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The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.

A fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.

The fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study.

The paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

Science and technology policy plays an important role in promoting the development of economic and social development in China. At present, the research on science and technology policy is mainly focused on the basic theories and some quantitative research. The analyses for contents of massive science and technology policies are relatively less. This paper makes use of semantic technologies to extract and analyze the relatively important information from massive science and technology policies. The purpose of this paper is to facilitate users to quickly and effectively obtain valuable information from the massive science and technology policies. The key methods and study results are presented in the paper. The study results can provide references for further study and application in China.

The paper presented the analysis model and method for science and technology policy in China. The terms and sentences are the important information in the science and technology policy. The study adopted the technology of natural language processing to analyze the linguistics characteristics of terms and combined with statistical analyses to extract the terms from Chinese science and technology policy. Then, the authors designed an algorithm, calculated and analyzed the important sentences in Chinese science and technology policies. The experiments were run on the Java test platform.

This paper put forward the analysis model and method for science and technology policy in China. The study obtained the following conclusions: term extraction of science and technology policy: the paper analyzed characteristic of terms in Chinese science and technology policy and designed a method of extracting a term that was suitable for the science and technology policy. The calculation of important sentences for science and technology policy: the paper designed an algorithm and calculated the importance of the sentences to obtain valuable information from the massive science and technology policies.

In our methods, there are some defects to be improved or solved in the future. For example, the precision of algorithm needs to be improved. The significance of this paper is to propose and use the analysis model to process Chinese science and technology policy; we can provide an auxiliary tool to help policy beneficiaries. Enterprises and individuals can be more effective to extraction and mining information from massive science and technology policy and find the target policy.

To verify the effectiveness of the method, the paper selected the real policies about the new energy vehicles as experimental data; at the same time, the paper added uncorrelated policies. It used the proposed analysis model of science and technology policy to calculate and find out the relatively important sentences. The results of study showed that the proposed method can obtain better performance. It verified the validity of this method. The model and method have been applied to actual retrieval system.

The proposed model and method in the paper have been applied to actual retrieval system for users.

The paper proposed the new analysis model and method to analyze science and technology policies in China. The presented model and method are a new attempt. According to the experimental results, this exploration and study are valuable. In addition, the idea and method will give a good start for improving information services of massive science and technology policies in China.

The technical level of aircraft failure analysis plays a special role in ensuring the safety of civil aviation flight. Using appropriate methods for functional failures analysis can provide a reliable reference for aircraft safety. The purpose of this paper is to provide a new and comprehensive measure based on conventional functional hazard analysis (FHA) and grey system theory to analysis and evaluate the class that each failure belongs to.

This paper integrates multiple methods including the FHA, the fixed weight cluster, the Delphi method and the analytic hierarchy process (AHP). To begin with, use FHA method to sort out the corresponding failure states of a certain system from the perspective of function and determine the evaluation index. And then using group decision and AHP, determine the expert weight and index weight in the fixed weight cluster. The fixed weight cluster function is used to determine the grey class to which a certain functional failure belongs in the complex system.

In the past, the risk assessment of aircraft was mostly dominated by the subjective judgment of the experts, but it was not possible to give an objective observation score for each failure state. This paper addresses the problem efficiently as well as the feature of “little data, poor information.” The risk degree of each failure state can ultimately be replaced by a quantitative value.

This paper uses the idea of clustering in grey system theory to evaluate the risk of landing gear system. In the expert evaluation stage, different experts evaluated the impact degree of the aircraft's failure caused by its functions, so the final risk classification is subjective to some extent.

This study analyzed the different conditions of the landing gear, including the front wheel steering, front wheel damping, front wheel steering system, brake system fault information and so on. It can effectively divide the different failure states and their effects, which is helpful to improve the safety of aircraft landing gear system and provide some useful methods and ideas for studying the safety of aircraft systems.

Based on the FHA analysis process and the grey system theory, this paper determines various potential risks and their consequences of various functions according to the hierarchy, so as to carry out further detailed analysis on the risks that may occur under various functional conditions and take certain measures to prevent them. It is helpful to improve the risk management and control ability of aircraft in the actual flight process and to guarantee the safety of people's lives and property.

This paper is a pioneer in integrating the FHA method and the grey system theory, which exactly can be used to address the problem with the character of “little data, poor information.” The model established in this paper for the defects of FHA can effectively improve the accuracy of FHA, which is of great significance for the study of safety. In this paper, a case about landing gear system is given to illustrate the effectiveness of the model.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

Devices for step estimation are body-worn devices used to compute steps taken and/or distance covered by the user. Even though textiles or clothing are foremost to come to mind in terms of articles meant to be worn, their prominence among devices and systems meant for cadence is overshadowed by electronic products such as accelerometers, wristbands and smart phones. Athletes and sports enthusiasts using knee sleeves should be able to track their performances and monitor workout progress without the need to carry other devices with no direct sport utility, such as wristbands and wearable accelerometers. The purpose of this study thus is to contribute to the broad area of wearable devices for cadence application by developing a cheap but effective and efficient stride measurement system based on a knee sleeve.

A textile strain sensor is designed by weft knitting silver-plated nylon yarn together with nylon DTY and covered elastic yarn using a 1 × 1 rib structure. The area occupied by the silver-plated yarn within the structure served as the strain sensor. It worked such that, upon being subjected to stress, the electrical resistance of the sensor increases and in turn, is restored when the stress is removed. The strip with the sensor is knitted separately and subsequently sewn to the knee sleeve. The knee sleeve is then connected to a custom-made signal acquisition and processing system. A volunteer was employed for a wearer trial.

Experimental results establish that the number of strides taken by the wearer can easily be correlated to the knee flexion and extension cycles of the wearer. The number of peaks computed by the signal acquisition and processing system is therefore counted to represent stride per minute. Therefore, the sensor is able to effectively count the number of strides taken by the user per minute. The coefficient of variation of over-ground test results yielded 0.03%, and stair climbing also obtained 0.14%, an indication of very high sensor repeatability.

The study was conducted using limited number of volunteers for the wearer trials.

By embedding textile piezoresistive sensors in some specific garments and or accessories, physical activity such as gait and its related data can be effectively measured.

To the best of our knowledge, this is the first application of piezoresistive sensing in the knee sleeve for stride estimation. Also, this study establishes that it is possible to attach (sew) already-knit textile strain sensors to apparel to effectuate smart functionality.