Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

An efficient manufacturing method for I-shaped cross-sectional CFRP beam with arbitrary arrangement of carbon fiber using electro-activated resin molding

Abstract

The I-shaped cross-sectional beam of CFRP (CFRP I-beam) is usually manufactured by the continuous protrusion method. Carbon fibers can only be arranged in the longitudinal direction. The CFRP I-beam with arbitrary arrangement of carbon fiber was manufactured with applying the electro-activated deposition molding method. The carbon fiber fabric was immersed in the deposition solution and energized, epoxy resin precipitated around carbon fiber and impregnated. The resin-impregnated fabric was installed to the mold, and the CFRP I-beam was fabricated. The CFRP I-beam was subjected to three-point bending tests, and the relationship between load-deflection was simulated by finite-element analysis.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

Survey on Passivity Based Control of Induction Machine

Induction machines (IM) constitute a theoretically interesting and practically important class of nonlinear systems. They are frequently used as wind generators for their power/cost ratio. They are described by a fifth‐order nonlinear differential equation with two inputs and only three state variables available for measurement. The control task is further complicated by the fact that IM are subject to unknown (load) disturbances and the parameters can be of great uncertainty. One is then faced with the challenging problem of controlling a highly nonlinear system, with unknown time‐varying parameters, where the regulated output, besides being unmeasurable, is perturbed by an unknown additive signal. Passivity‐based control (PBC) is a well‐established structure‐preserving design methodology which has shown to be very powerful to design robust controllers for physical systems described by Euler‐Lagrange equations of motion. PBCs provide a natural procedure to "shape" the potential energy yielding controllers with a clear physical interpretation in terms of interconnection of the system with its environment and are robust vis á vis to unmodeled dissipative effects. One recent approach of PBC is the Interconnection and Damping Assignment Passivity‐Based Control (IDA‐PBC) which is a very useful technique to control nonlinear systems assigning a desired (Port‐Controlled Hamiltonian) structure to the closed‐loop. The aim of this paper is to give a survey on different PBC of IM. The originality of this work is that the author proves that the well known field oriented control of IM is a particular case of the IDA‐PBC with disturbance.     

低觉醒脑电识别与唤醒的可穿戴系统研究

为智能化地识别警戒作业人员出现的低觉醒、注意力下降的生理状态，本文介绍了一种基于FPGA和脑电信号处理的低觉醒状态检测与唤醒系统，系统通过传感器从大脑头皮采集脑电信号，转换为数字信号，经傅里叶变换获取了脑电信号的θ相对能量、α相对能量、重心频率、谱熵等4个特征量，由4个特征量表征低觉醒状态并运用支持向量机对低警戒状态进行识别，当识别出低觉醒状态时采用声音报警模块发出声音，唤醒警戒作业人员。设计系统能够较好地识别出低觉醒状态，识别率达90.8%，可为提高警戒作业工作绩效提供一种可穿戴的智能装备。     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

高浓度聚合物驱提高采收率方法实验研究

为探索一种新的提高原油采收率的方法,在外形尺寸为4.5 cm×4.5 cm×30 cm、气测渗透率0.9~1.0μm2、变异系数为0.72的二维纵向非均质人造岩心上,模拟大庆油田油藏流体性质及温度条件,研究了高浓度聚合物(HPAM)驱注入时机、聚合物相对分子质量、聚合物段塞体积及段塞组合对驱油效果的影响。通过注入大分子量、高浓度聚合物,结合合理的注入方式,在化学剂成本与三元复合驱相当的情况下,采收率比水驱提高20个百分点以上,接近或超过三元复合驱的水平。实验表明,采用高浓度聚合物驱油是一种较好的提高原油采收率的方法。     

变频调速技术在定量包装机中的应用

介绍一种基于变频调速控制的全自动定量包装机，结构简单，组态灵活，性价比高，可广泛应用于食品饲料等行业，现场运行实践证明其通用性好，准确度和可靠性高、速度快、稳定性好。并论述变频器控制系统的设计及使用中的注意问题。     

双河油田注入水适应性及注水井酸化解堵研究

考察了双河油田双河联、江河联注入水堵塞地层的因素;膨胀性黏土，悬浮固体颗粒。细菌及悬浮污油。含膨胀性黏土的双河南、双河北及不含膨胀性黏土的双江岩心粉，在注入水中相对于地层水中的体积膨胀度分别为14．5％、11．1％及0．02％；注入100PV不含悬浮颗粒的等体积比地层水、注入水混合水使双河、双江岩心渗透率分别下降7％和4％、9％和7％。注入水中悬浮颗粒引起岩心渗透率下降，粒径越大、颗粒浓度越大、注入量越大，则渗透率越低。在粒径2．1μm或颗粒浓度3mg／L前后下降幅度变化较大。注入水中硫酸盐还原菌引起岩心渗透率下降，含菌量越大则渗透率开始下降时的注水量越小，注入含菌50个／L的水100PV使岩心渗透率下降7％。岩心对注水合油量敏感，注入含油量20mg／L的水50PV使岩心渗透率下降20％。在岩心注水实验中渗透率下降最严重的是双河南岩心，其次是双河北岩心．江河岩心较轻，注入精细过滤水的双河北岩心渗透率下降大大减少．说明悬浮固体是造成注水堵塞的主要因素。为了解除双河油田注水井的堵塞，研制了含黏土稳定剂、缓蚀剂、铁离子稳定剂、互溶剂的土酸液，与南阳油田使用的低伤害酸液一起，用于1口注水井的解堵，效果良好。图7表4参5。