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The problem of sliding mode control for a class of Takagi–Sugeno fuzzy model-based nonlinear one-sided Lipschitz systems is investigated in the paper. Due to the state components are not available, a state observer is designed based on an event-triggering mechanism. Meanwhile, the output measurements transmitted through the communication channels suffer from signal delays. Based on the estimated state, an integral sliding surface is proposed. Then, the sliding mode dynamics is obtained by virtue of equivalent control principle. Further, by constructing appropriate sliding mode controller, the finite-time reachability of predefined sliding surface is surely guaranteed. Moreover, the stability with an H ∞ performance analysis of sliding mode dynamics is undertaken via Lyapunov function theory and the criteria are established in terms of LMI. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed method.

This paper studies the problem of H ∞ control design for a class of singular switching semi-Markovian jump systems, in which the transition rates of jumping process are uncertain and the output measurements suffer from random sensor delays. Firstly, a Luenberger observer is designed to estimate the system state components, based on which an error dynamic is obtained. Then, stochastic stability analysis for the overall closed-loop system is given based on the upper and lower bounds of transition rates. Further, the state feedback controller gain matrices are given by solving a set of sufficient conditions in terms of strict linear matrix inequalities (LMIs), which also guarantee the closed-loop system to be stochastically stabilizable and has a prescribed H ∞ performance index γ . Finally, a numerical example is provided to verify the effectiveness of the obtained result.

Cooperative driver pathways discovery helps researchers to study the pathogenesis of cancer. However, most discovery methods mainly focus on genomics data, and neglect the known pathway information and other related multi-omics data; thus they cannot faithfully decipher the carcinogenic process. We propose CDPMiner (Cooperative Driver Pathways Miner) to discover cooperative driver pathways by multiplex network embedding, which can jointly model relational and attribute information of multi-type molecules. CDPMiner first uses the pathway topology to quantify the weight of genes in different pathways, and optimizes the relations between genes and pathways. Then it constructs an attributed multiplex network consisting of micro RNAs, long noncoding RNAs, genes and pathways, embeds the network through deep joint matrix factorization to mine more essential information for pathway-level analysis and reconstructs the pathway interaction network. Finally, CDPMiner leverages the reconstructed network and mutation data to define the driver weight between pathways to discover cooperative driver pathways. Experimental results on Breast invasive carcinoma and Stomach adenocarcinoma datasets show that CDPMiner can effectively fuse multi-omics data to discover more driver pathways, which indeed cooperatively trigger cancers and are valuable for carcinogenesis analysis. Ablation study justifies CDPMiner for a more comprehensive analysis of cancer by fusing multi-omics data.

As an effective way of improving traffic efficiency, vehicle platoon control has attracted extensive interest recently. Communication between vehicles tends to be affected by communication noises. Aimed at improving communication efficiency, an event-triggered vehicle platoon control under random communication noises is studied in this paper. First, for vehicle platoons with linear third-order dynamics, a time-varying consensus gain $c(t)$ is introduced to reduce the effects of the communication noises. Second, with the introduction of the algebraic graph theory and matrix analysis theory, conditions for internal stability and $l_{p}$ -string stability under random additive communication noises are derived. Third, by utilizing the Lyapunov approach and $It\hat {o}$ stochastic differential equations, the consensus of vehicle platoon under random additive communication noises is proved. Last, to reduce the frequent communication between vehicles, an event-triggered mechanism is introduced, and the design for the triggering parameter is derived. The effectiveness of the proposed method is verified with some numerical simulations.

Cell manipulation is a key technique involved in many cell surgery applications. Various automatic frameworks have been developed for optical manipulation of biological cells without consideration of stochastic perturbations, such as Brownian motion. In this brief, a stochastic control scheme for optical manipulation of biological cells is developed. On the basis of the stochastic dynamic model of an optically trapped biological cell, a vectorial backstepping control strategy and its adaptive case is formulated for cell trajectory tracking. The global noise-to-state stability of a closed-loop system in the presence of stochastic perturbations is analyzed. Simulation results demonstrate the performance of the proposed approach.

