Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

Effect of Luhong formula on the cardiac rehabilitation of patients with chronic heart failure

BACKGROUND Current treatments for chronic heart failure(CHF)are therapeutically ineffective.The optimization of treatments for this disease needs to be explored and analyzed.AIM To analyze the effect of using Luhong Formula in the cardiac rehabilitation of patients with CHF and its influence on cardiopulmonary function(CPF)and prognosis.METHODS In total,160 patients with CHF admitted between June 2022 and June 2023 were selected,including 75 receiving perindopril(control group)and 85 receiving Luhong Formula(research group).We conducted comparative analyses on the curative effects of traditional Chinese medicine(TCM)syndromes and cardiac function,CPF[oxygen consumption at the anaerobic threshold(VO2 AT)and at peak exercise(peak VO2)],echocardiographic indexes[left atrial volume index(LAVI),left ventricular muscle mass index(LVMI),left ventricular ejection fraction(LVEF)],and prognosis[major adverse cardiovascular events(MACEs)at 6 months follow-up].RESULTS The research group showed markedly higher curative effects of TCM syndromes and cardiac function than the control group.In addition,post-treatment VO2 AT,peak VO2,LVMI and LVEF in the research group were significantly higher,whereas LAVI was significantly lower,than those of the control group.Furthermore,fewer patients in the research group developed MACEs at the 6-month follow-up.CONCLUSION Luhong Formula is more therapeutically effective than perindopril for the cardiac rehabilitation of patients with CHF,specifically in enhancing CPF and prognosis.

Explosion of research on psychopathology and social media use after COVID-19:A scientometric study

BACKGROUND Despite advances in research on psychopathology and social media use,no comprehensive review has examined published papers on this type of research and considered how it was affected by the coronavirus disease 2019(COVID-19)outbreak.AIM To explore the status of research on psychopathology and social media use before and after the COVID-19 outbreak.METHODS We used Bibliometrix(an R software package)to conduct a scientometric analysis of 4588 relevant studies drawn from the Web of Science Core Collection,PubMed,and Scopus databases.RESULTS Such research output was scarce before COVID-19,but exploded after the pandemic with the publication of a number of high-impact articles.Key authors and institutions,located primarily in developed countries,maintained their core positions,largely uninfluenced by COVID-19;however,research production and collaboration in developing countries increased significantly after COVID-19.Through the analysis of keywords,we identified commonly used methods in this field,together with specific populations,psychopathological conditions,and clinical treatments.Researchers have devoted increasing attention to gender differences in psychopathological states and linked COVID-19 strongly to depression,with depression detection becoming a new trend.Developments in research on psychopathology and social media use are unbalanced and uncoordinated across countries/regions,and more indepth clinical studies should be conducted in the future.CONCLUSION After COVID-19,there was an increased level of concern about mental health issues and a changing emphasis on social media use and the impact of public health emergencies.

Empirical Study on the Reform of Water Resources Management in Xinjiang Rural Communities

With the case study of two rural communities of Hetian County and Shawan County in Xinjiang, the foundation, operation and development of the water management organizations in the two communities and their reform achievements were studied and compared. It was concluded that the reform of water resources management should be in accordance with the practical conditions of rural communities. Only with the same objectives of community people and by benefiting the farmers could the reform of water resources management be effectively implemented and achieve good results.

Analysis on Internal Growth Potential of Agriculture Under the Agricultural Structure Adjustment in Xinjiang

Agricultural structure adjustment has always been the important factors influencing Xinjiang agricultural internal income growth.On the basis of the planting scale,productivity and price in 2010,the paper measured the contributions made by agricultural structure adjustment to Xinjiang agricultural internal income growth through structural optimization.Linear programming model was established by Lingo software,to optimize the structure of planting,horticulture,animal husbandry in Xinjiang and analyze the internal agricultural income growth potential.After structure optimization,Xinjiang agricultural production value will reach 1231.44×108yuan and the farmers'income of production and management will reach 9851yuan.The greatest potential for Xinjiang agricultural internal income growth was husbandry accounting for 41.33% of the total production value.The next one was horticulture,whose share of the total production value has raised to 17.43% from 10%.Fruit industry and animal husbandry will become the pillars of future income growth.There are still some income growth spaces in Xinjiang internal agriculture though the agricultural structure adjustment.The agricultural production value will increase by 44.32%and farmers'production and management income will increase by 68.83%.That will be one of the most important ways to tap the future potential of agriculture internal income growth.

