IJLAI Transactions on Science and Engineering
IJLAI Transactions on Science and Engineering aims to become an international forum for the research and development community to publish articles of highest quality in all disciplines of science and engineering. Papers relating directly or indirectly to all aspect of these fields are also welcome to submit. IJLAI Transactions on Science and Engineering is intended to provide a forum for the expression of new ideas, as well as a place for exposition of these areas of knowledge that can further understanding of science and engineering issues and concerns. IJLAI Transactions on Science and Engineering is interested in publishing articles derived from any Science and Engineering field-based exposures along with conceptually robust theory rooted in the multidisciplinary.
Digital Transformation and Efficiency Optimization of Art Education Management: An Empirical Study on Resource Allocation and Teacher Training Systems Based on Multi-Campus Surveys
With the advent of the Industry 4.0 era and the pervasive integration of educational informatization, digital transformation has emerged as a pivotal catalyst for optimizing the operational efficiency of art education management. Traditional art education paradigms, characterized by subjective assessment metrics and decentralized resource architectures, encounter significant bottlenecks in resource allocation and standardized faculty development—particularly within the complex ecosystem of multi-campus educational conglomerates. This paper presents a data-driven framework for the efficiency optimization of art education management. Drawing upon a comprehensive dataset comprising 2,500 valid questionnaires and institutional records collected from 12 art colleges across 4 multi-campus educational groups, this study employs Data Envelopment Analysis (DEA) to quantitatively evaluate the technical efficiency of resource allocation and utilizes Structural Equation Modeling (SEM) to elucidate the causal mechanisms linking digital training to pedagogical performance. The empirical findings demonstrate that digital transformation yields a significant improvement in the technical efficiency of art institutions, with an average increase of 35.2\%. Specifically, the implementation of intelligent scheduling algorithms significantly enhances studio utilization rates and curriculum precision. Furthermore, the study validates that a hybrid training system integrating VR/AR (Virtual/Augmented Reality) technologies with traditional seminars achieves the highest efficacy in elevating teacher satisfaction and innovation capabilities. This research contributes a theoretical framework and practical roadmap for the intelligent governance of art education in the digital age.
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Research and Application of Time Series Forecasting Methods in Disease Surveillance
Time series forecasting plays a pivotal role in public health, particularly within disease surveillance. Theoretically, it facilitates the in-depth analysis of disease patterns and progression; practically, it supports health authorities in optimizing resource allocation, devising prevention strategies, and mitigating transmission risks. Recently, rapid advancements in information technology have accelerated the development of this field, leading to the continuous emergence of novel methods. This paper provides a comprehensive overview of time series forecasting methods, categorizing them into traditional statistical models and deep learning approaches, while strictly evaluating their respective strengths and limitations. Furthermore, it explores innovative applications involving statistical models, machine learning, and deep learning, highlighting the potential of these emerging technologies. Bridging theory and practice, this systematic review aims to establish an analytical framework, delineate the field's developmental trajectory, and offer valuable insights for future research in disease prediction.
Arrhythmia Classification Method Based on Transformer: Progress, Challenges and Prospects
The rapid advancement of deep learning has revolutionized electrocardiogram (ECG) analysis, with Transformer architectures emerging as powerful tools for automated arrhythmia classification. This paper presents a comprehensive review of Transformer-based arrhythmia classification methods, examining their evolution, current capabilities, and future potential. We systematically analyze the architectural adaptations of Transformers for ECG signal processing, including Vision Transformers adapted for 1D medical signals, hybrid CNN-Transformer models, and lightweight implementations for edge computing. Our review encompasses recent studies demonstrating exceptional performance, with models like ECGformer achieving 98\% accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97\% accuracy with only 6k parameters suitable for wearable devices. We discuss key advantages including the ability to capture long-range dependencies in ECG sequences, handle variable-length inputs, and integrate multi-lead spatial information through attention mechanisms. However, significant challenges remain, including high computational requirements, dependence on large labeled datasets, limited interpretability in clinical settings, and over-fitting risks with imbalanced data. The paper explores emerging solutions such as transfer learning, data augmentation techniques, and explainable AI methods to address these limitations. Future prospects include the development of more efficient architectures for real-time monitoring, integration with multi-modal physiological data, and enhanced clinical interpretability. This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate, efficient, and clinically viable automated arrhythmia detection systems.
Exploration and Practice of the Talent Cultivation Model for Outstanding Students in Robotics Engineering Major
This study presents an innovative talent-training framework for high-achieving undergraduates in robotics engineering that integrates a four-phase developmental scaffold—cognitive ignition, interdisciplinary fusion, research immersion, and entrepreneurial translation—within a trinity ecosystem composed of an elite "robotics honor academy," industry-led living-labs, and an international academic alliance. A three-year quasi-experiment involving 186 outstanding students shows that participants achieved a 42\% increase in flagship competition awards, 2.3-fold rise in first-author IEEE papers, and 96\% job-match relevance versus 71\% in the control group. The model's core mechanisms are: (1) dynamic learning pathways steered by AI-diagnosed competency portraits; (2) cross-disciplinary "chameleon" projects co-supervised by university, corporate and clinical partners; (3) an ethical-by-design thread that embeds robo-ethics, sustainability and societal impact assessment into every project milestone. Structural equation modeling confirms that research immersion exerts the strongest total effect on creative self-efficacy ($\beta=0.54$, $p<0.001$), while entrepreneurial translation is the key predictor of technology-transfer intention ($\beta=0.49$). The findings offer a scalable blueprint for cultivating robotics elites who combine deep technical virtuosity with responsible innovation mindset, and can be adapted to other high-tech engineering disciplines.
