The Zélus language excels at describing hybrid systems but has limitations in handling uncertainties, such as probabilistic transitions and random failures, in nuclear Instrumentation and Control (I&C) systems.Other languages for modeling uncertainty often choose to discretize the continuous behavior in the system, making it difficult to accurately describe the system.To address this, we propose Stochastic Zélus, a new language integrating probabilistic automata for modeling hybrid systems in uncertain environments.We also developed a quantitative analysis framework and a prototype tool for converting Stochastic Zélus models to NSHA (Networks of Stochastic Hybrid Automata) models supported by UPPAAL-SMC, to support quantitative analysis of stochastic Zélus models using UPPAAL-SMC.Finally, we demonstrate the effectiveness of this approach through a case study on a Reactor Protection System (PS), proving that the method is effective in verifying the reliability of the PS in uncertain environments.

Traditional Chinese medicine (TCM) utilizes intricate herbal formulations that exemplify the principles of compatibility and synergy. However, the rapid proliferation of herbal data has resulted in redundant information, complicating the understanding of their potential mechanisms. To address this issue, we first established a comprehensive database that encompasses 992 herbs, 18 681 molecules, and 2168 targets. Consequently, we implemented a multi-network strategy based on a core information screening method to elucidate the highly intertwined relationships among the targets of various herbs and to refine herbal target information. Within a non-redundant network framework, separation and overlap analysis demonstrated that the networking of herbs preserves essential clinical information, including their properties, meridians, and therapeutic classifications. Furthermore, two notable trends emerged from the statistical analyses of classical TCM formulas: the separation of herbs and the overlap between herbs and diseases. This phenomenon is termed the herbal combination model (HCM), validated through statistical analyses of two representative case studies: the common cold and rheumatoid arthritis. Additionally, in vivo and in vitro experiments with the new formula YanChuanQin (YanHuSuo-Corydalis Rhizoma, ChuanWu-Aconiti Radix, and QinJiao-Gentianae Macrophyllae Radix) for acute gouty arthritis further support the HCM. Overall, this computational method provides a systematic network strategy for exploring herbal combinations in complex and poorly understood diseases from a non-redundant perspective.

ABSTRACT Human activity recognition (HAR) has emerged as a critical element in various domains, such as smart healthcare, smart homes, and intelligent transportation, owing to the rapid advancements in wearable sensing technology and mobile computing. Nevertheless, existing HAR methods predominantly rely on deep supervised learning algorithms, necessitating a substantial supply of high‐quality labeled data, which significantly impacts their accuracy and reliability. Considering the diversity of mobile devices and usage environments, the quest for optimizing recognition performance in deep models while minimizing labeled data usage has become a prominent research area. In this paper, we propose a novel semi‐supervised HAR framework based on contrastive learning named S2C‐HAR, which is capable of generating accurate pseudo‐labels for unlabeled data, thus achieving comparable performance with supervised learning with only a few labels applied. First, a contrastive learning model for HAR (CLHAR) is designed for more general feature representations, which contains a contrastive augmentation transformer pre‐trained exclusively on unlabeled data and fine‐tuned in conjunction with a model‐agnostic classification network. Furthermore, based on the FixMatch technique, unlabeled data with two different perturbations imposed are fed into the CLHAR to produce pseudo‐labels and prediction results, which effectively provides a robust self‐training strategy and improves the quality of pseudo‐labels. To validate the efficacy of our proposed model, we conducted extensive experiments, yielding compelling results. Remarkably, even with only 1% labeled data, our model achieves satisfactory recognition performance, outperforming state‐of‐the‐art methods by approximately 5%.

Quasi-complementary sequence sets (QCSSs) have attracted sustained research interests due to the ability to support more users compared to complete complementary codes (CCCs) in multi-carrier code division multiple access (MC-CDMA) systems. In this paper, two classes of aperiodic QCSSs are constructed over the finite rings, which have new parameters in terms of the length of constituent sequences and set size respectively. Most notably, the length of constituent sequences of QCSS derived from the first construction is$ p^{n} + 1$, where p is a prime and n is a positive integer, which has not been covered in the literature. In the second construction, the set size of the proposed binary QCSS is$2^{n+1}$larger than the existing binary QCSS. In addition, both the proposed aperiodic QCSSs are asymptotically optimal with respect to the correlation lower bound.

