arXiv subjects

Chang-Jae Chun

Publications and source records attributed to Chang-Jae Chun.

7 recordsLinked to original sources

arXiv2026

LSR-Net: A Lightweight and Strong Robustness Network for Bearing Fault Diagnosis in Noise Environment

Rotating bearings play an important role in modern industries, but have a high probability of occurrence of defects because they operate at high speed, high load, and poor operating environments. Therefore, if a delay time occurs when a bearing is diagnosed with a defect, this may cause economic loss and loss of life. Moreover, since the vibration sensor from which the signal is collected is highly affected by the operating environment and surrounding noise, accurate defect diagnosis in a noisy environment is also important. In this paper, we propose a lightweight and strong robustness network (LSR-Net) that is accurate in a noisy environment and enables real-time fault diagnosis. To this end, first, a denoising and feature enhancement module (DFEM) was designed to create a 3-channel 2D matrix by giving several nonlinearity to the feature-map that passed through the denoising module (DM) block composed of convolution-based denoising (CD) blocks. Moreover, adaptive pruning was applied to DM to improve denoising ability when the power of noise is strong. Second, for lightweight model design, a convolution-based efficiency shuffle (CES) block was designed using group convolution (GConv), group pointwise convolution (GPConv) and channel split that can design the model while maintaining low parameters. In addition, the trade-off between the accuracy and model computational complexity that can occur due to the lightweight design of the model was supplemented using attention mechanisms and channel shuffle. In order to verify the defect diagnosis performance of the proposed model, performance verification was conducted in a noisy environment using a vibration signal. As a result, it was confirmed that the proposed model had the best anti-noise ability compared to the benchmark models, and the computational complexity of the model was also the lowest.

eess.SP↗

arXiv2026

FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation

Scaling Multimodal Large Language Models (MLLMs) to long-form speech is bottlenecked by the explosive growth of input tokens. Existing speech-language models project high-frame-rate acoustic features directly into the LLM input space, making long-context processing computationally prohibitive. Unlike images or videos, speech lacks spatial redundancy, making extreme token compression particularly challenging. To address this limitation, we propose FastSLM, a token-efficient architecture featuring the Hierarchical Temporal Abstractor (HTA), which progressively distills acoustic features across multiple temporal scales. HTA achieves an extreme compression rate of 1.67 tokens per second (97% reduction) while preserving essential linguistic information for downstream speech-language understanding. Experimental results demonstrate that FastSLM achieves competitive performance across diverse speech-language tasks while requiring substantially fewer speech tokens and FLOPs than existing speech-language models. The source code and model checkpoints are available at https://github.com/Lee-junseok1025/FastSLM.

eess.AS↗

arXiv2026

ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition

Knowledge distillation (KD) is one of the most effective paradigms for compressing large-scale foundation models into deployable architectures. In the context of Automatic Speech Recognition (ASR), previous studies have predominantly focused on forcing the student model to strictly mimic the predictive distribution of a massive teacher model. However, this static dependency often presents an inherent trade-off: while the student rapidly acquires basic linguistic representations, it simultaneously inherits the teacher's domain-specific blind spots and over-confident hallucinations, leading to a severe decline in out-of-distribution generalization capacity. To effectively mitigate this issue, we propose Adaptive Self-Knowledge Distillation (ASKD), a dynamic curriculum framework. ASKD systematically decays the dependency on the teacher's distribution as training progresses-thereby unlocking the student's independent reasoning capacity-and subsequently employs a self-knowledge distillation phase to act as a structural regularizer. By applying ASKD, we distill the massive Whisper architecture into a compact variant, ASKD-Whisper. In our comprehensive evaluations across diverse acoustic domains, ASKD-Whisper not only achieves a 5x speedup in inference latency but also outperforms its teacher model by yielding a 1.07% lower word error rate (WER). These results demonstrate that ASKD effectively prevents teacher-induced overfitting and establishes a new state-of-the-art for generalizable model compression.

cs.CL↗

arXiv2022

Transport Capacity Optimization for Resource Allocation in Tera-IoT Networks

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multi-device terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporate both rate and transmission distance into the objective function of our problem formulation with key features of THz bands, including the spreading and molecular absorption losses. More specifically, as a performance metric of Tera-IoT networks, we adopt the transport capacity (TC), which is defined as the sum of the rate-distance products over all users. This metric has been widely adopted in large-scale ad hoc networks, and would also be appropriate for evaluating the performance of various Tera-IoT applications. We then formulate an optimization problem that aims at maximizing the TC. Moreover, motivated by the importance of the transmission distance that is very limited due to the high path loss in THz bands, our optimization problem is extended to the case of allocating the subwindow, transmit power, and transmission distance. We show how to solve our problems via an effective two-stage resource allocation strategy. We demonstrate the superiority of our adaptive solution over benchmark methods via intensive numerical evaluations for various environmental setups of large-scale Tera-IoT networks.

cs.IT↗

arXiv2018

Deep Learning Based Joint Pilot Design and Channel Estimation for Multiuser MIMO Channels

In this paper, we propose a joint pilot design and channel estimation scheme based on the deep learning (DL) technique for multiuser multiple-input multiple output (MIMO) channels. To this end, we construct a pilot designer using two-layer neural networks (TNNs) and a channel estimator using deep neural networks (DNNs), which are jointly trained to minimize the mean square error (MSE) of channel estimation. To effectively reduce the interference among the multiple users, we also use the successive interference cancellation (SIC) technique in the channel estimation process. The numerical results demonstrate that the proposed scheme considerably outperforms the state-of-the-art linear minimum mean square error (LMMSE) based channel estimation scheme.

cs.IT↗

arXiv2018

Channel Tracking for Wireless Energy Transfer: A Deep Recurrent Neural Network Approach

In this paper, we study channel tracking for the wireless energy transfer (WET) system, which is practically a very important, but challenging problem. Regarding the time-varying channels as a sequence to be predicted, we exploit the recurrent neural network (RNN) technique for channel tracking. Particularly, combining the deep long short-term memory (LSTM) RNN with the deep feedforward neural network, we develop a novel channel tracking scheme for the WET system, which estimates the channel state information (CSI) at the energy transmitter based on the previous CSI estimates, and the current and previous harvested energy feedback information from the energy receiver. Numerical results demonstrate the superior performance and effectiveness of the proposed scheme.

cs.IT↗

arXiv2018

Dynamic Power Splitting for SWIPT with Nonlinear Energy Harvesting in Ergodic Fading Channel

We study the dynamic power splitting for simultaneous wireless information and power transfer (SWIPT) in the ergodic fading channel. Considering the nonlinearity of practical energy harvesting circuits, we adopt the realistic nonlinear energy harvesting (EH) model rather than the idealistic linear EH model. To characterize the optimal rate-energy (RE) tradeoff, we consider the problem of maximizing the R-E region, which is nonconvex. We solve this challenging problem for two different cases of the channel state information (CSI): (i) when the CSI is known only at the receiver (the CSIR case) and (ii) when the CSI is known at both the transmitter and the receiver (the CSI case). For these two cases, we develop the corresponding optimal dynamic power splitting schemes. To address the complexity issue, we also propose the suboptimal schemes with low complexities. Comparing the proposed schemes to the existing schemes, we provide various useful and interesting insights into the dynamic power splitting for the nonlinear EH. Furthermore, we extend the analysis to the scenarios of the partial CSI at the transmitter and the harvested energy maximization. Numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal scheme works very close to the optimal scheme at a much lower complexity.

cs.IT↗