Comprehensive Active Control of Booming Noise Inside a Vehicle Caused by the Engine and the Driveline

This study presents comprehensive active cancellation of booming noise caused by the engine and the driveline inside a passenger car. In modern noise control systems for vehicles, booming noise caused by engine harmonics could be effectively suppressed by employing active noise control. However, practical attempts or studies for the active suppression of driveline booming noise are scarce. One of the reasons may be that since the booming noise caused by the driveline is not harmonic with the engine speed, reference signals cannot be generated conventionally. Thus, passive approaches are generally employed to improve the driveline noise. To address this limitation, we propose a method for generating reference signals from engine revolution speed to suppress the driveline noise, such as propeller shaft and tire noise. Reference signals for driveline noise suppression were generated using the information from the torque converter, gear ratio, and final drive ratio. A practical active noise control system was implemented in a six-cylindered large sedan to validate the proposed method. The experimental results showed that the engine firing order was suppressed by 8.0 dB. Moreover, the first order of the propeller shaft and the second and third orders of the tires were suppressed by 5.5 dB, 3.9 dB, and 2.3 dB for entire seat positions. Furthermore, the results presented in this study were considered effective for improving annoyance perception through subjective evaluation

A Complementary Effect in Active Control of Powertrain and Road Noise in the Vehicle Interior

This study shows that a concurrent active noise control strategy for engine harmonics and road noise has a complementary effect. In particular, we found that engine booming noise is additionally attenuated when road noise control is concurrently used with engine harmonics control; an additional attenuation of 2.08 dB and 1.25 dB for the C1.5 and C2.0 orders, respectively, was achieved. A parallel multichannel feedforward controller for non-stationary narrowband engine harmonics and broadband road noise was designed and implemented to reduce noise in all four seats. Two control signals were considered independent because the reference signals, engine revolution speed for the engine harmonic controller, and acceleration signal for the road noise controller are uncorrelated. However, if the reference sensor for the road noise controller is installed along the overlapping transfer path between the engine noise and road noise, the engine noise may also be suppressed by the control signal for the road noise attenuation. Based on transfer path analyses for both engine harmonics and road noise, the optimal positions for the reference sensors were selected. In addition, we identified several overlapping transfer paths between the engine booming noise and road noise. A practical active noise control system combined with a remote microphone technique was implemented for a large six-cylinder sedan using a vehicle audio system to evaluate the noise attenuation performance. The experiments showed that the interior noise from the engine and road excitation was effectively suppressed by the proposed concurrent control strategy.

Learning Topology-Specific Experts for Molecular Property Prediction

Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert.Comment: 11 pages with 8 figure

Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt

Enhancing the zero-shot performance of instruction-following models requires heavy computation, either by scaling the total number of training datasets or the model size. In this work, we explore how retrieval of soft prompts obtained through prompt tuning can efficiently assist hard prompts in zero-shot task generalization. Specifically, we train soft prompt embeddings for each prompt through prompt tuning, store the samples of the training instances mapped with the prompt embeddings, and retrieve the corresponding prompt embedding of the training instance closest to the query instance during inference. While only adding 0.007% additional parameters, retrieval of soft prompt enhances the performance of T0 on unseen tasks by outperforming it on 10 out of 11 datasets as well as improving the mean accuracy of T0 on BIG-bench benchmark by 2.39% points. Also, we report an interesting finding that retrieving source embeddings trained on similar answer choice formats is more important than those on similar task types.Comment: EMNLP 2023 Finding