AI Scientific Illustration Gallery

Control flowchart of a fractional-order fuzzy neural network with dead-zone output.

Automated deployment and maintenance are fundamental to reducing operational costs and improving efficiency. The primary goal is to replace traditional manual, repetitive tasks with standardized, automated processes for system deployment, version upgrades, security patch distribution, and equipment maintenance. This approach reduces the risk of human error, enhances operational efficiency, and lowers labor costs. Traditional manual methods suffer from complex processes, difficulties in cross-system collaboration, and long execution cycles, leading to high labor costs and inefficient resource allocation. The core operational logic of this scenario follows a closed-loop management system: "Request Initiation - Solution Orchestration - Resource Scheduling - Automated Execution - Result Verification - Log Archiving." This creates a multi-level collaboration mechanism involving front-line execution and feedback, second-line solution design, and third-line standards development. Key constraints include task execution timeliness (efficient completion during off-peak hours), resource utilization thresholds (controlling hardware resource consumption), and process compliance rates. Standardized operation allows for the precise collection of core data such as hardware resource usage, labor input, and process efficiency, providing support for extracting cost-related metrics for feature systems. [An example diagram illustrating the closed-loop process of automated deployment and maintenance and multi-level collaboration should be inserted here, clearly showing the flow logic of each stage and the operational responsibilities of the first, second, and third lines of support.]

Fault prediction is a proactive approach to operations and maintenance (O&M) that aims to reduce repair costs. The core objective is to leverage historical data and real-time status information to identify potential system vulnerabilities and weaknesses in advance, predict failure types and their impact, and reduce unexpected failures through preventative maintenance, thereby lowering O&M costs and business losses. Traditional O&M lacks the means to identify hidden risks and relies on periodic maintenance, which is costly and has limited effectiveness. The core operational logic of this approach is "data acquisition - feature extraction - model prediction - early warning notification - optimization and improvement." Data sources include historical failure records and equipment operating data. Front-line personnel are responsible for troubleshooting and rectification, second-line personnel are responsible for model optimization, and third-line personnel are responsible for strategy development. Key constraints include prediction accuracy, early warning lead time, and vulnerability identification coverage. Application of this approach can significantly reduce failure rates and repair costs, providing support for feature system extraction of hardware resource status and system loss-related indicators. [An example diagram illustrating the fault prediction technical architecture and data flow is needed here, showing data input, prediction process, and early warning notification path.]

APPROVED Technical Illustration Request: Multi-modal Feature Fusion Neural Network Architecture Role: Technical Illustrator for Computer Science Research. Subject: A neural network architecture diagram illustrating 'Multi-modal Feature Fusion'. Style: Academic, IEEE standard, flat 2D vector, orthogonal lines, high contrast. White background. Layout & Components (Left to Right flow): 1. Input Phase (Left): Three parallel input vectors stacked vertically: * Top: A blue vector bar labeled '$V_{sem}$ (Semantic)'. * Middle: A green vector bar labeled '$V_{graph}$ (Graph)'. * Bottom: An orange vector bar labeled '$V_{stat}$ (Statistical)'. 2. Alignment Phase (Middle-Left): * The Top ($V_{sem}$) and Middle ($V_{graph}$) vectors pass through unchanged (identity). * The Bottom ($V_{stat}$) vector passes through a small neural network block labeled 'MLP Alignment'. * The output of this block is a new vector labeled '$H_{stat}$'. 3. Fusion Phase (Center): * Show the three vectors ($V_{sem}$, $V_{graph}$, $H_{stat}$) merging into one long vertical block. * Label this merging operation with the symbol '||' (Concatenation).