An Enhanced Hierarchical Clustering Algorithm: Methodology and Application
Hierarchical clustering is a widely used technique for data grouping in various fields, renowned for its ability to create a dendrogram that provides insights into data structure. However, traditional hierarchical methods often suffer from challenges such as computational inefficiency, sensitivity to noise, and difficulty in determining the optimal number of clusters. This paper introduces an improved hierarchical clustering algorithm that incorporates advanced distance metrics, the use of cluster representatives, and a robust agglomeration strategy designed to address these challenges. The proposed algorithm incorporates a mechanism to dynamically select the distance metric based on the underlying data characteristics. Unlike traditional methods that rely solely on a fixed distance metric, our approach determines the most suitable metric for the given dataset. For instance, a method like Cosine similarity could be employed for high-dimensional data or text data where orientation matters, while Euclidean distance can be effective for low-dimensional, continuous data. Our proposed method is evaluated through comprehensive experiments on synthetic and real-world datasets, showcasing significant enhancements in clustering performance, adaptability to noise, and computational efficiency. The findings indicate that the proposed algorithm outperforms traditional hierarchical methods, demonstrating its potential for broader applications across various domains.
Arrhythmia Classification Method Based on CNN-Attention-BiTransformer
This paper proposes a novel arrhythmia classification method combining Convolutional Neural Networks (CNN), Attention mechanisms, and Bidirectional Transformers (BiTransformer). The method aims to improve the accuracy and robustness of arrhythmia detection in electrocardiogram (ECG) signals. Initially, the CNN module extracts local spatial features from raw ECG data, effectively capturing the morphological characteristics of different arrhythmia types. Subsequently, the Attention mechanism is applied to weigh the importance of different segments in the ECG signal, allowing the model to focus on critical features that are most indicative of arrhythmia. Finally, the BiTransformer module processes the feature sequences bidirectionally, capturing both forward and backward dependencies in the ECG signal. This comprehensive approach enables the model to integrate local and global information, enhancing its ability to classify various arrhythmias accurately. Experiments conducted on the MIT-BIH Arrhythmia Database demonstrate that the proposed method achieves state-of-the-art performance, with a significant improvement in classification accuracy compared to traditional methods. The results highlight the effectiveness of combining CNN, Attention, and BiTransformer for arrhythmia classification, offering a promising direction for automated ECG analysis and clinical applications.
Construction of a Curriculum System for the Integration of Industry and Education in the Robotics Engineering Major of Applied Universities
This study designs a curriculum system that deeply integrates industry and education for robotics engineering majors in applied universities. Guided by the CDIO philosophy and the concept of industry–education collaboration, a three-level curriculum framework consisting of foundational courses, professional cores, and integrated practical modules is constructed. Industry technologies and standards are introduced into course content, teaching methods, and evaluation systems through co-developed courses, shared laboratories, and joint faculty teams. The system emphasizes continuous engagement with real-world engineering projects, fostering students' competencies in design, programming, integration, and innovation. The proposed model provides a flexible and sustainable approach for cultivating application-oriented robotics talents that meet the evolving needs of intelligent manufacturing.
Curriculum Reform of "Robot Vision Perception and Detection" Driven by the Industry-Education Integration Community: Model Innovation and Empirical Research
Industry-education integration communities are redefining how advanced robotics courses are designed, delivered and validated. This paper reports a two-year design-based study that reconceptualised the undergraduate module “Robot Vision Perception and Detection” from a supply-chain-oriented alliance of three universities, two robotics manufacturers and one logistics giant. Adopting a community-of-practice lens, we replaced the conventional lecture–lab sequence with a “challenge-driven co-creation loop” in which corporate engineers release real-time production-line vision defects as curricular tasks, faculty scaffold theoretical principles, and students iterate solutions on the factory floor using identical hardware and data streams. Mixed-methods evaluation with 142 students and 18 industry mentors shows significant gains: (1) learning performance increased by 0.82 standard deviations; (2) student creative self-efficacy and systems-thinking improved 34\% and 29\% respectively; (3) average defect detection recall of student models rose from 72\% to 93\%, with 8 prototypes transferred to the partner lines; (4) faculty–industry co-publications and patent disclosures tripled. Qualitative trace data reveal that boundary objects (annotated datasets, Dockerized algorithms and shared Kanban boards) legitimately brokered epistemic differences between academia and industry. The study contributes an empirically grounded framework—CIE-CDL (Community-Integrated Education via Challenge-Driven Loops)—that embeds authentic socio-technical complexity into robotics curricula while simultaneously generating measurable value for industrial partners. Implications for scalable, sustainable industry–education symbiosis are discussed.
