Computational drug repositioning technology can identify potential uses for existing drugs and reduce the time and cost required in the drug development process. How to find appropriate representations of drugs and diseases to predict the associations between the two is the main objective of such tasks. With the emergence of graph neural networks in recent years, researchers learned drugs and diseases via graphs in a bid to improve the prediction accuracy. However, there are three key problems that have not been adequately studied: 1) They usually place drug-disease association graphs, drug-drug similarity graphs, and disease-disease similarity graphs under the same semantic space for learning, which lose the higher-order features of the different graphs. 2) They assign equal weight to each neighbor node when aggregating based on the drug-disease association graph, but the effect and mechanism of a drug in treating different diseases are not consistent. 3) They adopt residual connections to enhance the role of the root node without considering that this operation amplifies the effect of anomalous features. In view of this, we first propose a graph attention network-based spatial decomposition method for drug repositioning. Specifically, we reduce the dimensions of the feature space by spatial decomposition and initialize the drug and disease embedding in the drug-similarity subspace, disease-similarity subspace, and drug-disease association subspace, respectively. The representations of drugs and diseases are jointly captured in the corresponding subspace based on similarity and therapeutic associations. Moreover, the extent of the associations is measured through the graph attention mechanism to explore higher-order neighborhood relationships between drugs and diseases. Finally, we introduce a targeted residual connection for personalized propagation of node features. Experiments on four benchmark datasets show that our proposed architecture outperforms current state-of-the-art approaches.

Offline reinforcement learning (offline RL) endeavors to learn effective policies from a large batch of pre-collected datasets without any costly or dangerous online exploration. Nevertheless, offline RL always suffers from substantial algorithmic extrapolation errors and may fail when bootstrapping from out-of-distribution (OOD) actions or states. In this work, we introduce a practical and effective Bayesian uncertainty weighted optimization (BUWO) to leverage the Bayesian uncertainty to account for the epistemic uncertainty associated with each training sample and penalize the state-action pairs with high uncertainty. We compare BUWO with other prevailing offline RL algorithms on D4RL benchmarks. The experimental results demonstrate that the algorithm can enhance the average reward score by almost 15% without additional computational costs compared to the current state-of-the-art algorithm.

Recent advancements in Swin Transformer-based image super-resolution (SR) methods have demonstrated remarkable progress through efficient modeling capabilities and feature extraction. However, existing approaches still have limitations: in hierarchical feature representation architectures, inter-layer feature propagation efficiency progressively degrades with increasing network depth. For this, the paper proposes an SR model, namely, SwinDPSR, composed of Dense-Parallel Swin Transformer (DPST) blocks. The proposed method innovatively constructs a densely connected parallel Swin Transformer architecture that enhances information flow through multi-level feature reuse mechanisms. Simultaneously, we introduce the Spatial Frequency Block (SFB) that establishes global contextual correlations in the frequency domain branch to precisely compensate for local detail loss. Experiments demonstrate significant improvements over the SwinIR across five benchmarks (Set5, Set14, BSD100, Urban100, and Manga109), achieving an average PSNR gain of 0.28[Formula: see text]dB in [Formula: see text] SR tasks. Theoretical analysis and experimental validation confirm the effectiveness of the parallel architecture, which improves FPS by 6.8% through enhanced computational parallelism. Ablation studies verify the synergistic optimization effect of dense connections and SFB.

With the rapid development of virtual reality (VR) technology, users are able to access a large number of new applications in their daily lives. VR expands users’ perceptual dimensions, bringing them entirely new experience. However, the user experience assessment for VR videos is still under exploration, which remains an unresolved issue. In such immersive scenarios, the methods based on user scoring require active feedback from users, which will interrupt the immersion experience. Besides, it is difficult to monitor the user experience status in real-time by user scoring. With the development of psychophysiological research, electroencephalographic (EEG) signal measurement is considered to have the potential to non-intrusively obtain the user experience. Hence, this paper employs EEG measurements to capture users’ EEG signals while watching VR videos with varying levels of stuttering, constructing a VR-EEG dataset. Subsequently, we analyze the dataset using time-frequency analysis methods to validate the feasibility of EEG signals reflecting user experience. Finally, we utilize machine learning methods to construct a QoE measurement network capable of analyzing users’ perceptual experience from single-trial EEG signals. Experimental results demonstrate that the proposed method establishes a relationship between brain activities and user experience and can effectively predict QoE scores from EEG signals. It provides a technical means for real-time, non-disturbing measurement of user experience in VR video playback.

Scene text detection is a challenging topic in computer vision, characterized by complex illumination, irregular shape, and arbitrary size. While recent advancements have been made in scene text detection, it remains difficult to simultaneously distinguish nearby text and accommodate irregularly shaped text. Therefore, this paper introduces HPNet, an enhanced text detector, based on the segmentation method that predicts two-scale results. To improve the shape robustness, the Hybrid Attentional Feature Fusion (HAFF) module is integrated into Feature Pyramid Networks (FPN) to dynamically perform feature fusion. Additionally, to distinguish nearby text, the model predicts the text region covering text instances and the text kernel covering the central region of the text. The improved Pixel Aggregation (PA) algorithm is then utilized to guide the expansion from the text kernel to the text region. Experiments on IC15, Total-Text, and CTW1500 validate the effectiveness of these improvements and the superiority of HPNet. Compared with the previous method PSENet for nearby texts, the proposed HPNet has improved inference speed by 63.6% and F-measure metric by 2.6%, 3.7%, and 2.5% on three datasets, respectively.

