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This paper is concerned with the observer-based controller for the stabilization of linear systems with multiple delays. We design a state observer for the delay systems and derive a separation property for the observer-based controller of the delay systems.

Inferring dynamic functional connectivity (dFC) from functional magnetic resonance imaging (fMRI) is crucial to understand the time-variant functional inter-relationships among brain regions. Because of the sparse property of functional connectivity networks, sparsity-promoting dFC estimation methods, which are mainly based on l 1 -norm regularization, are gaining popularity. However, l 1 -norm regularization cannot provide the maximum sparsity solution as the most natural sparsity promoting norm, the l 0 -norm. But l 0 -norm is seldom used to infer sparse dFC because an efficient algorithm to address the non-convexity problem of l 0 -norm is lacking. In this work, we develop a new l 0 -norm regularization-based inverse covariance estimation method for estimating dFC from fMRI. This novel method employs l 0 -norm regularizations on both spatial and temporal scales to enhance the spatial sparsity and temporal smoothness of dFC estimates. To overcome the non-convexity of l 0 -norm, we further propose an effective optimization algorithm based on the coordinate descent (CD). The performance of the proposed l 0 -norm-based sparse-smooth regularization (L0-SSR) method is examined using a series of synthetic datasets concerning various types of network topology. We further apply the proposed L0-SSR method to real fMRI data recorded in block-design motor tasks from 45 participants for the exploration of task induced dFC. Results on synthetic and real-world fMRI data show that, the L0-SSR method can achieve more accurate and interpretable dFC estimates than conventional l 1 -norm-based dFC estimation methods. Hence, the proposed L0-SSR method could serve as a powerful analytical tool to infer highly complex, variable, and sparse dFC patterns.

This brief gives an advanced observer-based attitude stabilization mechanism for quadcopter applications subject to parameter and load uncertainties. The results show two features: first, a parameter-independent pole-zero cancellation angular velocity observer for reducing the number of sensors, and second, state and disturbance observer-based pole-zero cancellation control for improving the robustness by guaranteeing the performance recovery property. Numerical evidence from realistic simulations confirms the effectiveness of the resultant closed-loop system.

For designs of computer experiments, column-orthogonality and space-filling property are two desirable properties. In this paper, we develop methods for constructing a new class of designs that include orthogonal Latin hypercube designs as special cases. These designs are not only column-orthogonal but also have good space-filling properties in low dimensions. All these appealing properties make them good choices for designing computer experiments. Based on orthogonal arrays, the proposed methods are easy to operate and flexible. Many new orthogonal designs with desirable space-filling properties are constructed and tabulated. Rotation matrices play a key role in the construction.

Polarization-adjusted convolutional (PAC) codes were recently proposed and arouse the interest of the channel coding community because they were shown to approach theoretical bounds for the (128,64) code size. In this letter, we propose systematic PAC codes. Thanks to the systematic property, improvement in the bit-error rate of up to 0.2 dB is observed, while preserving the frame-error rate performance. Moreover, a genetic-algorithm based construction method targeted to approach the theoretical bound is provided. It is then shown that using the proposed construction method systematic and non-systematic PAC codes can approach the theoretical bound even for higher code sizes such as (256,128)., Comment: 5 pages, 5 figures

An integral sliding mode control method for uncertain Takagi–Sugeno fuzzy systems is investigated in this paper. Considering the time-varying property of the fuzzy system control matrix , a dimensional-varying integral sliding mode controller is proposed. With a membership function piecewise linearization technique, the gain matrices of equivalent control law are derived. Then a dimension switching sliding model control scheme is designed to close the control loop. As a result, traditional restrictions on input matrix can be further relaxed. Finally, a numerical and a Diesel Engine Air-Path control examples are provided to certificate the merits and effectiveness of the proposed approach.

The conjugate gradient (CG) method is a method to solve unconstrained optimization problems. Moreover CG method can be applied in medical science, industry, neural network, and many others. In this paper a new three term CG method is proposed. The new CG formula is constructed based on DL and WYL CG formulas to be non-negative and inherits the properties of HS formula. The new modification satisfies the convergence properties and the sufficient descent property. The numerical results show that the new modification is more efficient than DL, WYL, and CG-Descent formulas. We use more than 200 functions from CUTEst library to compare the results between these methods in term of number of iterations, function evaluations, gradient evaluations, and CPU time.

Since any faulty operations could directly affect the composite property, making early prognosis is particularly crucial for complex equipment. At present, data-driven approach has been typically used for fault prediction. However, for part of complex equipment, it is difficult to access reliable and sufficient data to train the fault prediction model. To address this issue, this paper takes autoclave as an example. A Digital Twin (DT) model containing multiple dimensions for the autoclave is firstly constructed and verified. Then the characteristics of autoclave under different conditions are analyzed and presented with specific parameters. The data in normal and faulty conditions are simulated by using the DT model. Both the simulated data and extracted historical data are applied to enhance fault prediction. A convolutional neural network for fault prediction will be trained with the generated data which matches the feature of the autoclave in faulty conditions. The effectiveness of the proposed method is verified through result analysis.