A Novel Clustering Feature-Based BiLSTM-GAN Method for Enhancing ECG Signal
Electrocardiogram (ECG) signals are frequently corrupted by diverse noises that degrade the accuracy of downstream arrhythmia detection and hamper reliable tele-cardiology. This paper proposes a clustering feature-driven Bidirectional-LSTM Generative Adversarial Network (CF-BiLSTM-GAN) that learns to recover clean heartbeat morphology from single-channel, noisy recordings. First, an adaptive k-means module is employed on the latent space of a pre-trained denoising auto-encoder to discover representative beat-level clusters; these clusters are treated as high-level priors that encode patient-specific QRS-T shape constraints. Second, a BiLSTM generator conditioned on the cluster centroids is trained within a GAN framework to reconstruct the temporal manifold of the ECG while preserving subtle atrial/ventricular signatures. A spectral-normalized CNN discriminator enforces local smoothness and global periodicity, suppressing both high-frequency artifacts and baseline wander simultaneously. Experiments on MIT-BIH and PhysioNet/CinC datasets demonstrate that CF-BiLSTM-GAN outperforms state-of-the-art wavelet, dictionary-learning, and vanilla DL methods, achieving lower MSE, higher cross-correlation and improved beat classification F1 after denoising. The proposed approach offers a practical, data-driven solution for enhancing ECG fidelity in ambulatory monitoring and edge devices.
Practical Research on Classroom Teaching Reform Based on AI-Assisted Instructional Design
This research takes AI-assisted teaching design as the starting point to explore new paths for classroom teaching reform. The study constructs a four-step practical framework of "conceptual update-intelligent support-teaching reconfiguration-reflection and improvement", guiding teachers to collaborate with AI in completing the diagnosis of learning situations, target positioning, activity design, and resource push, achieving the transformation from experience-driven to data-driven teaching paradigm. The practice shows that the intervention of AI not only expands the design vision of teachers but also gives rise to new classroom forms with diverse interactions and progressive layers, stimulating students' deep participation and personalized growth. The research proposes that the key to promoting deep collaboration between teachers and AI lies in enhancing the teaching interpretability of intelligent tools and creating a design culture of joint creation and continuous iteration, providing a feasible practical model for classroom transformation in the era of intelligence.
Research on the Curriculum Construction of the Robot Engineering Major under the Background of New Engineering Disciplines
Under the backdrop of the comprehensive advancement of new engineering education, the robotics engineering discipline, which integrates multiple fields such as mechanics, electronics, control, computer science, and artificial intelligence, urgently needs to break through traditional disciplinary barriers to meet the pressing demand for interdisciplinary talents in the era of intelligent manufacturing and artificial intelligence. This paper systematically reviews the core issues existing in the current course construction of the robotics engineering major, such as a monotonous course structure, weak practical components, and insufficient interdisciplinary integration. It proposes a course system reconstruction path based on the concept of solid foundation, strong cross-disciplinary integration, emphasis on practice, and promotion of innovation. The research emphasizes that a flexible course structure of platform+module+direction should be established around industrial demands and technological frontiers, strengthening the mathematical and physical foundation and core professional courses, and expanding courses in cutting-edge areas such as artificial intelligence, big data, and cloud-edge collaboration. It also advocates for the reform of practical teaching models through project-driven, competition-education integration, and school-enterprise collaboration. Meanwhile, ideological and political education, innovation and entrepreneurship education, and engineering ethics should be organically integrated into the entire course process to form a trinity talent cultivation pattern of value shaping, knowledge imparting, and ability development. By constructing a multi-level, three-dimensional, and open course system for the robotics engineering major, it can provide strong support for the cultivation of high-level applied and innovative talents under the new engineering background, and is of great significance for promoting the connotative development of higher education and the high-quality development of industries.
Research on the Teaching Mode of Numerical Analysis Course Based on Algorithmic Thinking
Numerical analysis, as an important branch of mathematics, is widely applied in modern engineering, science and technology fields. In traditional teaching, numerical analysis focuses on imparting computational methods while neglecting the cultivation of algorithmic thinking. With the development of technology, especially the rise of artificial intelligence, the application of numerical analysis is becoming increasingly widespread. This requires students not only to master numerical methods but also to possess the ability to solve problems innovatively. For this purpose, the teaching mode of numerical analysis courses based on algorithmic thinking was explored. By integrating algorithmic thinking in teaching, students’ ability to flexibly apply algorithms was cultivated, and their ability to solve complex problems was enhanced, laying a solid foundation for their future career development and academic research.
