Miracle-dz/UniFAR-FTUs huggingface dataset:
MIDES: A Tool for Supervisor Synthesis via Active Learning

A tool, MIDES, for automatic learning of models and supervisors for discrete event systems is presented. The tool interfaces with a simulation of the target system to learn a behavioral model through interaction. There are several different algorithms to choose from depending on the intended outcome. Moreover, given a set of specifications, the tool learns a supervisor that can help ensure the controlled system guarantees the specifications. Furthermore, the state-space explosion problem is addressed by learning a modular supervisor. In this paper, we introduce the tool, its interfaces, and algorithms. We demonstrate the usefulness through several case studies.
Tactile-Based Gripper Localization on 1-D Deformable Objects

As part of automation processes, robotic manipulators are occasionally required to assemble deformable objects, e.g., installing an O-ring into a groove. However, deformable objects are characterized by high uncertainty due to shape and length change under external forces. These uncertainties make the assembly process complex and slow and may lead to errors between the actual and desired gripping location. In this paper, we present a localization technique to estimate the actual gripping point by using the grid localization algorithm based on tactile sensing. To reduce the dependency on complex and relatively slow vision sensors, the pose estimation process is based only on tactile feedback, by recognizing features, e.g., corners, along the deformable object. In simulations and experiments, the proposed algorithm converged to the correct gripping point after three detected features with an accuracy of less than 1 mm.
Membrane Thermodynamics for Osmotic Phenomena

In this chapter, we briefly review the thermodynamic ensembles and associated energy functions using the seven thermodynamic variables. The energy E , the entropy S , and the system volume V are used to derive the temperature T and pressure P . The chemical potential μ is derived as the change of the system energy with respect to the number of matters N in the isobaric-isothermal environment. A dilute solution is defined as a homogeneous mixture of solvent and inert solutes, where the total number and volume of solutes are much smaller than those of the solvent. Gibbs free energy of the dilute solution is used to rigorously derive the osmotic pressure by equilibrating chemical potentials of solutes and solvent. Nonequilibrium of the filtration systems is reviewed by introducing the irreversible thermodynamic model with Onsager ’ s reciprocal theorem. Direct applications of the irreversible thermodynamic model are currently limited due to the absence of the exact nonequilibrium statistical mechanics. We hope this chapter, containing a review of statistical mechanics, related to membrane separations and osmosis phenomena, helps researchers and especially graduate students, who seek an in-depth understanding of membrane separation from the theoretical statistical phys- ics as applied to chemical and environmental engineering. as gradients of quantities associated with the
A LiDAR and camera fusion-based approach to mapping and navigation

For the map building problem of laser and vision fusion, a novel fusion algorithm is proposed in this paper. At first, the algorithm preprocesses the original point cloud obtained by the depth camera with voxel filtering, coordinate transformation, direct-pass filtering, dimensionality reduction processing and conversion of the polar coordinates. Finally, two types of data fusion are performed. The paper designs three experiments on data fusion, map building, and robotics navigation. The experimental results show that the fused data of camera and LiDAR can reflect more useful information from the surrounding environment compared with the 2D LiDAR data, and the environmental map built by the robot with the fused data for SLAM presents more comprehensive environmental information. Similarly, when the robot navigates with the fused data, the actual effect of obstacle avoidance and navigation is satisfactory.
M2LC-Net: A multi-modal multi-disease long-tailed classification network for real clinical scenes

Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years. Nevertheless, most of the current studies only consider single-modal medical images, and the number of ophthalmic diseases that can be classified is relatively small. Moreover, imbalanced data distribution of different ophthalmic diseases is not taken into consideration, which limits the application of deep learning techniques in realistic clinical scenes. In this paper, we propose a Multimodal Multi-disease Long-tailed Classification Network (M2LC-Net) in response to the challenges mentioned above. M2LC-Net leverages ResNet18-CBAM to extract features from fundus images and Optical Coherence Tomography (OCT) images, respectively, and conduct feature fusion to classify 11 common ophthalmic diseases. Moreover, Class Activation Mapping (CAM) is employed to visualize each mode to improve interpretability of M2LC-Net. We conduct comprehensive experiments on realistic dataset collected from a Grade III Level A ophthalmology hospital in China, including 34,396 images of 11 disease labels. Experimental results demonstrate effectiveness of our proposed model M2LC-Net. Compared with the state-of-the-art, various performance metrics have been improved significantly. Specifically, Cohen's kappa coefficient κ has been improved by 3.21%, which is a remarkable improvement.
Adaptive Underwater Sonar Image Target Detection