In order to take advantage of the non-circularity property and low hardware cost and consumption of 1bit quantization in DOA estimation, in this letter, we propose a 1bit DOA estimation algorithm for strictly non-circular signals with non-circularity property. The relationship between approximated covariance without quantization and 1bit quantized covarince matrix, 1bit quantized pseudo covarince matrix is further derivation and obtained based on arcsin law. The approximated covariance can achieve DOA estimation via rotational invariance techniques (ESPRIT) type and gridless methods, simulations are provided to demonstrate the effectiveness of the proposed method.

We present a new gripper and exploration approach that uses a finger with very low reflected inertia for probing and then grasping objects. The finger employs a transparent transmission, resulting in a light touch when contact occurs. The finger elements are stiff and mounted on precise Cartesian axes for accurate proprioceptive sensing. Experiments show that the finger can safely move faster into contacts than industrial parallel jaw grippers or even most force-controlled grippers with backdrivable transmissions. This property allows rapid proprioceptive probing of objects. Contact information is leveraged to execute grasping actions with a contact-first strategy and to reduce environment state uncertainty. We evaluate a particle filtering algorithm that inputs contact information from either proprioception, or a combination of tactile sensing and proprioception, to estimate object location. Both methods can estimate location within 2 mm; combined tactile sensing and proprioception requires fewer observations.

Ocular axial length (AL) is an important property of eyes used for determining their health prior to surgery. Estimation of AL is also crucial while making artificial lenses to replace impaired natural lenses. However, accurate measurement of AL requires a costly and bulky benchtop optical system. The complex structural features of eyes can be captured by fundus images, which can be easily captured nowadays with portable cameras. Here, we suggest a deep learning method for predicting AL based on fundus images with evidence of decision. This visual interpretation of predictions is achieved by post-processing, separated from the training process, to ensure that the architecture can be freely designed. Through the visualization technique, discriminative regions on input images can be localized to demonstrate specific areas of interest for predictions. In the experiments, we found a significant relationship between the fundus images and AL with achieving a coefficient of determination ( R2 ) of 0.67 and accuracy of 90%, within an error margin of $ \pm 1$ mm. Furthermore, visual evidence proves that the network uses consistent regions for predicting AL. The visual results of this study also point to a link between AL and biological structure of eyes, which paves the way for future research.

This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property sets, and correlation and principal component analyses. The framework enables iterative definition of properties in the training and screening sets, guided by correlation relationships between properties, aiming to achieve optimal parametrizations for properties of interest. Specifically, the performance of increasingly complex potentials, Buckingham, Stillinger-Weber, Tersoff, and modified reactive empirical bond-order potentials are compared. Using MoSe2 as a case study, we demonstrate good reproducibility of training/screening properties and superior transferability. For MoSe2, the best performance is achieved using the Tersoff potential, which is ascribed to its apparent higher flexibility embedded in its functional form. These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.

The reservoir computing (RC) has recently gained considerable attention in practice and many methods have been developed to study its internal mechanism. However, the specific role played by the reservoir nodes of RC in time series prediction is still to be defined. An interpretable RC model wherein its reservoir network is designated as the directed acyclic network (DAN) is proposed with focus on time series prediction in this paper. In virtue of asymmetric transitivity and hierarchical structure of DAN, we present a directed network embedding method to identify the latent memory property of each node in the DAN. Such memory property is utilized to characterize the roles played by the reservoir nodes on the prediction performance of the RC. Meanwhile, it can also be leveraged to identify the corresponding memory community of DAN. As a result, we demonstrate how the reservoir network structure takes effect on the prediction performance from the perspective of memory community. In addition, two novel hyperparameters with the deterministic meaning are introduced to quantify the influence of the model initialization on the reservoir input so as to facilitate further dissection of the interpretable RC. The experimental results indicate that tuning these hyperparameters, which is explicable in terms of the Taylor expansion of the activation function, serves as an essential step in achieving superior prediction performance. Finally, comparative experiments with some other RC models on various time series benchmarks are also conducted.

The distinguishing property of Reconfigurable Manufacturing Systems (RMS) is that they can rapidly and efficiently adapt to new production requirements, both in terms of their capacity and functionalities. For this type of systems to achieve the desired efficiency, it should be possible to easily and quickly setup and reconfigure all of their components. This includes fixturing jigs that are used to hold workpieces firmly in place to enable a robot to carry out the desired production processes. In this paper, we formulate a constrained nonlinear optimization problem that must be solved to determine an optimal layout of reconfigurable fixtures for a given set of workpieces. The optimization problem takes into account the kinematic limitations of the fixtures, which are built in shape of Sterwart platforms, and the characteristics of the workpieces that need to be fastened into the fixturing system. Experimental results are presented that demonstrate that the automatically computed fixturing system layouts satisfy different constraints typically imposed in production environments.