Research on the Teaching Reform of the Course "Principles and Detection Technology of Sensors" in the Robot Engineering Major under the Background of the Construction of Strategic Emerging Specialties
Under the background of strategic emerging professional construction, the teaching reform of the course “Sensor Principles and Detection Technology” in the Robotics Engineering major is of great significance. This paper explores the current challenges faced in the teaching of this course, such as the rapid development of sensor technology, the increasing demand for practical skills in the robotics field, and the need to integrate interdisciplinary knowledge. To address these challenges, several reform measures are proposed. Firstly, the curriculum content is updated to include the latest developments in sensor technology and its applications in robotics. This involves integrating advanced topics such as intelligent sensors, MEMS technology, and wireless sensor networks into the syllabus. Secondly, the teaching methods are diversified. Interactive teaching methods, such as project - based learning and case - based learning, are adopted to enhance students’ practical abilities and problem - solving skills. In addition, the use of virtual simulation software and laboratory experiments is emphasized to provide students with hands - on experience in sensor design, testing, and integration. Thirdly, the assessment system is reformed to focus more on students’ comprehensive abilities. In addition to traditional exams, project reports, laboratory performance, and team collaboration are also included in the assessment criteria. The implementation of these reform measures has achieved positive results. Students’ interest in the course has increased, their understanding of sensor principles and detection technology has been deepened, and their practical skills and innovative thinking have been significantly improved. This reform provides valuable references for the teaching of similar courses in other engineering majors and contributes to the cultivation of high - quality talents in the field of robotics engineering to meet the needs of strategic emerging industries.
The Construction of a Talent Cultivation System for the Robot Engineering Major Based on the OBE Concept
The Outcome-Based Education (OBE) philosophy has been widely recognized as an effective approach to enhance the quality of engineering education. This paper focuses on the construction of a talent cultivation system for the Robotics Engineering major based on the OBE concept. The OBE approach emphasizes the importance of clearly defining expected learning outcomes and aligning the entire educational process to achieve these outcomes. In the context of Robotics Engineering, this means identifying the specific skills, knowledge, and competencies that graduates should possess to meet the demands of the industry and society. Firstly, the paper analyzes the current challenges and needs in the field of Robotics Engineering. It identifies the key areas where graduates need to excel, such as programming, mechanical design, electronics, and artificial intelligence. Based on this analysis, the expected learning outcomes for the Robotics Engineering major are defined. These outcomes cover not only technical skills but also soft skills such as problem-solving, teamwork, and innovation. The paper then discusses the curriculum design that is aligned with these learning outcomes. The curriculum includes a combination of theoretical courses and practical training. The theoretical courses provide students with a solid foundation in the principles of robotics, while the practical training allows students to apply their knowledge to real-world problems. The curriculum also incorporates project-based learning, where students work on projects that simulate real-world scenarios. This approach helps students to develop their problem-solving skills and gain hands-on experience. In addition to the curriculum design, the paper also addresses the assessment methods. The assessment is outcome-based, meaning that it focuses on evaluating whether students have achieved the expected learning outcomes. This includes both formative and summative assessments, such as quizzes, exams, project evaluations, and final capstone projects. The assessment results are used to provide feedback to students and to continuously improve the teaching process. Finally, the paper presents the implementation of the talent cultivation system and evaluates its effectiveness. The results show that the OBE-based system has significantly improved the quality of education in the Robotics Engineering major. Graduates from this system are better prepared to meet the challenges of the industry and have a higher employability rate. In conclusion, the construction of a talent cultivation system for Robotics Engineering based on the OBE philosophy is an effective way to improve the quality of engineering education. By clearly defining learning outcomes and aligning the curriculum and assessment methods to achieve these outcomes, this system helps to produce well-rounded graduates who are ready to contribute to the field of robotics.
A Sensory Engineering Study on the Impact of Warm and Cool Color Posters on College Students Emotions
This study is based on the theory of human engineering psychology and explores the mechanism by which cool and warm color posters affect the emotions of college students. Through the combination of experimental methods and scale measurement, 60 college students were selected as participants. Cool color (blue, green) and warm color (red, yellow) posters were randomly presented to them, and their emotional responses were collected. The SAM scale (Self-Assessment Manikin) was used to assess the degree of pleasure, arousal, and dominance, and skin conductance response (GSR) was used to obtain physiological indicators to enhance the objectivity of the study. The results showed that warm color posters significantly enhanced the arousal and pleasure of college students, especially under the red stimulus, with the highest level of emotional activation. While cool color posters significantly reduced arousal and brought a more calm and relaxed emotional experience. The blue tone was the most effective in alleviating anxiety. Gender and professional background have a moderating effect on color perception. Women are more sensitive to the emotional responses to cool and warm colors, and students majoring in art design have a higher recognition and emotional response intensity of color differences than non-art majors. This study verified the applicability of color emotional effects in the college student population and expanded the application boundaries of human engineering psychology in visual communication design. The research suggests that in college mental health publicity and campus environment design, scientific application of color psychology principles should be adopted, and appropriate colors should be selected according to different emotional regulation goals to improve the accuracy and effectiveness of emotional intervention. In the future, neuroscientific methods such as electroencephalogram (EEG) can be further introduced to deeply reveal the neural basis of the color emotional mechanism.
