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Subfield codes of linear codes over finite fields have recently received a lot of attention, as some of these codes are optimal and have applications in secrete sharing, authentication codes and association schemes. In this paper, a class of binary subfield codes is constructed from a special family of MDS codes, and their parameters are explicitly determined. The parameters of their dual codes are also studied. Some of the codes presented in this paper are optimal or almost optimal.

Smart passenger-seeking strategies employed by taxi drivers contribute not only to drivers’ incomes, but also higher quality of service passengers received. Therefore, understanding taxi drivers’ behaviors and learning the good passenger-seeking strategies are crucial to boost taxi drivers’ well-being and public transportation quality of service. However, we observe that drivers’ preferences of choosing which area to find the next passenger are diverse and dynamic across locations and drivers. It is hard to learn the location-dependent preferences given the partial data (i.e., an individual driver's trajectory may not cover all locations). In this paper, we make the first attempt to develop conditional generative adversarial imitation learning (cGAIL) model, as a unifying collective inverse reinforcement learning framework that learns the driver's decision-making preferences and policies by transferring knowledge across taxi driver agents and across locations. Our evaluation results on three months of taxi GPS trajectory data in Shenzhen, China, demonstrate that the driver's preferences and policies learned from cGAIL are on average 34.7% more accurate than those learned from other state-of-the-art baseline approaches.

Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

High-dimensional and sparse (HiDS) matrices are frequently found in various industrial applications. A latent factor analysis (LFA) model is commonly adopted to extract useful knowledge from an HiDS matrix, whose parameter training mostly relies on a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's learning rate is hard to tune in real applications, making it vital to implement its self-adaptation. To address this critical issue, this study firstly investigates the evolution process of a particle swarm optimization algorithm with care, and then proposes to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden, thereby innovatively achieving a novel position-transitional particle swarm optimization (P2SO) algorithm. It is subsequently adopted to implement a P2SO-based LFA (PLFA) model that builds a learning rate swarm applied to the same group of LFs. Thus, a PLFA model implements highly efficient learning rate adaptation as well as represents an HiDS matrix precisely. Experimental results on four HiDS matrices emerging from real applications demonstrate that compared with an SGD-based LFA model, a PLFA model no longer suffers from a tedious and expensive tuning process of its learning rate to achieve higher prediction accuracy for missing data.

The bipolar pad is one of the most promising topologies in inductive power transfer systems for electric vehicles. However, there is scant literature on the analytical model of the bipolar pad. In this paper, a 3-D analytical model of the inductive power transfer system including a bipolar transmitter and a square receiver is developed based on the superposition of two 2-D subdomain analytical models. Ferrite and the aluminum shields with finite dimension are taken into account on the transmitter and receiver sides. An analytical calculation of the mutual inductance is then carried out with respect to the main parameters of the inductive power transfer system, namely the dimension of the coils, the conductivity and the permeability of the shield. Two study cases are demonstrated to highlight how the proposed method can accelerate speed up the pad design process. Calculation results of the proposed model are compared with both a finite element analysis model and experimental measurements, demonstrating that the proposed model is 9 times faster than the finite element analysis method. When comparing with the experimental results, computational error of the proposed model is less than 6% in most of the study cases

Partial multi-view clustering has attracted various attentions from diverse fields. Most existing methods adopt separate steps to obtain unified representations and extract clustering indicators. This separate manner prevents two learning processes to negotiate to achieve optimal performance. In this paper, we propose the Joint Representation Learning and Clustering (JRLC) framework to address this issue. The JRLC framework employs representation matrices to extract view-specific clustering information directly from the presence of partial similarity matrices, and rotates them to learn a common probability label matrix simultaneously, which connects representation learning and clustering seamlessly to achieve better clustering performance. Under the guidance of JRLC framework, several new incomplete multi-view clustering methods can be developed by extending existing single-view graph-based representation learning methods. For illustration, within the framework, we propose two specific methods, JRLC with spectral embedding (JRLC-SE) and JRLC via integrating nonnegative embedding and spectral embedding (JRLC-NS). Two iterative algorithms with guaranteed convergence are designed to solve the resultant optimization problems of JRLC-SE and JRLC-NS. Experimental results on various datasets and news topic clustering application demonstrate the effectiveness of the proposed algorithms.

Multi-party set reconciliation is a key component in distributed and networking systems. It naturally contains two dimensions, i.e., set representation and reconciliation protocol. However, existing sketch data structures are insufficient to satisfy the new needs brought by the multi-party scenario simultaneously, including space-efficiency, mergeability, and completeness. The current reconciliation protocols, on the other hand, fail to achieve the global optimization of communication cost. To this end, in this article, we propose the marked cuckoo filter (MCF), a data structure for representing set members. Grounded on MCF, we implement the MCFsyn protocol to reconcile multiple sets. MCFsyn aggregates and distributes sets information represented by MCFs along with an underlying minimum spanning tree among the participants. The participants then identify the different elements by traversing the overall MCF which contains the information of all elements in the union set. For the identified missing elements, MCFsyn helps the participants to choose the optimal senders to fetch with the minimum communication cost. Comprehensive evaluations indicate that MCFsyn significantly outperforms existing alternatives in terms of both reconciliation accuracy and communication cost.

Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today's interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for its excellence in accelerating compute-intensive tasks such as deep learning algorithms in a high performance and energy efficiency manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based mobile interactive applications, and implement their backend computation engines on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80% while guaranteeing content freshness in heavily loaded ICVNs.

Solving constrained multiobjective optimization problems brings great challenges to an evolutionary algorithm, since it simultaneously requires the optimization among several conflicting objective functions and the satisfaction of various constraints. Hence, how to adjust the tradeoff between objective functions and constraints is crucial. In this article, we propose a dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm. In our approach, the selection preference of each individual is suitably switching from the objective functions to constraints as the evolutionary process. To be specific, the information of objective function, without considering any constraints, is extracted based on Pareto dominance to maintain the convergence and diversity by exploring the feasible and infeasible regions; while the information of constraint is used based on constrained dominance principle to promote the feasibility. Then, the tradeoff in these two kinds of information is adjusted dynamically, by emphasizing the utilization of objective functions at the early stage and focusing on constraints at the latter stage. Furthermore, to generate the promising offspring, two DE operators with distinct characteristics are selected as components of the search algorithm. Experiments on four test suites including 56 benchmark problems indicate that the proposed method exhibits superior or at least competitive performance, in comparison with other well-established methods.

Supercapacitors (SCs) is a promising energy storage approach to solve the intermittent problems of most renewable energy sources. N-doped hierarchically activated porous carbon materials (ACBS) for supercapacitor applicaitons are obtained by pre-carbonization and KOH activation of N-rich sword bean shells. ACBS containing 1–2% of N show hierarchical porosity, very high surface area and pore volume (equal to 2917 m2/g and 1.73 cm3/g, respectively). These materials are incorporated into supercapacitors. The device containing ACBS obtained using 700 °C as a post-treatment temperature shows a very high specific capacity (equal to 264 F/g at 1 A/g), which remains high (∼180 F/g) even at 20 A/g current density. Corresponding symmetric coin-cell supercapacitor demonstrats 12.5 Wh/kg energy density at 100 W/kg power density. This cell is capable of maintaining its energy density at the 11.1 Wh/kg level at 5000 W/kg power density and demonstrats almost 100% capacity retention after one thousand 1 A/g charge/discharge cycles. Such superior electrochemical performance of devices fabricated using ACBS as active materials is possible because of synergy between electrical double-layer capacitance and faradaic pseudocapacitance, which strongly depend on the surface area, pore volume and size distribution as well as dopant of heteroatoams.

In inductive power transfer (IPT) systems, misalignment and wide load range can lead to high current and control complexity. This can affect the performance of high-power systems. In this article, a method to minimize converter and primary resonant circuit currents based on an asymmetrical factor is proposed to improve IPT system performance over wide misalignment and load ranges. The proposed asymmetrical factor incorporates two design variables: an asymmetrical voltage factor and an asymmetrical compensation factor. These help to minimize current from two perspectives. First, they tend to redistribute zeroes and poles for power versus frequency characteristics. The power characteristic can be asymmetrical and monotonic over the working switching frequency range. Second, the input impedance angle can become insensitive to coupling factor and to load by adjusting the frequency that corresponds to the minimum input impedance angle. The current increases by only 15% over a 2:1 coupling coefficient variation range at rated load. Analysis and design guidelines are presented for the proposed method. A 2.1-kW prototype has been prepared to verify the approach.

Radio Frequency Identification (RFID) has been one of the critical technologies of the Internet of Things (IoT). With the rapid development of the IoT, the RFID systems are required to be more efficient and with high throughput capacity. In the widespread IoT application scenes, the collision problem of the RFID tags has become the increasingly remarkable problem in RFID systems. Traditionally, the anti-collision algorithms of RFID systems are always based on time division multiple access (TDMA). Although the TDMA based anti-collision algorithms are simple and easy to implement, it often misses tags and costs high time. Afterwards, the anti-collision algorithms based on blind source separation (BSS) have been introduced. These BSS based anti-collision algorithms are more efficient and stable, but they are mostly suitable for the determined or overdetermined case, i.e., the number of tags is less than that of the readers in RFID systems. Only a few anti-collision algorithms are taken into account of the underdetermined collision model. Because this underdetermined RFID collision model will give rise to more difficult solution but with very meaningfully practical IoT applications. Therefore, to investigate high quality underdetermined anti-collision algorithm for RFID system plays an important role in improving the efficiency of RFID system, and enable RFID implement more wide applications in future IoT systems. As a motivation, this paper proposes a new anti-collision algorithm for underdetermined RFID mixed system for performance improvement. In this work, the nonnegative matrix factorization (NMF) with minimum correlation and minimum volume constrains, i.e., the new MCV_NMF algorithm is proposed for anti-collision application in underdetermined RFID systems. This algorithm combines the independent principle of the tag signals with the NMF mechanism to achieve performance enhancement. The experimental results and analysis corroborate that this new algorithm can implement the underdetermined collision problem well and enhance the throughput capacity of RFID system.