Disparity estimation is an essential task taking part in many light-field applications. Due to the complexity of algorithms and high dimensional property of light-field data, performing this task involves a significant computational effort and results in very long processing time on CPU. Graphics processing units (GPUs), which is capable of massively parallel processing, is a promising solution to cover the computation requirement and speed up the task. In this paper, we develop a GPU-accelerated approach for light-field disparity estimation using a variational computation framework (GVLD). Our algorithm combines the intrinsic sub-pixel precision of variational formulation and the effectiveness of weighted median filtering to produce a highly accurate solution. The proposed algorithm is fully parallelized and optimized for the implementation using the OpenCL framework. An intensive evaluation including a quantitative comparison to related works and a detailed analysis of the proposed approach’s performance is presented. Experimental results demonstrate our superior performance compared to state-of-the-art approaches. The proposed approach is 10+ times faster than other approaches running on a similar GPU platform and provides the most accurate solution among optimization-based approaches. Compared to the implementation running on CPU, our GPU-accelerated method achieves up to $365\times $ speed up.

Despite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes.

The relationship between the structure and the shape in contemporary architecture has different formatsaccording to design and structural requirements. The integration is important formulas among these relationship, asthey form one unit in architecture, where the integration is characteristic by the important property which is theiterative system. One of the strategies to find iterative systems in contemporary architecture over traditional is calledstructural surface. Previous knowledge has been differed in explaining the functional of systems and it’smechanisms, especially in the relationship among the form and structure, so the problem has surfaced “the necessityto know the difference of the properties and types of iterative systems among solid and perforated structures withinthe structural surface strategy, and it’s role between the form and the structure”. to achieve the research’s goal “whatare the repetition and iterative systems in contemporary architectural structure and it’s role to determine the shape ofthe relationship among the structure and the form for building of different heights “, which has depended ondescriptive analytical method in three stage after defining the repetition in general, iterative system in particular andprevious knowledge criticism. First stage has focused on building a theoretical framework (characteristics and typesof the iterative systems in contemporary architectural structure and structural surface strategy). Second stage hasfocused on knowing the levels of the relationship among the form and the structure, By studying selected sampleswithin building of different height In addition to determine the important basic assumption of the research ,which is(iterative systems is differ in architectural structure (solid and perforated) , through characteristics of the iterativesystems with the system and the relationship of the surrounding environment according to it’s height (high ,medium)and it’s formation method (orthogonal ,free) ) . Third stage has focused on analyzing the results and conclusions as inthe role of the iterative systems (structural surface strategy) in producing the solid structures by adopting therepetition of the structural elements and generative rules in perforated structures, and use it to achieve a fusionamong the form and the structure to produce structures with efficiency and aesthetic appearance and structures thatreflect movement and dynamism. The level of this relationship are :( first: the compositionl, through theorganizational depth of the architectural structure. Second: the expressive, by finding three types of the relationshipare (the merging, the discrete, and the hybrid).

The system of operational control (SOC) of civil aircraft (CA) airborne equipment incorporates onboard equipment, as an object of control, means and programs of operational control, maintenance personnel of an operating enterprise, carrying out procedures using control means and organizing processes of operational control for the specified objects using control programs. Quality of A/C onboard equipment SOC becomes obvious in the process of operational control. Operational control is a set of processes for determining the technical condition (TC) of objects of control (OC) at the various operational stages: in flight, during operational maintenance (pre-flight and post-flight control), and periodic maintenance, after dismantling equipment from board. The process of determining OC TC of includes control, diagnostics, forecasting and recovery. The process of operational control is characterized by reliability of control – the property of TC control, which determines the extent of display objectivity as a result of monitoring the actual OC TC. Based on the SOC analysis as an object of research, the analysis of the problem of its forming and updating as well as the developed hierarchy of criteria for the effectiveness of interacting systems, the general problem will be formulated as follows: on a given set of parameters of onboard equipment SOC, let us determine the parameter values so that the system costs in the process of operational control reach minimum while performing all the required tasks and observing all the limitations for own parameters of the system as well as indicators of its technical efficiency.

Aiming at the phenomenon that the traditional measurement methods cannot complete large-scale measurement in a short time, and the image quality obtained by remote sensing in cloudy and rainy areas is difficult to meet the demand, this paper puts forward the idea of using UAV tilt photography to build three-dimensional modeling of urban real scene. The UAV tilt photography technology is used to collect the image data of about 200 km2 in Wuzhishan City. By laying a small number of ground image control points, the aerial triangulation is used to establish the connection, and the three-dimensional modeling of the collected data is carried out. Through the field verification to verify the attribute information of ground objects, the accuracy is verified by using CORS system field coordinate collection. The results show that the ground property and mathematical accuracy of UAV tilt photography model meet the requirements, and can be effectively used in real 3D modeling.

