A universal Wi-Fi fingerprint localization method based on machine learning and sample differences

Wi-Fi technology has become an important candidate for localization due to its low cost and no need of additional installation.The Wi-Fi fingerprint-based positioning is widely used because of its ready hardware and acceptable accuracy,especially with the current fingerprint localization algorithms based on Machine Learning(ML)and Deep Learning(DL).However,there exists two challenges.Firstly,the traditional ML methods train a specific classification model for each scene;therefore,it is hard to deploy and manage it on the cloud.Secondly,it is difficult to train an effective multi-classification model by using a small number of fingerprint samples.To solve these two problems,a novel binary classification model based on the samples’differences is proposed in this paper.We divide the raw fingerprint pairs into positive and negative samples based on each pair’s distance.New relative features(e.g.,sort features)are introduced to replace the traditional pair features which use the Media Access Control(MAC)address and Received Signal Strength(RSS).Finally,the boosting algorithm is used to train the classification model.The UJIndoorLoc dataset including the data from three different buildings is used to evaluate our proposed method.The preliminary results show that the floor success detection rate of the proposed method can reach 99.54%(eXtreme Gradient Boosting,XGBoost)and 99.22%(Gradient Boosting Decision Tree,GBDT),and the positioning error can reach 3.460 m(XGBoost)and 4.022 m(GBDT).Another important advantage of the proposed algorithm is that the model trained by one building’s data can be well applied to another building,which shows strong generalizable ability.

Exact Distribution of Difference of Two Sample Proportions and Its Inferences

Comparing two population proportions using confidence interval could be misleading in many cases, such as the sample size being small and the test being based on normal approximation. In this case, the only one option that we have is to collect a large sample. Unfortunately, the large sample might not be possible. One example is a person suffering from a rare disease. The main purpose of this journal is to derive a closed formula for the exact distribution of the difference between two independent sample proportions, and use it to perform related inferences such as a confidence interval, regardless of the sample sizes and compare with the existing Wald, Agresti-Caffo and Score. In this journal, we have derived a closed formula for the exact distribution of the difference between two independent sample proportions. This distribution doesn’t need any requirements, and can be used to perform inferences such as: a hypothesis test for two population proportions, regardless of the nature of the distribution and the sample sizes. We claim that exact distribution has the least confidence width among Wald, Agresti-Caffo and Score, so it is suitable for inferences of the difference between the population proportion regardless of sample size.

Design of the sample cell in near-field surface-enhanced Raman scattering by finite difference time domain method

The rational design of the sample cell may improve the sensitivity of surface-enhanced Raman scattering （SERS） detection in a high degree. Finite difference time domain （FDTD） simulations of the configuration of Ag film-Ag particles illuminated by plane wave and evanescent wave are performed to provide physical insight for design of the sample cell. Numerical solutions indicate that the sample cell can provide more ＂hot spots＂ and the massive field intensity enhancement occurs in these ＂hot spots＂. More information on the nanometer character of the sample can be got because of gradient-field Raman （GFR） of evanescent wave. OCIS codes： 290.5860, 240.0310, 240.6680, 999.9999 （surface-enhanced Raman scattering）.

Application of Transfer Learningin Mechanical Equipment Intelligent Diagnosis:Literature Review

Accelerating the process of intelligent manufacturing and the demand for new industrial productivity,the operating conditions of machinery and equipment have become ever more severe.As an important link to ensure the stable operation of the production process,the condition monitoring and fault diagnosis of equipment have become equally important.The fault diagnosis of equipment in actual production is often challenged by variable working conditions,large differences in data distribution,and lack of labeled samples,etc.Traditional fault diagnosis methods are often difficult to achieve ideal results in these complex environments.Transfer learning(TL)as an emerging technology can effectively utilize existing knowledge and data to improve the diagnostic performance.Firstly,this paper analyzes the trend of mechanical equipment fault diagnosis and explains the basic concept of TL.Then TL based on parameters,TL based on features,TL based on instances and domain adaptive(DA)methods are summarized and analyzed in terms of existing TL methods.Finally,the problems faced in the current TL research are summarized and the future development trend is pointed out.This review aims to help researchers in related fields understand the latest progress of TL and promote the application and development of TL in mechanical equipment diagnosis.