Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Categorical observers for metamerism

A method is proposed to generate categorical colour observer functions (individual colour matching functions) for any field size based on the CIE 2006 system of physiological observer functions. The method combines proposed categorical observer techniques of Sarkar et al with a physiologically-based individual observer model of Asano et al and a clustering technique to produce the optimal set of categorical observers. The number of required categorical observers varies depending on an application with as many as 50 required to predict individual observers' matches when a laser projector is viewed. However, 10 categorical observers are sufficient to represent colour-normal populations for personalized colour imaging. The proposed and recommended categorical observers represent a robust and inclusive technique to examine and quantify observer metamerism in any application of colorimetry.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

Correlation between motion of source bender element and feedback signal induced by self-monitoring

Since the introduction of bender element tests to soil testing, the reliability of the estimated travel time has been the most serious problem. The author has previously shown a potential solution whereby removing the response of the bender element subsystem from the whole response could dramatically improve the accuracy of the travel time estimation. In order to lay the foundation for estimating the response of the bender element subsystem, this paper examines the correlation between the displacements of the element and the induced feedback signals by employing a self-monitoring element. The response of the self-monitoring element is modeled as a transfer function involving two internal transfer functions that relate the input signals to the displacements and the displacements to the feedback signals, respectively. Using a laser displacement sensor, the displacements are directly measured through the entire surface and reveal the three-dimensional bending motion of the element oscillating in both longitudinal and width directions. The feedback signals are similar to, but inconsistent with, the tip displacements, and an attempt is made to correct the feedback signals. Finally, a conclusion is given on the potential for estimating the response of the bender element subsystem using the self-monitoring element.     

On one boundary problem for nonlinear stationary controlled system

The object of study is nonlinear stationary controlled system of ordinary differential equations with constant disturbance in the right part. The problem of constructing the synthesising control function providing the transfer of this system from the initial state to the origin is considered. The sufficiently simple for numerical implementation algorithm of solution of the above-mentioned problem is obtained. It is shown that for local null controllability of the considered system, it is sufficient that the conditions of the Kalman's type were satisfied. In addition, the estimates restricting the choice of initial conditions and external disturbances under which the transfer is guaranteed are obtained. The main idea of the method of construction of the desired control function consists in reducing the original problem to stabilisation of a special kind linear non-stationary system and solving the Cauchy problem for an auxiliary system of ordinary differential equations closed by stabilising control. The simplicity of the realisation of this algorithm is determined by the construction of the auxiliary system and its stabilisation that could be obtained by analytical methods. The effectiveness of the method is illustrated by solving the problem of crane control and its numerical simulation.     

Risk factors associated with online game addiction: A hierarchical model

Online gaming addiction has been increasingly recognized as a mental disorder. However, the predictive factors that lead to online gaming addiction are not well established. The aim of this study was to identify factors that may influence the development of online gaming addiction. A total of 263 patients with problematic online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ± 5.8 years) and153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ± 5.5 years, range) were recruited for participation in the current study. Hierarchical logistic regression analyses among each set of variables were conducted. Individual factors (sex and age), cognitive factors (IQ and perseverative errors), psychopathological conditions (ADHD, depression, anxiety, and impulsivity), and social interaction factors (family environment, social anxiety, and self-esteem) were evaluated in a stepwise fashion. All four factors were associated with online gaming addiction, with psychopathological conditions being the strongest risk factors for the addiction. Individual factors, psychological factors, and social interactions were associated with the development of pure online gaming addiction. As before, psychological factors (attention, mood, anxiety and impulsivity) were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction. Psychopathologies, including ADHD and depression, were the strongest factors associated with the development of online gaming addiction in individuals.     

YSZ/MoS2 self-lubricating coating fabricated by thermal spraying and hydrothermal reaction

Thermal sprayed ceramic coatings have extensively been used in components to protect them against friction and wear. However, the poor lubricating ability severely limits their application. Herein, yttria-stabilized zirconia (YSZ)/MoS₂ composite coatings were successfully fabricated on steel substrate with the combination of thermal spraying technology and hydrothermal reaction. Results show that the synthetic MoS₂ powders are composed of numbers of ultra-thin sheets (about 7 ~ 8?nm), and the sheet has obvious lamellar structure. After vacuum impregnation and hydrothermal reaction, numbers of MoS₂ powders, look like flowers, generate inside the plasma sprayed YSZ coating. Moreover, the growing point of the MoS₂ flower is the intrinsic micro-pores of YSZ coating. The friction and wear tests under high vacuum environment indicate that the composite coating has an extremely long lifetime (>?100,000 cycles) and possesses a low friction coefficient less than 0.1, which is lower by about 0.15 times than that of YSZ coating. Meanwhile, the composite shows an extremely low wear rate (2.30?×?10^?7 mm³ N^?1 m^?1) and causes slight wear damage to the counterpart. The excellent lubricant and wear-resistant ability are attributed to the formation of MoS₂ transfer films and the ultra-smooth of the worn surfaces of hybrid coatings.     

探针馈电圆柱共形微带天线阻抗特性的FDTD法分析

将圆柱坐标系下非分裂式理想匹配层吸收边界条件(UPML)引入到圆柱共形微带天线的全波分析中，并给出了其中的场量迭代方程，明显地减小了计算空间，简化了编程；提出了圆柱坐标系下考虑有限尺寸探针半径的新算法，使得计算结果更加精确；应用GPOF方法预估了时域晚时响应，使计算时间减少了70％～90％。本文给出的模型能够对任意多层圆柱共形结构的微带天线进行计算，因此对此类天线的CAD设计具有实际意义。     

常压炉炉管及转油线的腐蚀

通过对常压炉炉管及转油线腐蚀情况的分析,认为该炉发生转油线腐蚀泄漏的主要原因是原油含硫量升高、冲刷加剧、材质不适合以及设备老化等。针对腐蚀的成因采取了对整个转油线进行包盒子处理、材质升级、摸清活性硫在装置中的分布情况和腐蚀规律、开展高温部位定点测厚、避免高含硫原油直接进入装置等措施。