Efficient and intelligent exploration remains a major challenge in the field of deep reinforcement learning (DRL). Bayesian inference with a distributional representation is usually an effective way to improve the exploration ability of the RL agent. However, when optimizing Bayesian neural networks (BNNs), most algorithms need to specify an explicit parameter distribution such as a multivariate Gaussian distribution. This may reduce the flexibility of model representation and affect the algorithm performance. Therefore, to improve sample efficiency and exploration based on Bayesian methods, we propose a novel implicit posteriori parameter distribution optimization (IPPDO) algorithm. First, we adopt a distributional perspective on the parameter and model it with an implicit distribution, which is approximated by generative models. Each model corresponds to a learned latent space, providing structured stochasticity for each layer in the network. Next, to make it possible to optimize an implicit posteriori parameter distribution, we build an energy-based model (EBM) with value function to represent the implicit distribution which is not constrained by any analytic density function. Then, we design a training algorithm based on amortized Stein variational gradient descent (SVGD) to improve the model learning efficiency. We compare IPPDO with other prevailing DRL algorithms on the OpenAI Gym, MuJoCo, and Box2D platforms. Experiments on various tasks demonstrate that the proposed algorithm can represent the parameter uncertainty implicitly for a learned policy and can consistently outperform competing approaches.

Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data without considering the information in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning modules, while uniformity used to measure representation quality is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. This graph contrastive learning framework replaces the dropout-based data augmentation strategy by performing data augmentation in the embedding space, thereby better preserving the semantic information of the molecular graph. A more essential and effective drug representation can be learned through this graph contrastive framework without additional supervised data. Next, we design a new loss function that can be directly used to adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. Compared with the GraphDTA model, the relative improvement of the GraphCL-DTA model on the two datasets is 2.7% and 4.5%. The graph contrastive learning framework and uniformity function in the GraphCL-DTA model can be embedded into other computational models as independent modules to improve their generalization capability.

In this article, we propose a novel nonlinear observer based on neural networks (NNs), called neural observers, for observation tasks of linear time-invariant (LTI) systems and uncertain nonlinear systems. In particular, the neural observer designed for uncertain systems is inspired by the active disturbance rejection control, which can measure the uncertainty in real time. The stability analysis (e.g., exponential convergence rate) of LTI and uncertain nonlinear systems (involving neural observers) are presented and guaranteed, where it is shown that the observation problems can be solved only using the linear matrix inequalities (LMIs). Also, it is revealed that the observability and controllability of the system matrices are required to demonstrate the existence of solutions for LMIs. Finally, the effectiveness of neural observers is verified in three simulation cases, including the X-29A aircraft model, the nonlinear pendulum, and the four-wheel steering vehicle.

With the continuous development of multimedia technology, there is a growing demand for video applications. Among the huge number of videos, some video clips attract higher visual attention from viewers. Measuring this visual attention is not only crucial for evaluating the quality of experience (QoE) but also holds the potential for guiding video compression techniques. Therefore, there is an urgent need to propose an effective method to evaluate the changes of viewers' attention. To address this challenge, we employ electroencephalography (EEG) as a bridge to establish the relationship between video clips and visual attention. We introduce a visual attention measurement (VAM) method by combining EEG and machine learning approaches. Specifically, we design an EEG experiment to collect brain responses from viewers while watching various video clips, thus constructing a visual attention EEG dataset. Using these EEG signals, we propose a VAM network to identify whether viewers pay attention to the video clips. The experimental results show that our EEG experiment can capture the physiological signals related to visual attention. Moreover, the proposed VAM network can accurately determine viewers' attention levels to video clips. These results provide new insights into the evolution of human-oriented video communications.

Recently, the implementation of Industry 4.0 has become a new tendency, and it brings both opportunities and challenges to worldwide manufacturing companies. Thus, many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation. In this study, we propose a new model to measure the intelligent manufacturing readiness for the process industry, which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation. Although some models have already been reported to measure Industry 4.0 readiness and maturity, there are no models that are aimed at the process industry. This newly proposed model has six levels to describe different development stages for intelligent manufacturing. In addition, the model consists of four races, nine species, and 25 domains that are relevant to the essential businesses of companies’ daily operation and capability requirements of intelligent manufacturing. Furthermore, these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail. A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis. Using the new method, a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model. Overall, the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.

