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"Lin, Chih-Yang"
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الفنون الشعبية الصينية
تعد الفنون التشكیلیة التي یقوم بھا الشعب الصیني لتلبیة متطلبات الحیاة الاجتماعیة الشخصیة، وتتجلي تلك الفنون الشعبیة الصینیة في المقصوصات ولوحات رأس السنة الجدیدة والأقنعة والطائرات الورقیة والتشكیل بالعجین وتماثیل الصلصال وغیرھا. ویعمل بتلك الفنون الشعبیة مجموعات كبیرة من العمال العادیین الذین یعیشون بالمناطق الریفیة الواسعة، من ھؤلاء العاملین تتركز في النساء العاملات بالمناطق الریفیة. وتجمع الفنون الشعبیة في طیاتھا بین الحیاة الإنتاجیة وضروریات الحیاة الأساسیة والطقوس البشریة. وینضوي المضمون الثقافي والشكل الفني لتلك الفنون الشعبیة على تراث الثقافة التاریخیة للأمة الصینیة والتي امتدت لسبعة آلاف أو ثمانیة آلاف سنة منذ المجتمع من تلك الثقافة البدائیة التي تقوم على عبادة الطبیعة والطوطم وعبادة الأسلاف وحتي تلك الثقافة الاقتصادیة التجاریة الحدیثة، حیث یمكن القول بأنھا حجر نشاط الثقافة التاریخیة القومیة.
BOOK
Enhanced Human Activity Recognition Using Wi-Fi Sensing: Leveraging Phase and Amplitude with Attention Mechanisms
Wi-Fi-based human activity recognition (HAR) is a non-intrusive and privacy-preserving method that leverages Channel State Information (CSI) for identifying human activities. However, existing approaches often struggle with robust feature extraction, especially in dynamic and multi-environment scenarios, and fail to effectively integrate amplitude and phase features of CSI. This study proposes a novel model, the Phase–Amplitude Channel State Information Network (PA-CSI), to address these challenges. The model introduces two key innovations: (1) a dual-feature approach combining amplitude and phase features for enhanced robustness, and (2) an attention-enhanced feature fusion mechanism incorporating multi-scale convolutional layers and Gated Residual Networks (GRN) to optimize feature extraction. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on three datasets, including StanWiFi (99.9%), MultiEnv (98.0%), and the MINE lab dataset (99.9%). These findings underscore the potential of the PA-CSI model to advance Wi-Fi-based HAR in real-world applications.
Journal Article
Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD)
Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature pyramid network, known as De-tectoRS, utilized the recursive feature pyramid network technique to extract deeper multi-scale steel features by learning the feedback from the sequential feature pyramid network. Finally, Side-Aware Boundary Localization was used to precisely generate the output prediction of the defect detectors. We named our approach EnsGAN-SDD. Extensive experimental studies showed that the proposed methods improved the defect detector’s performance, which also surpassed the accuracy of state-of-the-art methods. Moreover, the proposed EnsGAN achieved better performance and effectiveness in processing time compared with the original ESRGAN. We believe our innovation could significantly contribute to improved production quality in the steel industry.
Journal Article
Novel Spatio-Temporal Continuous Sign Language Recognition Using an Attentive Multi-Feature Network
Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, either the full-frame image or details of hands and face. The scarcity of information for the CSLR training process heavily constrains the capability to learn multiple features using the video input frames. Moreover, exploiting all frames in a video for the CSLR task could lead to suboptimal performance since each frame contains a different level of information, including main features in the inferencing of noise. Therefore, we propose novel spatio-temporal continuous sign language recognition using the attentive multi-feature network to enhance CSLR by providing extra keypoint features. In addition, we exploit the attention layer in the spatial and temporal modules to simultaneously emphasize multiple important features. Experimental results from both CSLR datasets demonstrate that the proposed method achieves superior performance in comparison with current state-of-the-art methods by 0.76 and 20.56 for the WER score on CSL and PHOENIX datasets, respectively.
