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This article is concerned with set-membership global estimation for a networked system under unknown-but-bounded process and measurement noises. First, a group of local set-membership estimators is deployed to obtain the local ellipsoidal estimate of the true system state. Each estimator is capable of communicating with its neighbors within its communication range. Second, a global estimation approach is proposed which generates a trace-maximal ellipsoid within the intersection of all the local estimation sets with an aim to improve the difference of the local estimate at each time instant. Sufficient conditions for providing a global estimate under both complete and incomplete measurement transmissions are derived. Third, as an application, a modified distributed photovoltaic grid-connected generation system is provided to verify the effectiveness of the developed set-membership global estimation approach. Furthermore, an islanding fault detection scheme is derived based on the calculated global ellipsoidal estimate. Finally, simulation verification of the obtained theoretical results on the distributed generation system is presented.

This paper is concerned with the security problem for a networked control system subject to deception attacks. An adversary is present to corrupt both the sensor-to-controller channel and the controller-to-actuator channel with deception signals when sensor and control data are transmitted via the network medium. We model the malicious deception signals as unknown-but-bounded signals in such a way that no priori knowledge of the attack signals is required. First, a delicate set-membership performance index is proposed for meeting the system security requirement. Second, a novel set-membership estimation method is developed to obtain the desired ellipsoidal estimation set which guarantees the enclosing of true state of the system at every instant of time. Such an ellipsoidal estimation set is then used for deriving the sufficient conditions such that the resulting closed-loop system is set-membership input-to-state-stable and achieves the prescribed security requirement. Finally, a target tracking system is given to demonstrate the effectiveness of the proposed method.

Extensive epidemiological evidence suggests that nonalcoholic fatty liver disease (NAFLD) is the primary chronic liver disease worldwide. However, some studies have showed conflicting results on the effects of probiotics and synbiotics supplementation. Therefore, we conducted a systematic review and meta-analysis to investigate the effectiveness of the supplementation in subjects with NAFLD. We searched systematically PubMed, Cochrane, and Embase databases up to April 2018 and checked manually the bibliography of the original articles. The quality of the studies was evaluated using the Cochrane Risk of Bias Tool. This study analyzed 15 randomized, controlled trials involving 782 patients with NAFLD. Probiotics and synbiotics supplementation could significantly improve liver steatosis, alanine aminotransferase, aspartate aminotransferase, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, homeostasis model assessment-insulin resistance, liver stiffness and tumor necrosis factor-alpha (all P

With the prosperity of artificial intelligence, research on machine learning becomes a hot issue globally. Generative Adversarial Networks expose the huge security risks of machine learning. After the creation, GAN has achieved good performance in image generation, automatic image coloring, and data enhancement. With the improvement of the ability to generate samples against the deep learning network, generating malicious samples against the target learning model to achieve the deceptive discriminator becomes an effective and harmful attacking method. At present, some efficient attack methods have been proposed for different types of learning networks and different types of sample data. This paper mainly discusses the vulnerability of deep learning networks and several attack methods based on adversarial samples.

In these years, with the development of search engines, it is more convenient for people to get information via the Internet. However, despite the benefit, search engines bring new challenges to network security. More and more hackers search for vulnerable websites and computers with the help of search engines and make large-scale attacks. This is quite a challenge to traditional cybersecurity concepts. Honeypot is a technology that is widely used in capturing and analyzing hackers' behaviors. By building virtual objectives with vulnerabilities, honeypots attract hackers and analyze the interaction process with hackers. But traditional honeypot can't attract hackers well and just wait for attackers. We design and implement a new Web honeypot detection system based on search engines, which can not only attract hackers but also can renew the content automotive to synchronize with the new attack technologies. This new kind of honeypot makes up for the shortcomings of traditional honeypots and has great advantages in finding zero-day attacks.