Computational drug repositioning aims to discover new therapeutic diseases for marketed drugs and has the advantages of low cost, short development cycle, and high controllability compared to traditional drug development. The matrix factorization model has become the cornerstone technique for computational drug repositioning due to its ease of implementation and excellent scalability. However, the matrix factorization model uses the inner product operation to represent the association between drugs and diseases, which is lacking in expressive ability. Moreover, the degree of similarity of drugs or diseases could not be implied on their respective latent factor vectors, which is not satisfy the common sense of drug discovery. Therefore, a neural metric factorization model for computational drug repositioning (NMFDR) is proposed in this work. We novelly consider the latent factor vector of drugs and diseases as a point in the high-dimensional coordinate system and propose a generalized euclidean distance to represent the association between drugs and diseases to compensate for the shortcomings of the inner product operation. Furthermore, by embedding multiple drug (disease) metrics information into the encoding space of the latent factor vector, the information about the similarity between drugs (diseases) can be reflected in the distance between latent factor vectors. Finally, we conduct wide analysis experiments on three real datasets to demonstrate the effectiveness of the above improvement points and the superiority of the NMFDR model.

Computational drug repositioning technology is an effective tool to accelerate drug development. Although this technique has been widely used and successful in recent decades, many existing models still suffer from multiple drawbacks such as the massive number of unvalidated drug-disease associations and the inner product. The limitations of these works are mainly due to the following two reasons: firstly, previous works used negative sampling techniques to treat unvalidated drug-disease associations as negative samples, which is invalid in real-world settings; secondly, the inner product cannot fully take into account the feature information contained in the latent factor of drug and disease. In this paper, we propose a novel PUON framework for addressing the above deficiencies, which models the risk estimator of computational drug repositioning only using validated (Positive) and unvalidated (Unlabelled) drug-disease associations without employing negative sampling techniques. The PUON also proposed an Outer Neighborhood-based classifier for modeling the cross-feature information of the latent facotor. For a comprehensive comparison, we considered 6 popular baselines. Extensive experiments in four real-world datasets showed that PUON model achieved the best performance based on 6 evaluation metrics.

The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug discovery system. However, the number of validated drug-disease associations is scarce compared to the number of drugs and diseases in the real world. Too few labeled samples will make the classification model unable to learn effective latent factors of drugs, resulting in poor generalization performance. In this work, we propose a multi-task self-supervised learning framework for computational drug repositioning. The framework tackles label sparsity by learning a better drug representation. Specifically, we take the drug-disease association prediction problem as the main task, and the auxiliary task is to use data augmentation strategies and contrast learning to mine the internal relationships of the original drug features, so as to automatically learn a better drug representation without supervised labels. And through joint training, it is ensured that the auxiliary task can improve the prediction accuracy of the main task. More precisely, the auxiliary task improves drug representation and serving as additional regularization to improve generalization. Furthermore, we design a multi-input decoding network to improve the reconstruction ability of the autoencoder model. We evaluate our model using three real-world datasets. The experimental results demonstrate the effectiveness of the multi-task self-supervised learning framework, and its predictive ability is superior to the state-of-the-art model.

For stable motions of position controlled humanoid robots, ZMP (Zero Moment Point) control is widely adopted, but needs to be integrated with other controllers due to its relatively slow response and the execution error of robots. While CoM (Center of Mass) control is much more directly than ZMP feedback control in the view of rejecting unknown external disturbance. In the meanwhile, we hope the position-based humanoid robot can have the whole body compliance in standing maintenance. So we proposes a CoM compliance controller to achieve stable standing of position controlled humanoid robot under unknown disturbance. The controller uses the concept of force control and integrates virtual model control with admittance control, where an AMPM (Angular Momentum including inverted Pendulum Model)-based virtual model with variable gain is designed to not only generate desired recovery force but also take the GRF (Grand Reaction Force) constraints into account, while an admittance controller is employed to transform the desired force to expected CoM position and body attitude. The experiments were conducted on the humanoid robot ‘Kong’ by exerting external force disturbance and changing the slope of the ground to demonstrate the effectiveness and robustness of our method.

Temporal asynchrony between two cameras in the vision system is a usual problem in practice. In some vision task such as estimating fast moving targets, the estimation error caused by the tiny temporal asynchrony will become non-ignorable essentials. This paper will address on the asynchrony in the stereo vision system of humanoid Ping-Pong robot, and present a real-time accurate Ping-Pong ball trajectory estimation algorithm. In our approach, the complex Ping-Pong ball motion model is simplified by a polynomial parameter function of time t due to the limited observing time interval and the requirement of real-time computation. We then use the perspective projection camera model to re-project the ball's parameter function on time t into its image coordinates on both cameras. Based on the assumption that the time gap of two asynchronous cameras will maintain a const during very short time interval, we can obtain the time gap value and also the trajectory parameters of the Ping-Pong ball in a short time interval by minimizing the errors between the images of the ball in each camera and their re-projection images from the modeled parameter function on time t. Comprehensive experiments on real Ping-Pong robot cases are carried out, the results show our approach is more proper for the vision system of humanoid Ping-Pong robot, when concerning the accuracy and real-time performance simultaneously.

For ping-pong playing robots, observing a ball and predicting a ball's trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball's position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball's spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball's motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method.

