A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Research on Infrared Image Fusion Technology Based on Road Crack Detection

This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.

A Fusion Neutron Source Driven Sub-Critical Nuclear Energy System: A Way for Early Application of Fusion Technology

This paper proposes a sub-critical nuclear energy system driven by fusion neutron source, FDS, which can be used to transmute long-lived radioactive wastes and to produce fissile nuclear fuel as a way for early application of fusion technology. The necessity and feasibility to develop that system in China are illustrated on the basis of prediction of the demand of energy source in the first half of the 21th century, the status of current fission energy supply and the progress in fusion technology in the world. The characteristics of fusion neutron driver and the potential for transmutation of long-lived nuclear wastes and breeding of fissile nuclear fuel in a blanket are analyzed. A scenario of development steps is proposed.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Locality preserving fusion of multi-source images for sea-ice classification

We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information.The locality preserving fusion arises from two-fold,i.e.,the local characterization in both spatial and feature domains.We commence by simultaneously learning a projection matrix,which preserves spatial localities,and a similarity matrix,which encodes feature similarities.We map the pixels of multi-source images by the projection matrix to a set fusion vectors that preserve spatial localities of the image.On the other hand,by applying the Laplacian eigen-decomposition to the similarity matrix,we obtain another set of fusion vectors that preserve the feature local similarities.We concatenate the fusion vectors for both spatial and feature locality preservation and obtain the fusion image.Finally,we classify the fusion image pixels by a novel sliding ensemble strategy,which enhances the locality preservation in classification.Our locality preserving fusion framework is effective in classifying multi-source sea-ice images(e.g.,multi-spectral and synthetic aperture radar(SAR)images)because it not only comprehensively captures the spatial neighboring relationships but also intrinsically characterizes the feature associations between different types of sea-ices.Experimental evaluations validate the effectiveness of our framework.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.

Application of Dual-Energy CT Non-Linear Fusion Technology in Improving CTA Image Quality of Renal Cancer

Objective: To explore the significance of dual-energy CT non-linear fusion technique in improving the quality of CTA image of renal cancer. Methods: The CTA images of 100 patients who had been confirmed by pathology as renal cancer were collected and were randomly divided into experimental group and control group with 50 cases respectively. The two groups of patients were treated with iodine concentration of 300 mg/ml and 350 mg/ml non-ionic contrast agent, with a dosage of 1.5 ml/kg and an injection rate of 4 ml/s. The contrast agent intelligently tracking method was adopted bolus. The control group used the conventional CTA scanning, with a reference tube voltage/tube current of 100 kv/ref150 mas. The experimental group adopted the double energy scanning, with ball tube A and ball tube B. The reference tube voltage/tube current was 100 kv/ref250 mas and sn150 kv/ref125 mas respectively. The images of the experimental group were non-linear fused to obtain the Mono+ 55 kev single-energy images. The CT value, SNR contrast ratio of the abdominal aorta, renal artery and tumor tissue of the experimental group images and the 100 KV images and the Mono+ 55 kev images of the control group were compared. The objective evaluation and subjective evaluation of the image quality of the three groups of images was performed. Results: The results showed that the 100 kV images of the experimental group were statistically different from those of the control group (P05) in CT value, SNR and CNR (P 0.05). And there was no statistically significant difference between the non-linear fusion single-energy Mono+ 55 kev images and the control group images in CT value, SNR and CNR (P > 0.05). The subjective evaluation of image quality showed that there was no significant difference between Mono+ 55 kev images and control group images, and the quality of Mono+ 55 kev images was higher than that of experimental group 100 kV images. Conclusion: The dual-energy CT non-linear fusion technique can improve the quality of CTA image in patients with renal cancer, and it is possible to obtain high quality CTA images with low iodine concentration contrast agent.

Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

Application of Attributes Fusion Technology in Prediction of Deep Reservoirs in Paleogene of Bohai Sea

1 Introduction The Paleogene strata(with a depth of more than 2500m)in the Bohai sea is complex(Xu Changgui,2006),the reservoir buried deeply,the reservoir prediction is difficult(LAI Weicheng,XU Changgui,2012),and more

A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

The application of modern surveying technology in mining survey

With the unceasing appearance and widespread application of new surveying technology,the present age mining survey has meet huge change.However,lots of prob- lems occurred while using the new techniques since the number of mine is large in China and condition of the mine district is complex,it in some sense influenced the mine exploi- tation and management of China.Summarized the present situation of new technical ap- plication in mining survey,including the advanced instrumentation equipment,the '3S' technology,the information and the network technology and the information fusion tech- nology and so on,and analyzed the problems which exists in the current mining survey,it also provided new ways to present age mining survey from the sustainable development angle.

