量学认识论“分别识”和“无分别识”初探

量学把“认识”归纳为分别识和无分别识。无分别识是一种纯感性认识,分别识是在无分别识基础上产生的理性认识。本文从分别与无分别识之性相入手,分析此二种识的不同特征,旨在探讨量学在哲学认识论方面的理论价值。

族别识异民族的认同心理机制:来自普米人的证据

“族别识异”是指同一民族在族称上被界定为两个甚至多个民族。在20世纪50年代,民族识别使“西番”成为“普米”“藏族”“纳西族”三个不同民族。通过考察云南宁蒗、四川木里的多个普米族群村落,观察普米族人的微信群,从普米族人的民族认同、与周边民族交往交流交融角度考察“族别识异”对普米人民族认同的影响,发现“族别识异”导致同一族源不同称谓的普米人的民族认同趋向于复杂化与动态变化,个体对自身民族身份采取了心理区隔化或心理重塑的调适策略以达到认知平衡。普米人对周边民族的认同以及周边民族对普米人的认同,揭示了族别识异民族的认同心理机制。

别识心裁辩证分析——读《文史通义》札记

章学诚所著《文史通义》,既大力继承中华文化的优良传统又勇于创新,具有很高的理论价值。已有的研究成果对于总结其别识心裁、辩证分析的独具风格和理论新创成就尚有明显不足,由于沿袭“单纯考证尺度”而造成的认识误区尚未消除。在学术史研究领域留下的这一大缺憾早该认真补上,尤其是在当前,我们正处于创造性阐释中华优秀传统文化、大力发掘和总结先哲们著作中的中国精神和中国智慧的不平凡时代,章学诚面对时代大变局表现出的探索精神、理论建树、革新勇气和使命意识更显宝贵。对此深入剖析,不仅对于推进清代学术史研究有重要的学术价值,而且对于当下确立“文化自信”也具有重要的理论意义。

“别识心裁”的中国史学叙事理论探索--朱露川著《中国古代史书叙事的风格》平议

史学界从不缺少反省的声音。在众多的批判与自我批判中,关于选题碎片且重复、研究缺乏时代感、识见缺少独断之学的讨论尤其引人警觉。与此相对,拥有较好口碑的著作,大多问题意识鲜明,分析问题透辟,具有很强的学术个性,并由此形成学术牵引力。这两方面的评衡,实则指向治学利钝---“别识心裁”。大凡蕴含“别识”者,则其研究于起手处已先胜一筹,再运以“心裁”,故能新意迭出,发人深思。近日,读朱露川著《中国古代史书叙事的风格---从班荀二体到范袁二家》(社会科学文献出版社,2022,下称《风格》),时时想起实斋先生的这句四字诀,心有所感,故略作平议。

只有能分别识 没有所分别相

韩镜清老师于2003年1月17日舍报往生，享年92岁。为了纪念韩老，在这里不自量力介绍一点儿自己理解的韩老关于能相和所相问题的思考，以供大家继续探讨。

模型水轮机初生空化的特征谱提取识别方法

目前水轮机模型初生空化识别方法仍采用人工识别法,这种方法获得结果的周期较长、主观性强、准确度及效率都较低。针对于此,对水轮机模型初生空化识别方法进行了研究与优化创新,提出了一种基于炮声谱与特殊脉动谱特征提取的水轮机空化智能识别方法,即多态泡音智能识别方法 PSVFR。该方法依据自主开发的水轮机空化噪声多态算法MTCSPC,对数据进行处理,通过采集初生空化音态特征向量,建立矩阵模型,与样本数据库中的定性矩阵进行特征比对、计算、判断,以帮助机器完成对模型水轮机空化噪声的学习和识别。与现有技术相比,该方法能够提高机器对水轮机初生空化现象的识别准确度和识别效率,识别效率可达80%。

章学诚“别识心裁”说再议——兼论中西史学比较的本质与目标

在中国传统学术语境下,章学诚"别识心裁"说的理论基础(功力必兼性情)、获致途径(辨章学术,考镜源流)以及中心主旨(纲纪天人、推明大道以及成一家之言)皆有其自身的脉络。而处于中西学术交融的背景下,余英时以科林伍德的观点检讨章学诚"别识心裁"说,并由此揭示中西史学的本质、方法以及历史思维的一致性,意在"求同";张汝伦则立足中西思想文化传统的差异批判余英时的观点,主张"比较者对被比较者双方的特征都有深入细致的了解,以相对超然的态度进行比较",重在"存异"。余、张二人关于科林伍德所谓内在、外在、心灵、先验、建构自我权威与章学诚所谓言、事、心术、性情、史义之异同的争论,恰从正反两方面将主观与客观、主体与客体、观念、思维、方法等现代概念有机融入到传统史学之中。其实,中西史学词汇对译互释的过程也是在酝酿形成新的中西史学共通的学术语境,建构新的"异中之同"与"同中之异";中西史学比较的本质与目标是探寻普遍性(同)、确认独特性(异),即于古今嬗变、中西互镜中把握、阐明中西史学的主脉与精神,建构更加符合普遍性而又吻合自身传统的清晰明确的"自我"。

