Empirical examination of critical failure factors of continuous improvement deployments: stage-wise results and a contingency theory perspective

Organisations implement various Continuous Improvement (CI) practices such as Total Quality Management, Lean, Six Sigma and Lean Six Sigma (LSS) for improving their processes. Drawing from the success and failure stories of these structured CI practices, scholars enumerated Critical Success Factors and Critical Failure Factors (CFFs). This study empirically examines the occurrence of various CFFs across different stages of CI deployment. Further, from a contingency theoretic perspective, this study investigates their associations with contextual variables by collecting survey data from 213 business units from the USA, the UK, China, and India. Principal Component Analysis is used to group CFFs across five CI deployment stages leading to an empirically refined framework for CI. Crosstab analysis using the chi-square likelihood ratio presented associations of CFFs with contextual variables. Findings reveal significant differences in the occurrence of CFFs across countries. There is evidence that LSS is less prone to failures when compared with TQM, Lean and Six Sigma. The occurrence of CFFs has been relatively lower in smaller and medium-sized enterprises operating in the service sector. Findings also reveal that lessons learned from each CI deployment cycle, contribute to organisational learning, and thence, leading to success at the strategic CI level of maturity.     

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.     

Regional science in the Netherlands: A contextual interpretation of the “power of smallness”

This paper provides an overview of regional science research in the Netherlands since the 1960s. It adopts a contextual thematic approach in identifying and interpreting regional science research topics, practices and methodologies, inspired by Toynbee's challenge and response concept. It argues that place-based, cultural and historical conditions in the Netherlands have prompted a pragmatic and evidence-based scientific approach, in which in particular an innovative quantitative orientation accompanied by a methodological heterogeneity has given Dutch regional science a high international profile. Over the past decades, numerous regional scientists in the country have contributed to this achievement, too many to be mentioned in this overview. This paper has its limitations and offers mainly thematic examples of original—often quantitative—research undertaken by various Dutch regional scientists, that make a difference compared to general approaches in Europe and world-wide. From our concise review we conclude that – despite the small size of the country – the challenge and creative response model of Dutch regional science has been rather successful.     

基于时空序列模型的RBF神经网络在河流水位预测中的应用

河流水位预测一直以来都是水文预报中研究的热点。河流水位变化不定,具有时间上和空间上的变化性、多维性、动态性和不确定性等,给水位预测带来了挑战。本文综合考虑河流水位时空信息,建立基于时空序列的RBF神经网络预测模型来预测河流水位。实验中预测了金沙江下游向家坝水文站的水位信息,并将实验结果与其他多种水位预测方法比较,实验结果显示基于时空序列的RBF神经网络模型在河流水位预测中具有较高精度,证明了方法的可行性。     

结合限制密集轨迹与时空共生特征的行为识别

针对传统密集轨迹方法应用到真实场景后过多无效轨迹耗费存储与计算资源且严重影响有效特征提取的不足,提出一种新的人体行为识别算法。首先,检测视频帧中存在的人体目标并对获得的包含人体的矩形框进行扩展,利用扩展后的矩形框对传统密集采样特征点的范围进行筛选限制;然后,对筛选限制后的特征点在光流场中跟踪一定帧数获取限制密集轨迹,并在以限制密集轨迹为中心的时空体内构建一组包含轨迹的空间位置、时空上下文信息的特征描述子;最后在视觉词袋模型框架下,采用SVM对特征向量进行编码分类。结果显示:在KTH、YouTube和HMDB51 3个行为数据库上的识别准确率分别达到98.1%、89.7%和66.9%。证明本算法对复杂真实场景中的人体行为具有较高的识别能力。     

中心城区绿道智能选线规划研究——以宿迁为例

【目的】当前在绿道系统规划分析中较少应用自下而上的人本分析视角和时空间行为分析方法,缺乏对居民在绿道中行为方式的研究,在方法上对于机器学习的应用仍然处于探索阶段。【方法】以经典设计理论作为支撑,选取宿迁市中心城区作为案例地,整合手机信令数据、居民活动数据、街景数据等多源数据,借助人工智能、LSTM神经网络、时空行为分析等量化分析方法,分析居民真实出行的时空特征,提出绿道智能选线分析框架。【结果】结合LSTM神经网络、类A*算法等构建的轨迹模拟模型可有效开展绿道选线评估,模拟居民真实出行行为,提取潜在绿道网络。【结论】构建了中心城区绿道智能选线方法,将居民真实活动轨迹融入空间要素分析中,融合居民出行行为特征与街道环境要素,通过量化评估方法为绿道选线规划和建设提供指导。     

A novel prognosis and segmentation of necrosis (dead cells) in contrast enhanced T1-weighted glioblastoma tumor with automatic contextual clustering

