Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

Hierarchical Learning Water Cycle Algorithm

In order to improve the global searching ability of Water Cycle Algorithm (WCA), the hierarchical learning concept is introduced and the Hierarchical Learning WCA (HLWCA) is proposed in this paper. The underlying idea of HLWCA is to divide the solutions into collections and give these collections with hierarchy differences. One of the collections has a higher hierarchy than others and utilizes an exploration-inclined updating mechanism. The solutions in this high hierarchy collection are the exemplars of other collections. The other collections are sorted according to the exemplars’ function value and the solutions in these collections actively choose whether to follow their own exemplar or not. Through different updating mechanisms of collections, the global searching ability is improved while the fast convergence and strong local search ability of WCA are retained. The proposed HLWCA is firstly experimented on IEEE CEC 2017 benchmark suite to testify its performance on complex numerical optimization tasks. Then, it is tested on four practical design benchmark problems to verify its ability of solving real-world problems. The experimental results illustrate the efficiency of the proposed algorithm.     

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。     

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     

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。     

基于ASOS-ELM的湿式球磨机负荷软测量方法

针对湿式球磨机在磨矿过程中内部负荷靠专家经验难以准确预测的问题,提出一种基于改进的共生生物搜索(ameliorated symbiotic organisms search,简称ASOS)-极限学习机(extreme learning machine,简称ELM)的磨机负荷软测量方法。首先,利用ELM算法建立磨机负荷软测量模型,运用ASOS算法优化软测量模型的隐含层参数;其次,以筒体振动与振声信号的特征信息构建磨机负荷特征向量,并将其作为软测量模型的输入,将磨机负荷参数作为输出;最后,通过磨矿负荷检测实验和对比分析表明,磨机负荷软测量模型的负荷参数预测准确率较高,泛化能力较强,为磨机磨矿效率的提高及控制优化提供了有益的指导。     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。     

Gender difference in color preference across cultures: An archetypal pattern modulated by a female cultural stereotype

A gender difference in color preference among British participants has been repeatedly reported, in which both males and females show a preference for blue‐green colors, while females express an additional preference for pink‐purple colors. To investigate the robustness of gender difference in color preference in a different culture, we tested 81 young adult Indians from a school of design and compared them to 80 young British students in Psychology. The 35‐item International Personality Item Pool (IPIP) and Bem Sex Role Inventory (BSRI) questionnaires were also administered to explore possible links between personality traits, gender schemata, and color preferences. Results confirmed a gender difference in both cultures; participants collectively expressed a preference for cool over warm colors, while in addition females showed a preference for pink colors, with a warm bias for Indian females and a cool bias for British females. While these results extend gender difference to Indian culture and support the universality of an underlying pattern they also reveal a culture‐specific contribution essentially observed in females. In British participants, color preference was correlated exclusively with BSRI scores in females and overwhelmingly with IPIP scores in males; this gender‐specific pattern of correlation was not replicated in the Indian sample. Results point to an archetypal pattern of gender difference in color preference with a remarkable cross‐cultural similarity in men and a subtle but significant cultural difference in women whose origin is yet to be explained.     

Anti-Inflammatory Treatment with FTY720 Starting after Onset of Symptoms Reverses Synaptic Deficits in an AD Mouse Model

Therapeutic approaches providing effective medication for Alzheimer’s disease (AD) patients after disease onset are urgently needed. Previous studies in AD mouse models suggested that physical exercise or changed lifestyle can delay AD-related synaptic and memory dysfunctions when treatment started in juvenile animals long before onset of disease symptoms, while a pharmacological treatment that can reverse synaptic and memory deficits in AD mice was thus far not identified. Repurposing food and drug administration (FDA)-approved drugs for treatment of AD is a promising way to reduce the time to bring such medication into clinical practice. The sphingosine-1 phosphate analog fingolimod (FTY720) was approved recently for treatment of multiple sclerosis patients. Here, we addressed whether fingolimod rescues AD-related synaptic deficits and memory dysfunction in an amyloid precursor protein/presenilin-1 (APP/PS1) AD mouse model when medication starts after onset of symptoms (at five months). Male mice received intraperitoneal injections of fingolimod for one to two months starting at five to six months. This treatment rescued spine density as well as long-term potentiation in hippocampal cornu ammonis-1 (CA1) pyramidal neurons, that were both impaired in untreated APP/PS1 animals at six to seven months of age. Immunohistochemical analysis with markers of microgliosis (ionized calcium-binding adapter molecule 1; Iba1) and astrogliosis (glial fibrillary acid protein; GFAP) revealed that our fingolimod treatment regime strongly down regulated neuroinflammation in the hippocampus and neocortex of this AD model. These effects were accompanied by a moderate reduction of Aβ accumulation in hippocampus and neocortex. Our results suggest that fingolimod, when applied after onset of disease symptoms in an APP/PS1 mouse model, rescues synaptic pathology that is believed to underlie memory deficits in AD mice, and that this beneficial effect is mediated via anti-neuroinflammatory actions of the drug on microglia and astrocytes.