Objective Multi-source information fusion is a key technique in multi-robot cooperative localization, unmanned system navigation, and distributed perception because robots can improve localization accuracy by exchanging and fusing local state estimates obtained from different sensing and communication links. In practical distributed cooperative localization systems, however, local estimates are often affected by unknown cross-correlations caused by shared historical observations, repeated communication, and intermediate information propagation. Under such conditions, conventional fusion methods based on independence assumptions may underestimate the covariance and even yield inconsistent estimations. Inverse covariance intersection (ICI) is effective in ensuring consistent fusion without explicit computation of unknown cross-correlations. However, in sequential ICI fusion, the final result depends not only on the fusion rule itself but also on the order in which local estimates are introduced into the fusion process. This order sensitivity may lead to different covariance contraction results and localization performance, even when the same local estimates are used. To address this problem, this study investigates the order sensitivity of sequential ICI fusion and proposes a fusion-order optimization method based on a genetic algorithm. Methods First, the update characteristics of the sequential ICI fusion operator are analyzed from the perspective of pairwise recursive fusion. Because the ICI update contains nonlinear matrix inversion of covariance convex combinations, the sequential fusion operator generally does not satisfy commutativity or associativity, theoretically accounting for the different intermediate covariance evolution paths and, consequently, the different final fusion results obtained with different fusion orders. On this basis, the fusion-order selection problem is formulated as a permutation-based combinatorial optimization problem with the objective of minimizing the trace of the final fused covariance matrix. A genetic algorithm is introduced to perform a global search in the permutation space. In the proposed method, each chromosome represents a valid fusion order, the fitness of each chromosome is evaluated by executing the complete sequential ICI fusion process and computing the trace of the final covariance matrix, tournament selection is used to retain competitive individuals, and order crossover and swap mutation are implemented to preserve permutation feasibility and enhance search diversity. A one-dimensional search is embedded in each pairwise ICI fusion step to optimize the fusion weight, enabling the outer-layer genetic algorithm to optimize the fusion order while the inner-layer search refines the local fusion parameter. To validate the proposed method, two types of experiments are designed. The first is a static fusion-order analysis, in which several local state estimates with different directional uncertainty characteristics are constructed to simulate multi-source pose estimates that may occur in cooperative localization, and all feasible fusion orders are enumerated to study the influence of different orders on the covariance trace and error ellipse distribution. The second is a dynamic multi-robot cooperative localization simulation experiment, in which a five-robot cooperative localization environment with heterogeneous observation noise is established, and different order strategies, including fixed order, heuristic order, and genetic-algorithm-optimized order, are compared. In this experiment, the time-varying localization RMSE curve and the full-time average RMSE are used as performance indices. Results Sequential ICI fusion is inherently order-sensitive, and changing only the order of the local estimates can produce evident differences in the final covariance trace and fused error ellipse. The proposed genetic algorithm consistently obtains better fusion-order solutions than heuristic rules based on only a single factor, such as precision or relative distance, because it can more effectively exploit the directional characteristics and complementary uncertainty structures of local estimates. In the dynamic cooperative localization simulation, the proposed method maintains lower localization errors for most of the simulation, shows smaller fluctuations after convergence, and achieves the best full-time average RMSE among all tested strategies. Conclusions These results indicate that reasonable optimization of the sequential ICI fusion order can effectively reduce fusion uncertainty, improve localization accuracy, and enhance the stability of multi-robot cooperative localization systems. Therefore, the proposed method provides a practical framework for fusion-order optimization in distributed information fusion systems and facilitates the experimental evaluation of sequential fusion strategies in cooperative localization applications.