Spectral clustering is becoming more and more popular because it has good performance in discovering clusters with varying characteristics. However, it suffers from high computational cost, unstable clustering results and noises. This work presents a novel approximate spectral clustering based on dense cores and density peaks, called DCDP-ASC. It first finds a reduced data set by introducing the concept of dense cores; then defines a new distance based on the common neighborhood of dense cores and calculates geodesic distances between dense cores according to the new defined distance; after that constructs a decision graph with a parameter-free local density and geodesic distance for obtaining initial centers; finally calculates the similarity between dense cores with their new defined geodesic distance, employs normalized spectral clustering method to divide dense cores, and expands the result on dense cores to the whole data set by assigning each point to its representative. The results on some challenging data sets and the comparison of our algorithm with some other excellent methods demonstrate that the proposed method DCDP-ASC is more advantageous in identifying complex structured clusters containing a lot of noises.

Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive to collect. To address it, many efforts have been made on training sophisticated models with few labeled data in an unsupervised and semi-supervised fashion. In this paper, we will review the recent progresses on these two major categories of methods. A wide spectrum of models will be categorized in a big picture, where we will show how they interplay with each other to motivate explorations of new ideas. We will review the principles of learning the transformation equivariant, disentangled, self-supervised and semi-supervised representations, all of which underpin the foundation of recent progresses. Many implementations of unsupervised and semi-supervised generative models have been developed on the basis of these criteria, greatly expanding the territory of existing autoencoders, generative adversarial nets (GANs) and other deep networks by exploring the distribution of unlabeled data for more powerful representations. We will discuss emerging topics by revealing the intrinsic connections between unsupervised and semi-supervised learning, and propose in future directions to bridge the algorithmic and theoretical gap between transformation equivariance for unsupervised learning and supervised invariance for supervised learning, and unify unsupervised pretraining and supervised finetuning. We will also provide a broader outlook of future directions to unify transformation and instance equivariances for representation learning, connect unsupervised and semi-supervised augmentations, and explore the role of the self-supervised regularization for many learning problems., Comment: published in IEEE Transactions on Pattern Analysis and Machine Intelligence

Based on the nonlinearity of energy consumption systems and the influence of multiple factors, this paper presents a nonlinear multivariable grey prediction model with parameter optimization and estimates the parameters and the approximate time response function of the model. Next, a genetic algorithm is applied to optimize the nonlinear terms of the novel model to seek the optimal parameters, and the modelling steps are outlined. Then, to assess the effectiveness of the novel model, this paper adopts Chinese oil, gas, coal and clean energy as research objects, and three classical grey forecasting models and one time series method are chosen for comparison. The results indicate that the new model attains a high simulation and prediction accuracy, basically higher than that of the three grey prediction models and the time series method.

This article solves the problem of optimal synchronization, which is important but challenging for coupled fractional-order (FO) chaotic electromechanical devices composed of mechanical and electrical oscillators and electromagnetic filed by using a hierarchical neural network structure. The synchronization model of the FO electromechanical devices with capacitive and resistive couplings is built, and the phase diagrams reveal that the dynamic properties are closely related to sets of physical parameters, coupling coefficients, and FOs. To force the slave system to move from its original orbits to the orbits of the master system, an optimal synchronization policy, which includes an adaptive neural feedforward policy and an optimal neural feedback policy, is proposed. The feedforward controller is developed in the framework of FO backstepping integrated with the hierarchical neural network to estimate unknown functions of dynamic system in which the mentioned network has the formula transformation and hierarchical form to reduce the numbers of weights and membership functions. Also, an adaptive dynamic programming (ADP) policy is proposed to address the zero-sum differential game issue in the optimal neural feedback controller in which the hierarchical neural network is designed to yield solutions of the constrained Hamilton-Jacobi-Isaacs (HJI) equation online. The presented scheme not only ensures uniform ultimate boundedness of closed-loop coupled FO chaotic electromechanical devices and realizes optimal synchronization but also achieves a minimum value of cost function. Simulation results further show the validity of the presented scheme.

Recent decades have witnessed a trend that control-theoretical techniques are widely leveraged in various areas, e.g., design and analysis of computational models. Computational methods can be modeled as a controller and searching the equilibrium point of a dynamical system is identical to solving an algebraic equation. Thus, absorbing mature technologies in control theory and integrating it with neural dynamics models can lead to new achievements. This work makes progress along this direction by applying control-theoretical techniques to construct new recurrent neural dynamics for manipulating a perturbed nonstationary quadratic program (QP) with time-varying parameters considered. Specifically, to break the limitations of existing continuous-time models in handling nonstationary problems, a discrete recurrent neural dynamics model is proposed to robustly deal with noise. This work shows how iterative computational methods for solving nonstationary QP can be revisited, designed, and analyzed in a control framework. A modified Newton iteration model and an improved gradient-based neural dynamics are established by referring to the superior structural technology of the presented recurrent neural dynamics, where the chief breakthrough is their excellent convergence and robustness over the traditional models. Numerical experiments are conducted to show the eminence of the proposed models in solving perturbed nonstationary QP.

