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    Supply Chain Resilience: Research Review and Prospects
    Zhong-zhong JIANG, Jia-run GUO, Wei ZHENG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 59-70.   DOI: 10.12068/j.issn.1005-3026.2025.20250055
    Abstract2001)   HTML61)    PDF(pc) (2764KB)(2344)       Save

    In recent years, compounded crises such as geopolitical conflicts (e.g., the Russia-Ukraine conflict) and technological containment (e.g., the China-U.S. trade friction) have continuously exerted a profound impact worldwide, revealing the vulnerabilities of global supply chains. Enhancing the supply chain resilience has become a critical strategy to ensure the sustainable development of countries around the world, especially China, and it serves as a vital foundation for making China strong in manufacturing. On this basis, existing research on supply chain resilience was comprehensively reviewed, with particular focus on its origins, conceptual definitions, and driving factors. The evolution of the research was systematically analyzed, and prospective research directions were explored from four dimensions: collaborative optimization, resource allocation, dynamic response, and risk management. The findings aim to provide theoretical support and decision reference for enhancing supply chain resilience both globally and within China.

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    Development and Prospects for Software‑Defined Intelligent Control Systems
    Tian-you CHAI, Rui ZHENG, Yao JIA, Xin-yu HUANG, Yan-jie SONG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 1-10.   DOI: 10.12068/j.issn.1005-3026.2025.20250079
    Abstract1357)   HTML84)    PDF(pc) (4009KB)(664)       Save

    The current state of research on software-defined control systems was reviewed, and the role and development of control systems throughout the industrial revolutions were analyzed. The intelligent development direction for software-defined control systems was proposed. The case study of a software-defined end-edge-cloud collaborative PID(proportional-integral-derivative) tuning intelligence system was presented, which demonstrates that the tight conjoining and coordination between industrial artificial intelligence, industrial Internet, and other new-generation information technologies with software‐defined control systems has opened up a new way for the development of software-defined intelligent control systems. Finally, the principal research directions for software-defined intelligent control systems were pointed out by considering the challenges faced by software-defined control systems and those specific to their intelligent transformation.

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    Absolute Position Accuracy Calibration Algorithm for Robots Based on Joint Geometric Error
    Liang LIANG, Cheng-dong WU, Shi-chang LIU
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 1-7.   DOI: 10.12068/j.issn.1005-3026.2025.20230303
    Abstract1350)   HTML118)    PDF(pc) (1793KB)(771)       Save

    An industrial robot kinematic model with joint geometric error parameters and a calibration algorithm is proposed. Firstly, based on the DH model, six geometric error parameters are introduced for each joint to establish a more comprehensive error calibration model. The solutions of forward and inverse kinematic for the model are realized. Then, a differential kinematic Jacobian matrix containing 45 parameters, including joint errors, base coordinate errors, and tool coordinate errors is established. An iterative algorithm based on a small sample test set is used to solve the error parameters. Finally, experimental verification is carried out using a laser tracker on the SIASUN SR10C robot. The calibrated error parameters are then compensated into the model. Results show that, after calibration compensation, the maximum position error of the robot decreases by approximately 80%, the average position error decreases by approximately 80%, and the error variance decreases by approximately 97%, demonstrating that this method can significantly improve the absolute position accuracy and determinacy of industrial robots.

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    Review of Multi-type Energy Routers Research
    Qiu-ye SUN, Rong-da XING, Qian-xiang SHEN, Zhen-ao SUN
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 11-21.   DOI: 10.12068/j.issn.1005-3026.2025.20250063
    Abstract1055)   HTML17)    PDF(pc) (1261KB)(639)       Save

    Energy routers (ERs) are one of the core components of the energy Internet for achieving multi-port energy conversion and active energy flow control. This paper classified ERs into three categories: electrical ERs, information ERs, and multi-energy ERs. Based on the differences between these categories, the research on ERs is divided into four aspects: electrical conversion, focusing on topology and control of multi-port electrical conversion; energy routing control, primarily concerned with the regulation of power flow between ports of ERs; information processing and optimal control, emphasizing the acquisition and transmission of information and optimizing energy flow; and multi-energy coordination, with multi-energy comprehensive utilization as the main goal. Based on these four research aspects, this paper explored topology, control, communication, and multi-energy optimization of ERs, as well as the interrelationships between different aspects.