The mechanical properties of biological cells are known as effective label-free biomarkers in many cell physiological events. Several techniques have been proposed to manipulate, measure, and analyze cell mechanical properties, including micropipette aspiration, atomic force microscopy, optical tweezers, and magnetic tweezers. However, most of these methods have inherent problems in practice, including high equipment cost, complex operation, low accuracy, and low throughput. This paper presents a new method for the mechanical characterization of red blood cells (RBCs) on the basis of a dielectrophoresis (DEP) microfluidic system integrated with newly designed parallel ladder electrodes. The proposed microfluidic system not only can stretch RBCs in single-cell level exactly but also has the advantages of high efficiency and throughput. The DEP force generated by the ITO electrodes was theoretically studied, and a series of experiments were performed to stretch RBCs. The relationship between the DEP force and the strain of the RBCs was analyzed, which elucidated the elongation process of RBC under the domination of DEP force, as well as the maximum elongation ability of RBCs. These quantitative results are helpful for people to understand the characteristics of RBCs.

Micro surface modeling has been widely applied in micro scale systems, and the advanced modeling of micro surface is crucial to the development of micro scale systems. In this study, based on $\sqrt {3}$ subdivision schemes, an improved uniform micro subdivision algorithm is proposed for micro surface modeling. The original $\sqrt {3}$ subdivision scheme could not accommodate the sharp features on the micro surface in determining the micro local property of the surface. However, the proposed improved subdivision algorithm can overcome this difficulty without changing the topology of the subdivision surface. The proposed algorithm uses an interpolation subdivision method and obtains excellent results by preserving the dart, crease, corner, and cone. The micro subdivision surface can reach everywhere smoothly except for some infinite sharp features. The proposed algorithm can also deal with nano models, such as micro robots, nano artificial modeling, and micro 3D characters. The experimental results demonstrate the effectiveness of the proposed model.

This paper is concerned with the problem of finite-time H∞ control for stochastic singular systems with partly known transition rates (TRs). The transition of system parameters follows a finite-state Markov process. Firstly, based on stochastic functional method and linear matrix inequalities (LMIs) technique, sufficient conditions are proposed to ensure finite-time stochastic boundedness (FTSB) and finite-time H∞ stochastic boundedness (FTH∞SB) of considered stochastic singular system. Secondly, by designing a state feedback controller, strict LMI conditions are obtained to guarantee the closed-loop system with partly known TRs to be FTSB and FTH∞SB. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed methods.

This study is concerned with the problem of finite-time filter design for a class of Ito stochastic systems with Markovian switching and distributed time-varying delays. Firstly, a partially mode-dependent filter is designed to accommodate to unreliable network transmission. The attention is focused on deriving sufficient conditions for the filtering error system to ensure the finite-time boundedness and to satisfy a prescribed disturbance attenuation. Then based on stochastic functional theory, the existence of filter is presented by solving existing linear matrix inequalities optimisation problems. Furthermore, the result is extended to the case where the mode information is completely transmitted. Finally, a numerical example is provided to show the effectiveness of the proposed results.

Nano-blocking technology has become a key to overcoming a prominent bottleneck in shale gas development. In this paper, poly(ST-VS-B-E) was synthesized by Michael addition reaction using styrene, vinyl sodium sulfonate, ethyl methacrylate, and butyl acrylate. Poly(ST-VS-B-E) was characterized by infrared spectroscopy analysis, laser scattering analysis, and thermogravimetric analysis. The rheological properties of drilling fluid with poly(ST-VS-B-E) were studied by testing the drilling fluid’s properties. The results showed that the median particle size of poly(ST-VS-B-E) was 79.15 nm and it resisted a 359.25 °C high temperature. The yield point of the water-based drilling fluid with 2.5 wt% poly(ST-VS-B-E) added was 27 Pa, and the fluid loss was 3.1 mL, with good drilling fluid performance. Further test results from simulated mud cake filtration and the simulated core penetration showed that when the mass concentration of poly(ST-VS-B-E) was 2.5 wt%, the blocking rates were 66.67% and 93.33%, respectively, and the blocking performance increased gradually with the addition of poly(ST-VS-B-E). Poly(ST-VS-B-E) can be squeezed into the nano-pores of a shale formation under the action of pressure difference, so as to block the formation and prevent the entry of water-based drilling fluid filtrate. Poly(ST-VS-B-E) can serve as a potential nano-blocking material in water-based drilling fluids.