This paper investigates the problem of local synchronization of chaotic neural networks with sampled-data and actuator saturation. A new time-dependent Lyapunov functional is proposed for the synchronization error systems. The advantage of the constructed Lyapunov functional lies in the fact that it is positive definite at sampling times but not necessarily between sampling times, and makes full use of the available information about the actual sampling pattern. A local stability condition of the synchronization error systems is derived, based on which a sampled-data controller with respect to the actuator saturation is designed to ensure that the master neural networks and slave neural networks are locally asymptotically synchronous. Two optimization problems are provided to compute the desired sampled-data controller with the aim of enlarging the set of admissible initial conditions or the admissible sampling upper bound ensuring the local synchronization of the considered chaotic neural networks. A numerical example is used to demonstrate the effectiveness of the proposed design technique.

In this paper, a sampled-data fuzzy controller is designed to stabilize a class of chaotic systems. A Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic systems. Based on this general model, the exponential stability issue of the closed-loop systems with an input constraint is first investigated by a novel time-dependent Lyapunov functional, which is positive definite at sampling times but not necessary between the sampling times. Then, two sufficient conditions are developed for sampled-data fuzzy controller synthesis of the underlying T-S fuzzy model with or without input constraint. All the proposed results in this paper depend on both the upper and lower bounds on a sampling interval, and the available information about the actual sampling pattern is fully utilized. The proposed sampled-data fuzzy control scheme is successfully applied to the chaotic Lorenz system, which is shown to be effective and less conservative compared with existing results.

The balance between exploration and exploitation is a key problem for reinforcement learning methods, especially for Q-learning. In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems. In this approach, fidelity is adopted to help direct the learning process and the probability of each action to be selected at a certain state is updated iteratively along with the learning process, which leads to a natural exploration strategy instead of a pointed one with configured parameters. A probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the basic idea of probabilistic action selection. Then the FPQL algorithm is presented for learning control of quantum systems. Two examples (a spin-1/2 system and a Λ-type atomic system) are demonstrated to test the performance of the FPQL algorithm. The results show that FPQL algorithms attain a better balance between exploration and exploitation, and can also avoid local optimal policies and accelerate the learning process.

This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.

Real-time Ethernet (RTE) control systems with critical real-time requirements are called fast real-time (FRT) systems. To improve the real-time performance of Ethernet for plant automation (EPA), we propose an EPA-FRT scheme. The minimum macrocycle of EPA networks is reduced by redefining the EPA network frame format, and the synchronization process is modified to acquire higher accuracy. A multi-segmented topology with a scheduling scheme is introduced to increase effective bandwidth utilization and reduce protocol overheads, and thus to shorten the communication cycle significantly. Performance analysis and practical tests on a prototype system show the effectiveness of the proposed scheme, which achieves the best performance at small periodic payload in large scale systems.

In this paper, the problem of sampled-data synchronization for Markovian jump neural networks with time-varying delay and variable samplings is considered. In the framework of the input delay approach and the linear matrix inequality technique, two delay-dependent criteria are derived to ensure the stochastic stability of the error systems, and thus, the master systems stochastically synchronize with the slave systems. The desired mode-independent controller is designed, which depends upon the maximum sampling interval. The effectiveness and potential of the obtained results is verified by two simulation examples.

This paper investigates the problem of robust passive control for networked fuzzy systems, where randomly occurring uncertainties, variable sampling intervals, and constant network-induced delay are taken into account. A discontinuous Lyapunov functional is introduced for the closed-loop systems, which takes full advantage of the sawtooth structure of the time-varying interval delay induced by sample-and-hold and signal transmission. A sufficient condition is proposed to ensure the closed-loop system to be robustly stochastically passive. Then, the problem of robust passive control is solved. Two examples are utilized to illustrate the advantages and effectiveness of the results that are proposed in this paper.

In this paper, the problem of dissipativity analysis is discussed for discrete-time stochastic neural networks with time-varying discrete and finite-distributed delays. The discretized Jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms, and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically stable in the mean square and strictly (Q, S, R)-y-dissipative, which is delay-dependent in the sense that it depends on not only the discrete delay but also the finite-distributed delay. Based on the dissipativity criterion, some special cases are also discussed. Compared with the existing ones, the merit of the proposed results in this paper lies in their reduced conservatism and less decision variables. Three examples are given to illustrate the effectiveness and benefits of our theoretical results.

This paper studies the problem of sampled-data control for master-slave synchronization schemes that consist of identical chaotic Lur'e systems with time delays. It is assumed that the sampling periods are arbitrarily varying but bounded. In order to take full advantage of the available information about the actual sampling pattern, a novel Lyapunov functional is proposed, which is positive definite at sampling times but not necessarily positive definite inside the sampling intervals. Based on the Lyapunov functional, an exponential synchronization criterion is derived by analyzing the corresponding synchronization error systems. The desired sampled-data controller is designed by a linear matrix inequality approach. The effectiveness and reduced conservatism of the developed results are demonstrated by the numerical simulations of Chua's circuit and neural network.

This paper studies the problem of sampled-data exponential synchronization of complex dynamical networks (CDNs) with time-varying coupling delay and uncertain sampling. By combining the time-dependent Lyapunov functional approach and convex combination technique, a criterion is derived to ensure the exponential stability of the error dynamics, which fully utilizes the available information about the actual sampling pattern. Based on the derived condition, the design method of the desired sampled-data controllers is proposed to make the CDNs exponentially synchronized and obtain a lower-bound estimation of the largest sampling interval. Simulation examples demonstrate that the presented method can significantly reduce the conservatism of the existing results, and lead to wider applications.