Drug-induced liver injury (DILI) is difficult in diagnose, criteria used now are mostly based on history review. We tried to evaluate the value of these criteria and histopathology features in DILI to perform a method diagnosing DILI more definitely.We enrolled 458 consecutive hospitalized DILI patients from January 1, 2012 to December 31, 2018, using Roussel-Uclaf Causality Assessment Method (RUCAM), MariaVictorino scale (MV), and Digestive Disease Week-Japan criterion (DDW-J) combined with refined pathological scoring system respectively to perform the evaluation.A total of 458 DILI patients were enrolled, the area under receiver operating characteristics (AUROC) of the 3 clinical diagnostic criteria were 0.730 (95% confidence interval [CI]: 0.667-0.793), 0.793 (95% CI: 0.740-0.847), and 0.764 (95% CI: 0.702-0.826) respectively. Three hundred two DILI patients' liver biopsies were included: steatosis in 204 cases (67.5%), cholestasis in 151 cases (50%), cell apoptosis in 139 cases (46%), eosinophil granulocyte infiltration in 131 cases (43.4%), central and/or portal phlebitis in 103 cases (34.1%), iron deposition in 90 cases (29.8%), and pigmented macrophages in 92 cases (30.5%). The AUROC of refined pathological scale combined with 3 criteria were 0.843 (95% CI: 0.747-0.914), 0.907 (95% CI: 0.822-0.960), and 0.881 (95% CI: 0.790-0.942) respectively. In hepatocellular type, the AUROCs were 0.894 (95% CI: 0.787-0.959), 0.960 (95% CI: 0.857-0.994), and 0.940 (95% CI: 0.847-0.985); in cholestatic type, the AUROCs were 0.750 (95% CI: 0.466-0.931), 0.500 (95% CI: 0.239-0.761), and 0.500 (95% CI: 0.239-0.761); in mixed type, the AUROCs were 0.786 (95% CI: 0.524-0.943), 0.869 (95% CI: 0.619-0.981), and 0.762 (95% CI: 0.498 to -0.930).Combined with pathological scale can significantly improve the accuracy of clinical diagnostic criteria, no matter in alone or combined condition, MV might be more accurate in diagnosing DILI from suspected patients.

Drilling and riveting are commonly used in aircraft panel assembly process. Due to the fixture positioning error and the deformation of workpiece, the real position and orientation of the workpiece as well as its 3D geometry at the drilling position varies from the nominal CAD model, which would cause an unfavorable impact on assembly quality. Therefore, surface normal measurement and adjustment at the drilling position is of great importance. In this paper, a fast and effective non-contact measurement method for normal vector and height of moderately curved surfaces is accomplished by four laser displacement sensors, and a dedicated NC machine tool is also developed for normal adjustment. Firstly, a novel sensor calibration method based on laser tracker is introduced, which can acquire the sensors’ position and orientation in Tool Coordinate System (TCS) at the same time. The normal vector at hole position is calculated by cross product of any two non-parallel vectors constructed by the four laser projection points on the panel surface. Secondly, the kinematic model of the machine tool is established to calculate the adjustment of each axis of the machine tool with the Homogeneous Transformation Matrix (HTM). Besides, an innovative method to identify the distance of two rotary centers based on two laser interferometers is proposed. Finally, a series of experiments are conducted to validate the feasibility of the proposed method. The results show that the angle deviation can be reduced to less than 0.5° after adjustment, while the accuracy of the surface height is ±0.04 mm.

Press working causes structural deformation of the machine tool, especially the machine tool with large-span gantry or double columns used for the large-sized workpiece. The structural deformation makes the press tool out of accurate positioning, which leads to dimension deviation of the pressed workpiece. Even more, for thin-walled panel riveting, the structural deformation of the riveting machine will cause local deformations of the panel at rivet joints; this accumulated deformation will make the panel out of tolerance. An online deformation measurement and compensation method is developed for adaptive riveting with high accuracy and efficiency in this paper. During the process of servo driving riveting combined with force measurement, the force–displacement data points can be online obtained, and the recognized feature data points by a robust algorithm can represent the critical points during rivet deformation. Combined with the analytical springback of the rivet joint, the deformation of the machine tool can be accurately solved and compensated to the displacement of press tool for high­precision riveting. Simultaneously, with this innovative measurement method, the online quantity inspection for rivet joints can be realized synchronously. Also, a contrast experiment is conducted to validate the effectiveness of the online adaptive deformation compensation based on force–displacement data analysis. The results demonstrate the feasibility of the proposed method in riveting error compensation and riveting quantity inspection.

