State-of-health (SOH), state-of-charge (SOC), and state-of-energy (SOE) co-estimation is vital for reliable operation and longevity of Li-ion batteries (LIBs). However, the intricately coupled changes occurring in multiple states across varying operating stages and differing timescales under cycle aging challenge flexible, accurate, and robust co-estimation. To tackle them, this article proposes a novel co-estimation method, leveraging a large-scale pretrained language model (PLM) empowered by multistate explicit-implicit prompt learning. Specifically, a state-wise contextual synthesizer is introduced to augment battery data into state-dependent explicit prompts, serving as situational directives for PLM to make flexible estimates over cycles and charge-discharge phases. A cross-state disentangling scheme is devised to capture coupling relationships among states and unique intricacies within states by structuring shared and individual implicit prompts. It allows PLM to perceive capacity decay for accuracy loss compensation under cycle aging. A multistate synergistic regulator is built to calibrate inter- and intrastate knowledge and interact them with PLM, strengthening co-estimation robustness to state dynamics at differing timescales. Experiments demonstrate that the proposed method yields accurate co-estimates of fluctuating SOH over the lifecycle, as well as variable SOC and SOE during charge-discharge phases, with root-mean-square error (RMSE) reductions exceeding 38%, 29%, and 29%, respectively, compared with conventional deep co-estimators.

Accurate joint estimation of state-of-charge (SOC) and state-of-health (SOH) is essential for maximizing the reliability and lifespan of Lithium-ion batteries. However, the complex coupled dynamics of SOC and SOH across diverse operational stages on differing timescales challenge the flexibility and robustness of joint estimation. Hence, this paper proposes a multi-task explicit-implicit prompt learning method that exploits a large-scale pre-trained language model (PLM) for joint estimation. A per-state task interpreter is introduced to elaborate battery data into explicit prompt texts, directing PLM to grasp measurement representations and task semantics for flexible joint state estimates across varying operational stages. A cross-state task adaptor is designed to refactor implicit prompt vectors into a base type that encodes inter-state coupling and a custom type that perceives state-specific intricacies, enhancing PLM adaptability for joint estimation. An adaptive gated integrator is constructed to discriminatively aggregate base and custom prompts, shaping calibrated multi-state knowledge. It further engages extensively with the PLM space to inject knowledge, strengthening joint-estimation robustness against coupled state changes on differing timescales. Experiments demonstrate that the proposed method delivers accurate joint estimates of the persistently changing SOC during charge-discharge processes and the fluctuating SOH over cycles, ensuring high flexibility and robust performance.

Balise health prognostics is critical to proactively maintaining the reliable operation of high-speed railway wireless communication. However, the intricately evolving nature of balise health under coupled ground-train transmission dynamics across rail lines challenges generalized and accurate prognostics. To tackle it, this article proposes an explicit-implicit chain-of-thought (CoT) framework that endows a large pre-trained language model (PLM) with desired prognostic power via closed-loop reasoning comprising perception, adaptation, and feedback. Specifically, a stepwise instruction dispatcher is introduced to synthesize balise measurement data with situational context and logic rules, tailoring conditional directives for varying line scenarios. It can prompt PLM to progressively perceive distinctive health evolution signatures and deterioration cues throughout multi-step reasoning, shaping an explicit, causally traceable CoT for generalized prognostics. A dual-granularity adaptor is built to persistently capture coupling relationships among health deterioration factors and sequentially append their unique nuances, forming an implicit, highly adaptable CoT for PLM to improve prognostic accuracy. A regulatory routing mechanism is devised to harmonize explicit and implicit CoTs, while optimizing their collaboration via iterative feedback, mitigating drift of thought to boost prognostic performance. Experiments on hundreds of real-world balises across multiple lines demonstrate that the proposed method achieves accurate health prognostics with strong causal interpretability.

State-of-charge (SOC) estimation is critical for reliable operation of Li-ion batteries (LIBs). However, the distinct electrochemical characteristics coupled with harsh low-temperature environments make a single estimator struggle to robustly estimate the volatile SOC of multi-type LIBs. To address these issues, this article proposes a hard-soft hybrid prompt learning method to unleash the potential of a pretrained large language model (LLM) for SOC estimation. A textual encoder is introduced to convert LIB measurements into hard text prompts for language modeling, naturally eliciting the pretrained LLM to capture the intra-relations of measured values over time and their inter-relations with contextual semantics for accurate estimates. A side adapter network is constructed to reparameterize model adaptation towards different LIB tasks into optimizations within a low-dimensional subspace, strengthening the estimation generalization of the pretrained LLM in a parameter-efficient manner. A knowledge infusion mechanism is designed to encapsulate task-specific information as soft prompt vectors for model integration along forward propagation, dynamically conditioning the hidden states inside the pretrained LLM to enhance the estimation robustness against SOC volatilities. Extensive experiments verify that the hybrid prompt-driven LLM can simultaneously perform estimations for multi-type LIBs under diverse operations and sub-zero temperatures with superior accuracy, generalization, and robustness.