In this paper, a new 3D visualization technical method was developed for hydraulic fractures using micro-seismic monitoring. This technical method consists of four steps: i. interpret the geologic hydraulic fracture model based on seismic source location data from micro-seismic monitoring; ii. develop a hydraulic fracture indication model, relying on the 3D spatial freeze-frame of micro-seismic monitoring sources from hydraulic fracturing; iii. construct a hydraulic fracture density model using the intensity from the micro-seismic monitoring; and iv. implement a 3D visualization of the hydraulic fractures, relying on the spatial constraints of the density model, the hydraulic fracture indication model, and the properties of the hydraulic fractures. This proposed technical method was used to produce 3D visualizations of the hydraulic fractures in well X in the Jiao reservoir, China, and the 3D visualizations of the distribution, development, extent and cutting relationships of hydraulic fractures were successfully realized. The results show that this technical method can be used as a practical and reliable approach to characterize hydraulic fractures.

Random vector functional link networks ( RVFL ) is a class of single hidden layer neural networks based on a learner paradigm by which some parameters are randomly selected and contains more information due to the direct links between inputs and outputs. In this paper, combining the advantages of RVFL and the ideas of online sequential extreme learning machine ( OS-ELM ) and initial-training-free online extreme learning machine ( ITF-OELM ), a novel online learning algorithm which is named as initial-training-free online random vector functional link ( ITF-ORVFL ) is investigated for training RVFL. Because the idea of ITF-ORVFL comes from OS-ELM and ITF-OELM, the link vector of RVFL can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed. Besides a novel variable is added to the update formulae of ITF-ORVFL, and the stability for nonlinear systems based on this learning algorithm is guaranteed. The experiment results indicate that the proposed ITF-ORVFL is effective in estimating nonparametric uncertainty.

Aiming at the problem of poor prediction performance of rolling bearing remaining useful life (RUL) with single performance degradation indicator, a novel based-performance degradation indicator RUL prediction model is established. Firstly, the vibration signal of rolling bearing is decomposed into some intrinsic scale components (ISCs) by piecewise cubic Hermite interpolating polynomial-local characteristic-scale decomposition (PCHIP-LCD), and the effective ISCs are selected to reconstruct signals based on kurtosis-correlation coefficient (K-C) criteria. Secondly, the multi-dimensional degradation feature set of reconstructed signals is extracted, and then the sensitive degradation indicator IICAMD is calculated by fusing the improved independent component analysis (IICA) and Mahalanobis Distance (MD). Thirdly, the false fluctuation of the IICAMD is repaired by using the gray regression model (GM) to obtain the health indicator (HI) of the rolling bearing, and the start prediction time (SPT) of the rolling bearing is determined according to the time mutation point of HI. Finally, generalized regression neural network (GRNN) model based on HI is constructed to predict the RUL of rolling bearing. The experimental results of two groups of different rolling bearing data-sets show that the proposed method achieves better performance in prediction accuracy and reliability.

Nonlinear process modeling is a primary task in intelligent manufacturing, aiming at extracting high-value features from massive process data for further process analysis like process monitoring. However, it is still a challenge to develop nonlinear process models with robust representation capability for diverse process faults. From the new perspective of the correlation between process variables, this paper develops a nonlinear process modeling algorithm to adaptively preserve the features of both global and local inter-variable structures, in order to fully exploit inter-variable features for enhancing the nonlinear representation of process operating conditions. Specifically, a unidimensional convolutional operation with a self-attention mechanism is proposed to simultaneously extract global and local inter-variable structures, wherein different attentions can be adaptively adjusted to these two structures for the final aggregation of them. Besides, cooperating with a two-dimensional dynamic data extension, the unidimensional convolutional operation can represent the overall temporal relationship between process samples. Through stacking a collection of these convolutional operations, a ResNet-style convolutional neural network then is constructed to extract high-order nonlinear features. Experiments on the Tennessee Eastman process validate the effectiveness of the proposed algorithm for two vital process monitoring problems-fault detection and fault identification.

It is well known that a high voltage across the coupling plates of a Capacitive Power Transfer (CPT) system is beneficial to increase its power transfer capability, but a high voltage may lead to high insulation requirements and cause safety concerns. This paper proposes a compensation design method for achieving the maximum power of CPT systems under coupling voltage constraints. Basic on a simplified capacitive coupling model and thorough power transfer characteristics analysis, a family of compensation topologies are derived to maintain a 90 degrees phase shift between the input and output voltages across the CPT coupler against load variations, so that the coupling voltages (which practically need to be limited) are fully utilized for power transfer purposes. A full design process for determining the compensation parameters is presented, and an example 2kW CPT system is built and tested using LC compensation at the primary side to boost the input voltage to the coupler; and one of the proposed compensation topologies (CLC) at the secondary side for impedance transformation of a given load with a specified voltage and power requirements. Experimental results show a good agreement with theoretical analysis, which demonstrate the phase difference between the voltages before and after the coupler is kept nearly 90 degrees, and a maximum possible power transfer of 2.039kW is achieved under the given voltage limits and coupling conditions. A system end to end (DC-DC) efficiency of 90.29% is obtained when the air gap between the coupling plates is 150mm, and the coupling capacitance is only 13.84 pF.

