Accurate bearing fault diagnosis under varying operating conditions remains crucial for industrial reliability; however, the scarcity of labeled target data continues to significantly hinder the generalization of deep learning models. Existing unsupervised domain adaptation techniques often exhibit negative transfer by failing to distinguish between domain-invariant and domain-specific knowledge, leading to suboptimal adaptation. Therefore, this study proposes the Multisource Disentangled Expert Adaptation Network (MDEA-Net). MDEA-Net employs distinct shared expert networks to capture common fault characteristics across multiple source domains and private expert networks to model source-specific knowledge. Importantly, it explicitly disentangles these representations using an expert disentanglement loss, which raises both diversity and orthogonality. For efficient target adaptation, MDEA-Net utilizes frozen shared experts and introduces lightweight, target-specific private experts using low-rank adaptation. Domain alignment is accomplished by minimizing the maximum mean discrepancy specifically between shared feature spaces. Extensive experiments on benchmark datasets (Case Western Reserve University, Jiangnan University, Xi'an Jiaotong University and Changxing Sumyoung Technology Co., Ltd) demonstrate that MDEA-Net significantly outperforms state-of-the-art methods in cross-domain bearing fault diagnosis.

Smart building systems have recently achieved rapid development. However, a general software design framework for these systems is still lacking, due to their complexity in scale, structural intricacies, and multidisciplinary integration. To address this challenge, a framework called BuIlding Systems Modeling (BIS-Model) is proposed to enable systematic modeling, design, and behavior analysis of smart building systems. By extending SysML, BIS-Model introduces three types of modeling views: (1) a structure view called BIS-Block Definition Diagram that describes the complex system compositions, structure characteristics, and device properties of smart building systems; (2) a connection view called BIS-Internal Block Diagram that specifies device connections and interface properties of smart building systems; and (3) a behavior view called BIS-Sequence Diagram that specifies and analyzes the dynamic behaviors of smart building systems. Moreover, a support tool and roadmap are presented to integrate BIS-Model into each phase of smart building software development. The BIS-Model framework was empirically validated through a study using a real-world smart building system. The results demonstrate that BIS-Model enhances model quality by 45.17 % and reduces modeling time by 43.09 %, compared to the standard SysML, thereby significantly improving the design effectiveness and efficiency of smart building systems.

Rules are key elements for the collaborative interaction of different devices in IoT systems. However, the rule conflict problem is getting worse as IoT system complexity increases. And it is challenging to formally specify rules and detect rule conflicts for IoT systems with continuous states and running under uncertainty environment. To this end, this paper proposed a rule modeling and conflict detection approach called CredRule, aiming at construct “credible rules” for IoT systems. Firstly, CredRule provided a detailed definition for IoT rules, and proposed to specify rules, continuous system states and uncertainty environment with the NPTA (Network of Priced Timed Automata for short) model. Secondly, it provided a rule conflict taxonomy, and established a Rule Conflict Interaction Analysis model, providing an explicit description on rule conflicts. Finally, on basis of the established two models, it provided an automatic rule detection and analysis method based on the statistical model checking technique. To validate the effectiveness of CredRule, we carried out a case study on one ventilation and air conditioning system. The results demonstrate that CredRule can formally specify rules and effectively detect rule conflicts under uncertainty and continuous environment.

ABSTRACT Heterogeneous event mapping is a critical challenge in multi‐robot system, where diverse robots generate event logs with opaque names and inconsistent formats due to subsystem heterogeneity. However, existing techniques are inappropriate because they do not make full use of workflow features in event logs. We observe that several types of event constraints in event logs may serve as more discriminative features in event matching. Therefore, a novel approach is proposed to address the above problem by leveraging event structures as discriminative feature for heterogeneous event mapping. Specifically, the event structure captures behavioral constraints in event logs to provide a robust foundation for event mapping. To enhance matching efficiency, we devise an advanced A* search algorithm with a tight upper bound, which effectively prunes nonoptimal mappings and avoid the space explosion problem. Furthermore, a prototype system is implemented that automatically calculates the optimal mapping score between heterogeneous events. Based on this, extensive experiments on both real and synthetic data sets demonstrate that our approach outperforms state‐of‐the‐art approaches in accuracy, efficiency, and scalability. This work provides a robust solution for seamless coordination and data sharing in MRS, with potential applications in autonomous navigation, collaborative robotics, and human–robot interaction.

