Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.     

Semantic compared cross impact analysis

The aim of cross impact analysis (CIA) is to predict the impact of a first event on a second. For organization’s strategic planning, it is helpful to identify the impacts among organization’s internal events and to compare these impacts to the corresponding impacts of external events from organization’s competitors. For this, literature has introduced compared cross impact analysis (CCIA) that depicts advantages and disadvantages of the relationships between organization’s events to the relationships between competitors’ events. However, CCIA is restricted to the use of patent data as representative for competitors’ events and it applies a knowledge structure based text mining approach that does not allow considering semantic aspects from highly unstructured textual information. In contrast to related work, we propose an internet based environmental scanning procedure to identify textual patterns represent competitors’ events. To enable processing of this highly unstructured textual information, the proposed methodology uses latent semantic indexing (LSI) to calculate the compared cross impacts (CCI) for an organization. A latent semantic subspace is built that consists of semantic textual patterns. These patterns are selected that represent organization’s events. A web mining approach is used for crawling textual information from the internet based on keywords extracted from each selected pattern. This textual information is projected into the same latent semantic subspace. Based on the relationships between the semantic textual patterns in the subspace, CCI is calculated for different events of an organization. A case study shows that the proposed approach successfully calculates the CCI for technologies processed by a governmental organization. This enables decision makers to direct their investments more targeted.     

Quantitative cross impact analysis with latent semantic indexing

Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology.     

Web mining based extraction of problem solution ideas

The internet is a valuable source of information where many ideas can be found dealing with different topics. A few numbers of ideas might be able to solve an existing problem. However, it is time-consuming to identify these ideas within the large amount of textual information in the internet. This paper introduces a new web mining approach that enables an automated identification of new technological ideas extracted from internet sources that are able to solve a given problem. It adapts and combines several existing approaches from literature: approaches that extract new technological ideas from a user given text, approaches that investigate the different idea characteristics in different technical domains, and multi-language web mining approaches. In contrast to previous work, the proposed approach enables the identification of problem solution ideas in the internet considering domain dependencies and language aspects. In a case study, new ideas are identified to solve existing technological problems as occurred in research and development (R&D) projects. This supports the process of research planning and technology development.     

基于加速度信号的过山车监测预警系统设计

鉴于国家对特种设备安全的要求和大型游乐设施测试系统的现状,设计了一种过山车监测预警系统。系统的信号采集装置安装在过山车上,采用数字信号处理器(DSP)作为主控芯片,运算速度快,可实现数据的实时采集,硬件电路多采用3.3V的低功耗芯片,节能环保。系统采集高频振动加速度和低频运动加速度信号,通过WiFi将数据传输给上位机,上位机软件使用LabVIEW开发,运用小波分析处理数据,并采用BP神经网络算法诊断故障,同时,对采集的信号和标准信号进行快速傅立叶变换(FFT)分析并作频谱图,可直观的检验诊断结果。如果诊断出故障,系统可发出警报并通过通用分组无线业务(GPRS)通知相关工作人员。实验结果表明:系统的运算速度快,诊断结果可靠。     

基于小波变换的电力热线预警信息盲检测方法研究

电力电网系统在电力传输控制中节点分布具有动态性,容易产生热线预警,为了提高电力热线预警检测能力,提出一种基于小波变换的电力热线预警信息盲检测方法。构建电力热线预警的信号模型,采用随机线性时间序列分析方法构建电力热线预警的统计信号分析模型,对电力热线预警信号采用相关性检测方法进行时频分解,采用小波分析方法将电力热线预警信号从时域向频域转换,根据接收到的两路电力热线预警信息的关联性进行特征匹配和自动分离,实现对电力热线预警信息的盲分离和噪声干扰抑制,采用小波变换和谱特征检测方法,实现对电力热线预警的盲检测。仿真结果表明,采用该方法进行电力热线预警检测的盲分离性能较好,检测准确概率较高,提高了电力热线预警能力。     

Morphologically constrained ICA for extracting weak temporally correlated signals

Recently the constrained ICA (cICA) algorithm has been widely applied to many applications. But a crucial problem to the algorithm is how to design a reference signal in advance, which should be closely related to the desired source signal. If the desired source signal is very weak in mixed signals and there is no enough a priori information about it, the reference signal is difficult to design. With some detailed discussions on the cICA algorithm, the paper proposes a second-order statistics based approach to reliably find suitable reference signals for weak temporally correlated source signals. Simulations on synthetic data and real-world data have shown its validity and usefulness.     