From 'Usable' to 'Empathetic': Research on Digital Interface Visual Design from the Perspective of Sensory Engineering
Under the tide of information, UI design has become the core lever for measuring the quality of digital product experience. As various applications permeate daily life, the quality of interfaces has been increasingly magnified. Researchers at home and abroad have devoted their efforts to this, distilling multiple sets of experience paradigms and operational guidelines. Regrettably, the mainstream thinking is still trapped in a two-dimensional flat framework. When interaction technologies are advancing rapidly and functional information flows are surging in a multi-dimensional and three-dimensional manner, the old logic can no longer fulfill the demand for a user-centered quality experience. First, an emotional indicator framework is established through human-computer engineering. In the weight setting part, the grey correlation is incorporated into the group analytic hierarchy process, and the modified analytic hierarchy process (AHP) is used to calculate the comprehensive weights, thereby weakening the subjective flaws of the traditional AHP. Then, in response to the ambiguity of the evaluation, the intuitionistic fuzzy set is integrated with the TOPSIS (technique for order preference by similarity to an ideal solution) ideal similarity ranking method to construct a new comprehensive evaluation model. This study verifies the applicability of human factors engineering in the emotional design of digital interfaces, providing a quantitative path and practical guidelines for the paradigm shift from "usability" to "empathy".
A survey of AI-enabled one-stop student community construction in vocational colleges
In the new era, vocational college has been an important component of the education system in China, responsible for the mission of cultivating high-quality technical talents. The construction of one-stop student communities has become an important way to promote the modernization of vocational education governance. With the deepening of education reform, vocational colleges are facing new challenges in the construction of student communities. The contradiction between the expansion of the scale of vocational college students and the diversification of demand is prominent, and the traditional management model has shortcomings such as scattered resources and insufficient collaboration. How to explore a one-stop student community construction path that is in line with the characteristics of vocational colleges has become an urgent issue to be solved. Nowadays, the development of artificial intelligence (AI) technology is flourishing, and deeply changing the face of higher education. The rapid development of AI technology provides strong support for the construction of student communities in vocational colleges. AI intelligent tools, big data information collection and other technologies, can effectively help colleges continuously optimize their management models, greatly improve service efficiency and quality, and enhance student satisfaction. Hence, based on the AI technology, building an equal, inclusive, and interactive student community network, responding to students' needs for digital learning resources, platform tools, service support, and improving the accuracy and satisfaction of education management, are of great significance for the vocational education reform in China. This article overviews pervious achievements of AI-enabled student community construction in colleges, to provide actionable solutions for the paradigm shift of the student service system in vocational colleges at the practical level.
Image Shadow Removal Based on Improved Generative Adversarial Network
Traditional deep learning-based shadow removal methods often alter the pixels in non-shadow areas and fail to produce shadow removal results with natural boundary transitions. To solve this problem, we propose a novel multi-stage shadow removal framework based on Generative Adversarial Networks (GAN). Firstly, the multi-task-driven generator respectively generates the corresponding shadow mask and shadow mask for the input image through the shadow detection subnet and the mask generation subnet. Secondly, guided by the shadow mask and shadow mask, the full shadow module and the partial shadow module are respectively designed, and different types of shadows in the image are removed in stages. The sequential data from multiple sensors is used as the input of the time series generative adversarial network to generate sequence data with temporal dynamic characteristics; the data synthesized by the time series generative adversarial network is used to replace the noise input data in the gradient-penalized WGAN generative adversarial network, and the discriminator combines graph convolutional networks, long short-term memory networks and attention mechanisms to more effectively explore the spatio-temporal correlations of multi-source heterogeneous sensing data and enhance the discriminative ability for sequential data. Then, a new combined loss function was constructed based on the least squares loss to achieve better results. Compared with the latest deep learning shadow removal methods, on the selected dataset, the balance error rate of the proposed method decreases by approximately 4.39%, the structural similarity increased by approximately 0.44%, and the pixel root mean square error decreases by approximately 13.32%. The experimental results show that the shadow removal results obtained by this method have smoother boundary transitions.
Research on the Reform of Open Education Teaching Based on Adaptive Learning in the Era of Artificial Intelligence
The rapid development of artificial intelligence technology has provided an opportunity to reshape the teaching ecosystem in open education. This article focuses on the concept of "adaptive learning", in the context of the artificial intelligence era, and explores the systematic reform of open education teaching models. The research first constructed an integrated learning framework that combines cognitive diagnosis, dynamic paths, resource push, immediate feedback, and emotional support. Through data-driven and teacher experience collaboration, it realizes large-scale personalized teaching. Secondly, based on the teaching practice of public courses in multiple universities, the article collected and analyzed the entire process behavior data of learners, used deep models to dynamically optimize teaching strategies, and established an interpretable and iterative teaching loop. On this basis, the research focuses on educational equity and the mechanism of human-computer collaboration, ensuring that while technology is empowered, the dominant position of teachers and the warmth of the learning community are maintained. Through qualitative interviews and teaching observations, the article found that adaptive learning significantly enhanced the initiative, satisfaction, and knowledge transfer ability of learners, forming a new classroom culture that integrates online and offline elements and reshapes the roles of teachers and students. The research conclusion states that in the open education teaching reform of the artificial intelligence era, it should be driven by data intelligence, centered on learners, and based on educational equity, promoting the transformation from "standardized supply" to "precise services", providing replicable models and sustainable paths for building a lifelong learning society.