Objective With the implementation of the u201CTransparent Mineu201D and u201CIntelligent Miningu201D strategies, seismic geological interpretation in coal mines is shifting toward intelligent and data-driven approaches. However, traditional geophysics course experiments on fault identification still rely heavily on manual interpretation and seismic attribute analysis and lack practical training in intelligent modeling. To address this gap, this study designs an integrated experimental teaching scheme for the intelligent interpretation of seismic structural data based on a machine learning model. The aim is to cultivate studentsu2019 capabilities in data-driven geological analysis and intelligent fault recognition. Methods The proposed experiment is built upon the PCA-ISSA-SVM model, which integrates principal component analysis (PCA) for feature dimensionality reduction, an improved sparrow search algorithm (ISSA) for hyperparameter optimization, and a support vector machine (SVM) for fault classification. The experimental process comprises six modules: data construction, feature extraction and dimensionality reduction, model optimization, model training, multi-model comparison, and engineering validation using real seismic data. A synthetic fault model was first constructed to generate seismic data. Six seismic attributes (variance, instantaneous phase, frequency, reflection intensity, chaos, and root-mean-square amplitude) were extracted from the generated data. PCA was then applied to reduce data redundancy and select three principal components that retained 86.23% of the original information. The ISSA was used to optimize the SVMu2019s penalty factor c and kernel parameter g, with a population size of 50 and a maximum of 100 iterations. The optimized model was compared with the GA-SVM and PSO-SVM models. Finally, the trained model was validated using real 3D seismic data from a mining area in eastern Yunnan, China, with a dataset of 325 labeled samples. Results Experimental results on synthetic data showed that the PCA-ISSA-SVM model achieved a classification accuracy of 99.63%, a recall of 100%, and an F1-score of 0.986, notably outperforming the ISSA-SVM model without PCA, which achieved only 95.19% accuracy, 91.67% recall, and an F1-score of 0.835. The confusion matrix indicated that only one non-fault point was misclassified in the PCA-ISSA-SVM model, demonstrating the effectiveness of feature dimensionality reduction in improving model performance. Compared with PCA-GA-SVM (accuracy 97.04%, recall 91.67%, F1-score 0.880) and PCA-PSO-SVM (accuracy 96.30%, recall 88.89%, F1-score 0.843), the PCA-ISSA-SVM model shows higher convergence speed and better classification stability, with the lowest initial classification error and the fastest stabilization during iteration. On real seismic data, the model maintained a high recognition performance, with an accuracy of 93.52%, a recall of 96.30%, and an F1-score of 0.937, despite the increased noise and complexity of real-world conditions. Conclusions The integrated experimental design successfully bridges the gap between traditional seismic interpretation teaching and modern intelligent modeling techniques. It provides students with a complete workflow from data construction, feature engineering, and model optimization to engineering validation. Through comparative experiments, students gain a deep understanding of how feature processing and parameter optimization affect model performance. The use of both synthetic and real data enhances studentsu2019 ability to handle complex geological conditions and fosters data-driven geophysical thinking. This teaching reform not only improves studentsu2019 understanding of machine learning algorithms but also equips them with practical skills for intelligent coal mine construction, offering a valuable reference for modernizing geophysics education.
Objective The reactor control system, the core system of a nuclear power plant, controls most of its operational processes. As the system is extremely complex, with strong coupling among control parameters, any flaws in its design could lead to abnormal operation or accidents, substantially impacting the safety and economics of the nuclear power unit. Therefore, comprehensive verification of the reactor control system design is essential. Most of the existing verification methods, such as static verification and partial simulation-based calculation verification, are single-system-type methods, leaving a substantial gap in multisystem-coupling verification. Nuclear power simulation/engineering simulators are used for debugging and verification of the reactor control system, helping evaluate control configurations and dynamic responses prior to unit commissioning, thereby reducing the risks associated with field tests. Methods In this study, the characteristics of the reactor control system of the HPR1000 first unit project were investigated by defining the simulation scope, identifying the interrelationships among reactor control systems, and innovatively applying advanced digital simulation technology to develop high-precision models of the control objects and the control entities themselves. These models were coupled and interconnected to design digital twin models. The features of such a reactor control system were verified by combining mechanistic models, digital computation, analytical capabilities, and new technologies, and by employing a virtualu2013real collaborative testing method. In addition, precise mapping of the system using a software-defined virtual space, dynamic simulation, analysis, and evaluation helped study the operating conditions of the reactor control system. Through virtual verification experiments, design flaws, potential vulnerabilities related to upstream design, and technical or manual errors were identified and corrected to optimize the reactor control system design, prevent potential accidents, and avoid significant accident losses. Results Simulations and analysis under normal and transient operating conditions were performed to systematically verify the logic design and parameter settings of the reactor control system. Typical transient events in nuclear power plants were simulated and extrapolated, providing technical support for multisystem-coupling design verification while addressing the shortcomings of single-system verification. The approach was applied to the Hualong-1 project. During its application to the verification of normal system operation, transient operation, and equipment selection schemes, various design issues were identified and resolved, demonstrating the effectiveness of the approach. This application avoided unnecessary shutdowns and operational errors in the actual unit, thereby improving the safety and economic efficiency of nuclear power plant commissioning and operation and demonstrating significant value for engineering application. Conclusions The digital simulation verification platform for reactor control systems allows the flexible setting of various events, facilitating convenient analysis and identification of issues. Correct logic design, accurate interfaces, and appropriate parameter settings, adhering to the nuclear industryu2019s u201Csafety firstu201D norms, help resolve issues during the design and commissioning stages. This ensures the safe and economical operation of the nuclear power plant. Theoretical and technical references are provided on digital twin theory and technology, as well as for enterprises building and deploying digital twin systems. Based on the research findings, this study summarizes different innovative technologies and methods used in the reactor control system simulation and virtual commissioning, offering considerable reference value for the development and engineering application of complex systems in nuclear power plants.
