Motion-based method for estimating time required to attach self-adhesive insulators

A self-adhesive insulator is a component of a home appliance that is used to suppress vibration or prevent humidity affecting the internal parts of the appliance. There is a wide range of types and designs available, allowing them to be applied to areas having different shapes. At the design stage, once an insulator design has been developed sufficiently to identify its dimensions and features, the attaching time and baseline cost must be estimated with reasonable accuracy to enable a comparison of vendor quotes. However, the current estimation method is not sufficiently accurate in terms of the baseline cost. This paper presents a motion-based time-estimating scheme with which the time required for the attachment of such insulators can be calculated more accurately. The scheme has been developed by analyzing the motions needed to attach 350 insulators and then designating representative motions and their time values. For this purpose, a modular arrangement of predetermined time standards (MODAPTS) is adopted. Motion-based time-estimation method is useful in terms of simplicity and accuracy. It enables design engineers to estimate the time required for the attachment based only on a drawing of the insulator and a few MODAPTS rules. Estimates made with this method should vary from the actual value by no more than 9.5%.     

Modeling time criticality of information

In this paper, we continue the research on formal treatment of attributes of information, based on the computational approach. In this scenario, the usefulness of advisory information is measured by the decrease in complexity of a problem we need to solve. We propose to model the time criticality via usefulness of a piece of information which is received during the computation. As a modeling tool, we use deterministic finite automata.     

PET熔体输送系统中熔体温度自动控制方案

在热媒温度控制系统中以熔体温度作为主参数,组成串极调节系统。投入运行后,熔体温度稳定,调节阀动作平稳,效果良好。     

Determination of temporal information granules to improve forecasting in fuzzy time series

Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.     

Semantic weak signal tracing

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organization’s decision making in strategic early warning environment.     

Single frame correction of motion artifacts in PMD-based time of flight cameras

One of the leading time of flight imaging technologies for depth sensing is based on Photonic Mixer Devices (PMD). In PMD sensors each pixel samples the correlation between emitted and received light signals. Current PMD cameras compute eight correlation samples per pixel in four sequential stages to obtain depth with invariance to signal amplitude and offset variations. With motion, PMD pixels capture different depths at each stage. As a result, correlation samples are not coherent with a single depth, producing artifacts. We propose to detect and remove motion artifacts from a single frame taken by a PMD camera. The algorithm we propose is very fast, simple and can be easily included in camera hardware. We recover depth of each pixel by exploiting consistency of the correlation samples and local neighbors of the pixel. In addition, our method obtains the motion flow of occluding contours in the image from a single frame. The system has been validated in real scenes using a commercial low-cost PMD camera and high speed dynamics. In all cases our method produces accurate results and it highly reduces motion artifacts.     

Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

PLC上位机监控软件的开发

为提高硫化机控制水平，采用电阻式触摸平板电脑为上位机与PLC相结合。系统设计主要包括各监控画面的设计和上位机与PLC之间的通信设计，监控主画面设计成包含所有操作员需要了解的数据。实时曲线画面为圆盘型和直线型两种，上位机与PLC之间的通信采用VB6.0编程，实际运行证明状态良好。     

ADHESION OF ELASTOMERS: DWELL TIME EFFECTS

The strength of adhesion of elastomers to rigid substrates generally increases with time of contact. This effect has been studied for samples of butyl and chlorobutyl rubber adhering to some rigid substrates. The peel strength increased continuously over long periods of contact until in some cases failure became cohesive within the elastomer layer. At higher temperatures the strength increased more rapidly, consistent with the WLF relation governing molecular motions. It is postulated that slow molecular rearrangements occur at the interface and increase the bond strength. A criterion for the observed transition from interfacial to cohesive failure is suggested.     

宽带功率放大器PGSC建模方法及预失真研究

针对宽带功率放大器的强记忆效应特性,提出一种功放建模和数字预失真方法——PGSC模型。利用广义记忆多项式(GMP)、特定交叉项(SCT)及记忆时刻信号交叉项(CIMT)3个基函数来构造功放行为模型及数字预失真器,并搭建实际测试平台对模型的精度及线性化效果进行验证。测试结果表明,与PMEC方法相比,PGSC方法建模时的归一化均方误差减少了2.1 dB,数字预失真时输出信号的三阶邻信道功率比降低了4.94/2.03 dB;与GMP方法相比,PGSC方法仅利用73%的系数即可得到更高的模型精度和更好的线性化效果。