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lssue 2, 2026
Editors-in-Chief
Yunhe Pan, Aiguo Fei
Advisor Xicheng Lu
ISSN 3069-8928
CN 33-1439/TP
IF (2024 JCR) 2.9
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Volume 27 Issue 2，2026
Research Articles
CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network Enhanced Publication AI Introduction
“In the field of manufacturing efficiency, a new algorithm called CdualTAL has been introduced. This improved Transformer-based encoder–attention–decoder algorithm strategically leverages strong and weak features to predict tool wear accurately. Validated on tool wear datasets, CdualTAL outperforms 11 state-of-the-art methods, achieving superior prediction stability and accuracy.”Abstract：Accurate tool wear prediction is crucial for manufacturing efficiency, yet effectively using multi-domain sensor features is difficult due to redundant noise. There is a critical need to strategically leverage highly predictive strong features and potentially informative weak features. To address this issue, we propose CdualTAL, an improved Transformer-based encoder–attention–decoder algorithm. Its name represents the model’s key components: a correlation-adaptive feature selection algorithm module, a dual-channel Transformer encoder, an attention mechanism, and a long short-term memory (LSTM) decoder. CdualTAL employs a dual-channel encoder to independently process the full set of multi-domain features, along with a subset of strong features selected using a designed correlation-adaptive feature selection algorithm. A custom cross-attention mechanism is then used to fuse these representations, sharpening focus on strong features while judiciously integrating information from weak ones. Finally, a hierarchical LSTM decoder captures deep temporal dependencies. Validated on tool wear datasets, CdualTAL outperforms 11 state-of-the-art methods, achieving superior prediction stability and accuracy with an average R2 of 0.983 and a root mean square error (RMSE) of 4.373.Keywords：Multi-domain features;Dual-channel;Feature fusion;Tool wear;Attention mechanism;Feature enhancement25|31|0Updated：2026-02-11Research Articles
An attention mechanism-based multi-domain feature fusion approach for active sonar target recognition Enhanced Publication AI Introduction
“In the field of underwater acoustics, researchers have made significant progress in active sonar target recognition. They proposed an attention mechanism-based multi-domain feature fusion approach, using 1DCNN-LSTM and 2DCNN with channel attention to extract deep features. This method effectively eliminates redundant information and enhances feature representation, showing superior performance and stable generalization ability in low signal-clutter ratio scenarios.”Abstract：Due to the complex and changeable marine environment, the active sonar target recognition problem has always been difficult in the field of underwater acoustics. Deep learning-based fusion recognition technology provides an effective way to solve this problem, but relying on simple concatenation strategies to fuse multi-domain features can cause information redundancy, and it is not easy to effectively mine correlation information between domains. Therefore, this paper proposes an attention mechanism-based multi-domain feature fusion approach for active sonar target recognition. By preprocessing active sonar echo signals and constructing a multi-domain feature extraction and fusion network, this method uses a one-dimensional convolutional neural network with long short-term memory (1DCNN-LSTM) and a two-dimensional convolutional neural network (2DCNN) with channel attention introduced to extract deep features from different domains. Subsequently, combining feature concatenation and constructing multi-domain cross-attention, intra- and cross-domain feature fusion is performed, which can effectively eliminate redundant information and promote inter-domain information interaction, while maximizing the retention of target features. Experimental results show that compared with single-domain methods, the network using an attention mechanism for multi-domain feature fusion strengthens cross-domain information interaction and significantly improves feature representation capability. Compared with other methods, the proposed method has obvious advantages in performance and maintains stable generalization ability in scenarios with low signal-clutter ratios.Keywords：Acoustic target recognition;Neural network;Attention mechanism;Multi-domain feature fusion30|24|0Updated：2026-02-11Research Articles
A dual-band filtering push‒pull power amplifier with a large frequency ratio employing a hybrid-mode bandpass response balun Enhanced Publication AI Introduction
“Researchers have made significant strides in the development of a dual-band filtering push‒pull power amplifier. The team designed a filtering power dividing/combining network using a hybrid-mode filtering balun with microstrip line and substrate integrated waveguide. This innovative approach enables the amplifier to achieve a large frequency ratio and effective second-harmonic suppression. The prototype demonstrates excellent performance, with a peak output power of 36.8 dBm at low frequencies and 36 dBm at high frequencies, while maintaining out-of-band spurious rejection.”Abstract：A dual-band filtering push‒pull power amplifier (PA) with a large frequency ratio is presented in this paper. The proposed filtering power dividing/combining network is based on a hybrid-mode filtering balun using microstrip line (MSL) and substrate integrated waveguide (SIW). The MSL filtering balun operates in the S-band, with a frequency range of 2.6‒2.86 GHz. Meanwhile, the SIW filtering balun is designed for Ku-band operation, covering a frequency range of 13‒13.65 GHz. Under these conditions, the prototype is capable of attaining a frequency ratio as high as five times the original value. Due to the inherent differential characteristic of the hybrid-mode filtering balun with a large frequency ratio, the proposed push‒pull PA not only realizes filtering functionality but also achieves second-harmonic suppression. To validate the designed concept, the proposed prototype has been designed, fabricated, and measured. Measurement results demonstrate that the proposed PA achieves a 7 dB small-signal gain while maintaining out-of-band spurious rejection during active testing. The developed dual-band filtering push‒pull PA delivers excellent performance, with a peak output power of 36.8 dBm at low frequencies and 36 dBm at high frequencies. Moreover, by employing dual-band filtering baluns, the PA inherently suppresses even-order harmonics while simultaneously providing filtering characteristics in both operational bands, which effectively suppresses near-band spurious signals.Keywords：Large frequency ratio;Dual-band filtering balun;Harmonic suppression;Push‒pull power amplifier32|26|0Updated：2026-02-11
0 1 Hybrid-augmented intelligence: collaboration and cognition
0 4 Applications of artificial intelligence in intelligent manufacturing: a review*
0 5 Challenges and opportunities: from big data to knowledge in AI 2.0
0 6 Current trends in the development of intelligent unmanned autonomous systems
0 7 Recent advances in efficient computation of deep convolutional neural networks
0 8 Terahertz time-domain spectroscopy and micro-cavity components for probing samples: a review
0 9 Cross-media analysis and reasoning: advances and directions
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