Purpose Additive manufacturing of concrete (AMoC) is an emerging technology for constructing buildings. However, due to the nature of the concrete property and constructing buildings in layers, constraints and limitations are encountered while applying AMoC in architecture. This paper aims to analyze the constraints and limitations that may be encountered while using AMoC in architecture. Design/methodology/approach A descriptive research approach is used to conduct this study. First, basic notions of AMoC are introduced. Then, challenges of AMoC, including hardware, material property, control and design, are addressed. Finally, strategies that may be used to overcome the challenges are discussed. Findings Factors influencing the success of AMoC include hardware, material, control methods, manufacturing process and design. Considering these issues in the early design phase is crucial to achieving a successful computer-aided design (CAD)/computer-aided manufacturing (CAM) integration to bring CAD and CAM benefits into the architecture industry. Originality/value In three-dimensional (3D) printing, objects are constructed layer by layer. Printing results are thus affected by the additive method (such as toolpath) and material properties (such as tensile strength and slump). Although previous studies attempt to improve AMoC, most of them focus on the manufacturing process. However, a successful application of AMoC in architecture needs to consider the possible constraints and limitations of concrete 3D printing. So far, research on the potential challenges of applying AMoC in architecture from a building lifecycle perspective is still limited. The study results of this study could be used to improve design and construction while applying AMoC in architecture.

Detection of the property changes in the reservoir during injection and production is important. However, the detection process is very challenging using surface seismic surveys because these property changes often induce subtle changes in the seismic signals. The quantitative evaluation of the subsurface property obtained by full-waveform inversion allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational cost. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data. We have applied a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small zone of interest. The redatuming technique generates time-lapse virtual data for the target-oriented inversion. Considering that the injection and production wells are often present in the target zone, we can incorporate the well velocity information with the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network to learn the statistical relationship between the inverted model and the facies interpreted from well logs. The trained network is used to map the property changes extracted from the wells to the target inversion domain. We then perform another time-lapse inversion, in which we fit the predicted data difference to the redatumed one from observation, as well as fit the model to the predicted velocity changes. The numerical results demonstrate that our method is capable of inverting for the time-lapse property changes effectively in the target zone by incorporating the learned model information from well logs.

In this paper, exploratory and innovative research is done on the implementation technique of artistic color virtual reality for similarity image recovery. Based on similarity images, a nonlocal natural image before the regular term is proposed to deal with the single-image blind deblurring problem. This paper designs a new artistic color virtual reality realization technology based on similarity image restoration, which exploits the low-rank property between nonlocal similarity blocks in images and combines a strong convex term to enhance the convexity of the artistic color virtual reality model. We analyze virtual reality interaction design from the perspective of art color design, sort out the concept and content of design, analyze the elements, design principles, and evaluation criteria included in virtual reality interaction art color design, and explore the conceptual principles of virtual reality interaction art color design. A full understanding of the characteristics of the medium of the virtual reality interaction can help us to better use this medium as a tool to create works that aim to bring higher quality and experiential feeling with a perceptual communication method that is closer to natural interaction. Combining the power of technology, artistic colourful thinking, and a design approach paves the way forward. The study shows that virtual reality technology can effectively improve the status quo and promote the cultivation of professional practice ability in art color design, which is conducive to the cultivation of applied design talents.

Most of existing augmented designs are to add some runs in the follow-up stages. While in many cases, the level of factors should be augmented and these augmented designs are called level-augmented designs. According to whether the experimental domain is extended or not, they can be divided into range-extended and range-fixed level-augmented designs. For different types of initial designs, the symmetrical and asymmetrical level-augmented designs are discussed, respectively. Based on the property of robustness, a uniformity criterion is a suitable choice to obtain an optimal level-augmented design when the model is unknown. In this paper, the wrap-around $$L_2$$ -discrepancy (WD) is chosen as the uniformity measure. We give the expressions and the tight lower bounds of WD of level-augmented designs under some special parameters. A method to construct a special case of symmetrical level-augmented designs is given. Some examples and level-augmented uniform designs are also provided.

The interaction of the cerebellum with cerebral cortical dynamics is still poorly understood. In this paper, dynamical causal modeling is used to examine the interaction between cerebellum and cerebral cortex as indexed by MRI resting-state functional connectivity in three large-scale networks on healthy young adults (N = 200; Human Connectome Project dataset). These networks correspond roughly to default mode, task positive, and motor as determined by prior cerebellar functional gradient analyses. We find uniform interactions within all considered networks from cerebellum to cerebral cortex, providing support for the notion of a universal cerebellar transform. Our results provide a foundation for future analyses to quantify and further investigate whether this is a property that is unique to the interactions from cerebellum to cerebral cortex.