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    Intelligent Identification Method of Industrial Mixed Gases Based on ConvGRU Fusion Attention Mechanism
    Fan-li MENG, Shu-chang LI, Hao WANG, Zhen-yu YUAN
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 37-48.   DOI: 10.12068/j.issn.1005-3026.2025.20240164
    Abstract977)   HTML22)    PDF(pc) (4835KB)(323)       Save

    To address the issue of high data dependency and insufficient accuracy in mixed gas identification for traditional semiconductor gas sensors, a ConvGRUAttention network model that integrates gated recurrent units (GRU), convolutional layers, and attention mechanism is proposed. Empirical wavelet transform (EWT) is employed to convert raw signals into the time-frequency domain and perform multi-scale decomposition, which suppresses noise, reduces data dependency, and enhances the model’s robustness. The model extracts local dynamic features through convolutional layers, captures long-term dependencies using GRU, and optimizes feature weights across multi-scale signals via the attention mechanism, thereby improving feature extraction and generalization capabilities. Experimental results demonstrate 100% accuracy in qualitative identification and a root mean square error (RMSE) of 3.3×10⁻⁶ in quantitative detection. Compared with the traditional methods, the detection accuracy for mixed gases is significantly improved.

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    Progress and Application of Intelligent Manufacturing Technology
    Ya-dong GONG, Jia-hao GAO, Li-ya JIN, Heng ZHAO
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 84-93.   DOI: 10.12068/j.issn.1005-3026.2025.20250053
    Abstract901)   HTML30)    PDF(pc) (4156KB)(1056)       Save

    Based on an analysis of the extensive and profound impact of the development of artificial intelligence on manufacturing technology, it is expounded that intelligent manufacturing is the main direction for building a strong manufacturing country, and its important roles are discussed in the construction of a new industrialization system. Through an introduction to intelligent manufacturing empowerment technology, it is claimed that intelligent manufacturing technology by the deep integration of intelligent technology and advanced manufacturing technology will drive the progress of the new round of industrial revolution and industrial transfor-mation. Subsequently, typical research results and application examples of intelligent manufacturing are used to clarify the essential differences and engineering applications between intelligent manufacturing and traditional automation. Finally, it is pointed out that the advanced production mode of the deep integration of new generation information technology and advanced manufacturing technology demonstrates a new form of future manufacturing industry.

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    Research on Localization of Industrial Intelligent Inspection Robots in Cable Tunnel Environment
    Yu-tao WANG, Jun-wei AN, Chang-sheng QIN, Wei-fan GUO
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 49-58.   DOI: 10.12068/j.issn.1005-3026.2025.20240212
    Abstract892)   HTML13)    PDF(pc) (5943KB)(124)       Save

    The cable tunnel is closed and narrow, with repetitively laid cable racks and similar scene textures, which is a degraded scenario. To address this environment, a visual-inertial SLAM (simultaneous localization and mapping) algorithm based on point-line feature fusion is proposed. The algorithm improves the high-dimensional line features through length suppression and short line fitting to make it more effective in describing the structural features of tunnel scene. In addition, for the problem of loop closure detection failure due to feature similarity in cable tunnels, ArUco markers with efficient recognition and accurate pose estimation are introduced to limit the loop closure area, and the optimal loop closure frames are selected using the minimized pose transformation to improve detection accuracy and localization precision. Finally, dataset collection and experimental validation were conducted in actual cable tunnels. The results show that the absolute trajectory accuracy of the algorithm is improved by 69.73% on average relative to VINSMono(visual intertial system-Mono), which meets the application requirements of cable tunnel inspection.

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    Explosion Characteristics of NCM Lithium-Ion Battery Vent Gases After Thermal Runaway Under High Temperature Conditions
    Gang LI, Xiu-peng ZHANG, Wei-da CHANG, Wei ZHOU
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 78-86.   DOI: 10.12068/j.issn.1005-3026.2025.20230268
    Abstract886)   HTML4)    PDF(pc) (1911KB)(717)       Save