State-of-charge (SOC) estimation is important to ensure safe functioning of Li-ion batteries (LIBs). However, distinct measurements, harsh temperatures, and dynamic operations pose challenges in estimating intricate SOC changes of multiple LIBs with one estimator. Hence, this paper proposes a novel mixed prompt learning method to explore a large-scale pretrained language model (PLM) for SOC estimation. A new hard prompt generator is proposed to translate LIB data into instruction and answer text, eliciting the inherent representational capability of PLM to jointly learn the sequential patterns and contextual semantics of measurements through language modeling for accurate SOC estimation. A new soft prompt adapter is proposed to encode task-specific information of different LIBs into a small set of independent vectors, ensuring PLM adaptation with minimal parameters while maintaining good generalization. By integrating soft prompt vectors along the forward propagation, the hidden states of PLM are dynamically regulated to characterize volatile and variable SOC for robust estimation. Extensive experiments show that by leveraging mixed hard-soft prompts, PLM can make accurate, generic, and robust estimates for multiple LIBs simultaneously in subzero temperature, charge-pause-discharge, and low-capacity discharge scenarios, with the average MAE, RMSE, and MAX of all tasks as low as 1.20%, 1.51%, and 5.29%, respectively.

As software systems increasingly evolve towards intelligence, a multitude of new failure modes continue to emerge. Faced with a rapidly accumulating amount of complex software failures from multiple sources, quickly determining their categories of faults is a crucial step in enhancing the efficiency of fault resolution, optimizing resource allocation, and continuously improving software quality. The semantic understanding afforded by neural network language models paves the way for swift, automated categorization. However, the direct application of NLP models to software fault classification tends to require supplementary data from the development process and suffers from limited classifier generalization. To address the challenge of generating universal representations for software fault diagnosis, we introduce a novel classification framework leveraging a pre-trained BERT model for nuanced global feature extraction. This model harnesses deep learning techniques, including feedforward and transformer encoder layers, to capture and utilize the rich semantic nuances and domain-specific knowledge crucial for fault categorization. Validation on an independent dataset demonstrates that our BERT-Transformer outperforms competing models, achieving the highest precision and F1 score at 80% and 78.7%, show-casing its superior accuracy and robust generalization in fault classification tasks.

Epigenetics'flexibility in terms of finer manipulation of genes renders unprecedented levels of refined and diverse evolutionary mechanisms possible.From the epigenetic perspective,the main limitations to improving the stability and accuracy of genetic algorithms are as follows:(1)the unchangeable nature of the external environment,which leads to excessive disorders in the changed phenotype after mutation and crossover;(2)the premature convergence due to the limited types of epigenetic operators.In this paper,a probabilistic environmental gradient-driven genetic algorithm(PEGA)considering epigenetic traits is proposed.To enhance the local convergence efficiency and acquire stable local search,a probabilistic environmental gradient(PEG)descent strategy together with a multi-dimensional heterogeneous exponential environmental vector tendentiously generates more offsprings along the gradient in the solution space.Moreover,to balance exploration and exploitation at different evolutionary stages,a variable nucleosome reorganization(VNR)operator is realized by dynamically adjusting the number of genes involved in mutation and crossover.Based on the above-mentioned operators,three epigenetic operators are further introduced to weaken the possible premature problem by enriching genetic diversity.The experimental results on the open Congress on Evolutionary Computation-2017(CEC'17)benchmark over 10-,30-,50-,and 100-dimensional tests indicate that the proposed method outperforms 10 state-of-the-art evolutionary and swarm algorithms in terms of accuracy and stability on comprehensive performance.The ablation analysis demonstrates that for accuracy and stability,the fusion strategy of PEG and VNR are effective on 96.55%of the test functions and can improve the indicators by up to four orders of magnitude.Furthermore,the performance of PEGA on the real-world spacecraft trajectory optimization problem is the best in terms of quality of the solution.