Predicting the optimum number, location, and signal sound level of auditory warning devices for manufacturing facilities

An analytical method for predicting the optimum number, location, and signal sound level of auditory warning devices is proposed. Factors which influence the perception of alarm signals, namely, ambient sound level, machining workstations (locations and their generated sound levels), workers' locations, and recommended signal sound level reaching workers are considered in the development of the objective function and constraints. Solving the multiple alarm location problem with a nonlinear programming technique yields the minimum number of auditory warning devices, their locations, and the minimum required sound level of warning signals. Two examples are given to demonstrate the applications of the predictive model.

Relevance to industry

The method presented in this paper enables engineers to determine the optimum number and location of auditory warning devices for manufacturing facilities to ensure adequate perception of warning signals. Since the proposed method is analytical in nature, it helps reduce trial-and-error effort normally spent in locating the alarm devices, thus saving both time and money.     

基于面部动作时空特征的疲劳预警算法

目前疲劳预警算法多采用实时监测报警的方式,这在高速行驶中具有很大的安全隐患。鉴于人类疲劳状态的时序相关性,提出一种基于面部动作时空特征提取的预警算法。首先,构建加入空间变换结构的卷积神经网络,识别人脸区域,对脸部特征点进行检测标记;其次,建立时空特征提取网络,利用采集的人脸图像序列,对未来图像序列进行预测并输出;最后,在输出的图像序列中根据眼部、嘴部综合状态判断是否发出警告。实验结果表明,以15 fps的速率采集图像,预测未来2 s 30帧图像的方式下,该算法能以90%以上的准确率提前26帧（约1.5 s）预警,且提前15帧（1 s）预警的准确率达到97%。在我国高速公路平均100 km/h的车速下,相当于提前40 m预警,能进一步减少交通事故的发生。     

INFORMATION INFRASTRUCTURE MANAGEMENT

Corporate survival in an information economy requires that IS managers assume the new role of information infrastructure management, an architecture-focused approach that results in an integrated IS and business organization with an integrated strategic plan. The approach enables IS managers to anticipate future organizational demands and stimulate innovative redesign of business processes to continuously improve performance.     

基于MOMEDA与双谱分析的滚动轴承早期故障诊断

滚动轴承早期故障阶段,故障特征微弱且环境噪声干扰严重,采集数据包含大量噪声信息,传统的包络谱分析难以提取故障特征信息。双谱分析理论上可以抑制高斯噪声,但很难从强背景噪声下提取出微弱故障特征。而多点最优调整的最小熵解卷积（Multipoint Optimal Minimum Entropy Deconvolution Adjusted,MOMEDA）方法能增强信号中的冲击特征,但其效果和故障信号周期区间等参数有关。利用MOMEDA与双谱分析进行信号处理,将提取到的信号高阶谱特征作为滚动轴承早期故障分类依据。利用MOMEDA方法对采集信号进行滤波处理,提取出有冲击特征的时域信号;对特征增强的信号进行双谱分析,从高阶谱中提取故障特征。经过仿真信号分析和实际轴承故障信号验证,该方法能有效地提取出滚动轴承早期故障特征,实现故障诊断。     

Mitigating information asymmetry to acquire venture capital financing for digital startups in China: The role of weak and strong signals

This paper addresses how weak and strong signals affect venture capital funding acquired by digital startups at their early stage in various industries of China. We also articulate the interaction mechanism of these strong and weak signals by demonstrating their complementary or substitutive effects in alleviating information asymmetry on startup quality, which can help digital startups secure venture capital financing. Drawing on signalling theory and institutional legitimacy theory, we introduce application (app) downloads as a novel strong signal that can reduce market legitimacy concerns, and previous-round venture capitalist reputation as a traditional strong signal that mitigates regulatory legitimacy concerns. We treat founders' startup and IT experience as weak signals, as they provide rhetorical and indirect information indicating a startup's potential to establish regulatory and market legitimacy. The study empirically investigates our hypotheses using data of 163 digital startups in various industries of China. Results confirm the positive relationships between strong signals and venture capital funding secured by a digital startup. Furthermore, signals of similar strength are found to complement each other's effects in certain situations, while strong signals can reduce the effects of weak signals on a digital startup's financing performance under specific conditions that create these mixed effects. Implications for digital startup research and practice as well as limitations and suggestions for future research are discussed.     