The specific measures for the research on multi-modal data intelligent analysis to empower the reform and practice of university teaching mode are as follows: To ensure the realization of the research objectives, this project will focus on three core levels: "data foundation construction, analysis method research, and teaching practice closed-loop", which respectively correspond to solving the black box problem of teaching evaluation, the dormant problem of teaching data, and the open-loop problem of teaching optimization. The overall research framework is shown in Figure 1, and the specific measures for step-by-step implementation include: (1) Constructing a unified and standardized multi-modal teaching data base. First, we will focus on opening up and managing the data scattered in smart classrooms. The core task is to formulate and implement the "Teaching Multi-modal Data Governance and Privacy Security Specification" to systematically clean, desensitize, and spatio-temporally align the original data such as classroom videos, audios, courseware, and interactive texts. On this basis, relying on data lake warehouse technology, we will build a standardized and safely shareable teaching theme database. This database not only realizes the centralized storage and efficient management of data, but also ensures that all data applications are carried out within the compliance framework through strict data security protocols, providing a solid and reliable data foundation for subsequent intelligent analysis. (2) Developing intelligent analysis tools that deeply integrate with educational theories. The focus of this stage is to transform cutting-edge information technology into analytical tools with educational explanatory power. We will systematically introduce models in the fields of computer vision and natural language processing, and deeply adapt and innovatively apply them to educational scenarios. The specifics include: ① Dynamic analysis of teaching behavior: Going beyond simple "head-up rate" statistics, using pose recognition technology to analyze the dynamic changes of student group behavior patterns (such as listening, writing, and collaboration) under specific teaching events (such as group discussions and teacher questions), and visualize the teacher's classroom movement trajectory and interaction range. ② Classroom cognitive level assessment: Applying natural language processing technology to deeply analyze the transcribed teacher-student dialogue text to realize automated identification of the cognitive level of questions and construction of the logical structure map of classroom discussions, so as to quantitatively evaluate the depth and quality of thinking in classroom dialogues. The final result will be reflected in a set of interactive visualization dashboards embedded in the teaching process, providing teachers with intuitive and easy-to-understand "classroom teaching analysis reports" to help them reflect on their teaching. (3) Carry out data-based teaching practice closed-loop iteration and effect verification. In order to promote the effective transformation of analytical results into teaching productivity, we will form a "research-practice community" with front-line teachers and carry out empirical research using action research methods. By selecting typical courses in engineering majors, we will work with cooperative teachers to jointly establish an iterative closed loop of "data feedback-teaching intervention-effect evaluation". We will regularly provide teachers with data analysis reports and organize joint seminars to jointly interpret data, diagnose teaching problems, and design and implement precise teaching intervention strategies (such as optimizing question design and adjusting interaction methods). By systematically comparing the process data (behavioral and cognitive indicators), outcome data (academic performance), and subjective feedback (teacher-student surveys and reflections) before and after the intervention, we will comprehensively verify the actual effect of data analysis-driven teaching improvement, and continuously optimize the analysis model and method in the iteration. Through the above measures

The plan is to use a BERT-based approach for semi-supervised learning of anomalous text, which is divided into four parts: unsupervised pre-processing, clustering algorithms, pseudo-label incorporation, and active learning using large models.

Control Block Diagram Generation: This control block diagram embodies a hybrid force-position strategy of 'inner velocity loop + outer force loop'. The trajectory tracking module provides the desired end-effector velocity command v_pos for tangential motion, serving as the primary reference for the inner servo loop. Simultaneously, a six-dimensional force/torque sensor acquires contact force data, which is transformed to obtain the normal force F_z in the tool coordinate system. This F_z undergoes zero-offset calibration, amplitude limiting, and a second-order IIR low-pass filter to reduce noise. Subsequently, a Kalman filter is employed to online separate the slow-varying drift b(k), resulting in a stable normal force feedback F_z,f. The outer loop uses the desired normal force F_z,d and F_z,f to form a force error e_f, which is then used by a one-dimensional second-order impedance/admittance model Md ẍe + Bd ẋe + Kd xe = e_f to calculate the normal dynamic response. After two discrete integrations, the normal velocity correction v_z is obtained, leading to the construction of v_force=[0,0,v_z,0,0,0]^T. Finally, a selection matrix is used to synthesize v_pos and v_force at the velocity level, resulting in v_cmd=Sx v_pos+Sf v_force, which is sent to the robot's servo interface for execution at a period of Ts=2 ms. This enables the system to maintain tangential trajectory tracking while achieving constant force contact in the tool's z-direction, with the contact force fed back to the sensor to form a closed loop.

Generate an icon for information entropy calculation.

Generate a landscape-oriented, academic-style flowchart illustrating the "Automatic Copyright Revenue Distribution Process (Based on Blockchain Smart Contracts)." The process should flow from left to right, employing a clean, vector style suitable for academic paper illustrations. Step 1: On the left, "User (Licensee / Buyer)," represented by a person or terminal icon, labeled "Payment (Purchase / Lease)." Step 2: An arrow points to "Select Copyright Category + Units + Duration." Step 3: Entering the "Smart Contract (Copyright Token Contract)," display a contract box, internally labeled "Price Verification (Sale / Lease Price per Unit)." Step 4: Fees enter the contract pool, labeled "Payment Received." Step 5: The contract queries "Category Share Table ownershipValues[tokenId][category]," using branching arrows to represent multiple rights holders. Step 6: The contract automatically distributes revenue according to the share ratio, with each rights holder receiving a corresponding proportion of income, labeled "Automatic Revenue Distribution." Step 7: The process endpoint displays "Record License (Usage Right) or Transfer Shares (Ownership Transfer)." Add a small annotation in the lower right corner: "Revenue distribution is based on the copyright share ratio of the same category." The overall color scheme should be primarily black, white, and gray, with a small amount of blue or green to emphasize "Automatic Distribution." Use a simple font, clear logic, and a serious academic style.