Direct application of bio-oil from fast pyrolysis as a fuel has remained a challenge due to its undesirable attributes such as low heating value, high viscosity, high corrosiveness and storage instability. Solvent addition is a simple method for circumventing these disadvantages to allow further processing and storage. In this work, computer-aided molecular design tools were developed to design optimal solvents to upgrade bio-oil whilst having low environmental impact. Firstly, target solvent requirements were translated into measurable physical properties. As different property prediction models consist different levels of structural information, molecular signature descriptor was used as a common platform to formulate the design problem. Because of the differences in the required structural information of different property prediction models, signatures of different heights were needed in formulating the design problem. Due to the combinatorial nature of higher-order signatures, the complexity of a computer-aided molecular design problem increases with the height of signatures. Thus, a multi-stage framework was developed by developing consistency rules that restrict the number of higher-order signatures. Finally, phase stability analysis was conducted to evaluate the stability of the solvent-oil blend. As a result, optimal solvents that improve the solvent-oil blend properties while displaying low environmental impact were identified.

Traffic accidents forecasting represents a major priority for traffic governmental organisms around the world to ensure a decrease in life, property, and economic losses. The increasing amounts of ...

The purpose of this work is to study the possibilities of improving the quality of the processes of controlling the movement of the vessel along the course by combining individual standard controllers. Of the known scientific directions devoted to the problem being solved, the closest is the theory of systems with variable structure, in which, due to switching, a unique useful property is achieved, which are not possessed by individual switched structures. The article is devoted to the approach to the construction of the ship course control system, which is based on the principle of switching regulators during the transient process. This makes it possible to improve the quality of control processes in the system by using the features of individual regulators, in particular, the application of the switching principle made it possible to significantly increase the speed of the system in comparison with systems without switching and ensure the desired monotonic nature of the control process. The proposed approach is illustrated based on switchable P-controllers. The results of modeling the developed ship course control system are presented and discussed.

Summary In this article, the issue of adaptive finite‐time dynamic surface control (DSC) is discussed for a class of parameterized nonlinear systems with full state constraints. Using the property ...

The deep learning revolution introduced a new and efficacious way to address computational challenges in a wide range of fields, relying on large data sets and powerful computational resources. In protein engineering, we consider the challenge of computationally predicting properties of a protein and designing sequences with these properties. Indeed, accurate and fast deep network oracles for different properties of proteins have been developed. These learn to predict a property from an amino acid sequence by training on large sets of proteins that have this property. In particular, deep networks can learn from the set of all known protein sequences to identify ones that are protein-like. A fundamental challenge when engineering sequences that are both protein-like and satisfy a desired property is that these are rare instances within the vast space of all possible ones. When searching for these very rare instances, one would like to use good sampling procedures. Sampling approaches that are decoupled from the prediction of the property or in which the predictor uses only post-sampling to identify good instances are less efficient. The alternative is to use sampling methods that are geared to generate sequences satisfying and/or optimizing the predictor's desired properties. Deep learning has a class of architectures, denoted as generative models, which offer the capability of sampling from the learned distribution of a predicted property. Here, we review the use of deep learning tools to find good sequences for protein engineering, including developing oracles/predictors of a property of the proteins and methods that sample from a distribution of protein-like sequences to optimize the desired property.

We study a variant of the weighted consecutive ones property problem. Here, a 0/1-matrix is given with a cost associated to each of its entries and one has to find a minimum cost set of zero entries to be turned to ones in order to make the matrix have the consecutive ones property for rows. We investigate polyhedral and combinatorial properties of the problem and we exploit them in a branch-and-cut algorithm. In particular, we devise preprocessing rules and investigate variants of “local cuts”. We test the resulting algorithm on a number of instances, and we report on these computational experiments.

Multi-wall profile as one of the most widely approaches can enhance the crashworthiness properties in thin-walled tubular structures. In actual application process as energy absorbers, there may be...

Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable to small perturbations on the inputs. This has led to safety concerns on applying DNNs to safety-critical domains. Several verification approaches based on constraint solving have been developed to automatically prove or disprove safety properties for DNNs. However, these approaches suffer from the scalability problem, i.e., only small DNNs can be handled. To deal with this, abstraction based approaches have been proposed, but are unfortunately facing the precision problem, i.e., the obtained bounds are often loose. In this paper, we focus on a variety of local robustness properties and a(δ,ε)-global robustness property of DNNs, and investigate novel strategies to combine the constraint solving and abstraction-based approaches to work with these properties:We propose a method to verify local robustness, which improves a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. Specifically, the values of neurons are representedsymbolicallyand propagated from the input layer to the output layer, on top of the underlying abstract domains. It achieves significantly higher precision and thus can prove more properties.We propose a Lipschitz constant based verification framework. By utilising Lipschitz constants solved by semidefinite programming, we can prove global robustness of DNNs. We show how the Lipschitz constant can be tightened if it is restricted to small regions. A tightened Lipschitz constantcan be helpful in proving local robustness properties. Furthermore, a global Lipschitz constant can be used to accelerate batch local robustness verification, and thus support the verification of global robustness.We show how the proposed abstract interpretation and Lipschitz constant based approaches can benefit from each other to obtain more precise results. Moreover, they can be also exploited and combined to improve constraints based approach.We implement our methods in the tool PRODeep, and conduct detailed experimental results on several benchmarks