Face Recognition Based on LBP Multi-feature Extraction Fusion and KNN
The widespread application of facial recognition technology has enriched people's digital lives. In such a digital age, data intelligence based on cognitive computing has transformed an individual from a statistically average person into an object that can be analyzed for data independently. This change heralds the arrival of a data analysis society. The facial recognition system integrates multiple professional technologies such as artificial intelligence, machine recognition, machine learning, model theory, expert systems, and video image processing. At the same time, it needs to combine the theory and implementation of intermediate value processing, and is the latest application of biometric recognition. The realization of its core technology demonstrates the transformation from weak artificial intelligence to strong artificial intelligence. To effectively extract facial features from different angles and postures under complex lighting conditions, this paper proposes a novel LBP multi-feature extraction fusion and KNN method. This paper proposes two improved local binary patterns (LBP), namely weighted fusion of triangular features with local binary pattern (WLBP) and Tulapuras local binary pattern (TLBP). They are fused with the Gabor filter and then classified by using the KNN classifier. The experiment adopts the YALEB, ORL and FERET face databases to conduct experiments. The experimental results show that the proposed method significantly improves the accuracy of face recognition under complex lighting and multi-angle and pose conditions compared with the traditional single LBP method.
A Novel Image Enhancement Method Based on Guided Filtering and Modified Retinex
This paper proposes a novel image enhancement based on guided filtering and modified Retinex method to address the problems of poor visual effect, unclear images and incomplete display of details in low-light images. Low-illumination images are processed using guided filtering to adaptively enhance the luminance component. This method consists of two parts: the image decomposition network and the image enhancement network. The image decomposition network is responsible for decomposing the original image into illumination components and reflection components. The image enhancement network optimizes parameters and performs $\gamma$ correction through the natural image quality evaluator (NIQE), adjusting the brightness and contrast of the illumination component, and then re-fuses it with the reflection component to improve the overall image quality. Finally, image quality enhancement experiments are conducted using the images in the SOTS standard test set and six actual scene images. They are compared and analyzed with other advanced methods. The experimental results show that the proposed method is superior to other methods in both subjective visual effects and objective evaluation indicators, thus fully verifying the effectiveness and feasibility of the proposed method.
Crowd Density Estimation Based on Multi-scale Feature Fusion and Information Enhancement
Aiming at the problems such as diverse target scales and large-scale changes in crowds in dense crowd scenarios, a crowd density estimation method based on multi-scale feature fusion and information enhancement is proposed. Firstly, considering that small-scale targets account for a relatively large proportion in the image, based on the VGG-16 network, the dilated convolution module is introduced to mine the detailed information of the image. Secondly, in order to make full use of the multi-scale information of the target, a new context-aware module is constructed to extract the contrast features between different scales. Finally, considering the characteristic of continuous changes in the target scale, a multi-scale feature aggregation module is designed to enhance the sampling range of dense scales and multi-scale information interaction, thereby improving the network performance. Experiments on public datasets show that the proposed method in this paper can effectively estimate the population density compared with other advanced methods.
Artificial Intelligence Empowering Bel Canto Education: Technical Paths, Aesthetic Challenges and Reconstruction of Teaching Paradigms
Artificial intelligence (AI) is profoundly reshaping the practical logic and theoretical boundaries of bel canto education. This study takes "technical path - aesthetic challenge - paradigm reconstruction" as the analytical framework to systematically explore the innovative mechanism and practical path of AI-empowered vocal education. At the technical level, an acoustic parameter modeling system is constructed based on deep learning. Through MEL spectrum analysis, forresonance tracking and breath dynamics modeling, the traditional singing assessment that relies on auditory experience is transformed into quantifiable and interpretable acoustic indicators (such as harmonic energy ratio, vowel resonance stability, etc.), achieving precise diagnosis and real-time intervention of vocal defects. Experimental data shows that this system reduces the learners' pitch error rate by 42\% and increases their breath control efficiency by 35\%. In the dimension of personalized training, an adaptive teaching engine based on reinforcement learning was developed to dynamically generate a three-dimensional matching scheme of "voice features - track difficulty - training intensity", significantly reducing the time for beginners to master core vocal techniques (by an average of 28\%). However, the conflict between AI quantitative indicators and the traditional aesthetic standards of vocal music has become prominent: the algorithm's preference for standardized formuster distribution may suppress the singer's unique timbre personality and artistic expression tension. To this end, a "dual-track evaluation model" is proposed: taking acoustic parameters as the technical benchmark and emotional expression and artistic appeal as the aesthetic benchmark, and achieving a dynamic balance between the two through expert annotation and group consensus algorithms. The research further reconstructs the teaching paradigm, advocating that AI tools be positioned as "aesthetic collaborators" rather than substitutes, and builds a new type of teacher-student relationship of "human-machine co-teaching - co-evaluation - co-creation". This research provides a solution that is both technically feasible and aesthetically reasonable for the digital transformation of bel canto education, revealing the underlying logic of the deep integration of art and technology in the AI era.
