Effects and Mechanism of Combinational Chemical Agents on Solution‐Derived K0.5Na0.5NbO3 Piezoelectric Thin Films

Sodium potassium niobate (KNN) piezoelectric ceramic thin films were prepared by a chemical solution deposition method, in which the introduction of appropriately combinational organic stabilizing agents, including monoethanolamine (MEA), diethanolamine (DEA), and ethylenediaminetetraacetic acid (EDTA), dramatically suppressed the volatile loss of the alkali ions, improved the crystallinity, and promoted crystal orientation of the perovskite phase in the resulting KNN thin films. Theoretical analyses including ab initio calculation based on molecular model indicated that stronger binding structure could form comprising the alkali ions and the combinational EDTA‐(DEA‐MEA) chemical agents than the cases of using individual binding agents separately. Outstandingly large effective piezoelectric strain coefficient d₃₃ under the substrate clamping condition was achieved. The theoretical and experimental results provide the insight on the underlying interaction mechanism between the multiple stabilizing chemical agents and metal ions, and the guidance for establishing the methodology for producing high quality oxide thin films from chemical solutions with dedicatedly designed combinational stabilizing agents.     

Ambiguity-driven fuzzy C-means clustering: how to detect uncertain clustered records

As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods. However, the accuracy of FCM is subject to false detections caused by noisy records, weak feature selection and low certainty of the algorithm in some cases. The false detections are very important in some decision-making application domains like network security and medical diagnosis, where weak decisions based on such false detections may lead to catastrophic outcomes. They mainly emerge from making decisions about a subset of records that do not provide sufficient evidence to make a good decision. In this paper, we propose a method for detecting such ambiguous records in FCM by introducing a certainty factor to decrease invalid detections. This approach enables us to send the detected ambiguous records to another discrimination method for a deeper investigation, thus increasing the accuracy by lowering the error rate. Most of the records are still processed quickly and with low error rate preventing performance loss which is common in similar hybrid methods. Experimental results of applying the proposed method on several datasets from different domains show a significant decrease in error rate as well as improved sensitivity of the algorithm.     

A high performing meta-heuristic for training support vector regression in performance forecasting of supply chain

The prevalence of the use of third-party logistics (3PL) providers is noticeable. The complexity of the relationships pertinent to 3PL is greater than that of any traditional logistics supplier relationships. Moreover, they can be considered as truly strategic alliances. The use of the mentioned relationships to increase the flexibility of the organization to address the rapid changes occurring in market conditions has become popular while these relationships concentrate on the core competencies as well as the development of long-term growth strategies. A good number of studies have examined the selection of service providers. With respect to the selection of the service providers, the most recent studies approved the better performance of neural networks in comparison with the conventional methods to provide a solution for the real-world engineering problems, one of the sociopolitically inspired optimization strategies named imperialist competitive algorithm (ICA) is used. In order to select the 3PL, integration of the support vector regression (SVR) and self-adaptive ICA (SAICA) has offered a novel model, in which SAICA is utilized to adjust the parameters of the SVR. The suggested model is applied for cosmetics production. Moreover, the comparison of the suggested model and back-propagation neural networks, pure SVR, and ICA–SVR is presented. Higher estimation accuracy is achieved as the results of the proposed model reveal, which leads to the effective prediction.     

Modeling and analysis of MEMS disk resonators

Microsystem Technologies - In this work a very accurate process for modeling a microdisk resonator is presented and the dynamic behavior of the resonator is investigated. Using the minimization of...     

Localization of emergency acoustic sources by micro aerial vehicles

For micro aerial vehicles (MAVs) involved in search and rescue missions, the ability to locate the source of a distress sound signal is significantly important and allows fast localization of victims and rescuers during nighttime, through foliage and in dust, fog, and smoke. Most emergency sound sources, such as safety whistles and personal alarms, generate a narrowband signal that is difficult to localize by human listeners or with the common localization methods suitable for broadband sounds. In this paper, we present three methods for MAV‐based emergency sound localization system. The first method involves designing a new emergency source for immediate localization by the MAV using a common localization method. The other two novel methods allow localizing the currently available emergency sources, or other narrowband sounds in general, that are difficult to localize due to the periodicity in the sequence of sound samples. The second method exploits the Doppler shift in the sound frequency, caused due to the motion of the MAV and the dynamics of the MAV to assist with the localization. The third method involves active control of the robot's attitude and fusing acoustic and attitude measurements for achieving accurate and robust estimates. We evaluate our methods in real‐world experiments with real flying robots.     

The effect of mass irregularities of weft yarns on fabric surface protrusion

Fabric surface unevenness and defects are usually created by yarn irregularity and defects in weaving process. These unevenness and irregularities appear on fabric and affect various fabric properties. In order to investigate weft yarn mass irregularities on fabric surface unevenness and defects, various plain fabric samples were prepared in which they differ only in weft yarn mass irregularity. One of the effective factors on fabric surface unevenness is yarn protrusion in fabric structure. Yarn protrusion in fabric structure is influenced by yarn’s physical and mechanical properties and fabric structural characteristics. In this work, relationship between mass irregularities of weft yarn and fabric surface unevenness was investigated using angular power spectrum curve, a measure of yarn protrusion in fabric structures. The results showed a high significant correlation between these two parameters.     

Impact of a large penetration of wind generation on the GB gas network

Wind power is expected to be the major element of renewable electricity generation in Great Britain (GB) by 2020 with a capacity of around 30 GW. The potential impact of a large amount of wind generation on the GB gas network was investigated using a combined gas and electricity network model. The varying nature of gas and electric power flows, network support facilities such as gas storage and compressors, and the power ramping characteristics of various power plants were considered. Three case studies were modelled, one case uses the existing network and the other two make use of a hypothesised network in 2020 with two distinct levels of wind generation representing low and high wind periods. The simulation results show that a large penetration of wind generation will influence the electricity generation mix as the wind power varies. Gas-fired generation is used to compensate for wind variability. This will cause increased flows and compressor power consumption on the gas network. Linepack depletion during low wind periods was shown to limit the ability of the gas network to fully supply gas-fired generators.     

ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels

In the present study, an artificial neural networks-based model was developed to predict the ferrite fraction of microalloyed steels during continuous cooling. Fourteen parameters affecting the ferrite fraction were considered as inputs, including the cooling rate, initial austenite grain size, and different chemical compositions. The network was then trained to predict the ferrite fraction amounts as outputs. A multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the ferrite fraction.     

Stress Effects on Stabilizing Antiferroelectric Phase in Multilayer Ceramics

Controllable phase transformation between antiferroelectric (AFE) and ferroelectric (FE) states suggests multifunctional properties valuable for many device applications. Compared to AFE bulk ceramics with large voltage required for driving electric field‐induced phase transition, implementation of structures comprising multiple thin AFE ceramic layers can realize applications by reducing the switching operation voltage in the feasible range. Here, it is found that a compressive residual stress is developed in multilayer (Pb_0.97,La_0.02)(Zr_0.66,Sn_x,Ti_0.34?x)O₃ (PLZST) ceramic co‐fired with multiple Pd/Ag electrode layers, and the compressive residual stress can stabilize AFE phase. AFE phase forms in the PLZST multilayer ceramic with composition corresponding to FE in the bulk materials. Thermodynamic analysis based on free energy of FE and AFE phases well explains the FE to AFE phase transformation observed in the multilayer ceramic under the compressive stress. The findings exhibit a new strategy to tune structure and functional properties of multilayer ceramics through stress engineering for achieving device applications.