For the existing repetitive motion generation (RMG) schemes for kinematic control of redundant manipulators, the position error always exists and fluctuates. This article gives an answer to this phenomenon and presents the theoretical analyses to reveal that the existing RMG schemes exist a theoretical position error related to the joint angle error. To remedy this weakness of existing solutions, an orthogonal projection RMG (OPRMG) scheme is proposed in this article by introducing an orthogonal projection method with the position error eliminated theoretically, which decouples the joint space error and Cartesian space error with joint constraints considered. The corresponding new recurrent neural networks (NRNNs) are structured by exploiting the gradient descent method with the assistance of velocity compensation with theoretical analyses provided to embody the stability and feasibility. In addition, simulation results on a fixed-based redundant manipulator, a mobile manipulator, and a multirobot system synthesized by the existing RMG schemes and the proposed one are presented to verify the superiority and precise performance of the OPRMG scheme for kinematic control of redundant manipulators. Moreover, via adjusting the coefficient, simulations on the position error and joint drift of the redundant manipulator are conducted for comparison to prove the high performance of the OPRMG scheme. To bring out the crucial point, different controllers for the redundancy resolution of redundant manipulators are compared to highlight the superiority and advantage of the proposed NRNN. This work greatly improves the existing RMG solutions in theoretically eliminating the position error and joint drift, which is of significant contributions to increasing the accuracy and efficiency of high-precision instruments in manufacturing production.

Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.

Two narrow bandgap conjugated polymers, PTT-EFQX and PT-DFBT-T-EFQX, comprising a novel bis(2-alkyl) 5,8-dibromo-6,7-difluoroquinoxaline-2,3-dicarboxylate (EF-Qx) unit, are designed and synthesized through palladium-catalyzed Stille coupling reaction. The ester groups and difluorine substituents are introduced into the EF-Qx unit to improve the electron affinity. The polymers containing EF-Qx unit exhibit a relatively narrow bandgap of around 1.6 eV. To optimize the effects of solubility and molecular packing, D-A structure and D-A1-D-A2 structure synthesis strategies are adopted in this work and the structure-property relationship is systematically studied. The results morphology characterizations indicate that the active layer based on D-A structure polymer PTT-EFQX has a superior morphology compared to active layer based on D-A1-D-A2 structure polymer PT-DFBT-T-EFQX, which helps PTT-EFQX-based polymer solar cells (PSCs) to obtain better PCEs of up to 5.4%. Our work demonstrates that the effective design strategy for development of quinoxaline-based polymer donor is necessary for controlling the morphology and thus achieving high-performance PSCs.

Audio-visual matching aims to learn the intrinsic correspondence between image and audio clip. Existing works mainly concentrate on learning discriminative features, while ignore the cross-modal heterogeneous issue between audio and visual modalities. To deal with this issue, we propose a novel Adversarial-Metric Learning (AML) model for audio-visual matching. AML aims to generate a modality-independent representation for each person in each modality via adversarial learning, while simultaneously learns a robust similarity measure for cross-modality matching via metric learning. By integrating the discriminative modality-independent representation and robust cross-modality metric learning into an end-to-end trainable deep network, AML can overcome the heterogeneous issue with promising performance for audio-visual matching. Experiments on the various audio-visual learning tasks, including audio-visual matching, audio-visual verification and audio-visual retrieval on benchmark dataset demonstrate the effectiveness of the proposed AML model. The implementation codes are available on https://github.com/MLanHu/AML.

As an emerging technology, industrial Internet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this article, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.

Subcooled boiling is the most effective form of heat exchange in the water jacket of the cylinder head. Chen's model is the most widely used correlation for predicting boiling heat transfer, but the selection of the correlation for the nucleate boiling is controversial. The work of this paper is to simulate the heat transfer process in the water jacket of the cylinder head with a horizontal rectangular channel that is heated on one side. Using the coolant flow velocity, inlet temperature and system pressure as variables, the heat flux and heat transfer coefficient were obtained. The results show that the increase of the coolant flow velocity can effectively promote the convection heat transfer, and the change of inlet temperature and system pressure will affect the occurrence of nucleate boiling. However, the Chen’s model predictions doesn’t fit well with the experimental data. Four nucleate boiling correlations were selected to replace Chen's model nucleate boiling correlation. The correlation proposed by Pioro coincides best with the experimental data. The mean error after correction is 18.2%.

This paper continues to study the construction of uni-nullnorms on bounded lattices. At first, we introduce a new method for constructing the smallest uni-nullnorm on an arbitrary bounded lattice L having the elements e , a ∈ L , based on the existence of a uninorm on [ 0 , a ] 2 with the neutral element e and a triangular norm on [ a , 1 ] 2 . And then, we propose another new approach to obtain the largest uni-nullnorm on L via a uninorm on [ 0 , a ) 2 with the neutral element e and a triangular norm on [ a , 1 ] 2 . Furthermore, we provide some corresponding examples to illustrate that our construction methods differ from the existing ones.

Healthcare Internet-of-Things (IoT) is an emerging paradigm that enables embedded devices to monitor patients' vital signals and allows these data to be aggregated and outsourced to the cloud. The cloud enables authorized users to store and share data to enjoy on-demand services. Nevertheless, it also causes many security concerns because of the untrusted network environment, dishonest cloud service providers and resource-limited devices. To preserve patients' privacy, existing solutions usually apply cryptographic tools to offer access controls. However, fine-grained access control among authorized users is still a challenge, especially for lightweight and resource-limited end-devices. In this paper, we propose a novel healthcare IoT system fusing advantages of attribute-based encryption, cloud and edge computing, which provides an efficient, flexible, secure fine-grained access control mechanism with data verification in healthcare IoT network without any secure channel and enables data users to enjoy the lightweight decryption. We also define the formal security models and present security proofs for our proposed scheme. The extensive comparison and experimental simulation demonstrate that our scheme has better performance than existing solutions.

