Feature network-driven quadrant mapping for summarizing customer reviews

With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature’s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.     

Optimal bundling and pricing decisions for complementary products in a two-layer supply chain

This article explores bundling and pricing decisions for two complementary products in a two-layer supply chain consisting of a multi-product manufacturer and a retailer. We establish four different pricing models under cases of decentralized decision, while considering different portfolio-bundling strategies of the manufacturer and the retailer. A game-theoretical method is used to characterize the corresponding equilibrium outcomes in each scenario. By further analyzing and comparing the maximum profits of all four possible scenarios, optimal bundling and pricing decisions for the manufacturer and the retailer are obtained, respectively. Model extensions and numerical examples are enriched to highlight the factors affecting optimal decision-making. Finally, valuable and interesting managerial insights are summarized. Results show that the upstream manufacturer always profits more when he sells complementary products separately. However, the optimal bundling decision of the downstream retailer is jointly determined by product complementarity (as a major factor) and the difference of product profitability (as a secondary factor). Market power cannot exert an influence on both optimal bundling decisions, but it can partly affect pricing decisions.     

A partial robust optimization approach to inventory management for the offline-to-online problem under different selling prices

This study examines an optimal inventory strategy when a retailer markets a product at different selling prices through a dual-channel supply chain, comprising an online channel and an offline channel. Using the operating pattern of the offline-to-online (O2O) business model, we develop a partial robust optimization (PRO) model. Then, we provide a closed-form solution when only the mean and standard deviation of the online channel demand distribution is known and the offline channel demand follows a uniform distribution (partial robust). Specifically, owing to the good structural properties of the solution, we obtain a heuristic ordering formula for the general distribution case (i.e., the offline channel demand follows a general distribution). In addition, a series of numerical experiments prove the rationality of our conjecture. Moreover, after comparing our solution with other possible policies, we conclude that the PRO approach improves the performance of incorporating the internet into an existing supply chain and, thus, is able to adjust the level of conservativeness of the solution. Finally, in a degenerated situation, we compare our PRO approach with a combination of information approach. The results show that the PRO approach has more “robust” performance. As a result, a reasonable trade-off between robustness and performance is achieved.     

我国农业信息化的内涵、现状及发展对策

论述了农业信息化的涵义和内容,指出了家业信息化在新的农业技术革命中的地位与作用。阐明了我国家业信息化的研究现状,并在此基础上,提出了我国农业信息化的发展对策。     

有限随机性决策中展望的特征性质

对于由有限后果诱导生成的随机性决策的展望集,本文引入基的概念,并证明了它的唯一性。由此,给出了后果作为展望集的基的特征性质。     

联合概率数据关联中双门限跟踪算法研究

基于拟方向的基本思想 ,提出了一种方向跟踪门 ,并给出了此方向跟踪门的具体构造方法。将椭圆跟踪门和方向跟踪门相结合 ,获得了基于双门限的跟踪算法 ,以此来减少可行联合事件的个数 ,达到降低联合概率数据关联计算量的目的。对此跟踪方法进行了仿真研究 ,给出了仿真结果 ,并对如何减少可行联合事件的个数进行了实例验算。     

粗集与神经网络的集成技术研究

粗集和神经网络都是处理不确定、不完全信息的软计算方法 ,两者曾在决策支持和知识获取等领域取得了很大的成功并得到初步应用。但两者都有局限性 ,同时在许多方面有互补性。因此粗集和神经网络的集成成为当今智能混合系统的一个重要分支 ,或许也是开发下一代专家系统的主流技术。从认识论的角度分析了粗集和神经网络的特点 ,评述了目前粗集和神经网络集成的理论和方法 ,指出其中存在的主要不足。最后提出了粗集和神经网络进一步集成所需解决的问题。     

基于自适应提升小波变换多分辨率数据融合

建立了基于小波去噪的多分辨率多传感器数据融合模型 ,引入了提升法自适应离散小波变换 ,根据最小均方 (LMS)自适应法确定伯恩斯坦 (Bernstein)提升滤波器的权系数 ,使其匹配低分辨率传感器的数据序列 ,接着使其对高分辨率采样数据的小波分解的尺度系数进行数据更新 ,实现不同分辨率数据的融合。最后对不同分辨率三传感器测量系统进行了数值仿真。实验结果表明 ,该方法可以有效地实现多分辨率多传感器数据融合 ,而且消除了噪声干扰 ,提高了系统的测量精度。     

一种突发通信的IJF-OQPSK调制信号解调方法

提出了一种用于时分多址突发通信系统中的IJF-OQPSK调制信号解调方法.介绍了电路的组成及原理,分析了系统的性能.该方法具有电路结构简单、数据恢复快、相位抖动小的优点,对提高突发通信系统的效率有显著作用.实验结果表明在时分多址突发通信系统中,该方法具有很好的使用价值.