A parameter is a numerical or other measurable factor whose values characterize a system. Connectivity parameters have indispensable role in the analysis of connectivity of networks. Strength of a cycle in an unweighted graph is always one. But, in a fuzzy graph, strengths of cycles may vary even for a given pair of vertices. Cyclic reachability is a property that determines the overall connectedness of a network. This article introduces two connectivity parameters namely, cyclic connectivity index (CCI) and average CCI (ACCI) of fuzzy graphs, which can be used to represent the cyclic reachability. CCI of fuzzy graph theoretic structures, such as trees, blocks, $\theta$ -fuzzy graphs, and complete fuzzy graphs, are discussed. Vertices of a fuzzy graph are classified into three categories in terms of ACCI and their characterizations are obtained. Three algorithms are proposed. One of them is to help find CCI and ACCI of a given fuzzy graph. Another helps to identify the nature of vertices and the third to enhance the existing ACCI of a fuzzy graph. Also, future directions in the study of CCI and ACCI are proposed.

For the purpose of emulating realistic ray-based channels more exactly, two novel sum-of-cisoids (SOC) channel simulator parameters computation strategies, i.e., the bidirectional allocation (BA) and the simplified forward allocation (FA) methods, are proposed in this letter. Compared with the classical equal power method, the asymmetric computing property of these two methods further improves the spatial correlation emulation accuracy and significantly mitigates the intra-cluster correlation of sub-paths. As shown in the simulation results, both BA and FA methods enable the statistical spatial-temporal characteristics of emulated channel match extremely well with that of the target channel. More important, the proposed processes are deterministic, and will not introduce randomness in different realizations. Hence the proposed methods are always ergodic with arbitrary angle spreads.

This brief proposes an adaptive observer based on the super-twisting algorithm (STA) and its discrete-time realization with a predefined convergence time. In contrast to conventional adaptive STA that tries to adaptively reduces the gain sizes as much as possible in accordance with external disturbances, the proposed adaptive observer increases the gain sizes such that the convergence time is ensured to be within the predefined convergence-time period. The numerical chattering associated with these large gains is suppressed by employing the proposed discrete-time realization based on an implicit Euler discretization method. While keeping the property of predefined convergence time, the observation precision of the proposed discrete-time scheme is consistent with the STA, i.e., standard asymptotical second-order accuracy level. The superiority of the adaptive observer and its realization scheme is demonstrated through a circuit system example.

The state-of-charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This article proposes a novel method for online SOC estimation, which manifests itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of the system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy, which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.

With the increasing of vehicle volume and driving speed, traffic accidents and environmental safety have become social concerns. Vehicle traffic accidents, especially multi-vehicle chain accidents, cause damage to property and human lives. Meanwhile, traffic pollution will lead to continuous harm to living environment and health. This is a coupled human-vehicle-environment interaction system, which is difficult to model with traditional mathematical methods. Parallel theory is an effective method to solve such complex problems based on advanced artificial intelligence and computer technology. In this paper, a parallel system is built to analyze and control multi-intelligent connected vehicle based on parallel theory. The parallel system is also used to analyze and assess the exhaust emission of multi-intelligent connected vehicle. The parallel system is carried out with three steps: 1) modeling and representation of multi-intelligent connected vehicle system using artificial societies; 2) analysis and evaluation by computational experiments; 3) control, management and exhaust emission evaluation through parallel execution of real and artificial systems and big data. The parallel control methods, models and conclusions obtained from this paper can be used to enhance the experience of safety in multi-vehicle control under vehicle to everything environment and make the safety intervention measures more efficient.

Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images. These challenges are typically addressed using multi-column structures in existing methods. However, such an approach does not provide consistent improvement and transferability due to limited ability in capturing multi-scale features, sensitivity to large density shift, and difficulty in training multi-branch models. To overcome these limitations, a Single-column Scalere-invariant Network (ScSiNet) is presented in this paper, which extracts sophisticated scale-invariant features via the combination of interlayer multi-scale integration and a novel intralayer scale-invariant transformation (SiT). Furthermore, in order to enlarge the diversity of densities, a randomly integrated loss is presented for training our single-branch method. Extensive experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in counting accuracy and achieves remarkable transferability and scale-invariant property.

How detailed are long-term-memory representations compared with working memory representations? Recent research has found an equal fidelity bound for both memory systems, suggesting a novel general constraint on memory. Here, we assessed the replicability of this discovery. Participants (total N = 72) were presented with colored real-life objects and were asked to recall the colors using a continuous color wheel. Deviations from study colors were modeled to generate two estimates of color memory: the variability of remembered colors—fidelity—and the probability of forgetting the color. Estimating model parameters using both maximum-likelihood estimation and Bayesian hierarchical modeling, we found that working memory had better fidelity than long-term memory (Experiments 1 and 2). Furthermore, within each system, fidelity worsened as a function of time-correlated mechanisms (Experiments 2 and 3). We conclude that fidelity is subject to decline across and within memory systems. Thus, the justification for a general fidelity constraint in memory does not seem to be valid.

