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About the Journal
Governed by: Jiangsu Education Department
Sponsored by: Nantong University
Published by: Editorial Office of Journal of Nantong University (Natural Science Edition)
Issues per year: 4
ISSN: 1673-2340
CN: 32-1755/N
A survey on radio frequency fingerprint signal analysis and intelligent identification
YAN Gaoli;FU Xue;WANG Yu;GUI GuanIn the context of next-generation wireless communications and multi-source heterogeneous network systems, traditional cryptographic mechanisms and security protocols pose significant risks in Internet of things(IoT) environments. There is an urgent demand for more efficient and reliable identity authentication technologies. Radio frequency fingerprinting identification(RFFI), which leverages the inherent signal characteristics of wireless devices, provides a novel approach to addressing device authentication and security challenges. Unlike existing reviews that focus on selected aspects of RFFI from a broad perspective, this paper proposes a systematic and comprehensive framework.It begins by explaining the fundamental principles and characteristics of radio frequency fingerprint(RFF). Then, from the perspectives of statistical features and deep learning(DL)-based features, the paper presents an in-depth review of RFFI classification and identification methods, along with a comparative analysis of the two approaches supported by experimental validation. Finally, several potential research directions in intelligent RFFI are discussed, and future trends of RFF technology are explored, aiming to offer both theoretical insights and practical guidance for ongoing research and real-world applications.
A Q-learning method for creating a Hex opening library
XU Zhifan;LI Yuan;WANG Jingwen;LI Zhuoxuan;CAO YidingHex is a perfect-information board game, and its opening library — an essential component of the game system — has traditionally been generated based on human expertise and Monte Carlo tree search(MCTS) algorithms.However, this approach is computationally expensive and may not consistently ensure accuracy. This study proposes a self-play method based on Q-learning for the efficient construction of Hex opening libraries. The proposed method employs multi-threaded simulations and an improved upper confidence bound applied to trees(UCT) algorithm to identify promising opening moves. An enhanced ε-greedy strategy is incorporated to improve the convergence rate of the Q-learning algorithm. To further improve performance, Q-values are integrated into the upper confidence bound(UCB) formula as prior knowledge, which is intended to enhance decision-making accuracy during gameplay. Experimental results indicate that after 3 000 training iterations, the Q-values across the board converge, suggesting the method′ s potential for stable policy learning. In comparative evaluations, the generated opening library achieved a62.9% average win rate against the improved UCT algorithm. When Q-values were used as prior input to the UCB formula, the average win rate increased to 75.9%. The method was also applied in the Chinese Computer Game Competition, where the implementation received a first-place award, supporting the practical applicability of the approach.
Core loss prediction method for magnetic components based on machine learning
YAO Qida;PING Peng;ZHU Xinyi;ZHU XinfanMagnetic components play a key role in energy transfer, storage, and filtering, directly affecting the size,weight, loss, and cost of power converters. Therefore, accurate prediction of core loss is essential. To address the significant influence of excitation waveforms on core loss, an ensemble learning-based waveform classification strategy is proposed. Support vector machine(SVM), random forest(RF), and gradient boosting decision tree(GBDT) are used as base classifiers. The classification outputs are combined with original features to construct a new feature set, which is then used to train a meta-classifier to enhance generalization. XGBoost is selected as the core model for core loss prediction. A genetic algorithm is applied for multi-objective optimization to identify the optimal operating condition with minimal core loss and maximal magnetic energy transfer. Experimental results show that the ensemble classification model can accurately classify excitation waveforms. Compared with traditional core loss prediction models and other machine learning methods, the XGBoost model demonstrates higher prediction accuracy and better regression performance. The optimized framework demonstrates the capability to meet both loss reduction and energy efficiency objectives.
A dynamic uplink random access method based on non-orthogonal multiple access technology
YANG Jing;LIU Yuxin;GAO Rui;ZHAO Jing;DONG Zhicheng;ZHANG ZhenghuaTo address access collisions caused by massive user access in networks, a dynamic uplink random access method based on non-orthogonal multiple access(NOMA) is proposed. The method incorporates an adaptive resource allocation mechanism that dynamically adjusts time slot configurations and updates the access class barring(ACB) factor according to network conditions. After each round of random access, the system records the number of idle, successful, and collided time slots. These statistics are then used to estimate the number of remaining users. The process is iteratively repeated until all users successfully access the network. To evaluate the proposed method, a complete theoretical analysis framework is established under the Nakagami-m fading channel environment, from which closed-form expressions for access success probability and system throughput are derived. Simulation results show that, compared with the traditional dynamic ACB-based NOMA random access algorithm(DNRA) and the fixed ACB-based orthogonal random-access algorithm(FORA), the proposed method improves system throughput by 30.41% and 48.22%, respectively. Furthermore, under varying user loads, the method consistently achieves higher access success probabilities and maintains high system throughput across different network conditions.