Re-identifying beef cattle using improved AlignedReID++

Accurate and continuous identification of individual cattle is crucial to precision farming in recent years.It is also the prerequisite to monitor the individual feed intake and feeding time of beef cattle at medium to long distances over different cameras.However,beef cattle can tend to frequently move and change their feeding position during feeding.Furthermore,the great variations in their head direction and complex environments(light,occlusion,and background)can also lead to some difficulties in the recognition,particularly for the bio-similarities among individual cattle.Among them,AlignedReID++model is characterized by both global and local information for image matching.In particular,the dynamically matching local information(DMLI)algorithm has been introduced into the local branch to automatically align the horizontal local information.In this research,the AlignedReID++model was utilized and improved to achieve the better performance in cattle re-identification(ReID).Initially,triplet attention(TA)modules were integrated into the BottleNecks of ResNet50 Backbone.The feature extraction was then enhanced through cross-dimensional interactions with the minimal computational overhead.Since the TA modules in AlignedReID++baseline model increased the model size and floating point operations(FLOPs)by 0.005 M and 0.05 G,the rank-1 accuracy and mean average precision(mAP)were improved by 1.0 percentage points and 2.94 percentage points,respectively.Specifically,the rank-1 accuracies were outperformed by 0.86 percentage points and 0.12 percentage points,respectively,compared with the convolution block attention module(CBAM)and efficient channel attention(ECA)modules,although 0.94 percentage points were lower than that of squeeze-and-excitation(SE)modules.The mAP metric values were exceeded by 0.22,0.86 and 0.12 percentage points,respectively,compared with the SE,CBAM,and ECA modules.Additionally,the Cross-Entropy Loss function was replaced with the CosFace Loss function in the global branch of baseline model.CosFace Loss and Hard Triplet Loss were jointly employed to train the baseline model for the better identification on the similar individuals.AlignedReID++with CosFace Loss was outperformed the baseline model by 0.24 and 0.92 percentage points in the rank-1 accuracy and mAP,respectively,whereas,AlignedReID++with ArcFace Loss was exceeded by 0.36 and 0.56 percentage points,respectively.The improved model with the TA modules and CosFace Loss was achieved in a rank-1 accuracy of 94.42%,rank-5 accuracy of 98.78%,rank-10 accuracy of 99.34%,mAP of 63.90%,FLOPs of 5.45 G,frames per second(FPS)of 5.64,and model size of 23.78 M.The rank-1 accuracies were exceeded by 1.84,4.72,0.76 and 5.36 percentage points,respectively,compared with the baseline model,part-based convolutional baseline(PCB),multiple granularity network(MGN),and relation-aware global attention(RGA),while the mAP metrics were surpassed 6.42,5.86,4.30 and 7.38 percentage points,respectively.Meanwhile,the rank-1 accuracy was 0.98 percentage points lower than TransReID,but the mAP metric was exceeded by 3.90 percentage points.Moreover,the FLOPs of improved model were only 0.05 G larger than that of baseline model,while smaller than those of PCB,MGN,RGA,and TransReID by 0.68,6.51,25.4,and 16.55 G,respectively.The model size of improved model was 23.78 M,which was smaller than those of the baseline model,PCB,MGN,RGA,and TransReID by 0.03,2.33,45.06,14.53 and 62.85 M,respectively.The inference speed of improved model on a CPU was lower than those of PCB,MGN,and baseline model,but higher than TransReID and RGA.The t-SNE feature embedding visualization demonstrated that the global and local features were achieve in the better intra-class compactness and inter-class variability.Therefore,the improved model can be expected to effectively re-identify the beef cattle in natural environments of breeding farm,in order to monitor the individual feed intake and feeding time.

Schur Forms and Normal-Nilpotent Decompositions

Real and complex Schur forms have been receiving increasing attention from the fluid mechanics community recently,especially related to vortices and turbulence.Several decompositions of the velocity gradient tensor,such as the triple decomposition of motion(TDM)and normal-nilpotent decomposition(NND),have been proposed to analyze the local motions of fluid elements.However,due to the existence of different types and non-uniqueness of Schur forms,as well as various possible definitions of NNDs,confusion has spread widely and is harming the research.This work aims to clean up this confusion.To this end,the complex and real Schur forms are derived constructively from the very basics,with special consideration for their non-uniqueness.Conditions of uniqueness are proposed.After a general discussion of normality and nilpotency,a complex NND and several real NNDs as well as normal-nonnormal decompositions are constructed,with a brief comparison of complex and real decompositions.Based on that,several confusing points are clarified,such as the distinction between NND and TDM,and the intrinsic gap between complex and real NNDs.Besides,the author proposes to extend the real block Schur form and its corresponding NNDs for the complex eigenvalue case to the real eigenvalue case.But their justification is left to further investigations.