Objective Developing distributed renewable energy sources is essential for constructing modern power systems; however, the low output voltages (24u201348 V) of these sources do not allow direct connection to high-voltage grid-tied systems or standard 400 V DC buses. Although various high-gain DCu2013DC converters have been introduced, most suffer from hard-switching losses or lack common-ground characteristics, severely compromising their conversion efficiency and forcing the use of expensive differential probes during experimental testing. To resolve these efficiency and structural bottlenecks, this research designs a new quasi-resonant, soft-switching, high-gain DCu2013DC converter based on the Sepic topology. The objective is to simultaneously achieve high voltage gain, elevated efficiency, and common-ground characteristics to simplify waveform observation and reduce hardware costs. Crucially, the proposed topology deduction and soft-switching design methodologies are intended for extension into the practical sessions of the u201CPower Electronics Technologyu201D course to develop studentsu2019 complex engineering problem-solving and innovative capabilities. Methods A systematic methodology spanning topology derivation, mathematical modeling, and experimental prototyping is developed. Starting from the basic Sepic circuit, a Y-source network is integrated to utilize the coupled inductor turn ratio for high-ratio voltage boosting. A passive clamping circuit is implemented to recycle leakage energy and suppress switch-voltage spikes. Then, a three-winding coupled-inductor voltage multiplier cell is embedded, followed by a unique branch reconfiguration strategy to eliminate internal loop conflicts and achieve a stable, common-ground layout. Under continuous conduction mode, the converter exhibits four distinct operating modes within a single switching cycle. A complete mathematical model based on Kirchhoffu2019s voltage law and volt-second balance is established to derive the formulas for the voltage gain and component stresses. A quasi-resonant mechanism is established via the interaction between the leakage inductance and intermediate capacitors. In particular, the resonance frequency is configured higher than the switching frequency to achieve zero-voltage switching (ZVS) for the switch and zero-current switching (ZCS) for the diodes. Finally, a 200 W experimental prototype is fabricated to verify the operational performance of the proposed converter. Results Steady-state experimental tests were conducted on the 200 W prototype under a 37.5 V DC input voltage, with a duty cycle of 0.4 and turn ratios of n2 = 0.5 and n3 = 1.5. Under these conditions, the converter steps up the 37.5 V input to a stable 400 V output, verifying a practical voltage gain of 10.67, which closely matches that of the theoretical model. In addition, the main switch operates with ZVS at turn-on with its voltage stress clamped at 60 V, while the clamp and multiplier diodes achieve ZCS turn-off with minimized reverse recovery. Moreover, the average input current is 5.6 A, yielding a peak overall system efficiency of 95.23%. Furthermore, under full-load operation at 23 u2103, the peak component temperature stabilizes at 40.2 u2103, and the low temperature rise of 17.2 u2103 validates the effectiveness of the quasi-resonant loss reduction. Conclusions The proposed converter successfully fulfills all design expectations by effectively resolving the trade-off between ultra-high voltage gain and conversion efficiency. Experimental results confirm its high efficiency (95.23%), excellent thermal stability, and high suitability for low-voltage renewable energy integration. Crucially, the common-ground structure simplifies the measurement layout, eliminating the need for expensive differential probes while significantly reducing experimental research costs. Furthermore, the systematic engineering methodology encompassing rigorous topology deduction and soft-switching optimization provides a structured paradigm for the practical modules of the u201CPower Electronics Technologyu201D course, effectively guiding students through complex engineering problem-solving and enhancing their innovative practical capabilities.