This paper addresses a robust spectrum sensing scheme for non-circular (NC) primary signals within the framework of locally most power invariant test (LMPIT). The proposed method is referred as the NCJT detector because it is deemed to be the NC generalization of the circular signals based John test. The proposed method utilizes the full second-order statistical properties of the NC signals by taking into consideration both standard covariance and complementary covariance, thereby exhibiting superiority in detection capability and robustness. Unlike the eigenvalue decomposition inherent in the eigenvalue-based detectors, the NCJT statistic is expressed as the ratio between the Frobenius norm and the trace of augmented covariance matrix, which delivers computational efficiency. The properties of invariance and constant false-alarm rate (CFAR) of the proposed method are theoretically confirmed. In addition, the distributions of the test statistic under both hypotheses are established with the support of generalized Beta-based approximation after deriving the first two moments of NCJT statistic with or without primary signals, facilitating us to approximate the probabilities of false alarm and detection. Numerical results are provided to examine the precision of our theoretical results and compare the sensing performance of our method with other detectors in various sensing scenarios.

Synchronous language is routinely used to model safety-critical control systems. In recent years, it is gradually being applied to cyber-physical systems (CPS) which emphasise high levels of correctness and safety. It is based on the assumption that the system reacts instantaneously to input events and can compute the output before the next input event, so it is well suited for expressing temporal logic. However, it lacks effective constructs for expressing spatial properties in CPS. Moreover, spatio-temporal properties in CPS are indispensable, requiring not only qualitative analysis but also quantitative analysis. Therefore, we propose SELus, a new synchronous language based on Lustre, to provide the capability of modeling spatio-temporal properties in CPS, enabling the representation of spatial topological relationships and the performance of quantitative analysis on them. To formally verify the SELus model, we introduce a set of mapping rules to transform the SELus model into the Ptolemy II model. The resulting Ptolemy II model is used in Ptolemy II to perform quantitative analysis of the SELus model. Experiments are conducted on lane changing system, showcasing the usability and effectiveness of our language.

With the rapid development of computer technology, distributed systems have become an indispensable part of the field of information storage and management. In the process of large-scale data processing, it is an important issue to compress or replay files to ensure their integrity. In order to solve the problem of large-scale computing resources and data storage, the distributed file system emerged as a new system structure. The purpose of this paper to is study the key technologies of the distributed memory file system of high-performance computers is to improve the capability and efficiency of the distributed system. This paper mainly uses the experimental method and the comparative method to analyze the key technology of the distributed memory file system of the high-performance computer. Experimental results show that the maximum bandwidth value of DFMS in file memory processing can reach more than 2000, and the value becomes more stable as the file increases.

Source IP address spoofing has been a major vulnerability of the Internet for many years. Although much work has been done to study the problem extensively, spoofing continues to occur frequently and has led to many serious network attacks. Inter-AS source address validation (SAV) is considered an important defense method for AS to filter spoofed packets. However, existing work has been unable to drive inter-AS SAV deployment into practice due to the lack of deployment incentives and trust foundation.In this paper, we propose a practical and decentralized inter-AS SAV service framework, pSAV, to promote inter-AS SAV deployment. pSAV increases deployment incentives by treating SAV as a payable service and dividing the participant ASes into service subscribers, providers, and auditors. On the control plane, pSAV leverages blockchain as a trust foundation to provide service subscriptions and audits with automatic incentive allocation. On the data plane, pSAV leverages P4-programmable switches to provide flexible and high-performance SAV services. We prototype the pSAV control plane based on Hyperledger Fabric and implement various SAV techniques on Barefoot Tofino switches. The evaluation results show that (1) on the control plane, pSAV blockchain can provide high-performance service transactions (hundreds of transactions per second with second latency), and (2) on the data plane, pSAV can provide various high-throughput (hundreds of Gbps) SAV services using only one programmable switch.

In recent years, Distributed Denial-of-Service (DDoS) attacks have become more rampant and continue to be one of the most serious security threats facing network infrastructure. In a classic DDoS attack, the attacker controls numerous bots from many sources to send a significant volume of traffic to flood the victim end or the bottleneck link. In practical networks, it is inefficient and costly to request all partner routers to collaboratively mitigate DDoS attacks. The common feature of DDoS attacks is the abnormal distribution of traffic to the victim. In this paper, we propose TAP, a collaborative DDoS mitigation framework, based on traffic-aware probabilistic packet marking (PPM). TAP enables the victim to select a few hit routers as collaborators to mitigate attack traffic efficiently depending on the traffic distribution. Our evaluation results show that TAP greatly reduces attack traffic within seconds and mitigate the damage caused by DDoS with less overhead, which demonstrates that TAP is an effective, efficient, and rapid-response scheme for collaborative DDoS mitigation.

Apple nitrogen status is a key indicator for evaluating quality of apple fruits. In order to study and estimate the nitrogen content of apple leaves, it provides a basis for making a reasonable estimate of the yield of fruit trees. In this paper, the continuous wavelet transform method is used to screen out the sensitive bands. Among them, in the range of 500-650 nm, the correlation coefficient between the nitrogen content of the leaves and the original spectrum is significantly higher. The most relevant band is 621 nm with a correlation coefficient of 0.71. Modeling and verification by partial least squares method, the modeling accuracy is R2is 0.62, RMSE is 0.26g/(100g), NRMSE is 0.1, verification accuracy R2is 0.79, RMSE is 0.31 g/(100g) , NRMSE is 0.1. It can be seen that the model has good stability, high prediction ability and good fitting effect, and can be used as an estimation model for nitrogen content in apple leaves.