Blind estimation algorithm for frequency hopping parameters based on enhanced time-frequency ridge extraction
WAN Kai;CHANG Cheng;HOU Changbo;WU XiangyuIn existing blind estimation algorithms for frequency-hopping parameters based on time-frequency ridges,performance degrades significantly when the signal-to-noise ratio(SNR) falls below-5 dB. To address this issue, an improved blind estimation method based on time-frequency ridges is proposed. First, the time-frequency matrix of the received signal is obtained using the short-time Fourier transform(STFT). Then, Gaussian filtering is applied for signal smoothing and noise suppression. Since the energy distribution of fixed-frequency interference differs from that of frequency-hopping signals in the time-frequency matrix, an energy cancellation method is used to eliminate such interference. Next, OTSU thresholding is applied to the matrix after energy cancellation to further remove residual fixed-frequency interference and noise, yielding a clearer time-frequency representation. Time-frequency ridges are then extracted from this image, and the least-squares method is employed to fit the hopping instants along these ridges, enabling accurate estimation of frequency-hopping parameters. Simulation results show that the proposed method effectively produces clean time-frequency images even at SNR levels as low as-5 dB in the presence of fixed-frequency interference. The average relative estimation error of each parameter remains below 1%, and the method demonstrates strong robustness against varying intensity and quantity of interference.
Isolation and Functional Characteristics of Six Salt-Alkaline Tolerant Bacteria with Plant Growth-Promoting Traits
Chen Yuekun;Hu Fangjing;Qian Zhuyue;Long Xi'en;Soil salinization–alkalization poses a severe threat to global agricultural sustainability， underscoring the urgent need for efficient， low-cost amelioration strategies. In this study， six salt- and alkali-tolerant bacterial strains were isolated and screened from acid mine drainage and the rhizosphere of coastal salt-tolerant rice， and identified as Delftia tsuruhatensis (D11)， Acinetobacter junii (D37)， Bacillus aryabhattai (A44)， Pseudomonas koreensis (A34)， Pseudomonas hunenansis (K12)， and Pseudomonas putida (D3). Salt–alkali tolerance was evaluated by agar-plate screening and shake-flask assays; plant growth-promoting (PGP) traits were systematically characterized; and a pot experiment with wheat seedlings grown in saline–alkali soil was conducted to compare the growth-promoting effects of the strains. The results showed that all strains were capable of growing at 10% (w/w) NaCl， with D11 exhibiting the greatest salt tolerance and achieving an alkalinity reduction rate of 4.32%. All six strains displayed strong capacities for indole-3-acetic acid (IAA) production， extracellular polymeric substance (EPS) production， siderophore production， and phosphate/potassium solubilization: with tryptophan supplementation， strain D3 produced the highest IAA (124.93 mg L?1); D11 showed the highest relative siderophore level (69.90%) and the strongest inorganic phosphate-solubilizing capacity (89.02–202.78 mg L?1); EPS yields across the six strains ranged from 975.0 to 1，391.7 mg L?1. Moreover， by secreting metabolites and mobilizing soil nutrients， these strains exhibited synergistic potential for the amelioration of saline–alkali soils. The pot experiment confirmed their plant growth-promoting effects， with D11 demonstrating the most robust overall performance， thereby laying a foundation for the development of microbial inoculants adapted to saline–alkali conditions.
Segmentation of diabetic macular edema based on federated learning
CHEN Qiong;SUN Jingbo;LI Junlin;SHU Jiachen;DENG Yunjun;CHENG Xi;CHEN Zongcun;Deep learning technology plays a crucial role in the segmentation of spectral domain optical coherence tomography (SD-OCT) images for diabetic macular edema (DME). A DME segmentation algorithm based on federated learning (DMESA-FL) is proposed to address key challenges such as data privacy protection， computational cost control， and uncertainty quantification. Initially， a scale-aware pyramid fusion module and global pyramid guidance modules are incorporated into the convolutional neural network (CNN) to capture multi-scale contextual information and fuse the global contextual information flow with the features of the decoding path. Subsequently， the improved CNN is employed as the prediction model within the federated learning framework， and sequential training is adopted to update the global model， thereby enhancing data security. Ultimately， a feature discretization preprocessing module is introduced for all clients to reduce the computational burden of CNN and improve its generalization capability. During the feature discretization process， a fitness function based on rough sets is constructed to assess data uncertainty， and a genetic algorithm (GA) is utilized to search for the optimal breakpoints in SD-OCT images. Additionally， an uncertainty constraint term is introduced into the loss function of the network for effectively integrating the average approximation precision of rough sets as prior knowledge into CNN. The comparative results between DMESA-FL and the state-of-the-art SD-OCT fundus image segmentation algorithms demonstrate that DMESA-FL can efficiently train models across different clients without data sharing， thereby achieving precise segmentation of DME.