Currently, a large amount of heterogeneous event data with opaque event names (e.g., obscured IDs) are increasingly generated, e.g., the blockchain technology requires the transaction information to be recorded by different stakeholders in various ways from different blocks (i.e., distributed ledgers). Thus, a large amount of heterogeneous event data with opaque event names (e.g., obscured IDs) is generated on blockchains. To identify similar workflows on blockchains for collaborator selection, matching heterogeneous events is an important task. Existing techniques are inappropriate because they do not make full use of workflow features in event logs. We observe that several types of event constraints in event logs may serve as more discriminative features in event matching. Specifically, a general matching approach is proposed for identifying a right mapping that maximizes the similarity of event constraints for a pair of heterogeneous event logs. An advanced A* algorithm with a tight upper bound function is devised to improve the matching efficiency of the proposed approach. We conducted exr5tensive experiments to evaluate the effectiveness of our approach, and the experimental results demonstrate that our approach outperforms state-of-the-art matching approaches.

The demand for advanced monitoring and fault diagnosis technologies for critical mechanical components is growing rapidly. Early detection of rolling bearing faults is essential for preventing performance degradation, unplanned downtime, and safety risks. This article presents a novel fault diagnosis method that leverages digital twin technology and transfer learning to address the limitations of existing approaches in terms of data dependency and cross-domain effectiveness. Initially, a precise digital twin model is developed using finite element analysis to accurately simulate bearing dynamics under various operating conditions, generating extensive simulation data. These data compensate for the scarcity of fault data and are valuable for training diagnostic models. To reduce the noise level in real-world data, the snow ablation optimizer algorithm is employed to optimize variational mode decomposition for noise reduction. Subsequently, transfer learning techniques are utilized to treat the simulation data as the source domain and the actual vibration signals as the target domain, enabling domain-adaptive transfer learning. This approach facilitates cross-domain feature alignment and knowledge transfer, further optimized through adversarial loss and the maximum kernel mean discrepancy metric. Moreover, a deep learning model that combines residual convolutional neural networks with a Transformer is developed, significantly enhancing feature extraction and classification accuracy. Experimental validation conducted on the XJTU-SY dataset demonstrates that the proposed diagnostic method exhibits superior diagnostic performance under small sample conditions, outperforming existing diagnostic methods.

With the rapid development of rule engine-based smart building systems, it is urgent to address the potential conflicts between different control rules to ensure system usability and reliability. For this end, this paper proposed a rule conflict detection approach based on the statistical model checking technique. Firstly, it presented a conflict taxonomy for smart building rules, systematically considering conflicts between different rules, and rule conflicts against system invariant constraints. Then, it proposed depicting smart building control rules, dynamic trigger sources and actuators with the NPTA (Network of Priced Timed Automata for short) model, and specifying rule conflicts with temporal logic. Finally, it proposed carrying out conflict detection with the statistical model checking technique based on the above formal model and formal specification. To validate the effectiveness of this approach, we carried out a case study on a superstore ventilation and air conditioning system, and demonstrated the rule conflict detection processes of 8 types of rule conflicts. The results demonstrate that the proposed approach can explicitly depict smart building rule interactions, and can effectively diagnose and detect rule conflicts, ensuring usability and reliability of the rule-based smart building control rules.

教师队伍建设是军队院校落实为战育人培养要求的重要举措.文章以实战化教学为导向,对军队院校防抗专业教师专业素质要求进行研究分析,提出"学、跟、训、研、练、考"多措并举、交叉融合的教师培养机制,为防抗专业开展实战化教学与培养高素质军事人才建设复合型教师队伍.

在新时代军事教育方针的引领下,必须加强军队院校教学与科研的有机结合,把军事科研成果融入教学的全过程.为此,文章以国防工程信息管理专业为例,系统分析了军队院校专业课程教学中存在的不足,提出以"军事科研反哺教学"的思路开展专业课程教学改革,围绕"教员能力培养提升、教学内容和方法创新、实践创新体系构建"三个维度提出了科研反哺教学的具体实施途径,提出将军事科研过程中产生的关键技术、科学问题、工程案例、科研精神等要素融入教学实践的全过程,形成具有鲜明特色的军队院校军事科研反哺教学改革思路和举措.实践证明,该方法体系能够促进国防工程信息管理专业科研反哺教学的落地,有效提升专业课程教学的质量.

Self-adaptive software (SAS) is gaining in popularity as it can handle dynamic changes in the operational context or in itself. Time behaviors are of vital importance for SAS systems, as the self-adaptation loops bring in additional overhead time. However, early modeling and quantitative analysis of time behaviors for the SAS systems is challenging, especially under uncertainty environments. To tackle this problem, this paper proposed an approach called Timed-SAS to define, describe, analyze, and optimize the time behaviors within the SAS systems. Concretely, Timed-SAS: (1) provides a systematic definition on the deterministic time constraints, the uncertainty delay time constraints, and the time-based evaluation metrics for the SAS systems; (2) creates a set of formal modeling templates for the self-adaptation processes, the time behaviors and the uncertainty environment to consolidate design knowledge for reuse; and (3) provides a set of statistical model checking-based quantitative analysis templates to analyze and verify the self-adaptation properties and the time properties under uncertainty. To validate its effectiveness, we presented an example application and a subject-based experiment. The results demonstrated that the Timed-SAS approach can effectively reduce modeling and verification difficulties of the time behaviors, and can help to optimize the self-adaptation logic.