The underwater sonar image detection is a very important key technique. In the field of image detection, the detection of underwater sonar image is special and difficult because of its blurry image and underwater interference. To solve the difficult detection problems of underwater sonar image, the median filter algorithm and the Gamma transformation algorithm are used to preprocess the image, and an adaptive threshold method is designed to binarize the images. In addition, the corrosion expansion algorithm is used to further eliminate the interference of the image. Finally, the k-means algorithm is used to cluster the pixels of the image to achieve the final detection classification. To reduce underwater interference, a novel multi-frame detection algorithm is proposed based on multiple consecutive images and the detection performance is improved.
Consensus of multi-agent systems with unknown control directions by uniting dynamic and switching adaptive feedback

This paper investigates the asymptotic consensus for a class of uncertain multi-agent systems (MASs). The considered systems are affected by the unknown and nonidentical input coefficients whose unknown signs are called unknown control directions. Notably, "unknown" and "nonidentical" ingredients directly lead to the existence of the uncertainties and heterogeneities of the MASs, for which, a new consensus protocol is proposed by uniting switching scheme and continuous adaptive one. In the uniting strategy, the unknown signs of the input coefficients are estimated by the discontinuous switching scheme while the unknown values are compensated by the continuous adaptive scheme. Though Nussbaum-type gain function (continuous adaptive strategy) and traditional switching method (discontinuous strategy), have been verified to be solely effective for solving the problem considered in the paper, large control gains or terrible transient performances arguably exist. The paper intends to avoid the disadvantages of existing methods to some extend and unite them to develop a more effective uniting strategy for achieving consensus. Compared with the existing results, the protocol proposed in the paper guarantees that asymptotic consensus is achieved faster with smaller control gains, that is, better transient and steady state performances are pursued. The superiority of the developed uniting strategies in performance improving can be seen intuitively from the illustrative examples by simulation.
Robust Non-Fragile Event-Triggered Sampled-Data Control for Uncertain Distributed Parameter Systems

This paper introduces a robust non-fragile event-triggered sampled-data (ETSD) control for uncertain distributed parameter systems (DPSs). In order to reduce the communication burden and deal with the uncertainties in system and controller, a robust non-fragile ETSD control strategy presented by linear matrix inequalities (LMIs) conditions is given based on an appropriate Lyapunov functional, which can ensure the exponential stability of the closed-loop DPSs. Finally, a numerical example is offered to demonstrate the effectiveness of the proposed method.
Sheepdog Driven Algorithm for Sheep Herd Transport

So far, most of the existing works on swarm systems are concerned with the collective motions. In this work, a somehow reverse problem is investigated, i.e., how an agent can interact with/control a swarm system. In particular, we consider the sheep herd transport problem: a herd of sheep is driven by a single sheepdog from the start place to the sheepfold. The rules governing the behaviors of the sheep are preset and known to the sheepdog, and the objective is to design the rule for the sheepdog to fulfil the transport task. Inspired by the behaviors of true sheepdogs, we have developed a backward semi-circle reciprocation algorithm featuring dynamic turn-around point selection. Numerical results are provided to evaluate the proposed algorithm.
Finite-Time State Estimation for Discrete-Time Nonlinear Singularly Perturbed Complex Networks under New Event-Triggered Mechanism†

This paper investigates the finite-time state estimation problem for a class of discrete-time nonlinear singularly perturbed complex networks under a new dynamic event-triggered mechanism (DETM). This new DETM is devised to adjust the date packet transmissions flexibly with hope to save network resources. By constructing a new Lyapunov function dependent on the information of the singular perturbation parameter (SPP) and DETM, a sufficient condition is derived which ensures that the error dynamics of state estimation is finite-time stable. The parameters of the state estimator are given by means of the solutions to several matrix inequalities and the upper bound of the SPP can be evaluated simultaneously. The effectiveness of the designed state estimator is demonstrated by a numerical example.
Modelling and Prediction of Injection Molding Process Using Copula Entropy and Multi-Output SVR

Optimization and parameter adjustment of an injection molding (IM) process depend largely on a good modelling and prediction of industrial process, which has been received considerable attention in recent years. However, IM process is a typical multivariate production process with multiple product quality indices. It poses a great challenge for multi-output quality prediction problem to select key process variables as input with good interpretability. This study proposes a multivariate quality prediction method for IM process using copula entropy (CE) and multi-output support vector regression (MSVR). First, copula entropy is employed to characterize the association relationships between each process variable and the set of quality indices, thus key process variables can be selected by ranking CE. Then, the quantitative relationship between key process variables and quality indices is established by MSVR. Finally, the proposed method is tested by the experiment on a real IM process dataset. This study will provide an important reference for modelling and prediction of IM process and other multi-output problems.
Multi-edge Server Load Balancing Offloading Method Based On Ant Colony Algorithm