Dangerous driving behavior detection algorithm in infrared images based on improved YOLOv8
LI Congzheng;XUE Weibao;LIU Zixi;AI Jiaqiu;HU Min;In response to the current shortage of infrared datasets for detecting dangerous driving behavior and the serious false and missed detection of existing driver dangerous driving behavior detection models in low contrast infrared scenes， a low-contrast infrared image dataset named DDBD of driver dangerous driving behavior is specifically constructed， and a dangerous driving behavior detection model YOLO-CIE based on improved YOLOv8 in low-contrast infrared images is proposed. Firstly， in order to improve image quality， the model uses CLAHE for the image data pre-processing. CLAHE adaptively adjusts the contrast of the image locally， enhancing the details of dark areas and effectively improving the local contrast and details of the image， which is helpful for subsequent feature extraction. Secondly， in order to enhance the ability of the model to aggregate dangerous driving behavior features in infrared images， an improved EMA attention module (IEMA) is specifically proposed. IEMA can effectively enhance the representation ability of dangerous driving behavior features by integrating mean， standard deviation， and contrast statistics. Finally， integrating the IEMA module into the YOLOv8s network can significantly improve the accuracy of dangerous driving behavior detection in low-contrast infrared scenes. Experimental results on the DDBD dataset show that the average accuracy of the proposed YOLO-CIE has improved by 2.5 percentage compared to YOLOv8s， and is also superior to other object detection networks such as YOLOv5s and YOLOv7.
Research on the Mechanical Properties and Variability of Fiber Reinforced Polymer Rebars after High Temperatures
XIE Qinghai;ZENG Jie;WEN Zheming;ZHANG Haijin;ZONG Zhongling;To understand the influence of high temperatures on the mechanical properties of Fiber Reinforced Polymer (FRP) rebars， a large batch (1572 groups) of shear and compression tests were conducted after high temperatures to study the effects of different factors such as high temperature， constant temperature time， and protective layer thickness on Glass Fiber Reinforced Polymer (GFRP) bars and Basalt Fiber Reinforced Polymer (BFRP) bars with different diameters. The damage of GFRP and BFRP reinforcement due to high temperatures was revealed at the microscopic level through scanning electron microscopy (SEM). The test results show that the strength degradation of BFRP bars is slightly higher than that of GFRP bars. The smaller the diameter of the FRP bars， the higher their residual strength after high temperatures. However， when the diameter reaches above 16mm， the impact of diameter on strength is not significant. The strength of FRP bars decreased limitedly before 300 ℃ high temperature， and then rapidly decreases with increasing temperature. When reaching 350℃， the strength of GFRP and BFRP bars decreases by 37.7% and 36.4%， respectively. The degree of compressive strength degradation after high temperature was higher than that of shear strength， and the larger the diameter of FRP bars， the more significant the strength degradation. The reduction in shear strength of FRP bars was similar for a constant temperature of 1 hour and 2 hours， and was larger after 3 hours. The mortar protective layer can effectively protect the reinforcement material within 300 ℃. After 300 ℃， the rapid development of cracks in the specimen caused the disappearance of the protective effect. The shear strength of the internal FRP bars showed a degradation trend close to that of bare reinforcement. Based on experimental results， shear and compressive strength prediction formulas for GFRP and BFRP bars were established after high temperature. A method for establishing probabilistic strength model of FPR bar after high temperatures was proposed by combining Bayesian information criteria and probability testing methods. Finally， suitable probability models for the shear and compressive strength of GFRP and BFRP bars after high temperatures were established， and their variability was also quantified. The research results of this article can provide material models for the performance evaluation of FRP reinforced concrete structures after high temperatures.