    In order to evaluate the risk of deflagration in high temperature environments caused by NCM lithium-ion battery vent gas (BVG) after thermal runaway, the explosion characteristics and laminar burning velocity of BVG at different initial temperature θ0 (25~120 ℃) were tested using an 8 L explosive chamber and a Bunsen burner. At the same time, the influence mechanisms of laminar burning velocity(SL) at room temperature and high temperatures were further analyzed by CHEMKIN numerical simulations. The results show that the LFL doesn’t change significantly with the increase of the initial temperature, and UFL increases. When θ0 increases to 120 °C, pmax decreases from 0.62 MPa to 0.45 MPa, and the relationship with θ0 is exponential. Affected by both positive and negative effects, (dp/dtmax decreases to different degrees with the increase of θ0; LOC decreases exponentially from 7.39% to 7.03%; SL increases with the increase of θ0. It is also found that C2H4 and H2 are the decisive factors affecting the combustion and explosion damage degree of BVG. The research results can provide a reference for the risk assessment and prevention of environmental deflagration caused by thermal runaway in NCM lithium-ion batteries.

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    Research Progress on Development and Application of Digital Blast Furnace Ironmaking Technology
    Man-sheng CHU, Guo-dong WANG, Jue TANG, Quan SHI
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 113-130.   DOI: 10.12068/j.issn.1005-3026.2025.20250070
    Abstract882)   HTML14)    PDF(pc) (4726KB)(1485)       Save

    With the advancement of the digital information era, the digital transformation of blast furnaces has begun. Steel enterprises have applied intelligent closed-loop control, digital twins, and AI-based predictive models to develop intelligent systems for smart blast furnace operation, blast furnace condition assessment, and quality optimization. Research on digital blast furnaces primarily focuses on variable prediction, state diagnosis, and blast furnace condition optimization, with these domains evolving from traditional approaches toward complex optimization modeling, multidimensional comprehensive evaluation, and multi-objective collaborative optimization, respectively. However, current predictive models require enhanced online self-updating and integration of data and mechanisms; evaluation systems need to emphasize multidimensional and fine-grained diagnostics, and blast furnace condition optimization has to overcome single-indicator limitations by focusing on low-risk, low-cost, and multi-objective coupled strategies. According to the actual needs of the blast furnace site, a physical system of blast furnace information was developed, where data, mechanisms, and experience were reasonably matched and called upon to form an integrated technology encompassing data governance, rule mining, intelligent prediction, comprehensive evaluation, multi-objective optimization, and decision feedback, which was identified as one of the key directions for future development of digital blast furnace ironmaking.

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    Digital Twin Fault Diagnosis Method of Power Transformer Based on Industrial Intelligence
    Jian FENG, Bo-wen ZHANG, Ning ZHAO, Hui-jie JIANG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 22-29.   DOI: 10.12068/j.issn.1005-3026.2025.20240218
    Abstract836)   HTML18)    PDF(pc) (4393KB)(237)       Save

    As a key development direction integrating new-generation information technology with advanced manufacturing techniques, industrial intelligence leverages intelligent, digital, and automated methods to significantly enhance industrial production efficiency and optimize the prediction and maintenance management of industrial equipment. This paper focuses on the intelligentization of industrial equipment, with the goal of ensuring the efficient and stable operation of power transformers within power systems. A digital twin model for transformer inter-turn short circuit faults is constructed based on electromagnetic field equations and equivalent circuit models. The model analyzes the symmetry of the transformer in both normal and fault conditions from an electromagnetic field perspective, thereby integrating digital twin technology with fault diagnosis. Furthermore, through in-depth analysis of the virtual model of the transformer, the location of faults is accurately identified, ensuring the safe operation of the transformer, improving its reliability and efficiency, and advancing the intelligentization and modernization of the entire power system.

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    Effect of TiAlSiN Coating Structure on Its Mechanical Properties
    Xing-long LIU, Chen LI, Zeng LIN
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 33-42.   DOI: 10.12068/j.issn.1005-3026.2025.20230286
    Abstract820)   HTML14)    PDF(pc) (4777KB)(722)       Save

    TiSi (atomic ratio 80∶20) and AlTi (atomic ratio 67∶33) alloys were used as target materials by the vacuum arc ion plating technique. Two layers and multiple layers of TiAlSiN coating were deposited on the WC-Co substrates to study the effects of the coating structure on the microstructure, mechanical properties, and tribological properties of the coatings. TEM,SEM, EDS, XRD, nano-indentation instrument, microhardness instrument and binding force tester were used to analyze the cross sections of the coatings and the morphologies, compositions, microstructures, elastic moduli, microhardness and binding force of the coating. The tribological properties of the coatings were analyzed by the friction and wear testing machine. The results showed that the binding force (greater than 200 N) of the multilayer coatings is higher than that of the double-layer coatings. The double-layer coatings exhibit stronger resistance to plastic deformation, while the multilayer coatings show stronger resistance to elastic deformation. The friction coefficient of the coatings under low loads is greatly affected by the surface topography of the coating, while the surface topography of the coating under large load has little effect on the friction coefficient. Oxidation wear occurs only in the double-layer coatings, while abrasive wear occurs in the friction wear process of both coatings. The wear resistance of the multi-layer coatings is higher than that of the double-layer coatings.

