As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Detecting chromosome structural abnormalities in medical genetics is essential for diagnosing genetic disorders and understanding their implications for an individual\'s health. However, existing computational methods are formulated as a binary-class classification problem trained only on representations of positive/negative chromosome pairs. This paper introduces an innovative framework for detecting chromosome abnormalities with banding resolution, capable of precisely identifying and masking the specific abnormal regions. We highlight a pixel-level abnormal mapping strategy guided by banding features. This approach integrates data from both the original image and banding characteristics, enhancing the interpretability of prediction results for cytogeneticists. Furthermore, we have implemented an ensemble approach that pairs a discriminator with a conditional random field heatmap generator. This combination significantly reduces the false positive rate in abnormality screening. We benchmarked our proposed framework with state-of-the-art (SOTA) methods in abnormal screening and structural abnormal region segmentation. Our results show cutting-edge effectiveness and greatly reduce the high false positive rate. It also shows superior performance in sensitivity and segmentation accuracy. Being able to identify abnormal regions consistently shows that our model has demonstrated significant clinical utility with high model interpretability. BRChromNet is open-sourced and available at https://github.com/frankchen121212/BR-ChromNet.

Amid growing interest in the precise detection of volatile organic compounds (VOCs) in industrial field, the demand for highly effective gas sensors is at an all-time high. However, traditional sensors with their classic single-output signal, bulky and complex integrated structure when forming array often involve complicated technology and high cost, limiting their widespread adoption. Here, this study introduces a novel approach, employing an integrated YSZ-based (YSZ: yttria-stabilized zirconia) mixed potential sensor equipped with a triple-sensing electrode array, to efficiently detect and differentiate six types of VOCs gases. This innovative sensor integrates NiSb2O6, CuSb2O6, and MgSb2O6 sensing electrodes (SEs), which are sensitive to pentane, isoprene, n-propanol, acetone, acetic acid, and formaldehyde gases. Through feature engineering based on intuitive spike-based response values, it accentuates the distinct characteristics of every gas. Eventually, an average classification accuracy of 98.8% and an overall R-squared error (R2) of 99.3% for concentration regression toward six target gases can be achieved, showcasing the potential to quantitatively distinguish between industrial hazardous VOCs gases.

Discrimination of segmented Baijiu contributes to stabilizing the quality of products, improving revenue-generating effects. A fluorescence sensor array is constructed based on four fluorescence characteristic peaks of terbium@lanthanum metal-organic framework (Tb@La-MOF). Its fluorescence signal is specifically quenched, when Tb@La-MOF encounters acetaldehyde. Acetaldehyde may inhibit the absorption of energy by the organic ligands in MOF, or/and hydrogen bonding with -COOH on the organic ligand, resulting in energy transfer to Tb(Ⅲ). According to this, the quantitative detection of acetaldehyde is completed with a range of 10-300 μM and the detection limit of 5.5 μM. At the same time, it has been successfully applied to the discrimination of segmented Baijiu. Fifteen segmented from three wine cellars are 100 % discriminated with the combined processing of sensor arrays and analytical methods. Accuracy, simplicity, and low-cost are highlights of this fluorescence sensor array, which has considerable potential for application in detection, production, and food field.

High-resolution identification of chromium (Cr) species, especially various organic-Cr complexes, in a convenient and economically-feasible manner is the prerequisite for achieving the advanced treatment of chromium wastewater. To this end, a colorimetric nano-Au sensor array was developed by taking advantage of the UV-spectra shift of gold nanoparticles (Au NPs) upon interaction with Cr species; specifically, four molecular modifiers [i.e., iminodiacetic acid (IDA), tripolyphosphate (TPP), cetyltrimethylammonium bromide (CTAB), and 1,5-diphenylcarbazide (DPC)] were intentionally employed for assembling nano-Au array receptors, which showed respective responses toward different Cr species through the formation of coordination, hydrophobic interaction, electrostatic attraction, and redox reaction, respectively; the \"fingerprint\" differences of the unique optical properties were then integrated for semi-quantitatively recognizing Cr species by pattern recognition techniques. Eleven ubiquitous Cr species [i.e., Cr(III), Cr(VI), and various Cr(III)-organic complexes] served as the model samples, which could be sensitively identified, no matter in individual or mixture mode, by the developed nano-Au sensor array on the basis of the colorimetric responses resulted from diverse nano-Au-aggregation behaviors, with excellent anti-interference ability in the simulated or actual water scenario. Attractively, the nano-Au sensor array can achieve very sensitive detection limit of the quantitative analyses of Cr species in a prompt in-situ manner, which usually requires a two-step process of separation and detection for the conventional analytical methods. Such a convenient strategy of Cr species discrimination conduces to rationally designing specific protocols for the advanced treatment of chromium wastewater.

To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm-Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM.

Both microstructure and stress affect the structure and kinematic properties of magnetic domains. In fact, microstructural and stress variations often coexist. However, the coupling of microstructure and stress on magnetic domains is seldom considered in the evaluation of microstructural characteristics. In this investigation, Magnetic incremental permeability (MIP) and magnetic Barkhausen noise (MBN) techniques are used to study the coupling effect of characteristic microstructure and stress on the reversible and irreversible motions of magnetic domains, and the quantitative relationship between microstructure and magnetic domain characteristics is established. Considering the coupling effect of microstructure and stress on magnetic domains, a patterned characterization method of microstructure and stress is innovatively proposed. Pattern recognition based on the Multi-layer Perceptron (MLP) model is realized for microstructure and stress with an accuracy rate higher than 97%. The results show that the pattern recognition accuracy of magnetic domain features and micro-magnetic features simultaneously as input parameters is higher than that of micro-magnetic features alone as input parameters.

UNASSIGNED: Human gait pattern recognition and compliance control are key technologies for achieving high coordination and assistance between exoskeleton robots and human movements.
UNASSIGNED: In order to improve the adaptability of exoskeleton robots to the human body, this paper proposes an exoskeleton compliance control strategy based on dual interaction torque phase separation control method.
UNASSIGNED: A support phase swing phase split control strategy based on dual interaction torque is proposed. Utilize the interaction force of human joints and adopt a model-based method to control the support phase. By utilizing the interaction force of exoskeleton joints and using a torque closed-loop method to control the swing phase, a multi-state control method of motion is achieved.
UNASSIGNED: A lower limb exoskeleton knee joint testing platform is built to verify the proposed human gait recognition The effectiveness of human-machine interaction force identification and human-machine coupling system compliance control technology.
UNASSIGNED: The proposed control method can effectively adjust joint torque, enabling the exoskeleton robot to maintain balance and stability during the entire walking phase.

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The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.