According to the feature-based view of semantic representation, concepts can be represented as distributed networks of semantic features, which contribute with different weights to determine the overall meaning of a concept. The study of semantic features, typically collected in property generation tasks, is enriched with measures indicating the informativeness and distinctiveness of a given feature for the related concepts. However, while these measures have been provided in several languages (e.g. Italian, Spanish and English), they have hardly been applied comparatively across languages. The purpose of this paper is to investigate language-related differences and similarities emerging from the semantic representation of aggregated core features. Features with higher salience for a set of concrete concepts are identified and described in terms of their feature type. Then, comparisons are made between domains (natural vs. artefacts) and languages (Italian, Spanish and English) and descriptive statistics are provided. These results show that the characterization of concrete concepts is overall fairly stable across languages, although interesting cross-linguistic differences emerged. We will discuss the implications of our findings in relation to the theoretical paradigm of semantic feature norms, as well as in relation to speakers' mutual understanding in multilingual settings.

Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequently leverage them for interpreting the network's response to an input. The pruning objective -- selecting the smallest group of neurons for which the response remains equivalent to the original network -- has been previously proposed for identifying critical pathways. We demonstrate that sparse pathways derived from pruning do not necessarily encode critical input information. To ensure sparse pathways include critical fragments of the encoded input information, we propose pathway selection via neurons' contribution to the response. We proceed to explain how critical pathways can reveal critical input features. We prove that pathways selected via neuron contribution are locally linear (in an L2-ball), a property that we use for proposing a feature attribution method: "pathway gradient". We validate our interpretation method using mainstream evaluation experiments. The validation of pathway gradient interpretation method further confirms that selected pathways using neuron contributions correspond to critical input features. The code is publicly available., Comment: Accepted at CVPR 2021 (IEEE/CVF Conference on Computer Vision and Pattern Recognition)

In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, corresponding to intervals where one or more of the data streams behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data streams, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach, which we call MVCAPA, is shown to consistently estimate the number and location of the collective anomalies, a property that has not previously been shown for competing methods. MVCAPA can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation.

The sensory response threshold is a fundamental biophysical property of biological systems that underlies many physiological and computational functions, and its systematic study has played a pivotal role in uncovering the principles of neural computation. Here, we show that ant colonies, which perform computational tasks at the group level, have emergent collective sensory response thresholds. Colonies respond collectively to step changes in temperature and evacuate the nest during severe perturbations. This response is characterized by a group-size dependent threshold, and the underlying dynamics are dominated by social feedback between the ants. Using a binary network model, we demonstrate that a balance between short-range excitatory and long-range inhibitory interactions can explain the emergence of the collective response threshold and its size dependency. Our findings illustrate how simple social dynamics allow insect colonies to integrate information about the external environment and their internal state to produce adaptive collective responses.

Tactile sensations of fabrics are the primary property determining the wearing comfort of clothing; however, comprehensive evaluation of the fabric tactile property by considering the flexural buckling of fabrics under high curvature, hysteresis performance and thermal property has not been fully studied, leading to a clear gap between the existing measurement methods and application requirements. Herein, a simultaneous-integrated testing method, namely the Touch Sensation Tester for Fabrics (TST-F) was introduced to evaluate the mechanical–thermal sensory properties of woven fabrics. The introduced instrument used one device with a single mechanical sensor to test various mechanical properties by constructing different deformations of fabrics, and the thermal property was simultaneously measured using an infrared detector array, achieving an efficient characterization of the mechanical–thermal sensation properties of textiles. The measurement capacity and repeatability of the TST-F were statistically analyzed; the measurement indices and their relation with fabric mechanical–thermal sensation properties were also exhibited. Results showed that the TST-F was promising to characterize fabric touch sensations in terms of bending stiffness, compression softness with wrinkling, stretching tightness and thermal comfort by considering the infrared transmission and heat conductivity of textiles.