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    Electric Vehicle Charging Scheduling Strategy Based on Safe Reinforcement Learning Algorithm
    Heng-xin PAN, Run-da JIA, Shu-lei ZHANG
    Journal of Northeastern University(Natural Science)    2025, 46 (5): 1-9.   DOI: 10.12068/j.issn.1005-3026.2025.20230183
    Abstract781)   HTML35)    PDF(pc) (1237KB)(926)       Save

    As the number of electric vehicles (EVs) increases, reinforcement learning (RL) in EV charging scheduling faces challenges, particularly uncertainties and the curse of dimensionality from large‑scale applications. A microgrid model for residential areas, considering the vehicle‑to‑grid (V2G) mode and various nonlinear charging models is developed. The problem is formulated as a constrained Markov decision process (CMDP), with a model‑free RL framework to handle uncertainties. To address the curse of dimensionality, a strategy is designed where EVs are grouped by states, and agents send control signals to these sets, thus reducing the dimensionality of the action space. A Lagrangian deep deterministic policy gradient (LDDPG) algorithm is employed to solve the charging scheduling problem, with a safety filter ensuring constraint compliance. Numerical simulations validate the strategy’s effectiveness.

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    Evaluation and Optimization of Green Development Efficiency of Construction Industry in China
    Xi-jing QI, Meng-xing ZHANG, Sheng-jin ZHANG
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 115-123.   DOI: 10.12068/j.issn.1005-3026.2025.20230287
    Abstract754)   HTML6)    PDF(pc) (1523KB)(200)       Save

    An evaluation index system was established based on the green development and its efficiency in the construction industry.The static and dynamic efficiencies of green development in the construction industry at the national and provincial levels were estimated using the super efficiency SBM-ML model from 2008 to 2021. The results demonstrate that the static efficiency of green development in the construction industry in China is fluctuant rising. The efficiency value is greater than 1 for nine years. This implies that the green development in the construction industry is relatively effective. The green development efficiency in the construction industry has increased by an average of 1.7% annually, but the stability of dynamic efficiency in different years needs to be improved. The static efficiency of green development in the construction industry in each province shows a distribution of "high in the southeast and low in the northwest", and the efficiency value in each province shows an obvious differentiation of "high, medium and low". While there are fluctuations in the dynamic efficiency levels of green development in the construction industry in each province, there is no obvious manifestation of specific geographical distribution patterns. Through analyzing the calculation results, relevant suggestions are put forward from the aspects of policies and enterprises to optimize the green development efficiency and promote the high-quality development in the construction industry.

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    Experimental Study on Muck Improvement of Clay Stratum in Earth Pressure Balance Shield
    Meng LI, Wen ZHAO, Xin WANG, Xiao-di LIU
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 106-114.   DOI: 10.12068/j.issn.1005-3026.2025.20230273
    Abstract740)   HTML10)    PDF(pc) (1835KB)(510)       Save

    When tunneling in clay stratum, the shield may encounter some problems such as “mud cake” on the cutterhead, soil accumulation in the chamber, and poor soil discharge. Therefore, based on the shield project between Wenchu Road Station and Guanyin Road Station of Shenyang Metro Line 6, muck improvement was carried out based on foaming ratio and half-life test, dispersant settlement test, rotating shear test, mobility test and stirring test. Finally, scanning electron microscopy, Zeta potential and double electric layer theory were used to analyze the mechanism of clay improvement by dispersant. The results show that the performance of the modifier is better when the volume fraction of foam agent is 4% and the sodium hexametaphosphate mass fraction is 2%. The improvement effect is better when the moisture content of soil layer is 30%, the dispersant injection ratio is 10% and the foam injection ratio is 30%~60%. Scanning electron microscopy and Zeta potential tests showed that 2% sodium hexametaphosphate solution can increase the thickness of the clay double electric layer and disperse the clay.