Multi-view Dual Anchor Graph Fuzzy Clustering
ZHU Chenghao;DING Weiping;ZHANG Wei;In recent years， with the rapid development of multi-view learning， how to effectively integrate information from different views for clustering analysis has become an important research topic in both academia and industry， driving the emergence of a series of efficient methods. Despite significant progress in multi-view anchor graph clustering， current methods face three key limitations. First， suboptimal anchor graphs often result from the inherent uncertainty and low discriminability of real-world data. Second， prevalent approaches primarily focus on common information between views， overlooking valuable view-specific information. Third， effectively leveraging the learned anchor graph to improve clustering remains under-explored. To overcome these challenges， this paper proposes a novel dual anchor graph fuzzy clustering framework. Initially， a matrix factorization-based method extracts discriminative hidden representations from each view， enabling the derivation of both common and specific anchor graphs. Subsequently， a cooperative fuzzy clustering technique is developed to fully exploit these dual graphs. This method incorporates a fuzzy membership structure preservation mechanism utilizing both graphs to enhance performance. Furthermore， adaptive view weighting is achieved through negative Shannon entropy. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed DAG＿FC method. The results show that DAG＿FC outperforms competing methods on most metrics and datasets， with a significant improvement in NMI by approximately 30% and 20% on the Yale dataset compared to the other methods. Moreover， the experiments also confirm that anchor graph-based clustering methods generally perform better than traditional subspace-based clustering methods. By incorporating hidden representation extraction techniques and designing specialized clustering algorithms， this paper further enhances the clustering performance of the proposed method.
Application of Increasing PID Controlling Method in Temperature Controlling System
YAN Xiao-zhao,ZHANG Xing-guo(School of Mechanical Engineering,Nantong University,Nantong 226007,China)Temperature control is widely applied in scientific experiments and industrial processes.However,the temperature control system has characteristics of being nonlinear,time-varying and has hysteretic complicated large inertial system,and the effect of control is closely related to the algorithms adopted.In this paper,an experimental temperature control system is developed to meet the requirement of innovative ability training for mechatronic undergraduates.An increasing PID control algorithm is designed.The experimental results prove that the effect of the designed algorithm is better than the traditional PID algorithm.
Research and Development in Techniques of Dyeing Wastewater Treatment
JING Xiao-hui 1,YOUKe-fei 2,DING Xin-yu 2,CAI Zai-sheng 1(1.School of Chemistry and Chemical Engineering,Donghua University,Shanghai200051,China; 2.School of Chemistry and Chemical Engineering,Nantong University,Nantong226007,China)Reviewof the progress on treating methods of dyeing wastewater is presented,especially the advanced techniques are introduced,suchas membrane extraction,ultrasonic processes,high-energyphysical processes,advanced electrocatalytic oxidaˉtion processes and advanced photocatalytic oxidation processes.The treating trend for the dyeing wastewater is discussed.
The Application and the Development Foreground of Chitin and Chitosan
ZHANG Wei,LIN Hong,CHEN Yu-yue (School of Material Engineering,Soochow University,Suzhou 215021,China)The structure and the performance of chitin and chitosan are introduced and the application of chitin and chitosan in various fields is analyzed in this paper.The existing problems and the development foreground of chitin and chitosan are summarized in this paper.
Overview of SERS
LAN Yan-na,ZHOULing (Nantong Institute of Technology,Nantong226007,China)The principle of Raman spectrumis expounded first.Then the characteristics of SERS effect in experiment is summaˉrized and the mechanism of SERS is described.It's an accepted viewthat the mechanism of electromagnetic enhancement and the mechanismof chemical enhancement are both in existence.But which one is more important in different experiment depends on specific condition.
Application of ANSYS to Reinforced Concrete Beam
WANG Ya-ping 1,CHEN Jian-ping 2 ,CHEN Wu-zhou 3(1.Nantong Institute of Technology,Nantong226007,China;2.Nantong Architectural Design Institute of Industry,Nantong226001,China;3.Nantong Water Conservancy Construction Company,Nantong226005,China)In this article,taking features of reinforced concrete into account ,FEM software of ANSYS was used to calculate the beam' s deformation and the stress and strain of the normal section.At last,the answers to ANSYS(crack length,stress of reinforc-ing bar and concrete)and theoretical answers were compared in search of reasons and ways or measures that can amend it.
The Application and the Development Foreground of Chitin and Chitosan
ZHANG Wei,LIN Hong,CHEN Yu-yue (School of Material Engineering,Soochow University,Suzhou 215021,China)The structure and the performance of chitin and chitosan are introduced and the application of chitin and chitosan in various fields is analyzed in this paper.The existing problems and the development foreground of chitin and chitosan are summarized in this paper.