Objective Since the Ministry of Education (MOE) launched the first batch of philosophy and social science laboratories in 2021, these laboratories have become important platforms for advancing interdisciplinary research and developing China's independent knowledge systems. However, engineering universities with industry-specific strengths in geology, mining, and petroleum face significant structural challenges in participating in this initiative. Their strong emphasis on the natural sciences and engineering has left philosophy and social science disciplines comparatively underdeveloped, even though the industries they serve present urgent interdisciplinary challenges requiring deep integration across the natural sciences, engineering, and the social sciences. This study addresses this gap by examining how such universities should develop philosophy and social science laboratories with distinctive industry characteristics, as well as the underlying mechanisms, evolutionary pathways, and institutional safeguards needed to support them. Methods This study introduces multi-agent system theory and incorporates large language models (LLMs) as both an analytical framework and an organizational design metaphor. It proposes a novel three-dimensional collaborative model involving disciplineu2013technologyu2013governance (DTG) agents. Discipline agents represent contributors from economics, management, and industry-specific engineering disciplines, each maintaining an independent knowledge base while participating in structured interdisciplinary knowledge exchange. Technology agents represent intelligent infrastructure that serves as a catalyst for bridging disciplinary boundaries through data fusion and methodological transfer. Governance agents represent institutional mechanisms responsible for coordinating stakeholder interests and monitoring organizational performance. These agents interact through a hybrid centralizedu2013distributed architecture that combines top-down strategic coordination with bottom-up disciplinary self-organization. The framework is empirically validated through a case study of the MOE Social Science Laboratory of Mineral Resources Security Governance at China University of Geosciences (Beijing). Results The study presents four major findings. First, it identifies four structural challenges: disciplinary ecosystem imbalance, reflected in the u201Cstrong engineering, weak humanitiesu201D contradiction in resource allocation; superficial interdisciplinary integration that remains episodic rather than institutionalized; a disconnect between technology and research, in which engineering platforms fail to extend beyond instrumental applications in social science research; and fragmented governance caused by departmental silos that hinder cross-unit collaboration. Second, the DTG model reveals three operational mechanisms: a knowledge production mechanism driven by interactions among heterogeneous agents, with technology agents serving as bridges across disciplinary boundaries; an organizational coordination mechanism that integrates micro-level self-organization with macro-level strategic oversight through a three-tier governance structure; and an evolutionary mechanism driven by both endogenous disciplinary needs and exogenous strategic, policy, and technological forces. Third, the case study demonstrates three distinctive characteristics of industry-embedded laboratory construction: an industry problem-oriented research approach anchored in sector-specific challenges; an engineering technology reverse-feeding mechanism that transforms technological strengths into social science data assets and methodological tools; and a governmentu2013industryu2013universityu2013research integration pathway that establishes a closed-loop research ecosystem. Fourth, the study proposes a four-stage evolutionary model consisting of a disciplinary foundation stage for identifying anchor disciplines; a technology empowerment stage for developing an industry data hub and LLM-powered research assistant platforms; a collaborative integration stage for forming interdisciplinary teams through dual principal investigator arrangements and disciplinary bridge-builder programs; and an ecosystem construction stage for establishing multilayered open innovation networks that generate theoretical innovation, policy recommendations, and social service outcomes. Conclusions This study contributes to the field in three major ways. Theoretically, the DTG model moves beyond the prevailing paradigm of u201Ctechnology instrumentalismu201D by reconceptualizing intelligent technologies as organizational mechanisms that reshape interdisciplinary knowledge production. Practically, the proposed four-stage evolutionary model demonstrates that the u201Cstrong engineering, weak humanitiesu201D profile of industry-specific engineering universities can serve as a latent comparative advantage rather than a limitation when compared with comprehensive universities. Empirically, the case of the MOE Social Science Laboratory of Mineral Resources Security Governance validates both the explanatory power and practical applicability of the DTG framework. The study recommends establishing differentiated evaluation standards for industry-specific universities, promoting sectoral data sharing between government agencies and laboratories, creating interdisciplinary talent development programs that include bridge-builder positions, and reforming evaluation systems to recognize originality, interdisciplinarity, and practical impact as core assessment criteria.