An increasing number of cloud services are operated globally, where the service data are frequently replicated across geographically distributed datacenters to improve service quality and reliability. Such replication generates many one-to-many bulk data transfers over inter-datacenter networks from one datacenter to many receiver datacenters. To provide end-users with guaranteed services, these data transfers are usually required to be completed within designated deadlines. Despite the exponential growth in data demand, there has been little work on guaranteeing deadlines for one-to-many transfers, which is the subject of this paper. This paper proposes a centralized admission control coupled with a scheduling algorithm, named deAdline-Guaranteed transfEr (AGE), to guarantee the deadline of admitted data transfers and utilize the network capacity efficiently. The key idea is to flexibly select the source datacenter for receiver datacenters and allow the remaining receivers to obtain a replica from either the original source or the other receivers that have already received a copy. By jointly allocating the source for receivers and the bandwidth and routing paths for every data transfer, AGE maximizes the number of deadline-satisfied transfers. Our simulations show that compared to the state-of-the-art, AGE guarantees the deadline for up to 70% more transfers, achieves at least 2× higher network throughput, and reduces the completion time up to 80%.

The computer-based approach is required for the non-invasive detection of chronic liver diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we review the computer-aided diagnosis of hepatic lesions in view of diffuse- and focal liver disorders. This survey mainly focuses on three image acquisition modalities: ultrasonography, computed tomography, and magnetic resonance imaging. We present the insightful analysis with pros and cons for each preliminary step, particularly preprocessing, attribute analysis, and classification techniques to accomplish clinical diagnostic tasks. In preprocessing, we explore and compare commonly used denoising, deblurring and segmentation methods. Denoising is mainly performed with nonlinear models. In contrast, deep neural networks are frequently applied for deblurring and automatic segmentation of region-of-interest. In attribute analysis, the most common approach comprises texture properties. For classification, the support vector machine is mainly utilized across three image acquisition modalities. However, comparative analysis shows the best performance is obtained by deep learning-based convolutional neural networks. Considering biopsy samples or pathological factors such as overall stage, margin, and differentiation can be helpful for improving the prediction performance. In addition, technique breakthrough is expected soon with advances in machine learning models to address data limitation problems and improve the prediction performance.

Neighboring frames are more correlated compared to frames from further temporal distances. In this paper, we aim to explore the temporal correlations among neighboring frames and exploit cross-layer multi-scale features for action recognition. We present a Temporal Cross-Layer Correlation (TCLC) framework for temporal correlation learning. First, we introduce a context-aware reconstruction block to enable the exploration of neighboring context. This prediction block aims to reconstruct the past frame and the future frame in a unified way. It learns to mine frame correlations and aggregate long sequences at the same time. We demonstrate that this neighborhood mining process enhances the discriminative ability of the network. Second, we propose a novel cross-layer attention and a center-guided attention mechanism to integrate features with contextual knowledge from multiple scales. Our method is a two-stage process for effective cross-layer feature learning. The first stage incorporates the cross-layer attention module to decide the importance weight of the convolutional layers. The second stage leverages the center-guided attention mechanism to aggregate local features from each layer for the generation of a final video representation. We leverage global centers to extract shared semantic knowledge among videos. We evaluate TCLC on three action recognition datasets, i.e., UCF-101, HMDB-51 and Kinetics. Our experimental results demonstrate the superiority of our proposed temporal correlation mining method.

Subgraph matching finds all embeddings from a data graph that are identical to a query graph. Recent algorithms work by generating a tree-structured index on the data graph based on the query graph, ordering the vertices root-to-leaf path-by-path in the tree, and enumerating the embeddings following the matching order. However, we find such path-based ordering and tree-structured index based enumeration inherently limit the performance due to the lack of consideration on the edges among the vertices across tree paths. To address this problem, we propose an approach that generates the matching order based on a cost model considering both the edges among query vertices and the number of candidates. Furthermore, we create a bigraph index for candidate vertices and their selected neighbors in the data graph, and use this index to perform enumeration along the matching order. Our experiments on both real-world and synthetic datasets show that our method outperforms the state of the art by orders of magnitude.

This article addresses the tracking control problem for a class of nonlinear systems described by Euler-Lagrange equations with uncertain system parameters. The proposed control scheme is capable of guaranteeing prescribed performance from two aspects: 1) a special parameter estimator with prescribed-performance properties is embedded in the control scheme. The estimator not only ensures the exponential convergence of the estimation errors under relaxed excitation conditions but also can restrict all estimates to predetermined bounds during the whole estimation process and 2) the proposed controller can strictly guarantee the user-defined performance specifications on tracking errors, including convergence rate, maximum overshoot, and residual set. More importantly, it has the optimizing ability for the tradeoff between performance and control cost. A state transformation method is employed to transform the constrained optimal tracking control problem to an unconstrained stationary optimal problem. Then, a critic-only adaptive dynamic programming algorithm is designed to approximate the solution of the Hamilton-Jacobi-Bellman equation and the corresponding optimal control policy. Uniformly ultimately bounded stability is guaranteed via a Lyapunov-based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme.

