电装

In manufacturing systems, identifying the causes of failures is crucial for maintaining and improving production efficiency. In knowledge-based failure-cause inference, it is important that the knowledge base (1) explicitly structures knowledge about the target system and about failures, and (2) contains sufficiently long causal chains of failures to reach structural-level failure causes. In this study, we constructed Diagnostic Knowledge Ontology and proposed a Function-Behavior-Structure (FBS) model-based maintenance-record accumulation method based on it. Failure-cause inference using the maintenance records accumulated by the proposed method showed better agreement with the set of candidate causes enumerated by experts, especially in difficult cases where the number of related cases is small and the vocabulary used differs. In the future, it will be necessary to develop inference methods tailored to these maintenance records, build a user interface, and carry out validation on larger and more diverse systems. Additionally, this approach leverages the understanding and knowledge of the target systems in the design phase to support knowledge accumulation and problem solving during the maintenance phase, and it is expected to become a foundation for knowledge sharing across the entire engineering chain in the future.

software-defined vehicles (SDVs) consolidate safety- and nonsafety-critical functions onto centralized platforms, forming mixed-criticality system (MCSes) where applications like ADAS and infotainment coexist. While this reduces cost and complexity, it also introduces challenges in isolation, timing determinism, and safety certification. Lingua Franca (LF) offers deterministic coordination but lacks mechanisms for per-application monitoring and recovery. To address this, we 1) derive orchestration requirements from the "safety first for automated driving" white paper and 2) propose an LF-compatible framework with external monitors that detect faults and enable selective restarts of faulty applications without disrupting others. This work presents the first practical per-application failover solution for LF, improving the reliability of SDV mixed-criticality orchestration.

Dynamic wireless power transfer (DWPT) technology is emerging as a key enabler for in-motion charging of electric vehicles (EVs), allowing continuous energy transfer without the need for dedicated charging stops. This capability can reduce the required battery capacity and vehicle weight, thereby lowering the vehicle cost, extend battery life by minimizing deep discharge cycles, and support distributed charging that helps reduce grid peak loads. However, conventional receiver coil structures, such as the double-layered Double-D Quadrature (DDQ) configuration, require additional underbody space due to their two-layer winding structure, which limits their applicability in passenger EVs where the battery pack occupies most of the available area. To address this issue, a novel stackless onboard coil composed of four segmented coils is proposed. The midpoints of adjacent coil pairs are connected to create an additional current path, forming a composite magnetic structure within a single magnetic layer. This configuration eliminates the need for stacked windings and reduces the onboard coil thickness by 35% compared with conventional DDQ coils, while maintaining power transfer performance and efficiency without significant degradation. Theoretical analysis based on an equivalent circuit model, finite-element method, and dynamic circuit simulations was conducted to compare the proposed stackless and conventional DDQ systems. A 1.2 kW prototype with a 220 mm air gap was constructed, and experimental results confirmed continuous power transfer without a stacked structure. Compared with the DDQ coil, the proposed design increased the average output power by 13%, decreased the average efficiency by 1.8 percentage points, and improved power fluctuation by 21.5 percentage points. The proposed stackless coil is particularly suitable for passenger EV applications of DWPT systems, where minimizing the onboard coil thickness is prioritized over maximizing power transfer efficiency.

A marine vessel is a nonlinear system subject to irregular disturbances such as wind and waves, which cause tracking errors between the nominal and actual trajectories. In this study, a nonlinear vessel maneuvering model that includes a tracking controller is formulated and then controlled using a linear approximation around the nominal trajectory. The resulting stochastic linearized system is analyzed using a stochastic zeroing control barrier function (ZCBF). A stochastic safety compensator is designed to ensure probabilistic safety, and its effectiveness is verified through numerical simulations.

Post-training quantization (PTQ) enables rapid deployment of deep pretrained models. In the low-bit regime, recent PTQ methods for vision models adopt asymmetric quantization (AsymQ), introducing zero-point offsets to mitigate quantization errors. However, these offsets impose substantial hardware overhead and fail to fully capture the non-symmetric structure of pretrained weight distributions, leaving many quantization levels unused.In this paper, we reveal a hidden symmetry in the pretrained weights: after removing a few sparse outliers, the distribution becomes nearly symmetric.Accordingly, we propose Dense and Additive Sparse Quantization (DASQ), which decomposes the weights into dense and sparse matrices.The dense component captures the symmetric structure around zero, while the sparse component models the removed outliers, and both can be processed in parallel and can be implemented with efficient zero-point-free computation.Experiments on image classification, object detection, and instance segmentation show that DASQ surpasses state-of-the-art PTQ methods with lower BOPs. On an FPGA, DASQ also demonstrates higher accuracy and lower power consumption than AsymQ at comparable throughput.