Journal Article
Hierarchical Image Transformation and Multi-Level Features for Anomaly Defect Detection
Anomalies are a set of samples that do not follow the normal behavior of the majority of data. In an industrial dataset, anomalies appear in a very small number of samples. Currently, deep learning-based models have achieved important advances in image anomaly detection. However, with general models, real-world application data consisting of non-ideal images, also known as poison images, become a challenge. When the work environment is not conducive to consistently acquiring a good or ideal sample, an additional adaptive learning model is needed. In this work, we design a potential methodology to tackle poison or non-ideal images that commonly appear in industrial production lines by enhancing the existing training data. We propose Hierarchical Image Transformation and Multi-level Features (HIT-MiLF) modules for an anomaly detection network to adapt to perturbances from novelties in testing images. This approach provides a hierarchical process for image transformation during pre-processing and explores the most efficient layer of extracted features from a CNN backbone. The model generates new transformations of training samples that simulate the non-ideal condition and learn the normality in high-dimensional features before applying a Gaussian mixture model to detect the anomalies from new data that it has never seen before. Our experimental results show that hierarchical transformation and multi-level feature exploration improve the baseline performance on industrial metal datasets.
Journal Article
Targeting nerve growth factor-mediated osteosarcoma metastasis: mechanistic insights and therapeutic opportunities using larotrectinib
Osteosarcoma (OS) therapy presents numerous challenges, due largely to a low survival rate following metastasis onset. Nerve growth factor (NGF) has been implicated in the metastasis and progression of various cancers; however, the mechanism by which NGF promotes metastasis in osteosarcoma has yet to be elucidated. This study investigated the influence of NGF on the migration and metastasis of osteosarcoma patients (88 cases) as well as the underlying molecular mechanisms, based on RNA-sequencing and gene expression data from a public database (TARGET-OS). In osteosarcoma patients, the expression of NGF was significantly higher than that of other growth factors. This observation was confirmed in bone tissue arrays from 91 osteosarcoma patients, in which the expression levels of NGF and matrix metallopeptidase-2 (MMP-2) protein were significantly higher than in normal bone, and strongly correlated with tumor stage. In summary, NGF is positively correlated with MMP-2 in human osteosarcoma tissue and NGF promotes osteosarcoma cell metastasis by upregulating MMP-2 expression. In cellular experiments using human osteosarcoma cells (143B and MG63), NGF upregulated MMP-2 expression and promoted wound healing, cell migration, and cell invasion. Pre-treatment with MEK and ERK inhibitors or siRNA attenuated the effects of NGF on cell migration and invasion. Stimulation with NGF was shown to promote phosphorylation along the MEK/ERK signaling pathway and decrease the expression of microRNA-92a-1-5p (miR-92a-1-5p). In in vivo experiments involving an orthotopic mouse model, the overexpression of NGF enhanced the effects of NGF on lung metastasis. Note that larotrectinib (a tropomyosin kinase receptor) strongly inhibited the effect of NGF on lung metastasis. In conclusion, it appears that NGF promotes MMP-2-dependent cell migration by inhibiting the effects of miR-92a-1-5p via the MEK/ERK signaling cascade. Larotrectinib emerged as a potential drug for the treatment of NGF-mediated metastasis in osteosarcoma.
Journal Article
Global-and-Local Context Network for Semantic Segmentation of Street View Images
Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.
Journal Article
Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model
With the recent growth of Smart TV technology, the demand for unique and beneficial applications motivates the study of a unique gesture-based system for a smart TV-like environment. Combining movie recommendation, social media platform, call a friend application, weather updates, chatting app, and tourism platform into a single system regulated by natural-like gesture controller is proposed to allow the ease of use and natural interaction. Gesture recognition problem solving was designed through 24 gestures of 13 static and 11 dynamic gestures that suit to the environment. Dataset of a sequence of RGB and depth images were collected, preprocessed, and trained in the proposed deep learning architecture. Combination of three-dimensional Convolutional Neural Network (3DCNN) followed by Long Short-Term Memory (LSTM) model was used to extract the spatio-temporal features. At the end of the classification, Finite State Machine (FSM) communicates the model to control the class decision results based on application context. The result suggested the combination data of depth and RGB to hold 97.8% of accuracy rate on eight selected gestures, while the FSM has improved the recognition rate from 89% to 91% in a real-time performance.
Journal Article
TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition
Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications. However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. We evaluate our approach on a public Wi-Fi CSI dataset using a strict cross-subject protocol, where training and testing subjects do not overlap. The proposed TCN-MAML achieves 99.6% accuracy, demonstrating superior generalization and efficiency over baseline methods. Experimental results confirm the framework’s suitability for low-power, real-time HAR systems embedded in IoT sensor networks.
Journal Article
A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images
Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.
Journal Article