Coal is an important energy source worldwide. Objectively and accurately predicting coal consumption is conducive to healthy coal industry development, because such predictions can provide references and warnings that are useful in formulating energy strategies and implementing environmental policies. Population size and area economic development are the main factors that affect coal consumption. Considering the above influences, this paper first establishes a differential equation and proposes a novel multivariable Verhulst grey model (MVGM(1,N)) based on grey information differences. MVGM(1,N) extends classical model from single-variable to multivariate and diminishes the characteristics of Verhulst’s reliance on saturated S-shaped and single-peak data, making classical model more applicable to real situations. To prove the effectiveness of MVGM(1,N) simulation experiments are carried out in areas with high coal consumption. The result of this proposed model is more precise than that of NLARX, ARIMA and five classical grey models Finally, this novel multivariable model predicates coal consumption of Inner Mongolia and Gansu Provinces in China, the results show that MVGM(1,N) is preferable to other models, indicating that this model can effectively predict coal consumption.

This paper proposes a selective harmonic active tuning control method for hybrid active power filters (HAPFs). A HAPF is composed of a voltage source inverter (VSI) and an LC filter. With this method, the VSI in the HAPF is controlled as a virtual capacitance that varies with the dominant harmonic frequencies. Thus, the LC filter can be forced to be tuning at these frequencies, which significantly improves the harmonic filtering characteristic of the LC filter. Meanwhile, to enhance the filtering performance in the case of a small power grid impedance or to damp the resonances between an LC filter and the grid impedance, the harmonics in the grid source current are detected and proportionally fed back to the controller, which brings about an effect equivalent to adding a series harmonic resistance on the grid source side. The proposed control method has the advantages of a simple control structure and easy implementation. Simulation and experimental results confirm the effectiveness and feasibility of the proposed method.

Target detection based on deep convolutional neural network has achieved excellent performance. However, small target detection is still one of the challenges in the field of computer vision. In this paper, we present an efficient network for real-time small target detection. The proposed network performs feature extraction using a modified Darknet53, while utilizing scale matching strategy to select suitable scales and anchor size for small target detection. In the network, we design an adaptive receptive field fusion module to increase the context information around the small targets by merging the features with different receptive field. Furthermore, we also propose an image cropping method in data preprocessing, aiming to make the targets trained in a wider range of scales. We conduct experiments on VEDAI dataset and small target dataset. Comparative results show that the proposed network achieved 74.5% mean average precision (mAP) at 50.0 FPS on VEDAI dataset and 45.7% mAP at 51.1 FPS on small target dataset which is better than other advanced target detectors.

An inference approach is proposed by formulating reasoning processes as particular evolutions of Petri nets. It can be used to design an intelligent agent that executes tasks in a given environment. First, a symbol Petri net is defined to represent a Boolean variable describing a distinct aspect of an environment. Second, a propositional logic sentence in a conjunctive normal form, which may express some background knowledge or a sequence of percepts made by an agent, is formulated as a linear constraint, called as a semantic constraint. Third, an algorithm is constructed to design monitor places enforcing semantic constraints on symbol Petri nets, and its resultant net is called a knowledge Petri net representing relevant knowledge. Fourth, a reasoning algorithm is presented based on a newly defined transition-firing rule of the knowledge Petri net, and can be used to infer or reveal hidden facts. The proposed inference algorithm is efficient since its time computational complexity is proven to be polynomial with respect to the number of Boolean variables. The wumpus world problem is taken as an example to illustrate and verify it.

3D mesh subdivision is essential for geometry modeling of complex surfaces, which benefits many important applications in the fields of multimedia such as computer animation. However, in the ordinary adaptive subdivision, with the deepening of the subdivision level, the benefits gained from the improvement of smoothness cannot keep pace with the cost caused by the incremental number of faces. To mitigate the gap between the smoothness and the number of faces, this paper devises a novel improved mesh subdivision method to coordinate the smoothness and the number of faces in a harmonious way. First, this paper introduces a variable threshold, rather than a constant threshold used in existing adaptive subdivision methods, to reduce the number of redundant faces while keeping the smoothness in each subdivision iteration. Second, to achieve the above goal, a new crack-solving method is developed to remove the cracks by refining the adjacent faces of the subdivided area. Third, as a result, the problem of coordinating the smoothness and the number of faces can be formulated as a multi-objective optimization problem, in which the possible threshold sequences constitute the solution space. Finally, the Non-dominated sorting genetic algorithm II (NSGA-II) is improved to efficiently search the Pareto frontier. Extensive experiments demonstrate that the proposed method consistently outperforms existing mesh subdivision methods in different settings.

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly guarantee a satisfactory performance with the extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss and the shrink of the discrete solution space. In this article, we propose a novel Collaborative Learning strategy that is tailored for generating high-quality low-bit hash codes. The core idea is to jointly distill bit-specific and informative representations for a group of pre-defined code lengths. The learning of short hash codes among the group can benefit from the manifold shared with other long codes, where multiple views from different hash codes provide the supplementary guidance and regularization, making the convergence faster and more stable. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and have shown that the 8-bit hash codes generated by MAH achieve 94.3 percent of the MAP 1 1. Mean Average Precision (MAP) score on the CIFAR-10 dataset, which significantly surpasses the performance of the 48-bit codes by the state-of-the-arts in image retrieval tasks.