An approach of control design using fuzzy estimators (FEs) is proposed for quantum systems with uncertainties. Two types of quantum control problems are considered: 1) control of a pure-state quantum system in the presence of uncertainties and 2) control design of quantum systems with initial mixed states and uncertainties. For the first type of tasks, a partial feedback control scheme with an FE is presented to design controllers. In this scheme, an FE is trained to estimate the quantum state for feedback control of a quantum system, and controlled projective measurement is used to assist in controlling the system. For the second type of quantum control tasks, a probabilistic fuzzy estimator (PFE) is trained to estimate the quantum state for control design of a quantum system with an initial mixed state, and a corresponding control algorithm is proposed to design a control law that drives the system from the mixed state to a target pure state. Two examples of two-spin-1/2systems are also presented and analyzed to demonstrate the process of control design and potential applications of the proposed approach.

In this paper, the problem of reliable$H_\infty$control is investigated for discrete-time Takagi–Sugeno (T–S) fuzzy systems with infinite-distributed delay and actuator faults. A discrete-time homogeneous Markov chain is used to represent the stochastic behavior of actuator faults. In terms of a stochastic fuzzy Lyapunov functional, a sufficient condition is proposed to ensure that the resultant closed-loop system is exponentially stable in the mean-square sense with an$H_\infty$performance index. Based on the derived condition, the reliable$H_\infty$control problem is solved, and an explicit expression of the desired controller is also given. The case of no failure in the actuator is also considered. A numerical example is given to demonstrate that our results are effective and less conservative.

This paper is concerned with the problems of stability and dissipativity analysis for static neural networks (NNs) with time delay. Some improved delay-dependent stability criteria are established for static NNs with time-varying or time-invariant delay using the delay partitioning technique. Based on these criteria, several delay-dependent sufficient conditions are given to guarantee the dissipativity of static NNs with time delay. All the given results in this paper are not only dependent upon the time delay but also upon the number of delay partitions. Some examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.

This paper investigates the problem of master-slave synchronization for neural networks with discrete and distributed delays under variable sampling with a known upper bound on the sampling intervals. An improved method is proposed, which captures the characteristic of sampled-data systems. Some delay-dependent criteria are derived to ensure the exponential stability of the error systems, and thus the master systems synchronize with the slave systems. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalitys, which depend upon the maximum sampling interval and the decay rate. The obtained conditions not only have less conservatism but also have less decision variables than existing results. Simulation results are given to show the effectiveness and benefits of the proposed methods.

This paper describes the design and techniques of HAIBAO intelligent robot which is developed for Shanghai World Expo 2010. Compared with previous exhibition service robots, the HAIBAO robot has a more flexible motion ability, a more anthropomorphic interactive ability and a more intelligent cognitive and decision ability. Some key techniques including a four-wheeled omni-directional mechanism and its motion control and compensatory algorithm, the anthropomorphic interaction design, and the architecture of multi-tasks scheduling are introduced. During the Expo, totally 184 days, 37 HAIBAO robots have successfully serviced for the tourists by information providing, photography service, hall guiding, chat and various entertainments. Their robust, stability, flexibility and friendliness have been greatly commended.

Traditional classification algorithms can be limited in their performance on highly imbalanced and overlapping data sets, In this paper, we focus on modifying support vector machines (SVMs) to make it suitable for highly imbalanced and overlapping (HIO) classification. Based on the analysis of most SVM learning algorithms for imbalanced classification, we argue that in SVM-based algorithms, due to the linearity property of SVM, the key problem is that the increase of the number of correctly predicted minority samples will lead to even more majority samples be misclassified. Then a novel algorithm HIO-SVM is developed, it can recognize all minority samples while minimizing the error rate of majority ones. The proposed approach can identify the non-overlapping samples in one feature space, furthermore, by iteratively shifting kernel spaces, all non-overlapping samples in different kernel spaces are recognized. Because of the highly imbalanced distribution, the remaining overlapping samples can be regarded as minority. Then all minority samples can be predicted correctly and the error rate of majority samples can be guaranteed minimized simultaneously. Finally, numerous case studies show the properties and effectiveness of the proposed HIO-SVM algorithm.

OBJECTIVE: At present, most clinical data are exchanged between organizations within a regional system. However, people traveling abroad may need to visit a hospital, which would make international exchange of clinical data very useful. BACKGROUND: Since 2007, a collaborative effort to achieve clinical data sharing has been carried out at Zhejiang University in China and Kyoto University and Miyazaki University in Japan; each is running a regional clinical information center. Methods An international layer system named Global Dolphin was constructed with several key services, sharing patients' health information between countries using a medical markup language (MML). The system was piloted with 39 test patients. RESULTS: The three regions above have records for 966,000 unique patients, which are available through Global Dolphin. Data exchanged successfully from Japan to China for the 39 study patients include 1001 MML files and 152 images. The MML files contained 197 free text-type paragraphs that needed human translation. Discussion The pilot test in Global Dolphin demonstrates that patient information can be shared across countries through international health data exchange. To achieve cross-border sharing of clinical data, some key issues had to be addressed: establishment of a super directory service across countries; data transformation; and unique one-language translation. Privacy protection was also taken into account. The system is now ready for live use. CONCLUSION: The project demonstrates a means of achieving worldwide accessibility of medical data, by which the integrity and continuity of patients' health information can be maintained.

