Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making

Group/team decision-making is an integral part of almost all failure mode and effects analysis (FMEA) projects. A dysfunctional aspect of this decision-making fashion in fuzzy FMEA is that group/team members’ designs for membership functions and IF-THEN rules may be overshadowed by a member’s design. This problem is caused by groupthink, a pitfall known by the Organisational Behaviour science. This study aims to develop a fuzzy FMEA approach which is robust to the problem. We applied the Taguchi’s robust parameter design and investigated the effects of various control parameters namely Defuzzification, Aggregation, And and Implication operators for the fuzzy inference system (FIS). Our experiments illustrate that the control parameters, in the above-mentioned order, have the most effect on the signal-to-noise ratio (SNR). These factors’ optimal setting consists of the Centroid, Sum, Minimum and Minimum levels, respectively.     

PAPR Reduction in MIMO-OFDM Systems: Spatial and Temporal Processing

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is a promising solution for next generation wireless communications, due to high bandwidth efficiency, resistance to RF interference, and robustness to multipath fading. A major drawback of OFDM is its high peak-to-average power ratio (PAPR) which results in non-linearities in the output signal. In this paper, two methods based on spatial/temporal processing are proposed to reduce the PAPR of MIMO-OFDM systems. These methods divide the OFDM block at each transmit antenna into some subblocks. Then, spatial and temporal processing in the form of circular shifting or interleaving are applied to generate different candidate sequences. Finally, for each transmit antenna the candidate sequence with the lowest PAPR is chosen for transmission. Compared to the conventional PAPR reduction schemes such as ordinary partial transmit sequences (O-PTS), the proposed methods require lower computational complexity and have superior PAPR reduction performance.     

A multi-dimensional approach to the assessment of tunnel excavation methods

Selection of tunnel excavation method in rock calls for an exact assessment of engineering geology as well as geotechnical conditions of the tunnel strike. Moreover, uncertainties related to properties of engineering geology of rock often trigger complexity. In order to select the best possible excavation technique, one needs to take into account numerous operational, economic, environmental factors, and so forth. Using a hybrid methodology, including multiple attributes decision-making techniques and fuzzy set theory, the present study aims to evaluate the excavation methods of tunnels. The proposed approach is based on fuzzy-AHP and TOPSIS methods where the fuzzy-AHP determines weights of the criteria by decision makers in uncertain environment and TOPSIS concludes rankings of the excavation methods. Finally, a case study of Ghomroud project in Iran is presented to demonstrate the result of the proposed methodology.     

氧化锌-聚吡咯复合材料的介电性能和非线性特性（英文）

研究不同氧化锌(ZnO)含量的氧化锌-聚吡咯复合材料在200 Hz~5 MHz频率区间的非线性性能和频率特性。用热压法在130℃下制备样品。结果表明,当ZnO含量为70%时,击穿电压最小。击穿电压先从590 V降低到380 V,后从450 V增加到740 V,这是由于在晶界处没有聚吡咯。与击穿电压的变化不同,非线性系数随氧化锌含量的增加从4.2增加到9,这是由于随氧化锌含量的增加,晶界处的受主能级增加。电学参数如介电常数、介电损耗和样品的串联电阻对频率的依赖性很强,特别是在1 k Hz以下,这些参数随频率增加而下降,这与晶界上通过肖特基势垒的电荷传输有关。样品在1 kH z以下的高介电常数与晶界处的麦克斯韦-瓦格纳(Maxwell-Wagner)极化有关。在不同频率间隔区间存在不同的常数异常变化的现象,这与界面极化有关,是由于晶粒和晶界层结构的不同,导电性差异较大。     

Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS)

Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.     

Success criteria analysis in support of probabilistic risk assessment for nuclear power plants: application on SGTR accident

Success criteria analysis (SCA) bridges the gap between deterministic and probabilistic approaches for risk assessment of complex systems.To develop a risk model,SCA evaluates systems behaviour in response to postulated accidents using deterministic approach to provide required information for the probabilistic model.A systematic framework is proposed in this article for extracting the front line systems success criteria.In this regard,available approaches are critically reviewed and technical challenges are discussed.Application of the proposed methodology is demonstrated on a typical Westinghouse-type nuclear power plant.Steam generator tube rupture is selected as the postulated accident.The methodology is comprehensive and general;therefore,it can be implemented on the other types of plants and complex systems.     

Distributed fault detection and isolation in time-varying formation tracking UAV multi-agent systems

The problem of distributed fault detection and isolation (DFDI) in conjunction with time-varying formation control of unmanned aerial vehicle (UAV) multi-agent systems with multiple-leader leader–follower structure is studied, in this paper. It is assumed that the communication data of the agents are noisy, and faults may occur in either leaders or followers. The followers are not aware of the main trajectory and just follow the leaders. The first step in this paper is to reduce the communication noise. Besides, if a fault occurs in one of the leaders causing to exit the leader from the main trajectory, the followers should be able to detect the fault, isolate the leader, use the other leaders' data, and keep the main trajectory. Tracking the trajectory by followers in such a case is the second step of this paper. Preserving the time-varying formation despite a fault occurring in one of the followers is the next step. To reach these goals, a distributed array of Kalman filters is used for noise cancellation and data extraction. Next, the combination of state vector data fusion and some type of $\chi$² test are employed for DFDI, in agents. Besides, a controller is implemented in each agent for formation tracking. In this controller, estimated relative position and velocity vectors of the agents are employed to keep the time varying formation while tracking the leaders. The closed-loop stability is studied via Lyapunov stability theorem, and various simulations are carried out to demonstrate the capability of the proposed strategy.