In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Great efforts have been made by using Deep Neural Networks to recognize multi-label images. Since multi-label image classification is very complicated, people seek to use the attention mechanism to guide the classification process. However, conventional attention-based methods always analyzed images directly and aggressively. It is difficult for them to well understand complicated scenes. In this paper, we propose a global/local attention method that can recognize an image from coarse to fine by mimicking how human-beings observe images. Specifically, our global/local attention method first concentrates on the whole image, and then focuses on local specific objects in the image. We also propose a joint max-margin objective function, which enforces that the minimum score of positive labels should be larger than the maximum score of negative labels horizontally and vertically. This function can further improve our multi-label image classification method. We evaluate the effectiveness of our method on two popular multi-label image datasets (i.e., Pascal VOC and MS-COCO). Our experimental results show that our method outperforms state-of-the-art methods., Accepted by IEEE International Smart Cities Conference 2018

Cellular mechanical properties are closely related to cell physiological functions and status, and their analysis and measurement help understand cell mechanism. In this study, a microfluidic platform was built to measure the mechanical properties of cells by using dielectrophoretic (DEP) force. The electrodes generally used to stretch cells are made of indium tin oxide, Au, and Pt, which have inherent disadvantages. In this paper, galinstan alloy liquid metal was first introduced as microelectrode to form non-uniform electric filed for red blood cell stretching manipulation. The liquid metal microelectrode is easy to manufacture, low in price, stable at high voltage, and reusable. An effective microfluidic chip integrated with liquid metal electrode was designed and simulated, and a series of experiments to capture and stretch red blood cells was performed. The length of the red blood cells increased from 6 μm to 8 μm under the DEP force from 0 pN to 103 pN. This work also revealed the potential use of liquid metal as microelectrode to manipulate the microparticles and cells in a microfluidic chip.

In this paper, we propose a novel deep convolutional neural network (DCNN) for removing snowflakes from light field (LF) images. We observe that snowflakes in LF images always interrupt slopes in background scenes in epipolar plane images (EPIs), which means that snowflakes may be easily detected in EPIs. Our method takes 3D EPI volumes (i.e., stacked subaperture views along the same row or column of an LF image) as input. In this way, our snowflake detector based on a 3D residual network with a convolutional long short-term memory (ResNet-ConvLSTM) can utilize both contextual information and 3D scene structural information to effectively detect snowflakes of different sizes in LF images. Then, an encoder-decoder-based LF image restoration network is proposed to restore the background image. Finally, extensive experiments for comparison with the state-of-the-art methods demonstrate the effectiveness of our method for challenging scenes.

As one of the key components of aerospace power grid, the performance of on load tap changer (OLTC) of power transformer is very important. So it is necessary to realize real-time monitoring of its status. At present, there is no feasible monitoring scheme to carry out on-line monitoring and fault diagnosis for detecting faults of OLTC. The current waveform of the switching process will be abnormal due to the contact fault, the transition resistance fault, and most of the mechanical faults. This paper presents a fault diagnosis method for OLTC based on the current detection in the process of switching. Through on-line acquisition of current waveform in the switching process, empirical mode decomposition and Hilbert–Huang transform is used to calculate normalized time-frequency spectrum, and extract fault characteristic quantity, which can realize the comprehensive diagnosis of the running state of the equipment, and identify the early fault characteristics, and promote the process of the OLTC’s maintenance, from regular test to condition based maintenance. Experiment results prove that the designed fault diagnosis instrument can effectively monitor the working condition of OLTC during the switching process and make effective fault diagnosis.