This paper addresses the problem of creating a geometric map with a mobile robot in a dynamic indoor environment. To form an accurate model of the environment, we present a novel map representation called the ‘grid vector’, which combines each vector that represents a directed line segment with a slender occupancy grid map. A modified expectation maximization (EM) based approach is proposed to evaluate the dynamic objects and simultaneously estimate the robot path and the map of the environment. The probability of each grid vector is evaluated in the expectation step and then used to distinguish the vector into static and dynamic ones. The robot path and map are estimated in the maximization step with a graph-based simultaneous localization and mapping (SLAM) method. The representation we introduce provides advantages on making the SLAM method strictly statistic, reducing memory cost, identifying the dynamic objects, and improving the accuracy of the data associations. The SLAM algorithm we present is efficient in computation and convergence. Experiments on three different kinds of data sets show that our representation and algorithm can generate an accurate static map in a dynamic indoor environment.

In this paper, passivity analysis is conducted for discrete-time stochastic neural networks with both Markovian jumping parameters and mixed time delays. The mixed time delays consist of both discrete and distributed delays. The Markov chain in the underlying neural networks is finite piecewise homogeneous. By introducing a Lyapunov functional that accounts for the mixed time delays, a delay-dependent passivity condition is derived in terms of the linear matrix inequality approach. The case of Markov chain with partially unknown transition probabilities is also considered. All the results presented depend upon not only discrete delay but also distributed delay. A numerical example is included to demonstrate the effectiveness of the proposed methods.

In this paper, the problem of stability analysis is investigated for switched neural networks with time-varying delay using linear matrix inequality (LMI) approach. By taking advantage of the average dwell time method, two sufficient conditions are developed to ensure the global exponential stability of the considered neural networks, which are delay-dependent and formulated by LMIs. The state decay estimate is explicitly given. Numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed techniques.

This brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, a novel delay-dependent stability criterion is established for the considered recurrent neural networks via a new Lyapunov function. The obtained condition has less conservativeness and less number of variables than the existing ones. Numerical example is given to demonstrate the effectiveness of the proposed method.

This paper presents a novel method to design Petri net-based deadlock prevention controllers for flexible manufacturing systems. It starts from the computation of the complete deadlock markings by utilizing the conservativeness property of a Petri net model and the necessary and sufficient condition for deadlock. Then, it verifies a small state space including the dangerous and bad markings only by combining one-step look-forward through the original net and one-step look-backward via its reverse net. Subsequently, it defines the set of place invariants from the subset of marked operation places for the so called ¿elementary controlled bad markings¿. Finally, it synthesizes a deadlock prevention controller by a simplified invariant-based method. Its obtained deadlock-free controller allows more behavior of the closed-loop system than those obtained via a siphon-based control method. Its computational efficiency is higher than those based on a complete reachability graph-based control method.

The considered class of generalized mutual exclusion constraints (GMECs) on a controlled Petri net are such that the influence-uncontrollable subnets are forward-concurrent-free nets. Some structural properties of forward-concurrent-free nets are firstly proposed. Utilizing these properties, a method is then proposed to transform a given conjunction of GMECs into a conjunction of admissible GMECs. Furthermore, the necessary and sufficient condition of the existence of the permissive supervisor is obtained, and the optimal supervisor with a complexity of polynomial time is designed. The theoretic results are illustrated by an example that synthesizes a maximally permissive supervisor for a manufacturing system.

An estimation distribution algorithm for path planning of an autonomous mobile robot is proposed in this paper. Our algorithm encodes each path to a fixed-length real number string, updates the populations by building probabilistic model of solution distributions. Simulations results show that the proposed algorithm works well in both static and dynamic environments.

Robot motion simulation is one of the challenging topic both in the character animation and the robotics. In this paper, a simulation system HumRoboSim is introduced. A 'real' humanoid robot is created in a virtual world considering the dynamics and collision in HumRoboSim. We present a new technique for robot motion generation with the robot dynamics model and constraints within the physical engine. Virtual sensors which provide accurate sensor data are also simulated to aid robot's attitude control and interaction with environment. We also propose a multi-agent control method which obtains the information from virtual sensors and makes decision to control the robot action. Our simulator could provide users with a convenient interface for manipulation and programming. Theexperimental results show that the proposed robot motion generation method can be successfully applied both in the virtual robot motion animation and the real hardware robot system. And the multiple agent control policy could be applied in similar hardware robot system.

In this paper we explore the optimal weight setting to maximize QoS traffic admissible to a hop-by-hop routing network (such as the Internet). We try to answer the following questions: (1) How much ingress and egress traffic can be admitted to a hop-by-hop routing network (such as the Internet) without causing congestion of any link, and (2) what is the optimal link weight setting that can maximize the amount of traffic admissible to the network? We first present a mixed-integer programming formulation to compute the optimal link weights that can maximize the ingress and egress QoS traffic admissible to a hop-by-hop routing network. We then present a heuristic search algorithm to find the optimal weights. The heuristic algorithm has a performance comparable to the optimal link weights, but its computation complexity is much lower.