The deployment of multiple edge servers has a significant difference in the performance of mobile edge computing(MEC) due to the more diverse options for computing offloading. This paper proposes an Ant Colony Multi-edge Load-balancing Offloading (ACMLO) strategy based on ant colony algorithm. Under the premise of considering the existing load of each edge server, the problem is modeled as a general multi-constraint optimization problem, and the optimization goal is to minimize the weighted sum of energy consumption and delay. Then use the general constraint form of slack variable method and exponential method (PHR algorithm) to solve iteratively. In each iteration, the ant colony algorithm is used to make decisions on the division of subtasks and the allocation of channel bandwidth, so as to achieve load balancing. The simulation results show that the proposed strategy can effectively improve the computational offload performance and meet the differentiated needs of users.
Stabilization of Delayed Boolean Control Networks via Ledley Antecedence Solution

In this paper, we investigate the stabilization of Boolean control networks (BCNs) with time delay both in states and inputs. A necessary and sufficient condition to detect stabilization of BCNs with time delay is obtained using ledley antecedence solution. Moreover, a simple algorithm to design all valid state feedback controllers via Ledly antecedence solution is also given. In the end, an illustrative example is given to support our new results.
A Deep Reinforcement Learning Approach for Long-term Short-term Planning on Frenet Frame

Tactical decision-making and strategic motion planning for autonomous highway driving are challenging due to predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions. This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures than the surrounding actors' dynamics and traffic interactions. The agent receives time-series data of past trajectories of the surrounding vehicles and applies convolutional neural networks along the time channels to extract features in the backbone. The algorithm generates continuous spatiotemporal trajectories on the Frenet frame for the feedback controller to track. Extensive high-fidelity highway simulations on CARLA show the superiority of the presented approach compared with commonly used baselines and discrete reinforcement learning on various traffic scenarios. Furthermore, the proposed method's advantage is confirmed with a more comprehensive performance evaluation against 1000 randomly generated test scenarios. Code: https://github.com/MajidMoghadam2006/RL-frenet-trajectory-planning-in-CARLA
Graph Partitioning-Based Clustering for the Planning of Distribution Network Topology using Spatial- Temporal Load Forecasting

Planning the expansion and the new topology of distribution networks requires knowing the location and characterization of the load as well as its future growth. Spatial load forecasting is a key tool in this task, providing high spatial resolution and adequate temporal granularity. Nowadays, with the penetration of distributed energy resources, multiple microgrid connection strategies, and implementation of self-healing and protection schemes, it is necessary to identify load blocks to plan the new active network architecture. Based on spatial load forecasting information, this paper proposes a graph partitioning technique to create load clusters in the distribution feeders. A weighted graph is constructed by means of a minimum spanning tree that allows to consider adjacency relations. The results of the simulation, carried out in a real distribution network, have demonstrated the effectiveness of the proposed method.
Tacrolimus Intrapatient Variability After Switching From Immediate or Prolonged-Release to Extended-Release Formulation, After an Organ Transplantation

Background and Purpose: Several formulations of tacrolimus are available, but evidence of the benefit of changing to the most recent formulations is lacking. Tacrolimus intra-patient variability (tacrolimus IPV) is an emerging risk factor associated with poor graft outcomes after solid organ transplantations. Here, we examined the modifications of tacrolimus IPV after switching to a different formulation of tacrolimus. Experimental Approach: We identified 353 solid organ transplant recipients that were switched in our center from immediate-release (IR-tacrolimus) or prolonged-release tacrolimus (PR-tacrolimus) to extended-release, LCP-tacrolimus (LCP-tacrolimus). Among them, 54 patients underwent at least 3 available tacrolimus blood concentrations before and after the switch, allowing us to investigate tacrolimus IPV. Key Results: The switch was considered as a safe procedure since only four of the 353 patients presented a graft rejection after the switch, and no patient was hospitalized for tacrolimus overdose. The tacrolimus IPV estimated by the coefficient of variation (CV-IPV) was stable before and after the switch to LCP-tacrolimus (CV-IPV: 29.0% (IQR 25–75 (15.5; 38.5) before and 24.0% (15.8; 36.5) after the switch, p = 0.65). Conclusion and Implications: Switching from IR- or PR-tacrolimus to LCP-tacrolimus is a safe procedure. However, the CV-tacrolimus IPV was not impacted by the change of formulation.
A Framework for IoT Based Appliance Recognition in Smart Homes

