-hop retrieval on large KGs by building on symbolic KG formulations and executing traversal as hardware-efficient operations over decomposed subject, object, and relation representations. To scale to billion-edge graphs, LogosKG integrates degree-aware partitioning, cross-graph routing, and on-demand caching. Experiments show substantial efficiency gains over CPU and GPU baselines without loss of retrieval fidelity. With proven performance in KG retrieval, a downstream two-round KG-LLM interaction demonstrates how LogosKG enables large-scale, evidence-grounded analysis of how KG topology, such as hop distribution and connectivity, shapes the alignment between structured biomedical knowledge and LLM diagnostic reasoning, thereby opening the door for next-generation KG-LLM integration. The source code is publicly available at https://github.com/LARK-NLP-Lab/LogosKG, and an online demo is available at https://lark-nlp-lab-logoskg.hf.space/.

Sequential anomaly detection has received more and more attention because of its wide applications in various domains, such as debugging system failures via logs. Researchers have recently proposed many deep learning-based approaches for sequential anomaly detection. However, these approaches work as black-boxed models, not providing explanations for detected anomalies. On the other hand, explainability is a critical requirement to build trustworthiness in detection results. Moreover, domain experts would like to learn why a sequence is labeled as an anomaly. To overcome this challenge, in this paper, we propose a framework for Explainable Sequential Anomaly Detection (ESAD) in a semi-supervised setting. As there are various normal and abnormal behaviors in sequential data, ESAD derives multiple prototypes to describe diverse normal and abnormal sequences. Each prototype can encode one type of normal or abnormal behavior. Given a new sequence, if the sequence is similar to an abnormal prototype, the sequence will be detected as abnormal. After decoding the abnormal prototype as a prototypical sequence, domain experts can further understand the newly detected abnormal sequence by examining the prototypical sequence. We conduct experiments on one log dataset and two text datasets. Experimental results including quantitative and qualitative analysis on three datasets show the effectiveness of our model.

Sequential anomaly detection has been studied for decades because of its wide spectrum of applications and obtained significant improvement in recent years by utilizing deep learning techniques. As an increasing number of anomaly detection models are applied to high-stake tasks involving human beings, it is critical to understand the reasons why the samples are labeled as anomalies. In this work, we propose a Globally and Locally Explainable Anomaly Detection (GLEAD) framework targeting sequential data. Especially, considering that the anomalies are usually diverse, we make use of the multi-head self-attention techniques to derive representations for sequences as well as prototypes, which capture a variety of patterns in anomalies. The attention mechanism highlights the abnormal entries with high attention weights in the abnormal sequences for the local explanation. Moreover, the prototypes of anomalies encoding the common patterns of abnormal sequences are derived to achieve the global explanation. Experimental results on two sequential anomaly detection datasets show that our approach can detect abnormal sequences and provide local and global explanations.

Anomaly detection in sequential log data is a common data analysis task as it contributes to detecting critical information, such as malfunctions of systems. However, due to the scarcity of anomalies, the traditional supervised learning approaches cannot be applied for anomaly detection tasks. Meanwhile, most of the existing studies only focus on identifying the anomalous log sequences and cannot further detect the anomalous events in a sequence. In this work, we present InterpretableSAD, an interpretable log anomaly detection framework that can achieve both anomalous sequence and fine-grained event detection. Given a set of normal log sequences, we propose a data augmentation strategy to generate a set of anomalous sequences via negative sampling so that we can train a binary classification model based on the observed normal sequences and the generated anomalous sequences. After training, the classification model is able to detect real anomalous log sequences. We then consider the anomalous event detection as a model interpretation problem and apply an interpretable machine learning technique in a novel way to detect which parts of the sequences, a.k.a, anomalous events, lead to anomalous issues. Experimental results on three log datasets show the effectiveness of our proposed framework.

Switched reluctance motor (SRM) has the features of large starting torque, wide speed range and high reliability, which are appropriate for electric vehicles (EVs). A three-phase 12/8 pole SRM is designed for EVs application. The dynamic simulation model of SRM is built in Matlab/Simulink based on the flux linkage and torque characteristics. The EV simulation model is built combining SRM dynamic simulation model with vehicle dynamics equations by the forward simulation algorithm. Fix angle current chopping control (FA-CCC) is adopted in constant torque area, and adaptive variable angle current chopping control (AVA-CCC) is proposed in constant power area to widen the constant power speed range. Simulation analysis and experiment test results have been presented to demonstrate the performance of the proposed control strategies.

In order to improve the DC-bus voltage control performance and suppress the transient fluctuation of DC-bus voltage, a nonlinear disturbance observer (NDO) based DC-bus controller is proposed in this paper. The feed-forward compensation of active power disturbance is achieved by the proposed controller to reject the influence of parameter variations and the mismatched disturbance from the output terminals. The active power disturbance of feed-forward compensation can be effectively estimated by the NDO without requirement of measuring the load and sources power, which will reduce the hardware cost and also increase the reliabilities of DC microgird. The feed-forward compensation gain is designed by taking convergence rate of the observer and the influence of compensation gain on the stability of DC microgrid into account. Simulations are carried out to verify the performance of the proposed control method in consideration of different disturbances.

The main purpose of this paper is to explore the spontaneous synchronization of two Chua's circuits based on coupled memristors with cubic nonlinearity. An analog circuit is designed to simulate the magnetic flux controlled memristor with coupling effects and then applied to implement the coupled Chua's circuit. Simulation is carried out to numerically exhibit the strange attractors and spontaneous synchronization caused by increasing the coupling strength.

High voltage gain DC-DC converters as used in photovoltaic, fuel cells and other low-voltage distributed generation system have played a significant role in the large-scale promotion of new energy generation. A high step-up integrated DC-DC converter based on one active switch is proposed in this paper by making use of advantages of the quadratic Boost converter (QBC) and the voltage multiplying modular (VMM). Firstly, VMM was embedded into QBC, and this two modules share the same input DC source and the active switch. And then the output terminals of these two modules were connected in series sharing the same output diode and filter capacitor. The above combination constitutes the newly proposed integrated converter. One attractive performance of this converter is the gain can be not only controlled by the duty ratio but also the turn ratio of the coupled inductor in VMM. In addition, the low voltage stress across the switch can be achieved since the switch is not directly clamped at the high output voltage, which is conducive for optimizing the energy transmission efficiency. The zero input current ripples can be achieved by reasonably configuring the values of the fully coupled inductor as well as the auxiliary inductor. The operation principles and the steady state performances of the proposed converter are verified by the experimental prototype.