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Design experts need to fully understand the failure risk of a product to improve its quality and reliability. However, design experts have different understandings of and concepts in the risk evaluation process, which will lead to cognitive asymmetry in the product’s redesign. This phenomenon of cognitive asymmetry prevents experts from improving the reliability of a product, increasing the risk of product development failure. Traditionally, failure mode and effects analysis (FMEA) has been widely used to identify the failure risk in redesigning products and a system’s process. The risk priority number (RPN), which is determined by the risk factors (RF), namely, the occurrence (O), severity (S), and detection (D), is the index used to determine the priority ranking of the failure modes (FM). However, the uncertainty about the evaluation information for the RF and the coupling relationship within the FM have not been taken into account jointly. This paper presents an integrated approach for FMEA based on an interval-valued intuitionistic fuzzy set (IVIFS), a fuzzy information entropy, a non-linear programming model, and fuzzy PROMETHEE Ⅱ to solve the problem of cognitive asymmetry between experts in the risk evaluation process. The conclusions are as follows: Firstly, an IVIFS is used to present the experts’ evaluation information of the RF with uncertainty, and the fuzzy information entropy is utilized to obtain the weight of the experts to integrate the collective decision matrix. Secondly, a simplified non-linear programming model is utilized to obtain the weight of the RF to derive the weighted preference index of the FM. Subsequently, the coupling relationship within the FM is estimated by fuzzy PROMETHEE Ⅱ, where the net flow is given to estimate the priority ranking of the FM. Finally, the proposed approach is elaborated on using a real-world case of a liquid crystal display. Methods comparison and sensitivity analyses are conducted to demonstrate the validity and feasibility of the proposed approach.

Ultra Wideband (UWB)-based platform is a system which is suitable for high precision distance estimation and localization. A localization algorithm based on UWB platform can gain high accuracy of distance estimation and localization. Among localization methods, the maximum likelihood (MLL) is a simple and accurate location algorithm, which can meet the requirements of location algorithm based on UWB platform. In this paper, a method of maximum likelihood estimation based on outlier detection and UWB platform is studied. Firstly, we analyzed the principle of the UWB-based distance estimation and MLL localization algorithm. Then, the algorithm was verified by simulation analysis of localization accuracy and efficiency. Finally, indoor and outdoor localization experiments were carried out to compare and analyze the localization accuracy of the three localization algorithms in different environments, and the conclusions were drawn. The experimental results demonstrated that MLL has the highest accuracy in indoor and outdoor environments compared with LS and trilateration methods, and it is suitable localization algorithm for UWB localization.

When estimating the distance for wireless sensor networks (WSNs), we always suppose that a fixed curve model exists between the received signal strength indicator (RSSI) and communication distance. But there exist some negative factors in practice, which makes this assumption to contradict with the situation in real communication environment. It results in large distance estimation error with low efficiency. Thus, a lightweight WSN communication distance estimation method is presented, which is called distance estimation using distribution-based fingerprinting. First, we considered the uncertainty in RSSI values, and got the fingerprinting relationship in terms of RSSI data distribution, which is gained through a statistical calculation. Then, a data matching algorithm is implemented to estimate the communication distance. Finally, RSSI values in different conditions are utilized to validate this method. Experimental results demonstrated that the new method can obtain better results with high efficiency than other related methods, and can be applied in WSN localization system.

RSSI-based distance estimation method is widely used in wireless sensor network localization techniques, owing to its advantages including availability, low cost, flexibility and so on. However, RSSI measurement is easily affected by the adverse factors from the environment, which results in the localization result to be unhealthy. For the purpose of investigating the unhealthy degree of the localization result, we propose a new uncertainty analysis method for localization based on Received Signal Strength Indicator (RSSI) in three-dimensional (3-D) Wireless Sensor Network (WSN). Firstly, uncertain factors are obtained by analyzing the localization process; Secondly, sensitivities of different factors are gained by making use of the partial differential method; Finally, the uncertainty of localization result is expressed by the products of uncertainties and sensitivities of all factors, with the form of 2-norm. Experimental results in three different environments indicate that the proposed uncertainty analysis method has excellent performance on prediction of uncertainty in the localization result.