Reduced graphene oxide (RGO) samples with optimized types of surface functional groups were hybridized with TiO2 to achieve the selective adsorption and removal of various pollutants. A high ratio of hydroxyl groups was found to be remarkably advantageous for the adsorbtion and decomposition of rhodamine-B (and similar pollutants), while a high ratio of carboxyl groups was found to promote the ability to adsorb and decompose phenol. Moreover, the presence of carboxyl groups on the RGO edge provides a pre-condition to form a close chemical connection with TiO2, which has been proven by the obtained electron paramagnetic resonance (EPR) curve, infrared spectroscopy (IR) and electron lifetime. The resulting composite photocatalysts display excellent photocatalytic activities under both UV- and visible-light illumination, indicating that the well-designed surface micro-circumstances of the RGO are quite significant.

Possessing the unique and highly valuable properties, graphene sheets (GSs) have attracted increasing attention including that from the building engineer due to the fact that Graphene can be utilized to reinforce concrete and other building materials. In this work, the nonlocal elastic theory and classical plate theory (CLPT) are used to derive the governing equations. The element-free framework for analyzing the buckling behaviors of double layer circular graphene sheets (DLCGSs) relying on an elastic medium is proposed. Pasternak-type model is adopted to describe the elastic medium. Accordingly, the influences of boundary conditions, size of GSs and nonlocal parameters on the buckling behavior of DLCGSs are investigated. The results show that the OP buckling modes are only sensible to the van der Waals forces.

The problem of sliding mode control for a class of Takagi-Sugeno fuzzy model-based nonlinear one-sided Lipschitz systems is investigated in the paper. Due to the state components are not available, a state observer is designed based on an event-triggering mechanism. Meanwhile, the output measurements transmitted through the communication channels suffer from signal delays. Based on the estimated state, an integral sliding surface is proposed. Then, the sliding mode dynamics is obtained by virtue of equivalent control principle. Further, by constructing appropriate sliding mode controller, the finite-time reachability of predefined sliding surface is surely guaranteed. Moreover, the stability with an H

This paper studies the problem of H

Previous studies show that the indoor environment quality (IEQ) of buildings directly affects human health and comfort. This study aims to predict the change of indoor parameters at the next moment under the influences of the current indoor climate and outdoor climate and control the IEQ parameters based on the human thermal adaption in advance. We combine the simulation and the mathematical method to establish the office building model with air-conditioning and lighting systems and construct the bilinear model of the IEQ parameters and control variables. Unknown parameters are identified using the experimental method. Model predictive control (MPC) based on human thermal comfort is discussed by considering human thermal adaptation, and the neutral temperature is calculated through the dynamic relationship between outdoor and indoor temperatures. Results show that the temperature setpoint is adjusted in accordance with human adaptability, and the air-conditioning, fan, and lighting systems are controlled via MPC. The usage time of air-conditioning and light is reduced, and thus, energy is saved.

Airline disruption is universal phenomenon that block the originally scheduled flights, and the slow recovery scheduling causes a lot of losses to both the airline companies and the passengers. However, it is very difficult to generate a new recovery scheduling in a reasonable time by traditional methods, especially for large dimension airline disruption problem. To deal with this problem rapidly, the airline disruption problem will be reformulated as integer programmings and a distributed network, which is based on the fixed-point iterative method for integer programming, will be developed in this paper. In response to the airport closure, an airplane reschedule is constructed by these feasible flight lines. In the implementation of distributed network, a certain number of independent segments are obtained by dividing the solution space. As a feature of this fixed-point method, the number of partial feasible flight lines, which have to be calculated for finding an optimized airplane reschedule, is much fewer compared with the number needed by CPLEX CP Optimizer. This is the first distributed integer programming method to large airline disruption problems caused by airports closure. In the numerical results, the proposed distributed approach shows promising, especially for solving the large dimension airline disruption problem. CA extern

The execution of the airline operation is often deviated from the original schedule due to some unexpected disruptions, such as aircraft breakdowns and severe weather conditions. In this situation, a recovery plan is needed to get the irregular operation back to normal to minimize the losses of the airline. To produce recovery plans and solve the airline disruption problems, a novel modified traveling salesman problem model is proposed to generate sets of the feasible flight routes for each aircraft fleet type. Then, the feasible flight routes are reassigned to the available aircrafts in each fleet to form a recovery plan. Numerical results show that the approach proposed in this paper is efficient and promising.