The estrogen levels in the pre and post menstrual phases interact with brain-derived neurotrophic factor in a complex manner, which influences the overall state of the body. To study the role of oestradiol and brain-derived neurotrophic factor in modulating obesity related type 2 diabetes and the interactions between two factors, we enrolled 15 diabetic premenopausal women and 15 diabetic postmenopausal women respectively, the same number of healthy pre and postmenopausal women were recruited as two control groups. The fasting blood glucose, insulin, lipids, estrogen, and brain-derived neurotrophic factor levels were measured through clinical tests. Additionally, we set up obese female mouse model to mimic human trial stated above, to verify the relationship between estrogen and brain-derived neurotrophic factor. Our findings revealed that there is a moderately positive correlation between brain-derived neurotrophic factor and oestradiol in females, and decreased brain-derived neurotrophic factor may worsen impaired insulin function. The results further confirmed that high fat diet-fed mice which exhibited impaired glucose tolerance, showed lower levels of oestradiol and decreased expression of brain-derived neurotrophic factor mRNA in the ventromedial hypothalamus. The level of brain-derived neurotrophic factor reduced on condition that the level of oestradiol is sufficiently low, such as women in postmenopausal period, which aggravates diabetes through feeding-related pathways. Increasing the level of brain-derived neurotrophic factor may help to alleviate the progression of the disease in postmenopausal women with diabetes.

Shape of flexible part is easy to be out of tolerances due to deformations in the clamping and machining process. These deformations are generated randomly and hard to be predicted. Surface normal drilling and countersinking of flexible part is commonly necessary in aircraft assembly process. An online high precision surface normal measurement and cutter orientation compensation method is developed for adaptively drilling of flexible part in this paper. During the process of normal measurement and adjustment, the distance from tool nose to drilling point is accurately measured and adjusted synchronously, which is critical for countersinking. To accurately measure the current normal of the deformed workpiece, two 2D laser displacement sensors are applied to measure the surface profiles of the workpiece and sample two sets of geometrical points each time. Two crossed spatial curves are respectively fitted by the two sets of geometrical points, and the surface normal at the intersection point of the crossed curves is computed. After the deviation between the measured normal and tool orientation is calculated, an online compensation method is used to eliminate the deviation and meet the perpendicularity requirement of the cutter axis to the workpiece surface, and the distance from tool nose to drilling point is measured and adjusted to the setting value synchronously. The compensation method is based on the kinematic transformation of a five-axis machine tool and is implemented by NC compensation. Simulations and experiments are conducted to validate the feasibility and effectiveness of the online adaptive measurement and adjustment method.

In this paper, a set-membership filtering method is proposed in order to effectively track an Unmanned Surface Vessel (USV) in a dual antenna GPS positioning system. According to the relationship between position, velocity and the heading direction, a mathematical model is established to describe the considered USV system. The disturbances of wind, wave and current are modeled as unknown-but-bounded noises and a set-membership filtering scheme is designed which enables the states of the USV to reside in the ellipsoidal estimation sets at each time instant. A simulation experiment is conducted, which assumes that the USV is still in the water with the disturbances of wind, wave and current. Example results are carried out to illustrate the effectiveness of the proposed method.

This paper is concerned with the H ∞ control problem for a class of linear parameter-varying (LPV) systems with randomly multi-step sensor delays. A mathematical model which describes the randomly multi-step sensor delayed measurements for LPV systems is established. An improved Lyapunov functional is proposed to determine the asymptotically mean-square stability of the closed-loop system which depends on the varying parameters. The obtained full-order parameter-dependent dynamic feedback controller guarantees the considered system to be asymptotically mean-square stable and to satisfy the modified H ∞ performance for all possible delayed measurements. An extended cone complementarity linearization method (CCLM) is used to solve the constrained linear matrix inequality (CLMI). Simulation results illustrate the effectiveness of the proposed method.