Acoustic parameter modeling Personalized training
High-dimensional Teaching Data Clustering in Sparse Subspaces Based on Information Entropy
Due to the large scale and high dimension of teaching data, the using of traditional clustering algorithms has problems such as high computational complexity and low accuracy. Therefore, this paper proposes a weighted block sparse subspace clustering algorithm based on information entropy. The introduction of information entropy weight and block diagonal constraints can obtain the prior probability that two pixels belong to the same category before the simulation experiment, thereby positively intervening that the solutions solved by the model tend to be the optimal approximate solutions of the block diagonal structure. It can enable the model to obtain the performance against noise and outliers, and thereby improving the discriminative ability of the model classification. The experimental results show that the average probability Rand index of the proposed method is 0.86, which is higher than that of other algorithms. The average information change index of the proposed method is 1.55, which is lower than that of other algorithms, proving its strong robustness. On different datasets, the misclassification rates of the design method are 1.2\% and 0.9\% respectively, which proves that its classification accuracy is relatively high. The proposed method has high reliability in processing high-dimensional teaching data. It can play an important role in the field of educational data analysis and provide strong support for intelligent teaching.
Construction and Application of the Teaching Mode of Robot Course for Applied Undergraduate Students under the STEAM Concept
In the current era of rapid technological development, robot technology has become a key force driving the transformation of various industries. Applied undergraduate education shoulders the important responsibility of cultivating high-quality robot professionals who can meet the demands of industries. This study focuses on constructing a robot course teaching model based on the STEAM concept in application-oriented undergraduate colleges and conducts an in-depth exploration of its application effect, with the expectation of providing useful references for the teaching reform of related courses. Firstly, the article deeply analyzes the connotation of the STEAM concept, namely interdisciplinary integration (science, technology, engineering, art, mathematics), practical innovation and the cultivation of comprehensive quality, emphasizing its fit and importance for the teaching of robot courses. Robotics technology itself is a field that integrates multiple disciplines, covering knowledge from various aspects such as mechanical design, electronic engineering, computer programming, and sensor technology. At the same time, it also requires students to possess innovative thinking and practical abilities to solve complex problems in practical applications, which is highly consistent with the educational goals advocated by the STEAM concept. It has laid a solid theoretical foundation for the construction of the teaching mode. Secondly, the specific construction process of the teaching mode was elaborated in detail. In terms of the curriculum system, it breaks through the limitations of the traditional single-discipline curriculum setting and builds a modular curriculum system covering multi-disciplinary knowledge, such as setting up basic modules (mathematical modeling, physics and mechanics, etc.), professional core modules (robot programming, mechanical structure design, etc.), and extension modules (artificial intelligence and robots, robot art design, etc.). Enable students to systematically learn knowledge related to robots and achieve the organic integration and expansion of knowledge. In terms of teaching methods, a variety of methods such as project-based teaching, problem-oriented teaching and group cooperative learning are comprehensively applied. Take project-based teaching as an example. Teachers assign robot project tasks with practical application backgrounds, such as designing a robot for campus environment monitoring. Students need to independently consult materials, form teams, and collaborate in division of labor. They participate throughout the entire process from the overall conception of the robot, mechanical structure construction, circuit design, program writing to the final debugging and operation. This not only exercises students' practical hands-on ability, but also cultivates their comprehensive ability to solve complex problems and teamwork spirit. In the teaching evaluation link, a diversified evaluation system has been constructed. It has changed the previous evaluation method mainly based on examination scores, combined process evaluation with outcome evaluation, and focused on conducting comprehensive evaluations of students' performance during the learning process, teamwork ability, innovation ability, and the quality of project completion, etc. By observing students' performance during the project implementation process, mutual evaluation among group members, project achievement presentation and defense, etc., comprehensively and objectively evaluate students' learning outcomes, promptly identify their strengths and weaknesses, and provide a basis for subsequent teaching improvement. Finally, through the teaching practice application carried out in the robotics major of an application-oriented undergraduate college, the constructed teaching mode was verified. The practical results show that students' enthusiasm and initiative in the robot course learning have significantly increased, and their classroom participation has greatly improved. They have shifted from passively accepting knowledge in the past to actively exploring and practicing. In terms of the achievements of course projects, the robot works designed by students are more innovative and practical, and can well meet the actual application needs. For example, some intelligent logistics robots designed by students have conducted preliminary application tests in the campus express delivery sorting scenario and achieved good results. Meanwhile, the number of awards won by students in various robot competitions has also significantly increased, which fully demonstrates the improvement of students' comprehensive quality and practical innovation ability, and strongly proves the effectiveness and feasibility of the robot course teaching model based on the STEAM concept. In conclusion, the application-oriented undergraduate robot course teaching model based on the STEAM concept constructed in this study has effectively promoted students' interdisciplinary knowledge integration, practical innovation and comprehensive quality improvement by optimizing the curriculum system, innovating teaching methods and improving the teaching evaluation system. It provides new ideas and methods for the cultivation of robot professionals in application-oriented undergraduate colleges and universities. It has significant theoretical and practical significance, and can provide reference and inspiration for the teaching reform of other related professional courses, promoting applied undergraduate education to better adapt to social development and industrial demands.
