• Volume 48,Issue 2,2025 Table of Contents
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    • >Communication·Computer·Automation Engineering
    • Numerical differentiation for evaluating theoretical accuracy of device combination trajectory

      2025, 48(2):1-9. DOI: 10.11835/j.issn.1000-582X.2025.01.001

      Abstract (309) HTML (106) PDF 753.59 K (185) Comment (0) Favorites

      Abstract:The theoretical accuracy evaluation of device combination trajectories is a critical foundation for device allocation design and trajectory selection. Existing models for accuracy evaluation are based on the error propagation principle, using the Jacobian matrix of the trajectory with respect to measurement elements as their core. However, obtaining the analytic expressions for the Jacobian matrix elements in complex trajectory equations is challenging. This paper proposes and designs a theoretical accuracy evaluation algorithm for device combination trajectories based on numerical differentiation. By constructing numerical sequences and calculating the Jacobian matrix using numerical differentiation with spline interpolation, the theoretical accuracy of the device combination trajectory is determined. The algorithm’s effectiveness and practicality are validated by comparing the Jacobian matrix and accuracy values of the proposed method with those derived from a single device position equation.

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    • Research on software defect prediction based on machine learning

      2025, 48(2):10-21. DOI: 10.11835/j.issn.1000-582X.2025.02.002

      Abstract (435) HTML (90) PDF 2.03 M (236) Comment (0) Favorites

      Abstract:With the gradual penetration of machine learning technology into various fields, software testing in the software development process is very important. Software defect prediction faces class imbalance problem and accuracy issue. This paper proposes a supervised learning-based software prediction method for solving these two core problems. The method adopts sample balancing technique, combined with synthetic minority over-sampling technique(SMOTE) and edited nearest neighbor(ENN) algorithm, to test local weight learning(LWL), J48, C4.8, random forest, Bayes net(BN), multilayer feedforward neural network(MFNN), supported vector machine(SVM), and naive Bayes key(NB-K). These algorithms are applied to three different datasets (KK1, KK3 and PK2) in the NASA database and their effects are compared and analyzed in detail. The results show that the random forest model combining SMOTE and ENN exhibits high efficiency and avoiding overfitting in dealing with class imbalance problems, which provides an effective way to solve the problem in software defect prediction.

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    • Research on mechanism of conflict data fusion in multi-agent systems based on D-S evidence theory

      2025, 48(2):22-34. DOI: 10.11835/j.issn.1000-582X.2025.02.003

      Abstract (324) HTML (122) PDF 2.08 M (265) Comment (0) Favorites

      Abstract:The multi-agent information fusion(MAIF) system is smainly aimed at information fusion, regulation, communication, and conflict resolution among multiple agents. A multi-agent system conflict data fusion method combining reconstructed basic probability assignment and belief entropy is proposed to address the issue of D-S evidence theory failure under highly conflicting data conditions. This method uses reconstructed basic probability assignment and belief entropy to correct the reliability of evidence, obtaining more reasonable evidence. Then, the evidence is fused using the Dempster combination rule, and the results are obtained with a confidence level of over 90% in both 2 experiments. The experiment demonstrates the effectiveness of this method and improves the accuracy of the MAIF system identification process.

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    • Reconstructing missing health monitoring data using a deep network integrating EEMD and BiLSTM

      2025, 48(2):35-49. DOI: 10.11835/j.issn.1000-582X.2025.02.004

      Abstract (254) HTML (132) PDF 8.38 M (179) Comment (0) Favorites

      Abstract:In long-term monitoring processes, the structural health monitoring(SHM) system often encounters data incompleteness due to various factors, including sensor malfunctions, power interruptions, and network transmission issues. To address this challenge, this study proposes a missing data reconstruction method for structural monitoring based on ensemble empirical mode decomposition(EEMD) and bidirectional long short-term memory(BiLSTM) networks, leveraging their advantages in time-series processing. The proposed approach utilizes EEMD to adaptively decompose the monitoring time-series data into a set of intrinsic mode functions (IMFs), each representing different time scales. This decomposition effectively transforms the nonlinear and non-stationary time-series signals into stationary components. The IMFs are then input into a BiLSTM network for missing data reconstruction, enhancing the prediction accuracy of the BiLSTM model. Analysis is conducted on a six-story scaled structural model and a benchmark finite element simulation model. Experimental results demonstrate that, compared to the mainstream methods such as EEMD-LSTM, BiLSTM, and LSTM, the proposed EEMD-BiLSTM approach achieves the highest prediction accuracy. In cases of 5%, 10% and 15% missing data, the R2 value remains above 0.8. Therefore, the use of the EEMD method for preprocessing non-stationary structural acceleration response data significantly improves the prediction accuracy of BiLSTM, providing a more adaptive solution to the problem of missing data in structural monitoring.

