Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.

New pattern recognition system in the e-nose for Chinese spirit identification

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.

The Comparative Study of Chuangtse’s English Translations Under the Theory of Spirit Transmission and Meaning Conveyance

Cultural-loaded word is one of the most popular topics in translation studies.The theory of spirit transmission and meaning conveyance provides a new way for the translation of cultural-loaded word.Based on the A Happy Excursion,this paper makes a comparative analysis of the translation strategies of seven English versions.This study will reveal the similarities and differences between Chinese and foreign translators’translation strategies.

The partitioning patterns of nutrients between pods and seeds of Zanthoxylum fruits impacted by environmental factors

The nutritive quality in plant organs is related to the different partitioning patterns of nutrient resources among the organs under various environmental conditions.This study examined the relationship between the nutritive quality of pods and seeds in Zanthoxylum and environmental factors, such as temperature and preciptation by using numerous samples collected from Southwest China to the East China of Shandong peninsula. The increasing accumulations of N, P and C in seeds implied that the nutritive quality in seeds was higher at the regions with relative higher mean annual temperature(MAT) and mean annual precipitation(MAP), while that in pods was on the contrary. By contrast, pod nutritive content was relatively high, but seed nutritive content was relatively low at the regions with relative high MAT and MAP. In addition, C:N ratio in pods was significantly and negatively correlated with MAT and MAP, while that in seed was significantly and positively correlated with MAT and MAP. The partitioning patterns of N-compounds between pods and seeds reflected different nitrogen translocations in the plant organs under various climate condition. The N:P ratios were negatively correlated with MAP, implying a higher proportional allocation of P to seeds than that of N in the areas with a relative high MAP. Therefore, the strategies to assess pod nutritional quality should be taken into accountfor nutritive translocation under various environmental conditions.

Frequent Trajectory Patterns Mining for Intelligent Visual Surveillance System

A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.

Image Denoising Algorithm Considering Nonlocal Texture Pattern

Image denoising is indispensable for image processing.In this paper,image denoising algorithm based on Nonlocal Means(NLM)filter is proposed.Recently,abundant enhancements based on NLM filter have been performed.However,the performance of NLM filter is still inferior to that of other image processing approaches such as K-SVD.In this paper,NLM algorithm with weight refinement is utilized for image denoising.Weight refinement is performed to thoroughly take advantage of self-similarity of the image.Experimental results show good performance of the proposed method.

LOW FREQUENCY VARIABILITY OF INTERANNUAL CHANGE PATTERNS FOR GLOBAL MEAN TEMPERATURE DURING THE RECENT 100 YEARS

The TEEOF method that expands temporally is used to conduct a diagnostic study of the variation patterns of 1, 3, 6 and 10 years with regard to mean air temperature over the globe and Southern and Northern Hemispheres over the course of 100 years. The results show that the first mode of TEEOF takes up more than 50% in the total variance, with each of the first mode in the interannual oscillations generally standing for annually varying patterns which are related with climate and reflecting long-term tendency of change in air temperature. It is particularly true for the first mode on the 10-year scale, which shows an obvious ascending trend concerning the temperature in winter and consistently the primary component of time goes in a way that is very close to the sequence of actual temperature. Apart from the first mode of all time sections of TEEOF for the globe and the two hemispheres and the second mode of the 1-year TEEOF, interannual variation described by other characteristic vectors are showing various patterns, with corresponding primary components having relation with long-term variability of specific interannual quasi-periodic oscillation structures. A 2T test applied to the annual variation pattern shows that the abrupt changes for the Southern Hemisphere and the globe come closer to the result of a uni-element t test for mean temperature than those for the Northern Hemisphere do. It indicates that the 2Ttest, when carried out with patterns of multiple variables, seems more reasonable than the t test with single elements.

Fault Pattern Recognition based on Kernel Method and Fuzzy C-means

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

Spatial variations of terrain and their impacts on landscape patterns in the transition zone from mountains to plains—A case study of Qihe River Basin in the Taihang Mountains