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    Intelligent Microseismic Monitoring and Early Warning for Rock Burst During TBM Excavation of Deep Metal Mines
    Bing-rui CHEN, Xu WANG, Gui-peng JIANG, Fei HE, Jia-lin HAN, Jian-jun HAO
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 148-162.   DOI: 10.12068/j.issn.1005-3026.2025.20250085
    Abstract726)   HTML14)    PDF(pc) (10803KB)(738)       Save

    In response to the problem of insufficient automation and intelligence in the microseismic monitoring and early warning for rock bursts during tunnelling boring machine (TBM) excavation of deep metal mines, research on multi-dimensional parameter recognition of drilling holes based on deep machine vision DPED-AT method was conducted; automatic disassembly and assembly device for microseismic sensors was developed, and the decision-making system was designed, achieving automatic disassembly and assembly of microseismic sensors during TBM excavation. Microseismic intelligent frequency conversion acquisition technology was developed, realizing continuous and high-fidelity acquisition of rock rupture information during the rock burst incubation process. An improved neural network algorithm for identifying and picking up rupture signals was proposed, as well as a three-dimensional characterization algorithm for the probability field of microseismic sources induced by rock bursts incubation. Intelligent interpretation and refined early warning of rock burst incubation information during TBM excavation were preliminarily realized, and an intelligent monitoring and early warning technology system for rock burst that integrated intelligent drilling hole recognition, automatic sensor disassembly and assembly, and intelligent signal acquisition and interpretation was ultimately established. The application in Ruihai Gold Mine shows that it has achieved automatic microseismic monitoring, interpretation, and early warning of rock burst, providing strong support for less manned and unmanned TBM excavation in deep metal mines.

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    Visual-Inertial-GNSS Tightly Coupled Navigation and Positioning Method with Fusion of Point and Line Features
    Li-ming HE, Quan-you YUE, Zheng-lin QU, Yu ZHANG
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 124-133.   DOI: 10.12068/j.issn.1005-3026.2025.20230292
    Abstract714)   HTML5)    PDF(pc) (2317KB)(633)       Save

    A multi-sensor fusion positioning method was proposed to address the limitations of single-sensor localization in complex environments. In terms of vision, line features were added to point features to overcome the interference caused by repetitive textures in visual images. In the global navigation satellite system (GNSS), the introduction of carrier phase with higher accuracy was used to smooth the pseudorange observations, which improved the accuracy of single point positioning. The accuracy and stability of the algorithm were validated by using both public datasets and measured data. In both public datasets and actual data, the accuracy of the proposed method is improved by 32.2%, 23.3%, 24.5%, and 25.7%, 25.8%, and 14.1% in the XY, and Z directions, respectively, compared to the GVINS (visual inertial GNSS tightly coupled algorithm) in the geocentric coordinate system. In addition, in the environments where satellite signals are severely obstructed, the proposed method still has good positioning performance for a certain period of time, with a positioning accuracy of 0.74 m in plane and 0.91m in elevation. Research results provide new insights for multi-sensor fusion position in complex environments.

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    Hydration Characteristics of Slag-Fly Ash Cementitious System Activated by Lime-Sodium Sulfate Composite
    Ying WANG, Xiao-wei GU, Xiao-chuan XU, Qing WANG
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 87-96.   DOI: 10.12068/j.issn.1005-3026.2025.20230270
    Abstract697)   HTML9)    PDF(pc) (2250KB)(791)       Save

    In order to solve the problems of quick setting time and poor safety when using strong alkali activator such as sodium hydroxide in alkali-activated slag-fly ash cementitious system, a compound activator with the amount of substance ratio of lime to sodium sulfate of 1∶1 was used to activate the slag-fly ash cementitious system. The effects of activator dosage and fly ash content on the properties of lime-sodium sulfate compound-activated slag-fly ash cementitious system were analyzed. The hydration products and hydration process of the cementitious system were explored by XRD and other detection methods. The results show that the composite activator composed of lime and sodium sulfate can replace sodium hydroxide to activate the cementitious system of slag-fly ash, and the fluidity and setting time of the cementitious system can be controlled. The optimal dosage of the composite activator in the cementitious system is 10%, and when the fly ash content is less than 50%, the 28 d compressive strength of the cementitious system is above 36MPa. Lime-sodium sulfate compound activator can effectively destroy the shell of fly ash, promote fly ash to participate in the hydration reaction of cementitious system, and enhance the later compressive strength of cementitious system. C-(A)-S-H gel and ettringite cement slag and fly ash with different reaction degrees and particle sizes to form a compact matrix structure, which provides the main compressive strength for the cementitious system. This study provides a reference for the preparation of noval low-carbon cementitious materials.

