Between close and distanced links: Firm internationalization in a subsea cluster in Western Norway

Karlsen, A. & Nordhus, M. 2011. Between close and distanced links: Firm internationalization in a subsea cluster in Western Norway. Norsk Geografisk Tidsskrift–Norwegian Journal of Geography Vol. 65, 202–211. ISSN 0029-1951.

The cluster concept has attracted attention from scholars and become increasingly popular in regional politics, as exemplified by The Norwegian Centres of Expertise programme. External linkages of cluster firms have become a concern inspired by the notions ‘local buzz’ and ‘global pipelines’. Viable clusters are dependent on the quality of both internal industrial environment and external linkages. The article focuses on internationalization of SMEs from a cluster perspective and discusses how cluster firms take advantage of different dimensions of proximity in their internationalization endeavours. By applying the industrial network approach, firms’ strategies and positions at different stages of internationalization are integrated in the analysis. The cases are four SMEs located in a ‘subsea cluster’ close to Bergen, Norway. Data were collected through interviews with top managers and observations during cluster-related meetings. The cases differ with regard to size and international experiences. Firms that are highly internationalized rely on cognitive or organizational proximity when they internationalize further. Such firms provide opportunities for other cluster firms. In contrast, less internationalized firms rely on social and institutional types of proximity. As they collaborate with larger and more experienced cluster firms they can skip some of the resource-demanding steps in the internationalizing process.     

地方生产网络尺度、结构与组织研究及其对城市发展的启示

地方生产网络是分析地方经济十分有效的理论工具,而空间尺度、形态结构和组织方式是地方生产网络研究内容的3个重要方面。运用文献分析法,对地方生产网络的相关研究进行梳理,研究发现,在空间尺度方面,对网络行动者不同空间尺度的相互关系研究较多,地方内部关系的变化机理少有探讨;在形态结构方面,理想化类型的总结较多,具体案例分析较少;在组织方式方面,往往局限在某个时段内,缺少对不同时段组织方式演化动力的研究。未来可以与城市研究相结合,通过城市生产网络的研究,为城市产业竞争力的提升提供建议。     

新疆地区M≥7强震有序网络结构及其预测研究

自1800年以来,新疆及其邻区M≥7强震具有显著的可公度性和有序性,其主要有序值为30a×k(k=1,2,3)、11～12a、41～43a与18～19a等.根据翁文波信息预测理论和复杂网络技术,努力探索具有中国特色自主创新的强震预测方法,构建新疆地区M≥7强震信息有序网络结构,充分揭示了新疆地区近200a来M≥7强震链的活动规律,并据此较为成功地预测了2008年于田7.3级强震的发生.同时还提出新的预测意见:2014-2015年、2019-2020年以及2026年前后新疆地区仍有可能发生M≥7强震.研究结果表明强震是可以预测的.该方法对于强震的中长期跨越式预测具有独特效果.     

声速误差对圆走航水下控制点定位影响分析

针对圆走航定位声速剖面测量不准确引起水下控制点坐标解算存在偏差的问题,首先介绍了声速不确定性对测距误差的影响,并将声速测距误差分成背景声速剖面误差、随机误差、测距误差长周期项和测距误差短周期项,再通过分析坐标改正数方程得到不同声速测距误差项对控制点坐标定位的影响。最后,设计2组仿真实验对该理论进行验证,实验结果与理论推导的结论一致。     

城市地下管网管理信息系统中管线三维显示的实现

介绍了城市地下综合管网管理信息系统的目标、结构及其功能,详细阐述了在城市地下综合管网管理信息系统中管线三维显示的实现过程;基于OpenGL技术管线的三维显示功能将各种管线间的空间位置关系直观形象地表达,取得较为满意的效果.     

Neuro-fuzzy modelling in mining geochemistry: Identification of geochemical anomalies

Local and mine scale exploration models for anomaly recognition within known ore fields are discussed. Traditional geochemical exploration methods are based on multivariate statistical analysis, metallometry, vertical geochemical zonality and criteria of natural field geochemical associations, which suffer several shortcomings, including lack of a geostatistical generalised approach for separating anomalies from background. These shortcomings make the interpretation process time consuming and costly. Fuzzy set theory, fuzzy logic and neural network techniques seem very well suited for typical mining geochemistry applications. The results, obtained from applying the proposed technique to a real scenario, reveals significant improvements, comparing the results obtained from applying multivariate statistical analysis. Computationally, the introduced technique makes possible, without exploration drilling, the distinction between blind mineralisation and zone of dispersed ore mineralisation. The methodology developed in this research study has been verified by testing it on various real-world mining geochemical projects.     

Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS

This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed by employing field investigations and reinforcement working reports for the existing ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area. An erratum to this article can be found at     

The application of artificial neural networks to simulate meltwater runoff of Keqikaer Glacier,south slope of Mt. Tuomuer,western China

Because glacial melting provides a significant amount of surface water resources, especially in cold arid regions, it is critical that effective methods be developed for predicting their behavior. Glacier runoff differs from other types of stream flows, being characterized by large diurnal fluctuations, with maximum discharge during the summer months. Moreover, the size and remoteness of glaciers makes them difficult to study directly. Hence, developing effective modeling techniques is our best hope for understanding and predicting glacial melting phenomena. In the past, physics-based models have been used with some success. In this study, conducted in 2003 and 2004 on the Keqikaer Glacier on the south slope of Mt. Tuomuer, however, we used the newer artificial neural networks (ANNs) modeling technique. As the input nerve cell, we used the hourly wind speed, precipitation, air temperature, radiation balance, and ground temperature; the output nerve cell was the diurnal runoff at the glacial terminus. We then analyzed the simulated results under different scenarios by varying the input-nerve-cell parameters. It was found that ANN can simulate the process of glacier meltwater runoff successfully when basic parameters such as air temperature, precipitation and radiation balance are few. The results indicate that ANN can simulate the process of glacial meltwater runoff quite well, and that meteorological variables could in fact be used successfully to simulate glacier meltwater runoff using the ANN method.     

Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data

Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas of seismic data.     

Rockfill Strength Evaluation Using Cascade Correlation Networks

Performing comprehensive laboratory test programs to estimate rockfill strength for rockfill dam projects is a lengthy and onerous task because of the large sample-size. Accordingly, it has become a common practice to carry out limited experimental investigation, and extrapolate the results to the expected conditions in actual embankments. A number of investigators have established a function of the type τ = ασ^β, where τ and σ are the shear and normal stresses, respectively, and the constants α and β, which result from a fitting procedure, have no physical meaning. Results of laboratory tests on a variety of rockfills have shown that in addition to effective confining stresses the relative density, uniformity coefficient, maximum particle size and particle-breaking load influence rockfill strength. Thus these parameters must be included in any function for computing rockfill strength. Other parameters, whose influence is understood partially, are not included here. Given the non linear-multidimensional nature of the problem, in this paper a neuronal procedure is developed. This approach takes into account the influence of each of the parameters mentioned before. The network used in this article was defined after comparing the results obtained with a variety of algorithms. After several attempts, the Cascade Correlation Network (CCN) was found to yield most accurate strength predictions.