Ontology-based data mining approach implemented for sport marketing

Since sport marketing is a commercial activity, precise customer and marketing segmentation must be investigated frequently and it would help to know the sport market after a specific customer profile, segmentation, or pattern come with marketing activities has found. Such knowledge would not only help sport firms, but would also contribute to the broader field of sport customer behavior and marketing. This paper proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer knowledge from the database. Knowledge extracted from data mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to the case firm, Taiwan Adidas, for possible product promotion and sport marketing.     

Mining demand chain knowledge of life insurance market for new product development

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.     

A blind watermarking method using maximum wavelet coefficient quantization

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks.     

Data mining for adaptive learning sequence in English language instruction

The purpose of this paper is to propose an adaptive system analysis for optimizing learning sequences. The analysis employs a decision tree algorithm, based on students’ profiles, to discover the most adaptive learning sequences for a particular teaching content. The profiles were created on the basis of pretesting and posttesting, and from a set of five student characteristics: gender, personality type, cognitive style, learning style, and the students’ grades from the previous semester. This paper address the problem of adhering to a fixed learning sequence in the traditional method of teaching English, and recommend a rule for setting up an optimal learning sequence for facilitating students’ learning processes and for maximizing their learning outcome. By using the technique proposed in this paper, teachers will be able both to lower the cost of teaching and to achieve an optimally adaptive learning sequence for students. The results show that the power of the adaptive learning sequence lies in the way it takes into account students’ personal characteristics and performance; for this reason, it constitutes an important innovation in the field of Teaching English as a Second Language (TESL).     

抽油机在线故障监控器的设计与实现

油田安全生产是石油工业生产和发展的基础和前提.研究设计了一种基于C8051F020单片机的抽油机在线故障监控器.通过对抽油机采用多点监测,可以监测抽油机的运行状态、井口原油温度以及压力参数.详细介绍了系统结构、工作原理、硬件构成及软件设计.抽油机在线故障监控器在技术和经济上能适应油田生产发展的要求,并提高了故障判断的准确性和可靠性.     

改进工作集选择策略的序贯最小优化算法

针对标准序贯最小优化(sequential minimal optimization, SMO)算法采用可行方向工作集选择策略所带来的缓存命中率低下问题,给出了SMO类型算法每次迭代所带来的目标函数下降量的二阶表达式,并据此提出了一种改进的工作集选择策略.新策略综合考虑算法收敛所需的迭代次数及缓存效率,从总体上减少了核函数计算次数,因此极大提高了训练效率,并且,它在理论上具有严格的收敛保障.实验结果表明,核函数越复杂,样本维度越高,缓存容量相对训练样本的规模越小,改进工作集选择策略的SMO算法相较于标准SMO算法的性能提高就越显著.     

传感器网络中一种基于多元回归模型的缺失值估计算法

在无线传感器网络中,感知数据的缺失问题不可避免,并且给无线传感器网络的各种应用带来了巨大困难.解决该问题的最好办法是对缺失数据进行准确估计.提出了一种基于多元回归模型的缺失值估计算法.该算法首先依感知数据的时间相关性和空间相关性分别采用多元线性回归模型对缺失数据进行估计,然后根据回归模型的拟合优度对基于时间维和空间维求出的两个估计值分别赋予相应的权值系数,并将其加权平均值作为缺失数据的最后估计值.由于该算法在对缺失值进行估计时,同时考察多个邻居节点并联合地用其感知数据来共同估计缺失值,因此该算法具有可靠、稳定的估计性能.在两个真实的数据集合上对该算法进行了测试,实验结果表明提出的缺失值估计算法能够有效估计无线传感器网络中的缺失数据.     

基于GIS数据的植被实时虚拟可视化

针对大范围场景的植被实时虚拟可视化问题,提出一种在程序运行过程中实时生成各个体植被分布数据和属性数据的方法。该方法使用WangTiles和植被覆盖的地理信息系统数据。采用几何细节层次模型和基于纹理图像的方法对植被进行实时绘制。实验结果表明,该方法能在包含植被的大范围场景中取得较好的可视化效果,在场景漫游时可以保持交互的帧速率。     

Identify Congested Links Based on Enlarged State Space

When paths share a common congested link, they will all suffer from a performance degradation. Boolean tomography exploits these performance-level correlations between different paths to identify the congested links. It is clear that the congestion of a path will be distinctly intensive when it traverses multiple congested links. We adopt an enlarged state space model to mirror different congestion levels and employ a system of integer equations, instead of Boolean equations, to describe relationships between the path states and the link states. We recast the problem of identifying congested links into a constraint optimization problem, including Boolean tomography as a special case. For a logical tree, we propose an up-to-bottom algorithm and prove that it always achieves a solution to the problem. Compared with existing algorithms, the simulation results show that our proposed algorithm achieves a higher detection rate while keeping a low false positive rate.     

Output feedback robust MPC for LPV system with polytopic model parametric uncertainty and bounded disturbance

The output feedback robust model predictive control (MPC), for the linear parameter varying (LPV) system with norm-bounded disturbance, is addressed, where the model parametric matrices are only known to be bounded within a polytope. The previous techniques of norm-bounding technique, quadratic boundedness (QB), dynamic output feedback, and ellipsoid (true-state bound; TSB) refreshment formula for guaranteeing recursive feasibility, are fused into the newly proposed approaches. In the notion of QB, the full Lyapunov matrix is applied for the first time in this context. The single-step dynamic output feedback robust MPC, where the infinite-horizon control moves are parameterised as a dynamic output feedback law, is the main topic of this paper, while the multi-step method is also suggested. In order to strictly guarantee the physical constraints, the outer bound of the true state replaces the true state itself, so tightness of this bound has a major effect on the control performance. In order to tighten the TSB, a procedure for refreshing the real-time ellipsoid based on that of the last sampling instant is given. This paper is conclusive for the past results and far-reaching for the future researches. Two benchmark examples are given to show the effectiveness of the novel results.