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    Analytical Modeling and Performance Evaluation of Multi-stage Assembly Lines with Line-Side Buffers
    Peng-hao CUI, Qi-man ZHANG, Zhong-zhong JIANG, Guo-jun SHENG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 71-83.   DOI: 10.12068/j.issn.1005-3026.2025.20240193
    Abstract691)   HTML5)    PDF(pc) (2384KB)(344)       Save

    The output performance of assembly lines is not only affected by machine unreliability and limited buffer capacity but also constrained by line-side buffers. The analytical modeling and performance evaluation of multi-stage assembly lines with line-side buffers were investigated. Firstly, for the single-stage assembly lines, the steady-state probability distribution of system states was derived based on Markov chains. Secondly, for the two-stage assembly lines, each single-stage subsystem was modeled as a machine with one operational state and one failure state. A performance evaluation model was then established using Markov chains, and closed-form expressions for performance indicators were obtained. Thirdly, for the multi-stage assembly lines, an aggregation method was proposed to approximate the performance indicators. Furthermore, the accuracy of the performance evaluation method was validated through numerical experiments. Finally, utilizing the proposed method, numerical experiments were conducted to examine system properties, such as reversibility and monotonicity in the multi-stage assembly lines.

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    Research on Detection of Alzheimer Disease Based on Image Fusion Technology
    Zhi-gang LI, Ming-kai MU, De-an HU, Nan XIANG
    Journal of Northeastern University(Natural Science)    2025, 46 (6): 1-7.   DOI: 10.12068/j.issn.1005-3026.2025.20230338
    Abstract689)   HTML41)    PDF(pc) (2142KB)(432)       Save

    The plasma samples of Alzheimer disease(AD) patients are collected using Fourier transform infrared-attenuated total reflection (FTIR-ATR) spectroscopy technology. Based on the FTIR-ATR spectral data of the plasma membrane samples, the spectral data are encoded into two-dimensional images by utilizing the Gram angular field (GAF) and Markov transition field (MTF). Meanwhile, a neural network model based on the deep residual networks and attention mechanism is combined to conduct the screening and classification research on Alzheimer disease. The experimental results show that the GAF-MTF-CNN model can effectively improve the accuracy of spectral feature extraction. Additionally, the method of combining two-dimensional data with deep learning has better classification accuracy compared with traditional classification methods. Encoding spectrum into images using GAF and MTF techniques, and combining them with an improved residual neural network, effectively enhances the generalization ability and diagnostic accuracy of AD screening models, optimizing the screening performance.

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    Perception Technology and Application of Complex Urban Traffic Environment Based on Target Detection
    Aisan XIERAILI, De-fu CHE, Duo WANG, Tian YU
    Journal of Northeastern University(Natural Science)    2025, 46 (5): 29-36.   DOI: 10.12068/j.issn.1005-3026.2025.20230297
    Abstract677)   HTML13)    PDF(pc) (4528KB)(184)       Save

    Machine vision-based environmental perception technology is one of the key tasks in the field of intelligent transportation. Traditional deep learning algorithms typically meet the detection needs of individual targets in simple scenarios. However, they are not capable of addressing the intelligent perception requirements in complex traffic environment. To improve the intelligent perception capability of vehicles in such environment, this paper proposes an improved YOLOv8 object detection network model, integrating attention mechanisms, optimizers, and deformable convolutional layers to achieve multi-target detection in complex urban traffic environment. To verify the effectiveness of the algorithm, comparative experiment were conducted using YOLOv4, YOLOv8, and the improved YOLOv8 algorithm on sample images from complex traffic environments. The results show that, compared to YOLOv4 and YOLOv8, the improved YOLOv8 algorithm increased the average accuracy by 40.76% and 16.92%, respectively. The detection accuracy and real-time performance of the improved YOLOv8 algorithm meet the practical application requirements, and through multi-sensor information fusion, it can realize intelligent perception in complex urban traffic environment.

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