Detecting voltage faults in lithium-ion batteries is crucial in reducing potential property damage and injury risks. Current research focuses more on using measured values for voltage prediction, but many valuable operating condition information is ignored due to its difficulty in measurement. Therefore, a novel method using model fusion for voltage prediction and fault detection has been proposed in this paper, which especially focus on extracting implicit operating condition information for real-time prediction. It includes three modules: prediction module, similarity module, and fusion module, with CNN and LSTM as the basic unit. The incremental training scheme is adopted in the fusion module to ensure real-time performance. At the same time, the detrending optimization before incremental training has improved prediction accuracy. The predicted results can be used for threshold-based fault detection and differential-based fault detection methods. The experimental results demonstrate the satisfactory performance of the model in terms of predict accuracy, training efficiency and fault detection.

The powerful technique, symbolic execution, has become a promising approach for analyzing deep complex software failure modes recently. However, as the software scale grows rapidly in intelligent automatic control system, these methods unavoidably suffer the curse of path explosion and low global coverage. To solve the problem, an evolutionary strategy guided symbolic execution framework is proposed for triggering hard-to-excite input-relevant faults. A novel alternate asynchronous search strategy is adopted to enhance the breadth-search capability of symbol execution. Furthermore, by combining ANGR, a popular symbolic execution engine, and genetic algorithm, this method synchronously triggers the potentially hidden hybrid fault modes at different levels in the software architecture. Case studies on the SIR test suite demonstrate that the GA-enhanced symbolic execution greatly improves coverage and accelerates test convergence. Among them, the coverage rate has increased by up to 23.7%. With a baseline of 95% line coverage, the proposed method can reduce the number of iterations by at least 43.3%.

Software faults constantly appear during software development and evolution. The information on various platforms for bug knowledge recording, such as Stack Overflow, is mostly stored in weak entity relational database missing linkable relationships, which results in negative impacts on knowledge reuse. To enrich the relationships between entities and construct a software fault knowledge graph, we improve the SALKU model by considering the direction of prediction results between a pair of knowledge units, and utilize it to predict the class of linkable knowledge units. Experiment results show the improved model increases the ratio of knowledge unit pairs with equivalent link prediction results from 90.2% to 100% based on the premise of ensuring precision, recall, and F1-score. Eventually, we visualize the data from Stack Overflow in the knowledge graph based on the extracted relationships.

The flight reliability has been receiving considerable attention. However, the ability of the aircraft recovers to normal flight state from a perturbation were not considered under most circumstances. In this study, a simulation based intelligent analysis framework is proposed to identify the reliability, resilience and vulnerability states of Boeing 737 MAX aircraft disturbed by Maneuvering Characteristics Augmentation System (MCAS) system abnormal activation during the flight. Multiswarm particle swarm optimization (multiswarm PSO) algorithm based test cases generation strategy, aircraft failure behavior model which reflects aerodynamics of the aircraft after the horizontal stabilizer deflection caused by MCAS abnormal activation, JSBSim and FlightGear based co-simulation with aerodynamic and visual characteristics and neural network based flight states identification method constitute the proposed framework. Study results show that the proposed method can cover the margin of resilience and vulnerability quickly and the classification model can identify aircraft flight reliability, resilience and vulnerability states corresponding to different inputs accurately. The proposed framework can be used to validate the flight reliability and system resilience in a more efficient way.

As the control core of modern systems and infrastructures, the high-reliability operation of the software is of great significance. Software fault-tolerance is one of the widely used reliability design methods, and its quantitative analysis has always been a hot topic. Based on comparing the characteristics and limitations of traditional methods, such as mathematical models, fault injection, and model-driven methods, this paper introduces probabilistic model checking (PMC) technology into the modeling and evaluation of software fault-tolerance. This method is based on probabilistic models, such as continuous-time Markov chain, modeling the system behavior of fault-tolerant design, and defining system reliability properties by temporal logic language. In particular, the Erlang model is used to realize fixed time delay for modeling those designs considering timing monitoring. This paper systematically introduces how PMC is applied to the modeling and analysis of software structure fault-tolerance, information fault-tolerance, and fault detection. The experimental results show that the system reliability of the design mode adopting error detection and correction is higher, and the system availability is higher with the design mode of watchdog and redundancy integration. This paper provides a new software fault-tolerance evaluation technology to support researchers to compare different fault-tolerance methods in the early design stage.