Terrain plays a key role in landscape pattern formation, particularly in the transition zones from mountains to plains.Exploring the relationships between terrain characteristics and landscape types in terrain-complex areas can help reveal the mechanisms underlying the relationships. In this study, Qihe River Basin, situated in the transition zone from the Taihang Mountains to the North-China Plain, was selected as a case study area. First, the spatial variations in the relief amplitudes(i.e.,high-amplitude terrain undulations) were analyzed. Second, the effects of relief amplitudes on the landscape patterns were indepth investigated from the perspectives of both landscape types and landscape indices. Finally, a logistic regression model was employed to examine the relationships between the landscape patterns and the influencing factors(natural and human) at different relief amplitudes. The results show that with increasing relief amplitude, anthropogenic landscapes gradually give in to natral landscapes. Specifically, human factors normally dominate the gentle areas(e.g., flat areas) in influencing the distribution of landscape types, and natural factors normally dominate the highly-undulating areas(e.g., moderate relief areas). As for the intermediately undulating areas(i.e.,medium relief amplitudes), a combined influence of natural and human factors result in the highest varieties of landscape types. The results also show that in micro-relief areas and small relief areas where natural factors and human factors are more or less equally active,landscape types are affected by a combination of natural and human factors.The combination leads to a high fragmentation and a high diversity of landscape patterns. It seems that appropriate human interferences in these areas can be conducive to enhancing landscape diversity and that inappropriate human interferences can aggravate the problems of landscape fragmentation.

Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness

Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include： （1） the initial clustering points are preselected according to the density queue of data objects; and （2） Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common kmeans algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns＇ recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.

Defocus blur detection using novel local directional mean patterns(LDMP)and segmentation via KNN matting

Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction.Existing defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera configuration.Hence,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned limitations.This paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions.We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred regions.The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image.Additionally,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images.Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection.Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.

东北抗联精神的核心意蕴、时代价值及赓续路径

九一八事变后,中国共产党创建和领导的东北抗日联军坚持抗战14年之久,成为东北抗战的中坚力量,用血肉之躯铸造了一座铁血丰碑,也铸就了东北抗联精神。其核心意蕴为“忠诚于党的坚定信念,勇赴国难的民族大义,血战到底的英雄气概”,是中国共产党人的底气和志气、勇气和骨气、锐气与豪气的集中体现。深度凝练东北抗联精神的核心意蕴、时代价值和赓续路径,有助于传承红色基因,启人心智并催人奋进,为实现中华民族伟大复兴注入强大精神力量和精神滋养。

基于EBRCG的API结构模式信息增强方法研究

针对API调用模式缺乏结构信息及结果高冗余等问题,提出了基于扩展的分支保留调用图(the Extended Branch-Reserving Call Graph,EBRCG)的API结构模式信息增强方法。以Java开源项目源代码为研究对象,使用EBRCG来表示Java类的方法的结构信息,在EBRCG中,同时考虑了API调用语句、分支语句(将if语句和所有循环语句视为分支语句)、switch-case多分支语句、异常语句等,并提出了EBRCG裁剪算法来获取特定API调用模式的代码结构。同时,采用聚类和排序的方法对API调用模式的多个代码结构信息进行筛选,最终选择具有代表性的API调用模式的代码结构。为验证该方法的效果,将该方法与TextRank方法进行了3组实验比较。结果显示,该方法能有效地获取API调用模式的代码结构,相比TextRank方法能更准确地描述API的使用,有一定的研究意义,并为软件开发人员提供了参考。

构建新发展格局视域下的营商环境治理研究

营商环境治理作为中国国家治理的重要部分,是国家治理体系和治理能力现代化的重要标志,也是中国式现代化在经济领域的突出表现,彰显着国家治理现代化水平。因此,要牢牢把握营商环境发展方向,坚持以习近平新时代中国特色社会主义思想和党的二十大精神为指导、以中国式现代化为目标、以马克思主义中国化时代性成果和经验为基础,将营商环境的制度优势转化为国家治理效能。文章以中国式现代化作为营商环境治理的方向引领,以大国经济发展理论、新新贸易理论、马克思主义政治经济学为理论支撑,剖析了构建新发展格局视域下营商环境治理的内在机制,即大国经济理论从“人口规模巨大的现代化”出发,从人口规模多样性和市场多样性的现实角度,揭示了构建形成强大国内市场的重要意义;共同富裕、物质文明与精神文明相协调、人与自然和谐共生的中国式现代化目标,与新新贸易理论中基于企业异质性、挖掘企业潜力、关注企业参与度与社会责任感、最终实现产业整体效益的逻辑相符合;马克思主义政治经济学追求的国民经济良性循环目标,从国内国际“双循环”的角度,诠释了“和平发展道路”的政治经济学内涵。其次,文章基于三大理论基础,解析新发展格局视域下营商环境治理促进经济发展的多维动态逻辑,即基于大国经济发展理论的要素市场化配置逻辑、基于新新贸易理论的产业链供应链升级逻辑与基于马克思主义政治经济学的现代流通体系建设逻辑。最后,文章将营商环境治理的多维动态逻辑融入中国当前构建新发展格局的目标取向和战略选择中,提出构建新发展格局视域下营商环境治理的市场化改革路径、创新驱动路径和“放管服”改革路径,三条作用路径相辅相成、协同发力,以制度创新为新发展格局经济体系注入源源不断的活力。