Application of Increasing PID Controlling Method in Temperature Controlling System
YAN Xiao-zhao,ZHANG Xing-guo(School of Mechanical Engineering,Nantong University,Nantong 226007,China)Temperature control is widely applied in scientific experiments and industrial processes.However,the temperature control system has characteristics of being nonlinear,time-varying and has hysteretic complicated large inertial system,and the effect of control is closely related to the algorithms adopted.In this paper,an experimental temperature control system is developed to meet the requirement of innovative ability training for mechatronic undergraduates.An increasing PID control algorithm is designed.The experimental results prove that the effect of the designed algorithm is better than the traditional PID algorithm.
Preparation of functionalized carbon nanomaterials and their energy storage applications
LI Qi;QIN Tian;GE Cunwang;Over the past decades, functional carbon nanomaterials(FCMs) have attracted much attention from the materials science community owning to their outstanding physical and chemical properties, such as high electronic conductivity/rapid mass transfer, plentiful active sites, good chemical stability, and robust mechanical stiffness. In view of the anisotropic and synergistic effects stemming from the functionalization as well as small size effect at the nanoscale,these multifunctional FCMs exhibit high potential especially in lithium-ion batteries, sodium-ion batteries, potassiumion batteries, lithium-sulfur batteries, organic solar cells, and supercapacitors. In this review, the functionalization strategies of carbon nanomaterials that have been developed over the last five years are comprehensively summarized and then application of FCMs in energy storage and conversion is introduced exhaustively. Finally, the pressing challenges and research directions are discussed according to the development trend.
Preparation of biochar and its application in environmental pollution management
WANG Jiayue;LING Qian;ZHANG Yunhao;WANG Xinyu;LIN Jiaqi;ZHANG Weitao;LIU Zhixin;WANG Xiangke;Biochar is a kind of environmentally friendly porous material which can be easily synthesized at low cost and in large scale. It has a wide range of applications in environmental pollution control and pollutants′ remediation and immobilization due to its large specific surface area and abundant surface functional groups. In this review, we mainly summarized the preparation of biochar, discussed the effect of preparation conditions on the properties of prepared biochar. The application of biochar in the removal of various pollutants from wastewater and soil improvement,the immobilization and elimination of different pollutants in soils, were reviewed in detail and the interaction mechanism was discussed. The removal of heavy metal ions was mainly attributed to the sorption of metal ions through the formation of surface complexes and part of metal ions could be reduced from high valence to low valence and then immobilized on biochar through adsorption-reduction-solidification strategy. The removal of organic pollutants from solution to biochar was mainly attributed to surface complexation, H bonding and π-π interaction on biochar surfaces.The organic pollutants could also be photocatalytic degraded by biochar or biochar-based materials under visible light irradiation. In conclusion, further research and discussion on the interaction mechanism of pollutant molecular with biochar at molecular level are helpful for the application of biochar in wastewater treatment and soil remediation. This review is of scientific significance for reducing the migration and transformation of pollutants in the environment and reducing the risk of environmental pollutants in the natural environment.
Modeling and simulation of proton exchange membrane electrolyzer system
WANG Huidong;YAO Haiyan;GUO Qiang;XIA Hongjun;Proton exchange membrane(PEM) electrolyzer converts electrical energy into chemical and heat energy,which is a green hydrogen production method, featuring fast response, high current density, compact structure, and other advantages. In the modeling of proton exchange membrane electrolysis water hydrogen production system,existing literature lacks a lumped parameter model that comprehensively describes the voltage and current changes of the electrolysis cell, as well as the temperature dynamics of each component of the system. This study establishes a steady-state voltage model of PEM electrolyzer and the thermal dynamic model of the system based on the basic principles of electrochemistry and the laws of thermodynamics. The simulation analysis was carried out based on MATLAB/Simulink software, and the simulation results were compared with the experimental data. The results showed that the voltage error is less than 0.02 V, and the temperature error is less than 1.6 K, which verifies the validity of the model. The established model can describe and predict the behavior of system parameters and provide support for system design and control. According to the efficiency model of PEM electrolyzer and the simulation results, the influence of different temperature and pressure on the performance of the electrolyzer was analyzed. It is concluded that increasing the temperature and decreasing the pressure can improve the efficiency of the electrolyzer, with temperature being the main factor. Using the simulation model, a feedforward PID controller was employed for temperature control, achieving an overshoot of less than 0.6 K and a settling time within 400 seconds. Comparison with a traditional PID controller demonstrates that the feedforward PID controller has advantages in terms of reduced overshoot and faster response.