The problem of delay-dependent robust stabilization for a class of uncertain linear systems with time-varying delay is investigated. The parameter uncertainty under consideration is norm bounded and possibly time-varying. The time delay considered here is assumed to be size-bounded but otherwise arbitrarily fast time-varying. Based on a special Lyapunov-Krasovakii functional, the delay-dependent stability condition is formulated in terms of linear matrix inequalities. The delay-independent result can be derived as a particular case for certain values of the tuning parameters. A state-feedback control law is also given such that the resultant closed-loop system is stable for admissible uncertainties. Two numerical examples are given to demonstrate the efficiency of the obtained results.

The best measure quantifying FWL (finite word length) stability is the one that bases on the largest stable perturbation hypercube. But the computing of this FWL stability measure has not been solved. For second order digital systems, this paper develops an analytic computing method. Through solving 12 linear equations and 12 quadratic equations, the measure value can be obtained exactly.

In this paper, the design problem of /spl alpha/-robust H/sub /spl infin// state feedback controller for a class of linear parameter-varying (LPV) systems with time-delay is addressed. A sufficient condition for the existence of /spl alpha/-robust H/sub /spl infin//m state feedback controller is derived in terms of a new method and the corresponding design method of the controller is given. Furthermore, illustrative example is given to demonstrate the superiority of the presented method.

This paper addresses the state feedback control synthesis of discrete event systems on the forbidden state problem in which the forbidden states are finite, especially if they can not be expressed as linear inequality constraints using reported methods. The system is modelled by controlled Petri nets that ape bounded Petri nets or unbounded Petri nets with uncontrollable subnets satisfying the Reverse net Structurally Bounded Condition. Through the analysis of the reverse net, we obtain not only the weakly forbidden markings used to deal with uncontrollable transitions but also the maximally permissive state feedback control policy. Moreover, it is illustrated by an example in the reported literature that the method can be applied conveniently to a class of Petri nets whose uncontrollable subnets are Output Dominant Petri nets.

We introduce a new, simple and efficient evolutionary algorithm to multiobjective optimization problem, which based on neighborhood and archived operation (NAGA). The innovations contain two main parts: neighborhood identify procedure to obtain Pareto optimal solutions from the population and neighborhood crowding procedure to maintain the diversity of Pareto optimal solutions previously found. The neighborhood identify procedure is composed of two steps, first to identify the locally nondominated solutions from the population and then to obtain the global nondominated solutions among the locally solutions. The neighborhood crowding is introduced to maintain a widely distributed set of Pareto solutions along the Pareto optimal front, which through implementing a comparison among the neighborhood bounds of new identified Pareto solutions and those of solutions in the archive. The winners, which are not in any ranges of the solutions in the archive, will be copied to the archive. A well-tuned fitness assignment method is structured to guide the population converging to the true Pareto optimal front. This method is pragmatic compromise between the computational simplicity and efficiency. Four nicely balanced test problems are provided to check the performance of the approach.

This paper presents cost-based QoS routing and compares its performance with the commonly used widest-shortest-path routing. The advantages of cost-based routing are demonstrated in two major areas. First, it is stable under heavy-loaded conditions. Second, it requires a lower state-update overhead and is more robust to the inaccuracy of state information. The concept presented in the paper can be applied to any reservation-based QoS approach - like IntServ or MPLS type networks.

A multivariable nonlinear predictive control algorithm based on online fuzzy modeling and discrete optimization is presented for a family of complex systems with strong nonlinearity. The algorithm consists of two part: The first part is online fuzzy modeling using fuzzy clustering and linear identification, the second part is discrete optimization of the control action based on the principle of Branch and Bound method. In the process of fuzzy modeling, the unsupervised fuzzy competitive algorithm and a discarding criterion are introduced to ensure the fuzzy model can trace the system dynamics in time. The effectiveness and advantage of the presented algorithm are illustrated two numerical examples.

In this paper, the problem concerning a descriptor system approach to H/sub /spl infin// control for a class of uncertain Lur'e systems with both time-delays and parameter uncertainties are studied. A robust H/sub /spl infin// state feedback controller based on the Linear Matrix Inequalities (LMIs) is developed to guarantee both the robust stability and the H/sub /spl infin// performance of the resultant closed-loop system. The presented design approach is expected to be the lesser conservative as compared to reported design methods. Finally, some numerical examples are illustrated to demonstrate the superiority of the obtained method.

This paper is concerned with the problem of the robust H/sub /spl infin// filtering for a class of Lurie singular system with state time-delays, parameters uncertainties and unknown statistics characteristics but limited power disturbance. The purpose is to design a robustly stable filter such that the uncertain Lurie time-delay singular systems are not only regular, impulse free and stable, but also have a prescribed level of H/sub /spl infin// performance for the filtering error dynamics for all admissible uncertainties. A sufficient condition for the existence of such a filter is proposed in terms of linear matrix inequalities (LMIs). When a solution to this set of LMIs exists, the parametric matrices of a desired filter can be easily obtained using LMI toolbox. Finally, illustrative examples are given to demonstrate the superiority of the presented method.