The aboveground biomass of crops is an important index reflecting the growth status of crops. It is an important reference for achieving precise water and fertilizer management, yield monitoring and prediction. In this paper, the true height of the vegetation relative to the ground is obtained from the data preprocessing, the point cloud filtering and the normalization of the UAV radar data. The accuracy of biomass and height estimated by the average canopy height (Hcanopy) derived from canopy height model (CHM) is analyzed in a variety of resolution(5cm, 10cm, 15cm,20cm,25, 30cm,50cm, 100cm)respectively. The results suggest that the Hcanopyderived from CHM in 5cm resolution is highest correlated with biomass(R2=0.93, RMSE=698.90kg/ha), the Hcanopy derived from CHM in 50cm resolution is highest correlated with height (R2=0.96, RMSE=4cm). The crop growth parameters could be estimated based on UAV LiDAR across broad spatial scales.

Crop biomass is the basis of crop yield formation, and accurate biomass information is of great significance to ensure national food security. Taking winter wheat as the research object, the airborne radar data and wheat biomass information were used to study the difference of biomass inversion in the vertical distribution of laser point cloud at different scanning angles. The vertical distribution of point clouds is of great difference at 30 m above ground and a scan angle of ±60o. There is no correlation between the vertical distribution of point clouds and the scan angle of ±10oas a result of partial correlation analysis. Two lidar metrics (plot-level Hmea\mathfrakn and Dbelow) is strongly related to field-measured biomass (R2= 0.94,RMSE = 572.32kg/ha) at a scan angle of±10o.

Redundant robots performing multiple tasks of different priority levels are often handled by hierarchical control frameworks. This paper proposes a general hierarchical control approach that can handle not only a single standard lexicographic hierarchy, but also a complex priority network involving both strict and non-strict task priorities. In this approach, priorities can be defined by pairs of tasks and are encoded by a priority matrix. Priority modulations are achieved by the regulation of a novel generalized projector, which takes the priority matrix as input. This projector allows a task to be completely projected in the null-space of a set of tasks, while partially projected in those of some other tasks. Such a projector can be used to achieve multiple priority rearrangements simultaneously. The effectiveness of this approach is demonstrated on a KUKA LWR robot performing task priority rearrangements as well as task insertion and deletion.

Humanoid robots are expected to act in human environments, where some of the contacts can be non-rigid. A fairly large amount of work has been devoted to the whole-body control of humanoids under rigid contacts, but few of them take into account non-rigid contacts. Indeed, the handling of unknown compliant contacts to achieve goal directed actions and whole-body balance remains a challenge. This paper addresses this problem by proposing a control mechanism that solves whole-body tasks under non-rigid contacts. It is a reactive control approach that automatically regulates contact forces and whole-body motions based on the motion of contact points without the awareness of the rigidity properties of the contact material. Verification of this approach is conducted through experiments on the iCub humanoid robot in simulation.

This paper presents a new control framework for virtual humans (VHs) in a physics-based virtual environment. This framework combines multiobjective control with motion capture techniques. Each motion-tracking task is associated with a task wrench. Bounds are imposed on lower priority task wrenches to ensure the controller performance of higher priority tasks. An optimization problem is solved to compute optimal task wrenches that are based on wrench bounds. Finally, joint torques are computed using the optimal task wrenches. The novelty of our wrench-bound method is that it can handle inequality constraints on a higher priority task and maintain passivity as well. This control framework allows an operator to interact with the VH in real time, without the necessity of compromising the VH's balance. It also allows the VH to generate appropriate motions to handle interactions with the virtual environment, rather than to simply emulate captured motions. The effectiveness of our approach is demonstrated by a VH performing reaching and manipulation tasks.

This paper proposes a new framework of online hybrid control for virtual characters, which combines multi-objective control and motion capture techniques. At each time step, the motions of the operator are captured and sent to the character. These captured motions help the character understand what task the operator wants it to perform. Then, the character decides by itself how to execute proper actions while maintaining its balance, in a virtual environment which is different from the real world. Given a manipulation task, the desired virtual task wrench is computed thanks to a force control approach. The control system takes these task wrenches into consideration and solves a constrained quadratic problem for joint torques, which are then used to drive the virtual character. The whole control problem is solved online so that the virtual character can interact in real-time with the virtual environment as well as the operator. Examples using this control framework are presented as results from simulation.