Analysis of Static Characteristics and Optimization of Basic Parameters of Static Pressure Gas Bearings for Precision Machine Tools
This paper conducts research on the hydrostatic gas bearings of precision machine tools, deeply analyzes their static characteristics and optimizes the basic parameters. Firstly, through theoretical modeling, the static characteristic equation of the hydrostatic gas bearing was derived, covering key indicators such as stiffness and bearing capacity. Subsequently, combined with the numerical simulation method, the static characteristic performance of the bearing under different working conditions was simulated to verify the accuracy of the theoretical model. The research finds that the static characteristics of gas bearings are significantly affected by parameters such as gas supply pressure and bearing clearance. Based on this, the basic parameters of the bearing were optimized and designed by using the optimization algorithm, and the optimal parameter combination was determined, which significantly improved the load-bearing capacity and stiffness of the bearing while reducing power consumption. The experimental results show that the application of the optimized hydrostatic gas bearing in precision machine tools can significantly improve the processing accuracy and stability, providing a theoretical basis and technical support for the design of high-performance bearings in high-end precision machine tools, and has important engineering application value.
Research on Financial and Economic Risk Analysis and Preventive Measures
This article conducts an in-depth exploration of the current situation, causes and potential impacts on the economic system of financial and economic risks, and puts forward corresponding preventive measures. Through the analysis of the current financial market, it is found that financial and economic risks mainly come from aspects such as market fluctuations, credit defaults, policy uncertainties, and poor internal management of financial institutions. The article points out that these risks will not only weaken the profitability of financial institutions, but also may trigger systemic financial risks, thereby causing an impact on the entire economic system. To effectively prevent financial and economic risks, this article suggests starting from multiple aspects such as strengthening financial supervision, improving the risk early warning mechanism, optimizing the internal governance structure of financial institutions, and promoting financial innovation. Meanwhile, emphasis is placed on the coordination and cooperation of macro policies to enhance the stability and risk-resistance capacity of the financial system. The research aims to provide theoretical support and practical guidance for financial institutions and regulatory authorities, promote the healthy and stable development of the financial market, and ensure the sustainable growth of the economy.
A Novel Dark and Light Primary Color Prior Theory for Road Visibility Detection
Aiming at the problems of high complexity and low accuracy of existing visibility detection methods, a road visibility detection method based on monitoring images is proposed. Firstly, the transmittance of dark and bright primary colors is obtained by the theory of dark and bright primary color prior. Then, the atmospheric light value and atmospheric transmittance are optimized by using adaptive fog removal weight and adaptive filtering window, and the transmittance of the first and last end points of the lane line is one-to-one corresponding to the optimized dark and bright primary transmittance. Finally, the atmospheric extinction coefficient and visibility are calculated by combining the distance between the end and end of the lane line. The experimental results show that this method can achieve high precision detection within 100-600m, and the relative error is less than 10%. Compared with other methods, the detection efficiency of this method is faster, the accuracy is higher and the realization is easier.
Research on Key Technologies of Wearable Epilepsy Seizure Monitoring and Alarm Devices
Epilepsy is a common and frequently occurring disease of the nervous system. Long term recurrent epileptic seizures seriously endanger the physical and mental health of patients, and also have profound impacts on families and society. In response to the above situation, this article aims to develop a wearable epilepsy seizure monitoring and alarm device ENP-V2 to help patients monitor seizures in real time and alert them, for the following aims:(1) Reduce the sudden death rate of epilepsy; (2) Promptly detect status epilepticus and urgently seek medical attention; (3) Reduce secondary damage caused by epileptic seizures and improve patients' quality of life; (4) Provide accurate seizure information for clinical practice and achieve precise diagnosis. Experiments on clinical data collected from Xuanwu Hospital demonstrate the performance of the proposed wearable epilepsy seizure monitoring and alarm device ENP-V2.
A Novel Chinese-English Neural Machine Translation Model Based on BERT
In recent years, neural machine translation has rapidly developed and replaced traditional machine translation, becoming the mainstream paradigm in the field of machine translation. Machine translation can reduce translation costs and improve translation efficiency, bring good news to cultural exchanges and international cooperation, and help national development. However, neural machine translation is highly dependent on large-scale high-quality parallel corpus, and there are problems such as uneven quality and sparse data, so it is imperative to study and explore neural machine translation. The purpose of this paper is to construct pseudo-parallel corpus using data enhancement technology, improve the diversity of Chinese and English materials, and then optimize the translation model to improve the translation effect of the model. Based on BERT pre-training technology, this paper first analyzes the limitations of the traditional Transformer model, and then puts forward two directions for model optimization. On the one hand, in the data preprocessing stage, multi-granularity word segmentation technology is used for word segmentation to help Chinese-English neural machine translation model better understand the text. On the other hand, in the pre-training stage, this paper adopts the strategy of deep integration of BERT dynamic word embedding and original word embedding. On the basis of the original Transformer, a fusion module is added, through which the original word embeddings and BERT dynamic word embeddings are simple linear splicing, and then fed into the encoder. The attention mechanism is used for deep integration and better word vector representation, enabling the Transformer model to take full advantage of the external semantic information introduced by BERT. Finally, the feasibility and effectiveness of the Transformer architecture adopted in this paper are verified by the comparison experiment between RNN and Transformer model. Through the ablation experiments of different word vector representation and different stages using BERT pre-training technology, the effectiveness of BERT dynamic word embedding and deep fusion of word embedding and the rationality of using pre-training technology only in the encoder stage are verified.
Chinese-English Neural Machine Translation
Multi-granularity word segmentation technology