The Personalization Willingness Paradox: An Empirical Evaluation of Sharing Information and Prospective Benefit of Online Consumers

Online enterprises today use information about customers to improve service and design personalized offerings. To do this successfully, however, enterprises must collect consumer information. This study enhances awareness about a central paradox for firms investing in personalization; namely, that consumers who value information utility are also more likely to participate in personalization. We examine the relationship between prospective benefit and consumer willingness to share information for online personalization. Based on a survey of over 800 online consumers, we examine the question of whether customer perceived information valuable is associated with consumer willingness to be profiled online. Our results indicate that customers who desire greater profits will have a greater level of trust, and then more willing to be profiled. This result poses a dilemma for firms, is the bought information accurate and reliable? In order to manage this dilemma, we suggest that enterprises build trust for the core values and knowledge management systems that address the needs of consumers, and adopt a strategy of providing personalization features accepting that the privacy sensitive minority of consumers

Improved multi-point communication for data and voice over IEEE 802.11b

There is a growing demand for faster, improved data and voice services in rural areas without modern telecom infrastructure. A wireless network is often the only feasible solution for providing network access in this environment, due to the sparse populations and difficult natural conditions. A system solution that incorporates the Multipoint Communication System (MCS) algorithm created by TRLabs into the available IEEE 802.11b Wireless Local Area Network (WLAN) devices was proposed and studied in this thesis. It combines the advantages of both systems, that is, the MCS’ capability of integrating Voice over Internet Protocol (VoIP) and data services and the IEEE 802.11b standard, currently the most widely used in WLAN products. A system test bed was set up inside Network Simulator-2 (NS-2). The data and VoIP performance was tested. Modifications to the original MCS algorithm to improve system performance were made throughout this thesis. In a constant rate radio channel, data performance (throughput and transmission efficiency) was measured using the original MCS algorithm, which was comparable to the standard Distribution Coordination Function (DCF) operation of IEEE 802.11b when both were simulated at similar conditions. On an 802.11b platform, the Automatic Rate Fallback (ARF) feature was incorporated into the original MCS algorithm. However, when clients with different data rates were present in the same channel, all the clients involved received unacceptably low and equal data throughput, dragged down by the low rate clients. A modified MCS data polling algorithm was proposed with the capability of repeated polling, which eliminated the negative effect of low rate clients in a multi-rate channel. In addition, the original MCS algorithm was modified to be more efficient in the voice polling process. The voice performance and data throughput were tested at various conditions. However, the one-by-one polling still resulted in very low voice transmission efficiency. The time wasted became more severe with increasing relay distance and channel rate (only 8.5% in an 11 Mbps channel at 30 km). A new voice handling process similar to Time Division Multiple Access (TDMA) mode was implemented and simulated. Its voice efficiency can be kept at 25% at any setting of relay distance and channel rate. Data transmission in the same channel can also benefit from using the new voice scheme. The normalized saturation throughput could be improved by 13.5% if there were 40 voice clients involved in an 11 Mbps channel at the relay distance of 15 km, compared to the original MCS algorithm. More improvement in voice efficiency, voice capacity, and data throughput can be achieved at longer relay distance, or with more voice calls set up

A novel expert system of fault diagnosis based on vibration for rotating machinery

To avoid significant losses of rotating machinery which works in high-speed and heavy load for long-term, it is necessary to find faults by means of vibrations. A novel expert system of vibration fault diagnosis based on artificial intelligence for rotating machinery was presented, in which a equipment property database is established to obtain the symptom frequencies of fault of components, such as rotor, roll bearing and gear box, of equipment, so any fault can be found quickly and effectively and then the losses of fault can be reduced and further eliminated. The diagnostic reasoning engine of the system combined the forward reasons method and the forward-backward hybrid reasons method. It is proved by the diagnostic examples that the system is reasonable and scientific in structure, quick and reliable in diagnosis

A Novel Hybrid Evaluation Method for Transfer Efficiency Assessment between Rail Transit and Public Bicycles

This paper proposes a new hybrid evaluation method including Improved Analytic Hierarchy Process (IAHP), Entropy Method (EM), and Grey Comprehensive Evaluation Method (GCEM) to assess the transfer efficiency between rail transit and public bicycles. In particular, the IAHP method that replaces the nine-scale approach with three-scale approach to naturally meet the consistency requirements is applied to qualitatively calculate the weights of evaluation indices, the EM method is utilized to calculate the weights of evaluation indices with relatively high degrees of quantification, and the GCEM method is used to calculate the transfer efficiency between rail transit and public bicycles. In addition, a three-level evaluation-index system including target level, criteria level and index level is established. A numerical example is also provided to verify the feasibility of the proposed hybrid evaluation method and explore the reasons for low transfer efficiency between rail transit and public bicycles.</p

A Study on Image Reconfiguration Algorithm of Compressed Sensing

Compressed sensing theory is a subversion of the traditional theory. The theory obtains data sampling points while achieves data compression. The main content of this thesis is reconstruction algorithm. It’s the key of the compressed sensing theory, which directly determines the quality of reconstructed signal, reconstruction speed and application effect. In this paper, we have studied the theory of compressed sensing and the existing reconstruction algorithms, then choosing three algorithms (OMP, CoSaMP, StOMP) as the research. On the basis of summarizing the existing algorithms and models, we analyze the results such as PSNR, relative error, matching ratio and running time of them from image signal respectively. In the three reconstruction algorithms, OMP algorithm has the best accuracy for image reconstruction. The convergence speed of CoSaMP algorithm is faster than that of the OMP algorithm’s, but it depends on sparsity K quietly. StOMP algorithm on image reconstruction effect is the best, and the convergence speed is also the fastest