Electrocaloric effect in La-doped BNT-6BT relaxor ferroelectric ceramics

Relaxor [(Bi1/2Na1/2)0.94Ba0.06](1-1.5x)LaxTiO3 (x = 0, 0.03, 0.06, 0.09) ceramics (La-doped BNT-6BT) with composition close to the morphotropic phase boundary (MPB) were successfully prepared by using the conventional solid state reaction method. All samples present almost a pure perovskite phase with the coexistence of tetragonal and rhombohedral. With the increase of La doping content, the degree of the dielectric relaxor dispersion around the dielectric peak which is close to the room temperature increases, and also the transition temperature of ferroelectric-to-relaxor (TF-R) shifts 120 K towards a lower temperature at x = 0.09. The maximum value of the temperature change (ΔT) of the electrocaloric (EC) effect decreases sharply from 1.1 K at x = 0–0.064 K at x = 0.09. A large positive EC effect (maximum ΔT ~ 0.44 K) in a broad temperature range (~ 90 K) close to room temperature is achieved at x = 0.03, indicating that it is a promising lead-free material for application in solid state cooling system. Moreover, it is found that the Maxwell relationship can be well used to assess the EC effects of the La-doped BNT-6BT ceramics when the operating temperature is higher than that of the TF-R, indicating that these relaxor ceramics would perform as an ergodic

Enhanced energy storage performance of (1-x)(BCT-BMT)-xBFO lead-free relaxor ferroelectric ceramics in a broad temperature range

Relaxor ferroelectrics with high energy storage performances are very attractive for modern applications in electronic devices and systems. Here, it is demonstrated that large energy densities (0.52e0.58 J/cm3) simultaneously with high efficiencies (76è2%) and thermal stabilities (the minimum variation of efficiency < 4% from 323 K to 423 K at x ¼ 0.04) have been achieved in the (1-x)(BCT-BMT)-xBFO lead-free relaxor ferroelectric ceramics prepared using a conventional solid-state reaction method. Large dielectric breakdown strengths and great relaxor dispersion around the dielectric peaks are responsible for the excellent energy storage performances. The energy storage performances of as-prepared ceramics at high BFO doping amount (x ¼ 0.06 and 0.07) were deteriorated seriously due to low dielectric breakdown strengths. However, they could be greatly improved when aged, since the operable electric field was significantly enhanced from 10 kV/cm of as-prepared samples to 100 kV/cm of aged samples due to the reduced concentration of oxygen vacancies during the aging process. The excellent energy storage performances may make them attractive materials for applications in modern energy storage systems in a broad temperature range

Development of a prognostic nomogram and risk stratification system for upper thoracic esophageal squamous cell carcinoma

BackgroundThe study aimed to develop a nomogram model to predict overall survival (OS) and construct a risk stratification system of upper thoracic esophageal squamous cell carcinoma (ESCC).MethodsNewly diagnosed 568 patients with upper ESCC at Fujian Medical University Cancer Hospital were taken as a training cohort, and additional 155 patients with upper ESCC from Sichuan Cancer Hospital Institute were used as a validation cohort. A nomogram was established using Cox proportional hazard regression to identify prognostic factors for OS. The predictive power of nomogram model was evaluated by using 4 indices: concordance statistics (C-index), time-dependent ROC (ROCt) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI).ResultsIn this study, multivariate analysis revealed that gender, clinical T stage, clinical N stage and primary gross tumor volume were independent prognostic factors for OS in the training cohort. The nomogram based on these factors presented favorable prognostic efficacy in the both training and validation cohorts, with concordance statistics (C-index) of 0.622, 0.713, and area under the curve (AUC) value of 0.709, 0.739, respectively, which appeared superior to those of the American Joint Committee on Cancer (AJCC) staging system. Additionally, net reclassification index (NRI) and integrated discrimination improvement (IDI) of the nomogram presented better discrimination ability to predict survival than those of AJCC staging. Furthermore, decision curve analysis (DCA) of the nomogram exhibited greater clinical performance than that of AJCC staging. Finally, the nomogram fairly distinguished the OS rates among low, moderate, and high risk groups, whereas the OS curves of clinical stage could not be well separated among clinical AJCC stage.ConclusionWe built an effective nomogram model for predicting OS of upper ESCC, which may improve clinicians’ abilities to predict individualized survival and facilitate to further stratify the management of patients at risk

Using a machine learning approach for property market analysis

This report aims to predict the property market trend for Singapore and Hong Kong with Python and some packages including pandas and scikit-learn. A machine learning approach was applied to perform the predictions with three regression models selected. Raw data was collected from the region or country’s corresponding government website. Before performing the training and testing using regression models, the raw data went through data cleaning and preprocessing. In the end, the predictions with regression models were conducted. Linear regression fit the Hong Kong property market best, while the K-Nearest Neighbors with k equals 3 performs best in Singapore property market. However, the future trend for both markets cannot be obtained due to the lack of latest data for some macroeconomic factors.Bachelor of Engineering (Computer Science

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning

A Feature Discretization Method Based on Fuzzy Rough Sets for High-resolution Remote Sensing Big Data Under Linear Spectral Model

As one of the most relevant data preprocessing techniques, discretization has played an important role in data mining, which is widely applied in industrial control. It can transform continuous features to discrete ones, thus improving the efficiency of data processing and adapting to learning algorithms that require discrete data as inputs. However, traditional discretization methods have shortcomings, such as highly complex programs, excessive numbers of intervals obtained, and significant loss of necessary information in the preprocessing of high-resolution remote sensing big data. Moreover, the large number of mixed pixels in the image is a primary reason for the uncertainty of remote sensing information systems, and current discretization methods are based on the assumption that one pixel only corresponds to the spectral information of a single object, without considering the influence of the uncertainty caused by a mixed spectrum, which causes the classification accuracy to drop after discretization. We propose a discretization method for high-resolution remote sensing big data. We determine the membership degree of each pixel in training samples through linear decomposition, and establish the individual fitness function based on a fuzzy rough model. An adaptive genetic algorithm selects discrete breakpoints, and a MapReduce framework calculates the individual fitness of the population in parallel, to obtain the optimal discretization scheme in the minimum time. Our method is compared to the best state-of-the-art discretization algorithms on the authentic remote sensing datasets. Experiments verified the effectiveness of the proposed method, which provides strong support for the subsequent processing of images

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

Feature discretization can reduce the complexity of data and improve the efficiency of data mining and machine learning. However, in the process of multidimensional data discretization, limited by the complex correlation among features and the performance bottleneck of traditional discretization criteria, the schemes obtained by most algorithms are not optimal in specific application scenarios and can even fail to meet the accuracy requirements of the system. Although some swarm intelligence algorithms can achieve better results, it is difficult to formulate appropriate strategies without prior knowledge, which will make the search in multidimensional space inefficient, consume many computing resources, and easily fall into local optima. To solve these problems, this paper proposes a genetic algorithm based on reinforcement learning to optimize the discretization scheme of multidimensional data. We use rough sets to construct the individual fitness function, and we design the control function to dynamically adjust population diversity. In addition, we introduce a reinforcement learning mechanism to crossover and mutation to determine the crossover fragments and mutation points of the discretization scheme to be optimized. We conduct simulation experiments on Landsat 8 and Gaofen-2 images, and we compare our method to the traditional genetic algorithm and state-of-the-art discretization methods. Experimental results show that the proposed optimization method can further reduce the number of intervals and simplify the multidimensional dataset without decreasing the data consistency and classification accuracy of discretization

Tensor-Based Reduced-Dimension MUSIC Method for Parameter Estimation in Monostatic FDA-MIMO Radar

Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method