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    • An image dehazing algorithm based on improved multi-scale AOD-Net

      2025, 48(2):50-61. DOI: 10.11835/j.issn.1000-582X.2025.02.005

      Abstract (285) HTML (103) PDF 6.44 M (334) Comment (0) Favorites

      Abstract:To address the current issues of inefficient dehazing algorithms and poor detail recovery, we propose an improved multi-scale AOD-Net (all in one dehazing network) algorithm. This algorithm enhances the network’s feature extraction and recovery capabilities through three key improvements: adding an attention mechanism, adjusting the network structure, and modifying the loss function. Specifically, the first layer of the model incorporates the SPA (spatial pyramid attention) mechanism, which enables the network to avoid redundant information during feature extraction. Furthermore, the network structure is modified into a Laplacian pyramid structure, allowing the model to extract features at different scales and preserve high-frequency information in the feature maps. Additionally, the original loss function is replaced with the MS-SSIM (multi-scale structural similarity)+L1 loss function, thereby enhancing the model’s ability to retain structural information. Experimental results demonstrate that this method achieves better dehazing effects and richer details. Subjectively, the dehazed images exhibit superior quality compared to those produced by the original network. Objectively, compared to the original network, there is a 2.55 dB improvement in PSNR, a 0.04 increase in SSIM value, and a 0.18 increase in IE entropy value, which proves the algorithm’s excellent dehazing effect and stability.

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    • >Civil Engineering
    • Improved product scale method for quantitative evaluation of tunnel lining crack diseases

      2025, 48(2):62-73. DOI: 10.11835/j.issn.1000-582X.2024.253

      Abstract (272) HTML (91) PDF 1.11 M (256) Comment (0) Favorites

      Abstract:Cracking in tunnel lining structures is a critical issue requiring effective prevention and control during tunnel maintenance. The development of intelligent tunnel management and maintenance urgently requires a quantitative evaluation method for tunnel lining cracking diseases that is practical and broadly applicable. This study introduces an automatic crack image recognition technology and establishes a quantitative evaluation index system and threshold determination method for both single crack diseases and multi-crack diseases in tunnel lining sections. This is achieved by integrating the product scale method with established specification requirements. For single crack disease diagnosis, the primary evaluation indexes are crack length and width, with depth, orientation, and development serving as auxiliary indexes. For multi-crack disease diagnosis, the main indexes remain crack length and width, while depth, orientation, development, and distribution density are included as supplementary indexes. Threshold determination incorporates the probability distribution characteristics of uniform distribution functions alongside the safety characteristics of tunnel structures under stress conditions. The evaluation scores have been systematically standardized to eliminate dimensional inconsistencies, and software has been developed for practical application. A case analysis shows the scientific validity of this method, providing a novel approach to intelligent identification of tunnel cracking diseases and automated maintenance decision-making.

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    • Optimization of efficiency and energy-saving analysis of modular thermo-activated walls

      2025, 48(2):74-85. DOI: 10.11835/j.issn.1000-582X.2024.252

      Abstract (255) HTML (105) PDF 5.87 M (153) Comment (0) Favorites

      Abstract:A modular thermo-activated wall (MTAW) with specialized internal cavities for thermal diffusivity fillers was proposed to solve the problem of low-grade heat accumulation, which restricts the heat injection efficiency of thermo-activated walls. A dynamic heat transfer model of the MTAW was established., and its performance was compared with two reference walls under typical winter conditions in a cold climate zone. The study examined the effects of the filler cavity inclination angle(θ), cavity geometry ratio(ab), and thermal conductivity of the filling material(λf) on energy-saving potential and economic performance. Results show that incorporating filler cavities and thermal diffusing materials significantly reduces total operational energy consumption and costs. Compared with the reference walls, when the the MTAW filler cavity’s long axis is oriented transversely with an ab ratio of 12, the total operational energy consumption decreases by 2.60% and 14.13%, respectively. Compared with the reference walls, operational costs are reduced by 12.41% and 50.04%, respectively. When the long axis of the filler cavity is inclined toward the room side, heating energy consumption initially decreases and then increases as θ rises, with optimal performance observed at θL=60°. Additionally, ab and λf are inversely proportional to both total operational energy consumption and costs. For example, when λf is 12λc, heating energy consumption and gas operating costs are reduced by up to 3.03% and 34.53%, respectively.