Multivariate information bottleneck

The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural code, and spectral analysis. Here, we introduce a general principled framework for multivariate extensions of the IB method. This allows us to consider multiple systems of data partitions that are interrelated. Our approach utilizes Bayesian networks for specifying the systems of clusters and which information terms should be maintained. We show that this construction provides insights about bottleneck variations and enables us to characterize the solutions of these variations. We also present four different algorithmic approaches that allow us to construct solutions in practice and apply them to several real-world problems.     

运动状态下的智能远程心电监测预警系统设计

基于单导联方式设计了一款具有运动状态识别、心电分级预警、远程无线传输等功能于一体的新型心电监测系统.本设计通过基于STM32 F103芯片的心电监测终端,将人体表面采集到的微弱心电信号进行滤波和放大处理,并结合加速度传感器实时获取人体的运动信息.将采集到的心电信号进行分析处理,有针对性地提出建议和警示,达到提前预警及紧急救助的目的.实验结果表明,采集的心电波形具有良好的医学参考价值,可为未来心血管疾病的远程智能医疗提供一定的技术支持.     

哈密市山洪灾害预警信息平台设计与实现

哈密市山洪灾害具有峰量大且集中、涨峰持续时间较短、破坏力大等特点，山洪的监测监视和实时预警是灾害防御的重要手段。哈密市山洪灾害预警信息平台的设计与实现，遵循哈密市防汛减灾“以时间换空间、以空间换时间”的思想理念，在哈密市水利一张图、一张网、一个库的基础上，汇聚气象、雨量、水位、 视频、应急预案和防汛工程图及相关责任人通讯录等防汛减灾联合调度的关键信息，以前端感知、信息监测和分析研判为主线，实现汛期雨水情的实时监测、监视、预警等，做到提前监测发现和应急准备，为防汛值班、 预报预警、应急处置、安全度汛提供技术支撑和保障。同时，实现哈密市中小型水库视频监视的汇聚，形成统一的视频监视平台，为哈密市委市政府组织应急局、气象局、水利局等多部门协同作业，发挥联合决策、调度优势提供支撑。     

变分自编码器在机械故障预警中的应用

由于往复压缩机、航空发动机等复杂机械振动信号呈现非平稳性,目前应用较多的单特征门限报警方法存在报警准确率低的问题。针对该问题,依据机械响应点的响应信号特点,提出一种基于变分自编码器的故障预警方法。该方法基于机械振动信号的高维特征参数,利用变分自编码器自学习出高维特征的统计分布模型,将正常工况模型作为基准模型,通过计算实时工况模型与基准模型间的差异度,并将其与自适应预警阈值相比较,实现故障预警。通过与单特征门限报警方法、基于状态子空间的预警方法进行比较,验证了该方法的优越性。实验结果表明,该方法能够提高机械故障预警的准确率并大幅提前故障的报警时间点,具有较高的时效性和较强的适应性。     

基于机器学习的车用主动防撞预警雷达信号识别系统研究

为了避免同向和相向干扰信号对识别精准度影响,引入机器学习,研究车用主动防撞预警雷达信号识别系统。在机器学习支持下,设计预警雷达信号识别系统总体架构,采用BGT24MTR12E6327XUMA1型号原装雷达收发器,通过TendaA9信号放大器将混频信号送到信号处理系统之中,以此控制汽车行驶速度。TMS320F206 DSP通过CAN总线连接外部设备和TJA1041A总线收发器,使PC和DSP之间能够串行通信。使用抗干扰流水线结构转换方式,基于机器学习获取无干扰实时状态信号。通过计算雷达信号相似度,设计具体识别流程。依据各个子雷达在汽车上分布情况设计实验,由实验结果可知,相向干扰下机器学习技术信号识别精准度最高可达到96%;同向干扰下机器学习技术信号识别精准度最高可达到94%,为车辆安全行驶提供设备支持。     

信息安全在物联网时代面临的挑战

物联网安全研究主要集中在物联网安全体系、物联网个体隐私保护模式、物联网安全相关法律的制定等方面。首先举例说明物联网在智能电网等生产生活领域的应用,然后讨论了物联网安全技术架构。最后根据物联网的安全架构分析了物联网安全面临的挑战。由此警示我们应提早应对物联网发展带来的信息安全等挑战。     

An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).