A new type of electronic voltage transformer is proposed in this study for big data background. By using the conventional inverted SF_6 transformer insulation structure, a coaxial capacitor sensor was constructed by designing a middle coaxial electrode between the high-voltage electrode and the ground electrode. The measurement of the voltage signal could be obtained by detecting the capacitance current i of the SF_6 coaxial capacitor. To improve the accuracy of the integrator, a high-precision digital integrator based on the Romberg algorithm is proposed in this study. This can not only guarantee the accuracy of computation, but also reduce the consumption time; in addition, the sampling point can be reused. By adopting the double shielding effect of the high-voltage shell and the grounding metal shield, the ability and stability of the coaxial capacitor divide could be effectively improved to resist the interference of stray electric fields. The factors that affect the coaxial capacitor were studied, such as position, temperature, and pressure, which will influence the value of the coaxial capacitor. Tests were carried out to verify the performance. The results showed that the voltage transformer based on the SF_6 coaxial capacitor satisfies the requirements of the 0.2 accuracy class. This study can promote the use of new high-performance products for data transmission in the era of big data and specific test analyses.

Classic core-based instrument transformers are more prone to magnetic saturation. This affects the measurement accuracy of such transformers and limits their applications in measuring large direct current (DC). Moreover, protection and control systems may exhibit malfunctions due to such measurement errors. This paper presents a more accurate method for current measurement based on a circular magnetic field sensing array. The proposed measurement approach utilizes multiple hall sensors that are evenly distributed on a circle. The average value of all hall sensors is regarded as the final measurement. The calculation model is established in the case of magnetic field interference of the parallel wire, and the simulation results show that the error decreases significantly when the number of hall sensors n is greater than 8. The measurement error is less than 0.06% when the wire spacing is greater than 2.5 times the radius of the sensor array. A simulation study on the off-center primary conductor is conducted, and a kind of hall sensor compensation method is adopted to improve the accuracy. The simulation and test results indicate that the measurement error of the system is less than 0.1%.

Graphene sheets are widely applied due to their unique and highly valuable properties, such as energy conservation materials in buildings. In this paper, we investigate the vibration behavior of double layer graphene sheets (DLGSs) in thermal environments which helps probe into the mechanism of energy conservation of graphene sheets in buildingmaterials. The nonlocal elastic theory and classical plate theory (CLPT) are used to derive the governing equations. The element-free method is employed to analyze the vibration behaviors of DLGSs embedded in an elastic medium. The accuracy of the solutions in this study is demonstrated by comparison with published results in the literature. Furthermore, a number of numerical results are presented to investigate the effects of various parameters (boundary conditions, nonlocal parameter, aspect ratio, side length, elastic foundation parameter, and temperature) on the frequency of DLGSs. OA gold - CA extern

The knapsack feasibility problems have been intensively studied both because of their immediate applications in industry and financial management, but more pronounced for theoretical reasons, as knapsack problems frequently occur by relaxation of various integer programming problems. In this work, the large-scale knapsack feasibility problem is divided into two subproblems. The first subproblem is transforming of the knapsack feasibility problem into a polytope judgement problem which is based on lattice basis reduction. In the next subproblem, a distributed implementation of Dang and Ye’s fixed-point iterative algorithm is introduced to solve the polytope judgement problem generated in the former subproblem. Compared with the branch and bound method, numerical results show that this distributed fixed-point method is effective.