Background: Drug-induced liver injury (DILI) is a potentially severe adverse drug reaction especially in susceptible patients. But there are no sensitive or specific parameters to detecting DILI. The specific expression of miR-122 in the liver has been a hotspot in the evaluation of hepatic toxicity due to its high stability and sensitivity. Methods: We performed a systematic literature review through July 31, 2017 to identify studies which evolved DILI patients testing miR-122 without limiting a certain drug. According to the PRISMA statement, a meta-analysis: the diagnostic role of miR-122 in DILI was made. QUADAS-2 quality evaluation table was used to evaluate the quality of the documentary evidence, PRISMA flowchart and quality evaluation table were drawn with RevMan, use Stata to calculate the sensitivity and specificity of miR-122 in diagnosing DILI, ROC curve and Deeks funnel plot were also drawn by STATA. Results: Eleven studies involved 194 DILI patients and 251 controls, all were tested miR-122 (fold change). Sensitivity of miR-122 in diagnosing DILI was [0.85 (95% CI, 0.75–0.91), I2 = 53.46%] and specificity was [0.93 (95% CI, 0.86–0.97), I2 = 65.10%], the area under ROC curve was 0.95 (95% CI, 0.93–0.97). While in acetaminophen (APAP)-induced liver injury, the sensitivity was [0.82 (95%CI, 0.67–0.91), I2 = 65.77%] specificity was [0.96 (95%CI, 0.88–0.99), I2 = 31.46%], AUROC was 0.97 (95% CI, 0.95–0.98). Conclusions: In this systematic review and meta-analysis, we found miR-122 have a high specificity in DILI, and a modest positive diagnostic effects. On the basis of the limited evidence, further research is needed to evaluate the long-term observation and more clinical data to testify miR-122 in diagnosing DILI.

The paper presents a hybrid strategy in a soft computing paradigm for the optimisation of the plastic injection moulding process. Various plastic injection molding process parameters, such as mold temperature, melt temperature, injection time and injection pressure are considered. The hybrid strategy combines numerical simulation software, a genetic algorithm and a multilayer neural network to optimise the process parameters. An approximate analysis model is developed using a Back-propagation neural network in order to avoid the expensive computation resulting from the numerical simulation software. According to the characteristic of the optimisation problem, a nonbinary genetic algorithm is applied to solve the optimisation model. The effectiveness of the improved strategy is shown by an example.

The gate is one of the most important functional structures in an injection mould, as it has a direct influence on the quality of the injection products. The design of a gating scheme includes the selection of the types of gate and calculation of the sizes and determination of the location, which depends heavily on prior experience and knowledge and involves a trial-and-error process. Due to the vagueness and uncertainty in the design of a gating scheme, classical rough set theory is not effective. In this paper, a fuzzy rough set model is proposed, which is not based on equivalent relationships but on fuzzy similarity relationships. An inductive learning algorithm based on the fuzzy rough set model (FRILA) is then presented. Compared to decision tree algorithms, the proposed algorithm can generate fewer classification rules; moreover, the generated rules are more concise. Finally, an intelligent prototype system for the design of a gating scheme based on an induced fuzzy knowledge base is developed. An illustrative example proves the effectiveness of the proposed method.

The notion of Divisible Online/Offline Signature (DOS) was recently introduced by Gao et al. in CT-RSA 2009. In a DOS system, the idea is to perform the signing process in two phases. The first phase outputs offline tokens before the arrival of a message to be signed, where the offline tokens are regarded as a part of full signature, and the second phase is performed very efficiently after knowing the message. The DOS schemes are useful in many devices that have low bandwidth of the communication channel. Gao et al. also proposed a efficient DOS scheme, which is based on Boneh and Boyen(BB)’s short signature scheme. However, their scheme is approximately 480 bits in length. In this paper, we introduce a simple chameleon-hash-based transformation which changes traditional online/offfline signature scheme into DOS scheme. With the Boneh, Lynn and Shacham (BLS)’s short signature as an example, we demonstrate a efficient DOS scheme which the length is only 320 bits, and prove its security in random oracles model.