In recent years, mortality rate with high-grade tumor has been increased significantly especially with glioblastoma (GBM) brain tumor while compared to other malignant brain tumor. Here, the amount of dead cells accommodated with the tumor tissue in GBM brain tumor play a vital task and necessitate an earlier diagnosis of malignancy with the GBM tumor. It inspires to implement new automatic diagnosis system which detects the dead cells and tumor tissue with the GBM brain tumor, such that the survival rate of the diseased can easily be prognosis by the Radiologist and Neurosurgeon. The main objective of this article is to detect the amount of dead cells with respect to tumor tissue associated with the GBM brain tumor which desires the impact factor of the brain tumor. In this framework, initially, the new contrast enhancement modality is incorporated to enhance the gray information over the dead cells and the tumor tissue with the T1-weighted MRI GBM brain tumor. In this enhancement, the edges of the tumor cells and its dead cells are magnified efficiently. As the noises and outliers with MR image is unpredictable and it experiences the variable amount of noises over the local window, the contextual information over each pixel of the image is adaptively modified with respect to the amount of noise over local window using adaptive contextual clustering. The performance evaluation of the framework is investigated which exhibits the overwhelming result compared to conventional detection techniques.     

Spatio-temporal analysis of precipitable water vapour over northwest china utilizing MERSI/FY-3A products

Northwest China is considered as the arid and semi-arid temperate continental climate, where the precipitation is closely related to precipitable water vapour (PWV) content. In this paper, the Medium-Resolution Spectral Imager (MERSI) water vapour products were first used to study the spatial and temporal characteristics of water vapour over Northwest China, which were developed by the National Satellite Meteorological Center of China from the Chinese second-generation polar orbit Meteorological Satellite Fengyun 3A (FY-3A). In order to utilize the MERSI water vapour products, the MERSI 5 min water vapour product is compared respectively with global positioning system (GPS), Aerosol Robotic Network (AERONET) and Radiosonde water vapour data in situ datasets. The results show that the water vapour values of the MERSI product are a slightly lower than referenced data, and the accuracy of MERSI product compared with GPS water vapour is the most agreeable, with a mean absolute percentage error (MAPE) of 22.83%. The PWV content displays a typical spatial distribution pattern in Northwest China that it is the highest in the southeast, the second for the northwest and the lowest in the south-centre. The water vapour content over each province in a descending order is Shaanxi, Ningxia, west of Inner Mongolia, Xinjiang, Gansu and Qinghai. The seasonal variation of water vapour content over Northwest China appears to be lowest in winter, followed by spring, then for autumn, and highest in summer. The PWV content of each province in Northwest China shows the periodic inner-annual variation, that is, the PWV content is lowest in January, and gradually increases with time till it peaks in July, and then decreases monthly afterwards, which agrees with the quadratic polynomial model by months. The standard deviation of the water vapour content in summer is 0.533–1.027 mm, while that in winter is 0.262–0.527 mm.     

Cartographic visualization of human trajectory data: overview and analysis

With the prevalence of mobile computing systems and location based services, large amounts of spatio-temporal data are nowadays being collected, representing the mobility of people performing various activities. However, despite the increasing interest in the exploration of these data, there are still open challenges in various application contexts, e.g. related to visualisation and human–computer interaction. In order to support the extraction of useful and relevant information from the spatio-temporal and the thematic properties associated with human trajectories, it is crucial to develop and study adequate interactive visualisation techniques. In addition to the properties of the visualisations themselves, it is important to take into consideration the types of information present within the data and, more importantly, the types of tasks that a user might need to consider in order to achieve a given goal. The understanding of these factors may, in turn, simplify the development and the assessment of a given interactive visualisation. In this paper, we present and analyse the most relevant concepts associated to these topics. In particular, our analysis addresses the main properties associated with (human) trajectory data, the main types of visualisation tasks/objectives that the users may require in order to analyse that data and the high-level classes of techniques for visualising trajectory data. In addition, this paper also presents an overview on a user study, conducted in function of this analysis, to compare two classes of visualisation techniques, namely static maps and space-time cubes, regarding their adequacy in helping users completing basic visualisation tasks.     

Detecting modes of transport from unlabelled positioning sensor data

Global positioning systems (GPS) logs recorded in personal devices contain rich information such as travel patterns, locations of frequent visits and place–event associations. There have been rather successful attempts in detecting the mode of transport from GPS logs such as walking, driving or taking a bus, which has found varied applications. However, the best-known schemes either require tedious manual labelling or pre-training process (or both). We present MoDetect (MD), a unsupervised scheme which eliminates the need of manual labelling and pre-training while attaining equal or greater accuracy compared with the best-known supervised methods. MD can also cater for differences in individual's behaviours, and hence may be more widely applicable than the existing schemes. To achieve this, MD relies on Kolmogorov–Smirnov test which offers a theoretical assurance when computing similarity between segments of records. Our analysis shows that the higher speed modes can be better differentiated through a weighted bootstrapping procedure. We also augment the decisions with reference to the transfer probabilities between different modes at locations identified from the GPS records.