The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

Application of the novel Droplet digital PCR technology for identification of meat species

The authenticity and traceability of meat products are issues of primary importance to ensure food safety. Unfortunately, food adulteration (e.g. the addition of inexpensive cuts to minced meat products) and mislabelling (e.g. the inclusion of meat from species other than those declared) happens frequently worldwide. The aim of this study was to apply a droplet digital PCR assay for the detection and quantification (copies μL⁻¹) of the beef, pork, horse, sheep, chicken and turkey in meat products. The analysis conducted on commercial meat showed the presence of traces of DNA from other animal species than those declared. We show that the method is highly sensitive, specific and accurate (accuracy = 100%). This method could be adopted by competent food safety authorities to verify compliance with the labelling of meat products and to ensure quality and safety throughout the meat supply chain, from primary production to consumption.     

Type II fuzzy set-based data analytics to explore amino acid associations in protein sequences of Swine Influenza Virus

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Adaptive fuzzy control for nontriangular form systems with time-varying full-state constraints

This article investigates an adaptive fuzzy tracking control problem for a class of nontriangular form systems with asymmetric time-varying full state constraints. Unknown functions are approximated by the fuzzy logic systems. A domination approach is employed to tackle the nontriangular form structure. Time-varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure full-state constraints satisfaction. Based on the backstepping technique and time-varying ABLFs, an adaptive controller is proposed and guarantees that all the signals in the closed-loop system are ultimately bounded and the time-varying full state constraints are met. Simulation examples are presented to further demonstrate the effectiveness of the proposed approach.     

Word length and lexical activation: Longer is better.

Many models of spoken word recognition posit the existence of lexical and sublexical representations, with excitatory and inhibitory mechanisms used to affect the activation levels of such representations. Bottom-up evidence provides excitatory input, and inhibition from phonetically similar representations leads to lexical competition. In such a system, long words should produce stronger lexical activation than short words, for 2 reasons: Long words provide more bottom-up evidence than short words, and short words are subject to greater inhibition due to the existence of more similar words. Four experiments provide evidence for this view. In addition, reaction-time-based partitioning of the data shows that long words generate greater activation that is available both earlier and for a longer time than is the case for short words. As a result, lexical influences on phoneme identification are extremely robust for long words but are quite fragile and condition-dependent for short words. Models of word recognition must consider words of all lengths to capture the true dynamics of lexical activation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于改进模糊综合评价法的水利工程社会评价研究

通过对水利工程社会评价及模糊综合评价法的介绍,讨论了模糊层次分析法中的一致性检验问题,并采用改进的模糊综合评价法对江垭水利枢纽工程进行社会评价,得到综合评价结果。     

微构造的地震识别与应用效果

胜利油田的油气勘探具有较大的剩余勘探潜力，且大部分油气蕴藏在微构造油气藏中。微构造油气藏在胜利油田的勘探开发老区均有分布，纵向上从沙四段到东营组均有发育。该类油气藏埋深浅、储层物性好、产量高，具有较高的经济效益。微构造油气藏在国内外研究较少，国内外较注重目的层沉积微构造的研究，且主要是沉积微构造对注采关系的影响。在对复杂断块的精细研究中，以前研究的重点也主要集中在应用钻井资料对断层的成因和对断层封堵性的研究上，而对微构造圈闭的成因、识别及成图，一直没有进行系统的分类研究。文章划分了微构造的类型，研究了微构造的特点，并认为构造作用、沉积作用和后期改造作用是微构造形成的３种主要原因。同时，利用地震资料对微构造圈闭的识别方法和描述技术进行了探索，形成了一套具有针对性的技术系列，取得了显著的效果，这对油气田老区的滚动勘探开发具有重要的意义。     

一种零知识证明协议的安全分析与改进

论文指出了文献[1]中的零知识证明协议在公钥参数选取上的安全漏洞,并从求解离散对数的角度进行了分析证明。然后吸取了DSA数字签名算法中安全密钥选取高效简单的优点,提出了一种比Schnorr身份识别方案快1／3的简单、安全、高效的零知识证明身份识别方案。