场所精神理论下传统街区文化空间呈现与感知

随着城市的快速发展,传统街区面临着文化空间的传承问题。地方文脉的传承,与地区空间格局的演变、空间特性的延续、人们对区域氛围的感知变化等问题密切相关。选择场所精神理论作为切入点,以北京天桥地区为例,使用质性研究中扎根理论三级编码和地理空间展示与分析方法,从空间格局演变、场所精神分析、文化空间的呈现与场所精神的传承三个方面,对城市传统街区文化空间呈现与感知问题进行研究。结果显示:人们对天桥地区的整体认知体现在“古今具有高知名度的艺术表演区域”这一内涵上。人们通过观看展览演出等活动,体验区域的整体空间氛围,进而感受地方文化。这样的游览模式,适合此类新旧文化空间同时呈现的传统街区。

写生之本体意涵与当代中国画写意精神的回归

阐释写生的本体意涵以厘清中西写生概念的历史脉络,传统中国画学语境中的写生除专指花鸟画科外,实为写万物之生意、生机、生趣,与西方基于造型训练的写生本无瓜葛。历史的原因造成了写生概念的置换和误读,并导致当代中国画的发展脱离中国传统文化之“轨辙”。写生意涵的正本清源呼唤民族绘画写意精神和传统的回归。

朴素贝叶斯下新型建筑室内空调瞬态能耗预测

新型建筑通常具有较为复杂的结构,使得室内空调系统的能耗受到如建筑朝向、室内人员活动以及设备使用情况等因素影响,导致无法精准预测空调能耗。为此,提出新型建筑室内空调瞬态能耗朴素贝叶斯预测方法。采用K均值聚类(K-means clustering,K-Means)算法将新型建筑室内空调划分为除湿模式、制冷模式、通风模式、制热模式和自动模式,并分析影响空调瞬态能耗的因素,建立朴素贝叶斯预测模型,剔除空调瞬态能耗异常数据,输入至朴素贝叶斯预测模型中,实现新型建筑室内空调的瞬态能耗预测。仿真结果表明,所提方法可有效完成不同模式和运行时间下的瞬态能耗预测,与实际能耗之间的偏离程度较低,期望误差仅为2.31%,变异系数为4.76%,预测误差小。

构建新安全格局的重要意义、基本遵循和实践要求

构建新安全格局是我国在新形势下有效应对国内外复杂形势、掌握国家安全战略主动权、主导权的战略部署。构建新安全格局为推进中国式现代化提供坚强安全保障,为实现高质量发展提供战略支撑,为建设更高水平的平安中国指引方向。为此,必须坚持党中央对国家安全工作的全面领导,坚持以习近平总书记总体国家安全观为理论指导和行动指南,坚持以人民安全为宗旨,充分发扬斗争精神,加强国家安全法治建设,以国际共同安全构建新安全格局。

Machine acceleration time series prediction for dimensional accuracy of 3D printed parts

This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop a long short-term memory(LSTM)network capable of assessing this impact.We conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s bed.The LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy.This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model.Specifically,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill pattern.Rigorous testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s precision.Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of 0.007328.Notably,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration data.The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.

基于模式识别的舰船机械电子设备故障自动监测

舰船机械电子设备故障数据量较为庞大,且模式复杂多样,为满足其复杂性的要求,提出基于模式识别的舰船机械电子设备故障自动监测方法,采集舰船机械电子设备运行中的温度、压力、振动等数据作为故障监测的原始数据,计算数据间的相似系数和欧氏距离,结合K均值算法实现数据聚类处理。通过小波包算法对聚类后的数据进行特征提取,将其输入到卷积神经网络中,通过对监测模型进行训练,最终实现对舰船机械电子设备故障自动监测。通过实验分析,该方法与相关人员进行监测的故障情况高度一致,在不同故障类型监测的时间均能够保持在5 ms以内,具有较高的监测效率和监测精准度。