Fully distributed intelligent building systems can be used to effectively reduce the complexity of building automation systems and improve the efficiency of the operation and maintenance management because of its self-organization, flexibility, and robustness. However, the parallel computing mode, dynamic network topology, and complex node interaction logic make application development complex, time-consuming, and challenging. To address the development difficulties of fully distributed intelligent building system applications, this paper proposes a user-friendly programming language called SwarmL. Concretely, SwarmL (1) establishes a language model, an overall framework, and an abstract syntax that intuitively describes the static physical objects and dynamic execution mechanisms of a fully distributed intelligent building system, (2) proposes a physical field-oriented variable that adapts the programming model to the distributed architectures by employing a serial programming style in accordance with human thinking to program parallel applications of fully distributed intelligent building systems for reducing programming difficulty, (3) designs a computational scope-based communication mechanism that separates the computational logic from the node interaction logic, thus adapting to dynamically changing network topologies and supporting the generalized development of the fully distributed intelligent building system applications, and (4) implements an integrated development tool that supports program editing and object code generation. To validate SwarmL, an example application of a real scenario and a subject-based experiment are explored. The results demonstrate that SwarmL can effectively reduce the programming difficulty and improve the development efficiency of fully distributed intelligent building system applications. SwarmL enables building users to quickly understand and master the development methods of application tasks in fully distributed intelligent building systems, and supports the intuitive description and generalized, efficient development of application tasks. The created SwarmL support tool supports the downloading and deployment of applications for fully distributed intelligent building systems, which can improve the efficiency of building control management and promote the application and popularization of new intelligent building systems.

Cyber-physical systems (CPS) are complex systems, which integrate computational and physical components with the control loops. And an early modeling and quantitative analysis of CPS behaviors can reduce development difficulties and improve system reliability. However, CPS behaviors, especially the behaviors of the control loops and time behaviors, lack a systematic description and quantitative analysis. For this end, this paper proposes an approach called OODA-CPS, which integrates the modeling processes of visual modeling, formal modeling and quantitative analysis of CPS behaviors. The OODA-CPS approach is created based on the OODA (observe, orient, decide, act) system architecture, and incorporates the SysML model and the Network of Priced Timed Automata (i.e., NPTA) model. Concretely, OODA-CPS: 1) presents an explicit description on CPS behaviors by extending the SysML sequence diagram, 2) provides an automatic transformation from the CPS visual model (i.e., the extended SysML sequence diagram) to the CPS formal model (i.e., the NPTA model), and 3) provides a quantitative analysis on the OODA control loop behaviors and the time behaviors of CPS. To evaluate the approach, we present an example application, and the results demonstrate that the approach can effectively reduce modeling and analysis difficulties of the CPS behaviors.

Motivation Image dehazing, as a key prerequisite of high-level computer vision tasks, has gained extensive attention in recent years. Traditional model-based methods acquire dehazed images via the atmospheric scattering model, which dehazed favorably but often causes artifacts due to the error of parameter estimation. By contrast, recent model-free methods directly restore dehazed images by building an end-to-end network, which achieves better color fidelity. To improve the dehazing effect, we combine the complementary merits of these two categories and propose a physical-model guided self-distillation network for single image dehazing named PMGSDN. Proposed method First, we propose a novel attention guided feature extraction block (AGFEB) and build a deep feature extraction network by it. Second, we propose three early-exit branches and embed the dark channel prior information to the network to merge the merits of model-based methods and model-free methods, and then we adopt self-distillation to transfer the features from the deeper layers (perform as teacher) to shallow early-exit branches (perform as student) to improve the dehazing effect. Results For I-HAZE and O-HAZE datasets, better than the other methods, the proposed method achieves the best values of PSNR and SSIM being 17.41dB, 0.813, 18.48dB, and 0.802. Moreover, for real-world images, the proposed method also obtains high quality dehazed results. Conclusion Experimental results on both synthetic and real-world images demonstrate that the proposed PMGSDN can effectively dehaze images, resulting in dehazed results with clear textures and good color fidelity.

