Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.

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Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, the sparsity and stability of RSS fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated RSS Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse RSS acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of RSS measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey.

This thesis tries to offer a guidance for the work connecting between two systems through the building of a new type rural social endowment insurance (hereinafter referred to as NTRSEI ) system and design a new interface of a financial interface software (hereinafter referred to as FIS) which includes confirm the interface means and interface implementation. The building of this system enables us to standard the professional business under help of modern computer communication technology, and also speed up the managerial Scottification progress of NTRSEI.

]: In this paper, a rural credit risk control factor was studied from the perspective of financial institutions. It was used of 386 farmer households from Heilongjiang province credit investigation data to carry out empirical. The article from the farmer’s farmers professional cooperative level of wealth, the level of debt, level of education, use of the loan in four aspects the establishment of rural credit risk identification index system, Construction of support vector classification model and the Logistic regression model. The results show that, Wealth and education levels have positive correlation, indebted level and use of the loan has negative correlation.Keywords: Management System; Support vector machine; Farmers Professional Cooperative;