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    • Axial compressive behavior of thin-walled multi-cavity concrete-filled double-skin (square inner and square outer) steel tubular stub columns

      2025, 48(2):86-101. DOI: 10.11835/j.issn.1000-582X.2024.259

      Abstract (246) HTML (95) PDF 5.43 M (413) Comment (0) Favorites

      Abstract:To improve the axial performance of concrete-filled double-skin steel tubular(CFDST) stub columns, a novel thin-walled multi-cavity concrete-filled double-skin tubular(MCFDST) stub column was proposed. Experimental investigations were conducted to evaluate the axial compressive behavior of these columns. A total of fifteen MCFDST stub columns and three CFDST stub columns were designed and fabricated, with four key parameters examined: concrete compressive strength(CCS), width-to-thickness ratio(WTR) of the outer tube, hollow ratio(HR), and the presence of tensile ribs. The study assessed deformation, load-displacement behavior, damage patterns, and ductility coefficient to determine the ultimate bearing capacity, failure mode, and ductility performance of the columns. Experimental results show that increasing the CCS from 58 MPa to 90 MPa enhances the bearing capacity by 46%, while reducing the ductility coefficient by 74%. A decrease in WTR from 39 to 29 results in a 12.5% improvement in bearing capacity alongside a notable increase in ductility coefficient. The HR increase from 0.31 to 0.38 yields marginal improvements in bearing capacity(1.3%) and ductility coefficient(1.0%). Notably, the presence of tensile ribs significantly increases the bearing capacity and ductility coefficient by 14.2% and 282%, respectively. Moreover, the experimental data validated the effectiveness and accuracy of numerical modeling, which facilitated extensive finite element parameter analyses. The applicability of current design methods for predicting axial bearing capacity was also discussed, indicating that the prediction formula in Japanese standard AIJ is suitable for estimating the axial compressive bearing capacity of MCFDST stub columns.

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    • >化学工程
    • Performance of copper slag based ferrous oxalate cement after exposure to elevated temperatures

      2025, 48(2):102-109. DOI: 10.11835/j.issn.1000-582X.2024.267

      Abstract (260) HTML (120) PDF 2.01 M (152) Comment (0) Favorites

      Abstract:Copper slag based ferrous oxalate cement(CS-FOC) exhibits significant potential for applications in high temperature kiln repair and nuclear waste stabilization/solidification, owing to its rapid setting properties and high early-age strength. This study comprehensively investigates the evolution of properties, phase compositions, and structure of CS-FOC following exposure to elevated temperatures(150~1 000 ℃), building on prior research. The results show that CS-FOC achieves a compressive strength of 55.1 MPa after 28 days of natural curing, primarily composed of newly-formed ferrous oxalate dihydrate(FeC2O4·2H2O) and unreacted fayalite(Fe2SiO4). Exposure to elevated temperatures induces regular changes in both the compressive strength and structural integrity of CS-FOC. At temperatures exceeding 250 ℃, FeC2O4·2H2O decomposes into iron oxide, leading to structural degradation and a consequent reduction in strength. Despite this, the material maintains a stable compressive strength of about 15 MPa after exposure to temperatures as high as 1 000 ℃. These findings highlight the superior thermal stability of CS-FOC, alongside its ability to retain a relatively high compressive strength under extreme thermal conditions.

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    • Mechanical activation enhances cadmium purification from zinc powder in zinc sulfate solution

      2025, 48(2):110-122. DOI: 10.11835/j.issn.1000-582X.2024.256

      Abstract (243) HTML (117) PDF 7.25 M (106) Comment (0) Favorites

      Abstract:Excessive zinc powder consumption and low cadmium content in purified cadmium removal residues are persistent challenges in cadmium removal from zinc sulfate solution via zinc powder replacement. The primary cause is the reduced reactivity of zinc powder due to encapsulation by product layers. To address this, a novel two-stage countercurrent replacement method with mechanical activation was proposed. This method involves adding low-coefficient zinc powder in the first stage to facilitate cadmium enrichment and high-coefficient zinc powder in the second stage to achieve the desired cadmium removal from zinc sulfate solutions, meeting solution purification standards. Experimental results show that, under optimal conditions (total zinc powder addition coefficient of 1.02, reaction temperature of 60 °C, and reaction time of 60 min), using a primary zinc powder addition coefficient of 0.9 and a secondary zinc powder addition coefficient of 1.2 reduced cadmium concentration in the solution from 1 530 mg/L to 60-70 mg/L after the first stage. This process enriched the cadmium content in the primary purification residue to 81.54%, with zinc content below 10%. Following secondary purification, cadmium concentration in the solution further decreased to 2 mg/L to 5 mg/L, while the purification residue contained 16.09% cadmium and 56.04% zinc. Phase analysis revealed that the primary purification residue predominantly consisted of elemental cadmium, whereas the secondary residue contained unreacted zinc and some elemental cadmium. Compared with traditional primary purification methods, the introduction of fluid shear stress and mechanical activation effectively disrupted the encapsulation layer on zinc powder surfaces, eliminating the the wrapping effect, enhancing zinc powder utilization efficiency, reducing consumption, and increasing cadmium content in the purification residues.

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