Under the current COVID‐19 global pandemic, most of the world is operating online, which has increased the importance of better understanding the perceived color quality of video conference calls We performed two experiments to evaluate the white balance appearance preference for images simulating a scene from a video conference call where a person is using a virtual background Due to the dissimilarities of the light sources used for the subject and the background scene, the overall picture may look aesthetically unappealing The first experiment was designed to assess the preference of white balance for images containing a foreground subject, with three different skin tones: light, medium, and dark, and a background scene, with five different color temperature appearances, cool to warm The background scenes include famous attractions in the United States, water bodies, foliage, and a few less common scenes like a sculpture at the Rochester Institute of Technology (RIT) and a ColorChecker Observers were presented with a pair of images of the same subject with same background scene, but with different white balance appearance These comparisons were performed for each foreground subject with all background scenes Both experiments were performed by naive observers, who were from around the globe with no knowledge of color science and observers from the Munsell Color Science Lab (MCSL) at the RIT The results show that observers' preference increases as we go from cooler to warmer appearance for the Canyon, RIT, Snow, Flower, and Autumn backgrounds, and vice versa for the Lake background The Golden Gate background results are the most scattered among all scenes with very small differences in their scale values MCSL observers show a strong agreement in preference to warmer appearance for light and dark skin tones for the RIT scene, and neutral and warmer appearance for medium skin tone for the RIT and Golden Gate scenes, respectively To understand the relation between the background scene and foreground subject's white balance appearance, a second experiment was performed The subjects had three different appearances: cool, neutral, and warm Based on the results of the first experiment, five scenes with two white balance appearances, the most preferred rendition and the neutral rendition (original appearance at the time of the scene was captured), were used for the second paired comparison study The results indicated that preference varied based on the foreground subject skin tone and background scene However, the background preferences follow similar trends to the first experiment There were variations in the degree of agreement, some showed very strong agreement, like dark skin tone with the RIT background, whereas for others, like autumn background with light skin tone, preferences were more scattered Overall, the differences in the scale values were smaller as compared to the first experiment, which indicates that, as we present the observers with more options, the decision became harder [ABSTRACT FROM AUTHOR] Copyright of Color Research & Application is the property of John Wiley & Sons, Inc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Cellulosic yarns are the most fundamental and important materials for making a broad range of fashion structures and composites. Property of cellulosic yarns is mainly determined by their constitute fibers, and the internal structural properties, particularly the configurations and migration patterns of fibers inside the yarns. Tracer fiber technology is a popular method to measure fiber migration. The image mosaic and segmentation for two-viewed tracer fiber images are mainly conducted by manual operation. This paper is reporting the recent development of an intelligent method and automatic system for automatic mosaic and segmentation of tracer fiber images to analyze cellulosic yarn structural properties, including three-dimensional fiber configurations and migrations. Also a database composed of fifty series of tracer fiber images (total 872 images) with five different count densities of lyocell yarns (10Ne– 60Ne) was prepared and used to fully evaluate the qualities of the proposed image processing system with respect to conventional manual method. Evaluation results showed that the proposed method works well in automatic mosaic and segmentation for tracer fiber images for the intelligent structural analysis and evaluation. The proposed system presents a much higher efficiency than the conventional method, demonstrating a promising method and system for the structural analysis and evaluation of cellulosic yarns for fashion products.

In this article, a novel sampled-data control method is proposed for direct current to direct current converter. According to its switching property, the direct current to direct current converter is described as a switched affine system. A novel error tracking switching law is designed based on the multi-Lyapunov functions method and sampled-data control strategy with variant sampling intervals. The sufficient condition concerning the state of the constructed switched affine system converging to a finite region is developed, which guarantees the outcome voltage can approach the desired value and achieve the voltage adjustment. Based on it, the condition under uncertain parameters is developed as well, which would be more desirable in applications. The effectiveness of the proposed method is verified by numerical simulations. The proposed method is also applicable to other types of power converters.

Several methods for dynamically adapting the local consistency property applied by a CP solver during search have been put forward in recent and older literature. We propose the classification of such methods in three categories depending on the level of granularity where decisions about which local consistency property to apply are taken: node, variable, and value oriented. We then present a detailed review of existing methods from each category, and evaluate them theoretically according to several criteria. Taking one recent representative method from each class, we then perform an experimental study. Results show that simple variable and value oriented methods are quite efficient when the older dom/ddeg heuristic is used for variable ordering, while a carefully tuned node oriented method does not seem to offer notable improvement compared to standard arc consistency propagation. In contrast, under the more realistic setting of dom/wdeg, the variable and value oriented methods cannot compete with standard propagation, while the node oriented method is very efficient. Finally, we obtain a new adaptive propagation method by integrating the variable and value oriented approaches and adding an amount of randomization The resulting method is simple, competitive, and almost parameter-free.

History matching algorithms usually converge to the most prominent solution in the hypercube of parameter space defined by bound values. Here, we present a workflow to partition the parameter space into subdomains by defining a set of constraints. Then, a constrained history matching algorithm is developed to search each subdomain for a solution. This algorithm enables the engineers to solve the history matching problem subject to a set of general nonlinear/linear constraints on model parameters. The history matching problem definition follows a Bayesian framework, where the solution is obtained by maximizing the parameter’s posterior probability density conditioned to the field data. With the proposed constrained algorithm, the optimization is subject to a set of constraints on model parameters. The optimizer is an iterative ensemble smoother and the constraints are enforced in a secondary update step at each optimization iteration by projecting the solutions to the feasible domain. The projection operator is derived from the Lagrangian form of the constrained problem, and based on linearizing the active set of constraints at the ensemble updates. The proposed constrained history matching algorithm and multi-solution search workflow are tested on an optimization test problem to validate its robustness and efficiency. Then history matching of a reservoir under water flooding is investigated where the history matching variables are the parameters for the relative permeability curves and the multipliers for the regional rock property fields. The constraints include relations between porosity and permeability multipliers as well as the relative permeability curve parameters. The constrained history matching algorithm could robustly find the feasible solutions which provided acceptable data matches. Moreover, with the application of the presented workflow, multiple solutions could be obtained for the history matching problem.