接层输出作为脑电信号时频图的特征表示，最后利用支持向量机（SVM）、BP神经网络、长短期记忆网络（LSTM）、基于稀疏表达分类算法（SRC）、线性判别分析（LDA）等分类算法进行特征分类。基于开源脑电数据集采用十折交叉验证的方法对算法进行了验证,比较6种分类器的效果，得到SVM算法的平均特异性为8881%，灵敏度为8807%，准确率为8844%，证明了该方法识别致痫区脑电信号的有效性。 .txt     

Protein quality and antioxidant properties of soymilk derived from black soybean after in vitro simulated gastrointestinal digestion

Protein quality and antioxidant properties of soymilk derived from black soybean (eight varieties) in China were analysed following in vitro simulated gastrointestinal digestion (including dialysis). Soymilk from black soybean possessed high okara weight but low yield, protein content and sensory scores. The in vitro digestibility of protein in all black soymilk samples was higher than 60%, and the Shenmu black soybean exhibited the highest digestibility. Non-digested milk from the black soybean exhibited significantly high total phenolic content (TPC) (127.15–173.04 mg/100 mL), ferric-reducing antioxidant power (FRAP) (272.18–366.27 μmol L⁻¹) and DPPH free radical-scavenging activity (61.20–83.81%). These parameters were significantly lower in the non-digested soymilk than those in soymilk after gastric digestion but higher than those of soymilk in the dialysed fraction. Gastric digestion significantly increased bioactive compound levels released from black soymilk, and the bioaccessibility of phenolic compounds was 24.37–36.05%. Hence, black soymilk was sufficiently available for human absorption.     

基于康复训练中力信号的人体参与度建模

设计一种基于人机接触力的人体主动参与程度评估模型,用于康复机器人应用中的主动康复训练。通过人体的肌电信号得到肌肉活跃度,确定肌电信号与人体参与度的关系。分析这一过程中人机接触力的变化,将腿部力信号平均值以及腰部力信号方差作为模型输入,对基于肌电信号的参与度模型进行拟合,得到基于力信号的参与度模型,使用肌电信号参与度模型对力信号参与度模型进行验证,证明了该模型的有效性。该方法避免了肌电信号采集时干扰较大且准备工作繁琐等缺点,具有良好的实用性。     

重力坝坝基多斜面抗滑稳定模糊体系可靠度研究

以基岩具有多斜面、多滑移通道的某实际重力坝工程为例,基于刚体极限平衡法,采用模糊理论和几种典型的体系可靠度计算方法,研究其典型坝段的坝基深层抗滑稳定模糊体系可靠度,探讨模糊理论中隶属函数形式、隶属度以及隶属函数中关键参数的取值对可靠度计算结果的影响。研究结果表明,采取限制失效概率模糊界限比的方法可使计算结果更具有参考性,考虑模糊性的抗滑稳定可靠指标的下限较常规可靠度计算值偏小,这在重力坝坝基抗滑稳定分析中应引起重视。     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

离子液体/低共熔溶剂在煤基液体分离中的应用

为了实现煤基液体各组分利用价值最大化，本文综述了离子液体和低共熔溶剂对组分组成复杂的煤基液体进行高效萃取分离的研究进展。首先介绍了离子液体和低共熔溶剂的性质及分类；其次根据分离目标的不同，将离子液体和低共熔溶剂对煤基液体典型组分的萃取分离分为四个方面进行阐述：煤基液体提酚、燃料油萃取脱硫、燃料油萃取脱氮、芳烃和脂肪烃的分离。分析表明，离子液体和低共熔溶剂对实际煤基液体的提酚效果较好，能分离出绝大多数的酚类化合物；燃料油萃取脱硫时，离子液体和低共熔溶剂对实际煤基液体的单次脱硫率均不高，需3～5次重复萃取后才能获得理想效果；燃料油中的碱性及非碱性含氮化合物很难被同一种离子液体或低共熔溶剂一次性分离出，导致实际油品的脱氮率较低；大多数离子液体和低共熔溶剂进行芳烃和脂肪烃的分离时不能获得理想的分配系数和选择性，尚无法用于实际芳烃和脂肪烃的分离。氢键、π-π、CH-π、范德华力等分子间相互作用的差异是实现离子液体或低共熔溶剂进行煤基液体典型组分分离的主要原因。依据分离对象，设计合适的离子液体和低共熔溶剂，提高实际煤基液体分离时的萃取率和选择性；分析并解决离子液体和低共熔溶剂用于实际煤基液体各组分分离时可能出现的问题，势必会推进煤基液体高值化分离的工业化进程。