In this paper, the new absolute stability and stabilization conditions for a class of Lurie uncertain time-delay systems are proposed. Based on Lyapunov functions combined with linear matrix inequality (LMI) technology, the sufficient delay dependent absolute stability and stabilization conditions are derived for a class of Lurie uncertain time-delay systems with time-delay feedback either in states or nonlinear part through introducing a new state transformation. Finally, the absolute stability and stabilization conditions are illustrated by the detailed examples, and the result shows that there has been a distinct amelioration in conservation.

In this paper, a genetic algorithm based on Infeasibility Degree (IFD) selection is proposed for constrained optimization problems. Initial solutions and intermediate solutions are allowed to be feasible as well as infeasible as penalty function methods. The infeasibility degree of a solution (IFD) is defined as the sum of the square value of all the constraints violation and the infeasibility degree selection of the population is designed through checking whether the IFD of a solution is less than or equal to a threshold value or not to decide the candidate solution is acceptable or refusable. The method is divided into two stages: first, initial IFD selection is carried out to produce enough initial feasible solution; then the GAs based on Annealing IFD selection is applied to search for the feasible optimum solution. Two selected problems are used to test the algorithm performance.

A new robust model predictive control (MPC) approach is proposed for constrained linear systems in this paper. It adopts a contractive state set as terminal set in MPC problem and employs a novel function of the state with respect to this terminal set as the stage cost of the objective function. When the controlled systems are subject to linear constraints, explicit MPC law can be obtained as a piecewise affine linear state feedback control law via multi-parameter linear programming method. The presented MPC, along with linear variable-structure control law associated with the terminal set, is essentially a dual mode control scheme to stabilize the constrained uncertain linear system. Compared to the existing MPC methods, the proposed approach have both better real-time applicability and more general robust stability guarantee.

This paper addresses the enforcement of a class of linear inequality constraint defined on the marking of a Petri net (PN). The constraint may be regarded as the conjunction of 'less-than-or-equal-to' inequality and 'greater-than-or-equal-to' inequality. The extended Petri nets such as inhibitor arc PN and its complementary net, the so-called enabling arc PN, are exploited to design a PN supervisor such that the constraint is enforced in the controlled net. The supervisor is optimal in the sense that it allows the net evolves with least restriction while the given constraint is satisfied. An example is provided for illustration.

Based on linear matrix inequality (LMI) technique, a sliding mode control approach is presented for a class of uncertain time-delay systems in the delay-independent and delay-dependent case. The corresponding sufficient conditions for the existence of sliding mode are proposed. Different from the reported results, the conclusion presented in this paper is only relating to the original system parameters and owns simple and legible form. A numerical example illustrates the effectiveness of the presented method.

This paper addresses the supervisory control of the class of discrete event system (DES) modeled by a Petri net. The control specification described by a linear 'less-than-or-equal-to' inequality defined on the place marking of the net has been extensively studied in the literatures. However, in this paper we consider the control specification in the form of linear 'greater-than-or-equal-to' marking inequality. The supervisory control of the DES with 'greater-than-or-equal-to' constraint is implemented via an enabling arc, which is a recently proposed arc by Uzam (1998) and Uzam et al. (1999) and can be regarded as complementarity of inhibitor arc. An example illustrates the supervisory control method is presented in this paper.

A Petri net (PN) with weighted inhibitor arc is exploited to solve the forbidden state problem of discrete event systems (DES). The forbidden state problem considered is described as the linear inequality constraint of the place marking. We first review the relevant work on the control of DES using inhibitor arcs. Then, the design of the PN controller is introduced with two steps. The first step of the design is to track the state of the system. Then, the weighted inhibitor arcs are exploited to disable the relative transitions in the case that the firing of these transitions will violate the constraints. A simple example of a discrete manufacturing system in the reported literature is used to show the detailed procedure of the controller synthesis and the advantages of the presented method.

In the reported literatures on the control of discrete event systems (DES) modeled by Petri nets with the constraint of a logical intersection of some linear inequalities or just a single one, the constraint usually has to be transformed into the form of logical union when there are some uncontrollable transitions in the net. In this paper, we propose a hierarchical control method for DES with logical union of constraints based on colored Petri nets though the plant (uncontrolled DES) is modeled as a noncolored Petri net. The low-level gets the state information from the plant and sends it to the high-level. The high-level plays the role of controller and ensures that there is at least one of the constraints is satisfied at any time for any system state. In addition, it is proved that the proposed hierarchical control is maximal control.

The previous work of Boel et al. (1996) on the forbidden state problem for the class of discrete event systems modeled by controlled state graph is extended in this paper. The class of Petri nets we consider is a generalized net in which the weight of the arcs may exceed 1. Furthermore, the limit to the output places number of a transition in the net is removed and consequently the Petri net is capable of firing more than one process simultaneously. Based on the calculation of weakly forbidden conditions, the maximally permissive supervisor is obtained.