The main goal of this article is to consider the fixed time control problem of perturbed chaotic systems by virtue of sliding mode control. For this aim, this article presents a novel fixed time stability theorem at first by the Lyapunov tools. Then combining the obtained stability theorem and sliding mode technique, a new sliding mode surface is constructed and some novel controllers are designed appropriately to stabilize the discussed chaotic system. The proposed controllers have two main advantages: (1) The control criteria is robust against the effects of perturbations. (2) The convergence time, which is only dependent on the control parameters regardless of the initial conditions, is bounded by a fixed constant. Finally two typical systems are taken as the numerical examples to verify the validity of the control strategy.

The system call sequence is widely used as raw data due to its prospective performance in host-based intrusion detection methods using machine learning. However, evolutionary intrusion attacks such as the obfuscation technique can achieve the same invasion purpose and effect while changing the malicious system call combination to bypass the abnormal identification, which makes the detection results not robust and even invalid. In this paper, we present a behavioral semantics enhancement method of system call sequence to overcome the problem. This method combines sequence completion to extend behavior information capacity with system calls abstraction and invocation switching differential encoding to improve abstractive representation ability. To complete behavioral semantics features extraction and data classification, the enhanced sequences are transformed to vector matrices and input into the multi-channel Text-CNN. Evaluation experiments show that the proposed method outperforms all of the compared works significantly, which suggests it has a more accurate and robust performance in detecting obfuscation attacks.

Measuring similarity among data objects is important in data analysis and mining. SimRank is a popular link-based similarity measurement among nodes in a graph. To compute the all-pairs SimRank matrix accurately, iterative methods are usually used. For static graphs, current iterative solutions are not efficient enough, both in time and space, due to the unnecessary cost and storage by the nature of iterative updating. For dynamic graphs, all current incremental solutions for updating the SimRank matrix are based on an approximated SimRank definition, and thus have no accuracy guarantee. In this paper, we propose a novel local push based algorithm for computing and tracking all-pairs SimRank. Furthermore, we develop an iterative parallel two-step framework for local push to take advantage of modern hardwares with multicore CPUs. We show that our algorithms outperform the state-of-the-art methods.

International audience; The Passivity-Based Control (PBC) is recognized as an effective energy shaping approach to guarantee the asymptotic stability of the whole system by using the passivity property. However, the model-based and sensor-based characteristics limit its development and application. The combination of the PBC and online estimation technique can solve these problems. The purpose of this paper is to propose a controller and an observer, which are designed simultaneously based on Hamiltonian framework and Lyapunov criterion. It leads to the system design without separation of the dynamics of the controller and the observer. The uncertainties in the model and parameters are considered as equivalent voltage and current sources. To reduce the number of sensors, input voltage, output current, and equivalent sources are estimated together. The steady-state error is eliminated by using this estimation technique. The exponential stability of the whole system (converter, controller, and observer) is proved by using a proper Lyapunov function. Simulation and experimental results from a 3 kW 270–350 V DC/DC boost converter with a Constant Power Load (CPL) are performed to confirm the proposed control algorithm. Since the system parameter values may vary with temperature and the equilibrium point, the robustness of the proposed method is verified without and with parameters uncertainties

Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale. Secondly, training sets contain only video-level labels indicating the existence of an abnormal event during the full video while lacking annotations of precise time durations. To tackle these problems, we contribute a new Large-scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence, etc. with large scene varieties, making it the largest anomaly analysis database to date. 2) It provides the annotation data, including video-level labels (abnormal/normal video, anomaly type) and frame-level labels (abnormal/normal video frame) to facilitate anomaly detection. Leveraging the above benefits from the LAD database, we further formulate anomaly detection as a fully-supervised learning problem and propose a multi-task deep neural network to solve it. We first obtain the local spatiotemporal contextual feature by using an Inflated 3D convolutional (I3D) network. Then we construct a recurrent convolutional neural network fed the local spatiotemporal contextual feature to extract the spatiotemporal contextual feature. With the global spatiotemporal contextual feature, the anomaly type and score can be computed simultaneously by a multi-task neural network. Experimental results show that the proposed method outperforms the state-of-the-art anomaly detection methods on our database and other public databases of anomaly detection. Codes are available at https://github.com/wanboyang/anomaly_detection_LAD2000., Publication in IET Image Processing

Evolution in digital technology is bringing improvements in the lifestyle and behaviors of ordinary people throughout the world. In recent years, research is undertaken to investigate the penetration of web-based sports applications in students' recreational activities. This study uses a systematic thematic analysis to analyze the literature on the research topic. We perform a rigorous thematic analysis on the qualitative information from ten chosen studies, following the particular data analysis model with five steps: compilation, disassembling, reassembling, interpreting, and concluding. This study's results show that technology-based motivation among students is widely explored in the literature. The exercise environment and behavioral diversity are other identified codes (data units) in the chosen literature. This study finds that the use of technology can effectively promote higher participation of students in low-intensity exercises.