Efficient extraction of phenol from wastewater by ionic micro-emulsion method:Anionic and cationic

Phenolic wastewater is one of the priorities in the field of wastewater treatment,which poses a serious threat to the human health and nature environment.In this paper,cationic cetyltrimethylammonium bromide(CTAB)and anionic sodium oleate(Na OL)microemulsions were utilized to extract phenol from the wastewater.The optimal extraction factors were investigated by exploring the effects of microemulsion composition ratio and extraction conditions on the phenol extraction performance.Furthermore,the enhanced extraction mechanism of phenol by cations microemulsions is illustrated by studying the extraction process of cationic and anionic microemulsions in the extraction of phenol.The optimum components were obtained:surfactant concentration of 0.2 mol·L^(-1),isoamyl alcohol volume of 30%,internal aqueous phase concentration of CTAB microemulsion of 0.05 mol·L^(-1),and internal aqueous phase concentration of Na OL microemulsion of 0.09 mol·L^(-1).The extraction efficiencies were 96.44%and 82.0%when using CTAB and Na OL microemulsions under optimal conditions(water-emulsion ratio of 5,contact time of 9 min,extraction temperature of 298.15 K,and p H of 9),confirming the enhanced extraction of phenol by CTAB cationic microemulsion.It was analyzed that the enhanced extraction of CTAB microemulsion was due to the electrostatic adsorption of cations with phenol root ions.

Air target recognition method against ISRJ for radio frequency proximity sensors using chaotic stream encryption

The interrupted-sampling repeater jamming(ISRJ)can cause false targets to the radio-frequency proximity sensors(RFPSs),resulting in a serious decline in the target detection capability of the RFPS.This article proposes a recognition method for RFPSs to identify the false targets caused by ISRJ.The proposed method is realized by assigning a unique identity(ID)to each RFPS,and each ID is a periodically and chaotically encrypted in every pulse period.The processing technique of the received signal is divided into ranging and ID decryption.In the ranging part,a high-resolution range profile(HRRP)can be obtained by performing pulse compression with the binary chaotic sequences.To suppress the noise,the singular value decomposition(SVD)is applied in the preprocessing.Regarding ID decryption,targets and ISRJ can be recognized through the encryption and decryption processes,which are controlled by random keys.An adaptability analysis conducted in terms of the peak-to-side lobe ratio(PSLR)and bit error rate(BER)indicates that the proposed method performs well within a 70-k Hz Doppler shift.A simulation and experimental results show that the proposed method achieves extremely stable target and ISRJ recognition accuracies at different signal-to-noise ratios(SNRs)and jamming-to-signal ratios(JSRs).

Magneto-hydrodynamic simulation study of direct current multi-contact circuit breaker for equalizing breaking arc

This work is based on a direct current(DC)natural current commutation topology,which uses load-carrying branch contacts carrying rated current and multiple sets of series arcing branch contacts in parallel to achieve circuit breaking.The proposed topology can meet the new requirements of higher voltage DC switches in aviation,aerospace,energy and other fields.First,a magneto-hydrodynamic arc model is built using COMSOL Multiphysics,and the different arc breaking characteristics of the arcing branch contacts in different gas environments are simulated.Then,a voltage uniformity coefficient is used to measure the voltage sharing effect in the process of dynamic interruption.In order to solve the dispersion of arcing contact action,a structural control method is adopted to improve the voltage uniformity coefficient.The uniform voltage distribution can improve the breaking capacity and electrical life of the series connection structure.

Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Repeater jamming suppression method for pulse Doppler fuze based on identity recognition and chaotic encryption

Pulse Doppler(PD) fuze is widely used in current battlefield. However, with the threat of repeater jamming, especially digital radio frequency memory technology, the deficiency in the anti-repeater jamming of a traditional PD fuze increasingly emerges. Therefore, a repeater jamming suppression method for a PD fuze based on identity(ID) recognition and chaotic encryption is proposed. Every fuze has its own ID which is encrypted with different chaotic binary sequences in every pulse period of the transmitted signal. The thumbtack-shaped ambiguity function shows a good resolution and distance cutoff characteristic. The ability of anti-repeater jamming is emphatically analyzed, and the results at different signal-to-noise ratio(SNR) show a strong anti-repeater jamming ability and range resolution that the proposed method possesses. Furthermore, the anti-repeater jamming ability is influenced by processing gain, bit error rate(BER) and correlation function. The simulation result validates the theoretical analysis, it shows the proposed method can significantly improve the anti-repeater jamming ability of a PD fuze.

Construction and Implementation Approaches of Ecological Compensation Mechanism for Coordinated Control of Air Pollution in Beijing-Tianjin-Hebei Region

The spatial spillover of air pollution in Beijing-Tianjin-Hebei region makes the territorial governance model inefficient.At the same time,it is facing the contradiction between environmental governance and economic development.So far,there is still no regional long-term mechanism for coordinated governance of air pollution.Ecological compensation theory is becoming an effective guiding theory to coordinate economic and environmental benefits.There is relatively little research on ecological compensation mechanism in the coordinated control of regional air pollution.Ecological compensation mechanism is an effective tool to promote air pollution control.At the same time,regional cooperation can realize the efficient control of air pollution.Scientifically constructing the cross regional cooperative control model of Beijing,Tianjin and Hebei is a favorable way to implement comprehensive control of air pollution.This paper discusses the necessity of ecological compensation mechanism for regional coordinated control of air pollution.Referring to practical experience of provinces and cities that have implemented the ecological compensation mechanism for air pollution control,and the effective way to distribute the compensation amount,compensation subject and compensation standard determination method that can be implemented in Beijing-Tianjin-Hebei region are analyzed,further proposing an effective way to establish an ecological compensation mechanism for regional air pollution collaborative control according to its reality.

Bichromatically pumped artificial cnoidal wave breathers in optical microresonators

Breathers are localized structures that undergo a periodic oscillation in their duration and amplitude. Optical microresonators, benefiting from their high-quality factor, provide an ideal test bench for studying breathing phenomena. In a monochromatically pumped microresonator system, intrinsic breathing instabilities are widely observed in the form of temporal dissipative Kerr solitons which only exist in the effectively red-detuned regime.Here, we demonstrate a novel bichromatic pumping scheme to create compulsive breathing microcombs via respectively distributing two pump lasers at the effectively blue-and red-detuned sides of a single resonance.We experimentally discover the artificial cnoidal wave breathers and molecular crystal-like breathers in a photonic chip-based silicon nitride microresonator and theoretically describe their intriguing temporal dynamics based on the bichromatic pumping Lugiato–Lefever equation. In particular, the corresponding breathing microcombs exhibit diverse comb line spacing ranging from 2 to 17 times the free spectral range of the microresonator.Our discovery not only provides a simple yet robust method to harness microcombs with reconfigurable comb line spacing but also reveals a new class of breathing waves in driven dissipative nonlinear systems.

Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

In this paper, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design, and performance optimization for the plasmonic waveguide-coupled with cavities structure(PWCCS) based on artificial neural networks(ANNs). The Fano resonance and plasmon-induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyperparameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by the Monte Carlo method, the transmission spectra predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs.More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for the transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS that is expected to have significant applications in the device design, performance optimization, variability analysis,defect detection, theoretical modeling, optical interconnects, and so on.

Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network

To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from the back-end servers and uncertainty is an inherent characteristic of the spatio-temporal data. How to deal with the uncertainty thus becomes a basic and challenging problem. A lot of researches have been rigidly conducted on the uncertainty of a moving object itself and isolated from the context where it is derived. However, we discover that the uncertainty of moving objects can be efficiently reduced and effectively ranked using the context-aware information. In this paper, we focus on context- aware information and propose an integrated framework, Context-Based Uncertainty Reduction and Ranking （CURR）, to reduce and rank the uncertainty of trajectories. Specifically, given two consecutive samplings, we aim to infer and rank the possible trajectories in accordance with the information extracted from context. Since some context-aware information can be used to reduce the uncertainty while some context-aware information can be used to rank the uncertainty, to leverage them accordingly, CURR naturally consists of two stages： reduction stage and ranking stage which complement each other. We also implement a prototype system to validate the effectiveness of our solution. Extensive experiments are conducted and the evaluation results demonstrate the efficiency and high accuracy of CURR.

Annealing Induced Microstructure and Mechanical Property Changes of Impact Resistant Polypropylene Copolymer

The effects of annealing on microstructure and mechanical properties of an impact resistant polypropylene copolymer （IPC） were investigated. Different annealing temperatures ranging from 80 ℃ to 160 ℃ were selected. The phase reorganization of IPC during annealing process was studied through morphological characterization technologies, including scanning electron microscopy （SEM） and transmission electron microscopy （TEM）. The crystalline structure changes in the IPC sample, including the iPP matrix and PE component, were investigated using wide angle X-ray diffraction （WAXD） and differential scanning calorimetry （DSC）. Dynamic mechanical analysis （DMA） was used to analyze the relaxation extent of 1PC before and after annealing. The results showed that annealing induced phase reorganization in IPC and the degree of phase reorganization depended on annealing temperature. The annealed IPC samples exhibited largely increased crystallinity compared with the unannealed one. Intensified damping peak with increased molecular chain mobility was achieved for the annealed IPC samples. At an appropriate annealing tem. perature （140 ℃）, largely enhanced impact strength was achieved for the annealed IPC sample. The toughening mechanisms were analyzed based on the phase reorganization and relaxation behavior.

ENHANCING CHAIN SEGMENTS MOBILITY TO IMPROVE THE FRACTURE TOUGHNESS OF POLYPROPYLENE

As a part of a serial work about the annealing inducing improvement of fracture toughness of polypropylene （PP） articles, in this work, a highly efficient mobilizer was introduced into PP and the injection-molded samples were annealed at different temperatures. The mobility of chain segments of PP was investigated by measuring the glass transition temperature. Differential scanning calorimetry （DSC） and wide angle X-ray diffraction （WAXD） were used to characterize the variation of crystalline structure of PP during the annealing process. The fracture behaviors including notched Izod impact fracture and universal tensile fracture were investigated to detect the mechanical properties in response to the variations of both chain segments mobility and crystalline structures. It was found that the mobilizer greatly improved the chain segments mobility. Further results showed that the mobilizer also induced apparent changes of the glass transition temperature and the degree of crystallinity of PP during the annealing process. Consequently, the annealed PP samples containing a few amount of mobilizer exhibited largely increased fracture toughness.

Conservation and tourism development of house settlements in Moso matriarchate in Lugu Lake area

This paper analyzes the present situation of the settlement and the influence of tourism development and construction.It discusses the attitude toward the conservation and development of the settlement as well as the way to maintain the production,life,and culture of the Mosian and the way to promote the sustainable development of the settlement by tourism.

论智慧监狱条件下现代警务运行监管模式的构建思路

智慧监狱条件下的现代警务运行监管模式是现代监狱改变传统的监管值勤模式,即对监管区域实施在线值守、智能监控、信息处理、预警报警、指挥调度、应急处置等一系列勤务警务监管业务,具有信息化、智能化、扁平化、集约化、实战化等特点,是监狱落实总体国家安全观、强化教育改造工作、推进监狱治理能力现代化、推动监狱安全管理转型升级的现实需要,可以在监控、指挥、预警、处置"四个模式"上积极构建,并在监管的理念、硬件、技术、管理、能力、法治"六大基础"同时具备的智慧监狱条件下实施。