Internet of Things (IoT) technologies will play an important role in enabling the smart grid achieving its goals in monitoring, protecting, and controlling by incorporating sensors, actuators, and metering devices while supporting various network functions and system automation. In this regard, home energy management systems (HEMS) enable customers efficiently use energy by managing their consumption, providing feedback information and improving control of major appliances. This work proposes a novel framework for IoT based appliance recognition in smart homes. It consists of two parts: training framework and inference framework. The proposed framework allows incorporating different loads in the monitoring system and enables selecting and testing specific parameters related to dataset configuration, feature extraction, and classifier model setting. The work contributes by developing an easy-to-use tool that allows customization of the training/prediction parameters according to the user criterion. Once the data and all its parameters are loaded, a novel feature extraction algorithm is used to obtain a total of ten statistical features. For the classification task, three machine learning models are included: a feed-forward neural network, a long short-term memory and a support vector machine. In addition, the user can apply a set of techniques to handle imbalanced classes, and also measure the influence of the selected features in the classifiers’ prediction by performing a feature importance analysis.
Lyapunov Spectrum Local Assignability of Linear Discrete Time-Varying Systems by Static Output Feedback

We consider a linear discrete time-varying input-output system. Our goal is to study the problem of local assignability of the Lyapunov spectrum by static output feedback control. To this end we introduce the notion of uniform consistency for discrete-time linear systems which is the extension of the notion of uniform complete controllability to input-output systems. The property of uniform consistency is investigated, some necessary and sufficient conditions for this property are obtained. The notion of uniform local attainability is introduced for the closed-loop system. We prove that uniform consistency implies uniform local attainability of the closed-loop system. The property of local Lyapunov reducibility is introduced for the closed-loop system. We prove that uniform local attainability implies local Lyapunov reducibility. We prove that, for a locally Lyapunov reducible system, the Lyapunov spectrum is locally assignable, if the free system is diagonalizable or regular (in the Lyapunov sence) or has the stable Lyapunov spectrum.
Transverse Function Control for Underactuated MSV With Stochastic Disturbances Under Output Error Constraints

This paper investigates the trajectory tracking control problem of an underactuated marine surface vehicle (MSV) with stochastic disturbances, where the tracking errors are required to preserve within the predefined constraint bounds. Since the vehicle system is exposed to the environmental stochastic disturbances, the difficulty arisen from Hessian terms induced by stochastic differentiation rule is overcome by introducing a quartic Lyapunov function. To tackle the difficulties caused by underactuation and nonzero off-diagonal terms in the system matrix, we develop a transverse function control approach to introduce an additional control input in backstepping procedure. A tan-type barrier Lyapunov function is introduced to guarantee the satisfaction of the prescribed tracking error constraints. Based on backstepping procedure, barrier Lyapunov function, transverse function control approach, and Lyapunov synthesis, feedback control laws are proposed to ensure that the tracking errors converge exponentially to a small neighborhood of zero, and the constraint requirements on the tracking errors are not violated in the course of operation in probability despite the presence of stochastic disturbances. Simulation results illustrate the performance of the proposed tracking control.
Gut Dysbiosis, Bacterial Colonization and Translocation, and Neonatal Sepsis in Very-Low-Birth-Weight Preterm Infants

Gut dysbiosis may precede neonatal sepsis, but the association is still not well-understood. The goal of this study is to investigate the association between gut microbiota and neonatal sepsis, and to seek the evidence of colonization of pathogenic bacteria in the gut before evolving into an invasive infection. A prospective cohort study examined fecal microbiota composition in preterm infants with and without sepsis. Thirty-two very-low-birth-weight (VLBW) preterm infants and 10 healthy term infants as controls were enrolled. The fecal samples collected from the participants at the first, fourth, and seventh weeks of life underwent 16S rRNA amplicon sequencing for measurement of the diversity and composition of the microbiota. The bacterial isolates causing neonatal sepsis were genome sequenced. PCR was performed to confirm the translocation of the bacteria from the gut to the blood. The results showed that VLBW preterm infants with sepsis had lower microbial diversity in the gut at birth compared to preterm infants without sepsis and term infants. The composition of gut microbiome in preterm infants was similar to healthy terms at birth but evolved toward dysbiosis with increasing Proteobacteria and decreasing Firmicutes weeks later. The strain-specific PCR confirmed the presence of causative pathogens in the gut in 4 (40%) out of 10 VLBW preterms with sepsis before or at onset of sepsis, and persistence of the colonization for weeks after antibiotic treatment. The same bacterial strain could horizontally spread to cause infection in other infants. Prolonged antibiotic exposure significantly reduced beneficial Bifidobacterium and Lactobacillus in the gut. In conclusion, preterm infants with gut dysbiosis are at risk for neonatal sepsis, and the causative pathogens may be from the gut and persist to spread horizontally. The association of increased Proteobacteria abundance and decrease in microbiome diversity suggests the need for interventions targeting the gut microbiome to prevent dysbiosis and sepsis in VLBW preterm infants.