Self-adaptive software (SAS) systems are gaining increasing popularity in recent years. However, the changing and dynamic running environment and the diverse user requirements have introduced uncertainty, especially the random uncertainty into behaviors of the SAS systems. And the above uncertainty has posed huge challenges in software modeling and decisionmaking of the SAS systems. For this end, this paper presents ProbaSAS: an MDP (Markov Decision Process) based approach for SAS modeling and decision making under uncertainty. Firstly, the uncertainty within the SAS systems has been systematically analyzed, and the modeling and decision-making framework is proposed. Then, the modeling approach for three kinds of uncertainty (i.e., the probabilistic behaviors, the non-deterministic processes, and the non-functional characteristics) is created based on the MDP model. Finally, the self-adaptation reasoning and decision-making approach for two kinds of selfadaptation (i. e., the structure self-adaptation and the behavior self-adaptation) is proposed based on the probabilistic model checking technique. Taking the Ship-Supplying Information System as an example, we have evaluated the effectiveness of the ProbaSAS approach in uncertainty modeling and decision-making of the SAS systems.

对分课堂教学模式提倡教与学在课堂上的结合,对促进学生综合能力的培养具有正面作用.文章以军校专业课工程伪装与防护课程为例,在对对分课堂理论与应用分析的基础上,利用对分课堂相关理论研究作为指导,研究设计了对分课堂教学模式下的教学内容、教学组织,并通过教学实践对该教学模式进行了检验.阶段性实践结果表明,通过对分课堂教学模式能够有效提升学生的课堂积极性,帮助学生高效掌握课程知识,提高学生的知识归纳总结与解决问题能力.

文章首先分析了"数字信号处理"课程的教学现状,然后论述了"数字信号处理"课程的教学改革,包括研究多媒体课件与板书的最佳结合、充分利用MATLAB作为教学辅助工具、加强实践环节、设计拓展型作业和开放性作业、设置灵活的成绩考核方式.

Self-adaptive software (SAS) is gaining popularity as it can reconfigure itself in response to the dynamic changes in the operational context or itself. However, early modeling and formal analysis of SAS systems becomes increasingly difficult, as the system scale and complexity is rapidly increasing. To tackle the modeling difficulty of SAS systems, we present a refinement-based modeling and verification approach called EasyModel. EasyModel integrates the intuitive Unified Modeling Language (UML) model with the stepwise refinement Event-B model. Concretely, EasyModel: 1) creates a UML profile called AdaptML that provides an explicit description of SAS characteristics, 2) proposes a refinement modeling mechanism for SAS systems that can deal with system modeling complexity, 3) offers a model transformation approach and bridges the gap between the design model and the formal model of SAS systems, and 4) provides an efficient way to verify and guarantee the correct behaviour of SAS systems. To validate EasyModel, we present an example application and a subject-based experiment. The results demonstrate that EasyModel can effectively reduce the modeling and formal verification difficulty of SAS systems, and can incorporate the intuitive merit of UML and the correct-by-construction merit of Event-B.

Insect intelligent building (I2B) is a novel decentralized, flat-structured intelligent building platform with excellent flexibility and scalability. I2B allows users to develop applications that include control strategies for efficiently managing and controlling buildings. However, developing I2B APPs (applications) is considered a challenging and complex task due to the complex structural features and parallel computing models of the I2B platform. Existing studies have been shown to encounter difficulty in supporting a high degree of abstraction and in allowing users to define control scenarios in a concise and comprehensible way. This paper aims to facilitate the development of such applications and to reduce the programming difficulty. We propose Touch, a textual domain-specific language (DSL) that provides a high-level abstraction of I2B APPs. Specifically, we first establish the conceptual programming architecture of the I2B APP, making the application more intuitive by abstracting different levels of physical entities in I2B. Then, we present special language elements to effectively support the parallel computing model of the I2B platform and provide a formal definition of the concrete Touch syntax. We also implement supporting tools for Touch, including a development environment as well as target code generation. Finally, we present experimental results to demonstrate the effectiveness and efficiency of Touch.

For the problems existing in most of the researches,such as weak anti-noise ability,incompatible signal size and insufficient feature extraction of deep-learning-based Wi-Fi human activity recognition,a kind of sequential image deep learning-based recognition method was proposed.Based on the idea of sequential image deep learning,a series of image frames were reconstructed from time-varied Wi-Fi signal to ensure the consistency of input size.In addition,a low-rank decomposition method was innovatively designed to separate low-rank activity information merged in noises.Finally,a deep model combining temporal stream and spatial stream was proposed to automatically capture the spatiotemporal features from length-varied image sequences.The proposed method was extensively tested in WiAR dataset and self collected dataset.The experimental results show the proposed method could achieve the accuracy of 0.94 and 0.96,which indicate its high-accuracy performance and robustness in pervasive environments.

该文针对我校研究生"数字信号处理"课程在教学实践中存在的问题,结合课程内容特点、教学对象特点和教学活动特点,以人才培养和能力养成为主要目标,从课程优化设置、教材建设两个方面提出了具体改革举措,为课程教学效果提升提供了可行的解决方案.