以ChatGPT为代表的AI技术与高等教育的深度融合

随着人工智能技术飞速发展,以ChatGPT为代表的人工智能技术已经逐渐成为人们关注的焦点。旨在探讨ChatGPT技术与高等教育的深度融合研究,分析其对高等教育的影响和挑战,并提出相应的解决方案。首先,介绍了ChatGPT技术的基本原理和特点,包括其语言生成模型的构建、预训练和优化等;然后,通过分析ChatGPT技术在高等教育中的应用场景,如智能教育、在线教学、个性化推荐等,探讨了其对高等教育的影响和挑战;最后,提出了一些解决方案,比如加强ChatGPT技术与高等教育的深度融合、建立完善的教育监管机制等,以应对ChatGPT技术在高等教育中面临的挑战。

高温合金熔化焊的研究现状及发展趋势

综述了高温合金熔化焊的研究进展.对电弧焊、电子束焊和激光焊等常见高温合金熔化焊工艺技术的优势和应用范围进行了阐述;介绍了常见的焊接裂纹类型,并对凝固裂纹、晶界液化裂纹、应变时效裂纹和失塑裂纹的形成机理及影响因素进行了概括总结;此外,从热输入、材料的成分和微观结构以及焊接残余应力等方面探讨了提高熔化焊焊接性的主要技术方法.由于母材合金成分和组织结构与焊接性能密切相关,在未来的发展中,应加强对新兴高温合金和传统不可焊高温合金等的成分设计,此外,也应关注焊接工艺技术的完善以及焊前和焊后处理方法的改进,协同提高高温合金的焊接性能,促进高温合金熔化焊的广泛应用.

人工智能技术赋能我国档案工作发展的现状与建议

为了解当前我国档案领域人工智能技术应用的发展现状,论文运用内容分析法对全国及各省档案事业“十四五”规划、档案领域人工智能应用实践案例及国家档案局科技项目立项进行分析。研究发现我国鼓励并支持人工智能技术在档案领域的应用,正在初步探索人工智能助推档案工作的路径和方法。在此基础上,论文从三个方面提出建议,分别是:推动人工智能技术在档案全流程管理中的应用,推动人工智能技术与档案工作的深度融合,以及关注人工智能技术成功应用于档案工作的影响因素。

3D打印联合影像融合技术在主动脉腔内治疗教学中的应用

探讨3D打印联合影像融合技术在血管外科主动脉疾病腔内治疗教学中应用的效果。2020年3月至2023年10月在解放军总医院第一医学中心血管外科进行住院医师规范化轮转培训的60名学员的教学结果显示,该方法能提升学员学习兴趣,使其快速掌握主动脉瘤的解剖结构,了解腔内治疗的基本原理和技能,为血管外科临床教学模式改进提供新的思路。

基于虚拟现实的大视差图像网格优化拼接算法

图像亮度、局部区域信息强度影响图像清晰度,导致图像拼接效果不佳,为提升图像拼接效果,提出一种基于虚拟现实的大视差图像网格优化拼接算法。基于虚拟现实技术建立一个虚拟现实拼接环境,在此环境下开展图像拼接处理,对待拼接图像实施灰度化处理,增强图像局部区域信息强度;基于投影算法建立低密度的网格,并根据待拼接图像的匹配点分布结果建立网格矩阵,结合全局最佳相似变换矩阵实施矩阵加权叠加,对图像重叠区域展开失真校正;使用内容感知算法对图像重叠区域展开感知识别,找出其中的积累最小像素线,实施图像融合处理,通过融合结果完成大视差图像网格优化拼接。实验结果表明,使用该方法开展图像拼接时,图像拼接时间较短,均方根误差较低,拼接效果较好。

准确把握加快形成新质生产力的十大关系

新质生产力是马克思主义生产力理论中国化的重大创新,故需从辩证视域出发全面科学地把握新质生产力的形成、发展进程中面临的多重关系,并厘清对应的科学规律。发展新质生产力应以新旧动能转化为基础,以科技创新引领产业创新为动力源,以基础研究和应用研究结合为重点,以产业链和创新链贯通为进路,以创新和人才双轮驱动为支撑,以制度和文化创新为源泉,以数实融合为新动能,以军民融合为特色阵地,以有为政府促有效市场为引领,以国内国际双重资源为蓄能,不断向全球产业链价值链中高端攀升。