Wafer Defect Map Pattern Recognition Based on Improved ResNet

The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in the yield of wafer manufacturing.Therefore,for the pattern recognition of wafer defects,this paper uses an improved ResNet convolutional neural network for automatic pattern recognition of seven common wafer defects.On the basis of the original ResNet,the squeeze-and-excitation(SE)attention mechanism is embedded into the network,through which the feature extraction ability of the network can be improved,key features can be found,and useless features can be suppressed.In addition,the residual structure is improved,and the depth separable convolution is added to replace the traditional convolution to reduce the computational and parametric quantities of the network.In addition,the network structure is improved and the activation function is changed.Comprehensive experiments show that the precision of the improved ResNet in this paper reaches 98.5%,while the number of parameters is greatly reduced compared with the original model,and has well results compared with the common convolutional neural network.Comprehensively,the method in this paper can be very good for pattern recognition of common wafer defect types,and has certain application value.

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

Knowledge Graph Based Method for Tracing Quality of Aerospace Products

Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.

Region-Aware Fashion Contrastive Learning for Unified Attribute Recognition and Composed Retrieval

Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.

基于感知模式纹理的人脸识别

稀疏表示分类法和局部二值模式是目前广泛运用的人脸表示方法.虽然稀疏表示分类法通过重构算法能很好的稀疏表示人脸,但在人脸的局部特征表示上不够理想.局部二值模式是基于局部纹理特征表示人脸,其提取的局部纹理简单、不连续、不光滑,造成纹理损失严重.提出局部纹理特征的感知模式纹理的概念,克服了稀疏表示分类法在局部特征表现不佳的缺点,同时又比局部二值模式纹理光滑、鲁棒.利用感知模式纹理特征在Yale和ORL数据库取得了较好的识别效果.

章学诚与柯林伍德的历史观念差异比较——与余英时先生商榷

中国的思想传统和学术传统使得章实斋的历史观念不可能与柯林伍德的历史观念"如此地不谋而合",更谈不上"这两人的相似之处可以说是十分惊人"。比较哲学要求比较者对被比较者双方的特征都有深入细致的了解,以相对超然的态度进行比较。由各自的历史性造成思想间巨大的差异,是任何严格的比较研究不能不注意的。

Support vector machines for emotion recognition in Chinese speech

Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.

Automatic discrimination of sedimentary facies and lithologies in reef-bank reservoirs using borehole image logs

Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extreme heterogeneity of reef-banks, it is very difficult to discriminate the sedimentary facies and lithologies in reef-bank reservoirs using conventional well logs. The borehole image log provides clear identification of sedimentary structures and textures and is an ideal tool for discriminating sedimentary facies and lithologies. After examining a large number of borehole images and cores, we propose nine typical patterns for borehole image interpretation and a method that uses these patterns to discriminate sedimentary facies and lithologies in reeI^bank reservoirs automatically. We also develop software with user-friendly interface. The results of applications in reef-bank reservoirs in the middle Tarim Basin and northeast Sichuan have proved that the proposed method and the corresponding software are quite effective.

Seismic low-frequency-based calculation of reservoir fluid mobility and its applications

Low frequency content of seismic signals contains information related to the reservoir fluid mobility. Based on the asymptotic analysis theory of frequency-dependent reflectivity from a fluid-saturated poroelastic medium, we derive the computational implementation of reservoir fluid mobility and present the determination of optimal frequency in the implementation. We then calculate the reservoir fluid mobility using the optimal frequency instantaneous spectra at the low-frequency end of the seismic spectrum. The methodology is applied to synthetic seismic data from a permeable gas-bearing reservoir model and real land and marine seismic data. The results demonstrate that the fluid mobility shows excellent quality in imaging the gas reservoirs. It is feasible to detect the location and spatial distribution of gas reservoirs and reduce the non-uniqueness and uncertainty in fluid identification.

Zoeppritz equation-based prestack inversion and its application in fluid identification

Prestack seismic inversion methods adopt approximations of the Zoeppritz equations to describe the relation between reflection coefficients and P-wave velocity, S-wave velocity, and density. However, the error in these approximations increases with increasing angle of incidence and variation of the elastic parameters, which increases the number of inversion solutions and minimizes the inversion accuracy. In this study, we explore a method for solving the reflection coefficients by using the Zoeppritz equations. To increase the accuracy of prestack inversion, the simultaneous inversion of P-wave velocity, S-wave velocity, and density by using prestack large-angle seismic data is proposed based on generalized linear inversion theory. Moreover, we reduce the ill posedness and increase the convergence of prestack inversion by using the regularization constraint damping factor and the conjugate gradient algorithm. The proposed prestack inversion method uses prestack large-angle seismic data to obtain accurate seismic elastic parameters that conform to prestack seismic data and are consistent with logging data from wells.

P-SV wave elastic impedance and fluid identification factor in weakly anisotropic media

The P-SV wave reflection coefficients in VTI and HTI media were obtained by approximation of the Jilek（2002a and b）equation in orthotropic anisotropic media.An approximate equation for P-SV wave elastic impedance can be derived from the combination of the new coefficients with S-wave elastic impedance（Duffaut et al.,2000）.On this basis, the fluid identification factor in weakly anisotropic media was constructed and used to identify the Castagna and Smith（1994）lithologic combination and achieved good results. Finally,we specifically analyzed the anisotropic parameter impacts P-SV wave elastic impedance and fluid factor trends.