Collaborative Distribution Alignment for 2D image-based 3D shape retrieval

Retrieving 3D shapes with 2D images has become a popular research area nowadays, and a great deal of work has been devoted to reducing the discrepancy between 3D shapes and 2D images to improve retrieval performance. However, most approaches ignore the semantic information and decision boundaries of the two domains, and cannot achieve both domain alignment and category alignment in one module. In this paper, a novel Collaborative Distribution Alignment (CDA) model is developed to address the above existing challenges. Specifically, we first adopt a dual-stream CNN, following a similarity guided constraint module, to generate discriminative embeddings for input 2D images and 3D shapes (described as multiple views). Subsequently, we explicitly introduce a joint domain-class alignment module to dynamically learn a class-discriminative and domain-agnostic feature space, which can narrow the distance between 2D image and 3D shape instances of the same underlying category, while pushing apart the instances from different categories. Furthermore, we apply a decision boundary refinement module to avoid generating class-ambiguity embeddings by dynamically adjusting inconsistencies between two discriminators. Extensive experiments and evaluations on two challenging benchmarks, MI3DOR and MI3DOR-2, demonstrate the superiority of the proposed CDA method for 2D image-based 3D shape retrieval task.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

锌铝水滑石的制备及其在制革少铬鞣中的应用

通过共沉淀法制备了锌铝水滑石(ZnAl-LDH),采用XRD、FTIR、SEM对其结构进行了表征.将其配合用量为皮质量2％的铬粉(记为2％铬粉)应用于制革鞣制工艺中.考察了ZnAl-LDH制备时的金属源物质的量比、陈化时间对鞣制坯革收缩温度、鞣后废液中的Cr2O3质量浓度的影响.结果表明,ZnAl-LDH是尺寸在100～400 nm之间的片状材料.当n〔(Zn(NO3)2?6H2O)〕:n〔Al(NO3)3?9H2O〕=2.0:1.0,陈化时间为6 h时,制备的ZnAl-LDH-4具有优异的性能.与单独使用2％铬粉鞣制坯革相比,ZnAl-LDH-4(用量为皮质量的4％)配合2％铬粉鞣制坯革收缩温度由75℃提高到94℃,废液中的Cr2O3质量浓度由99.58 mg/L降低至46.98 mg/L,且加入ZnAl-LDH-4鞣制后,鞣后废液化学需氧量和生物需氧量均降低.     

氯乙烯变温吸附装置的研究

介绍了采用变温吸附技术对氯乙烯进行干燥脱水的工艺流程及运行效果,阐述了变温吸附装置运行过程中出现的问题及相应的解决方法。     

Investigation on thermal conductivity and mechanical properties of Si3N4 ceramics via one-step sintering

High thermal conductivity Si₃N₄ ceramics were fabricated using a one-step method consisting of reaction-bonded Si₃N₄ (RBSN) and post-sintering. The influence of Si content on nitridation rate, β/(α+β) phase rate, thermal conductivity and mechanical properties was investigated in this work. It is of special interest to note that the thermal conductivity showed a tendency to increase first and then decrease with increasing Si content. This experimental result shows that the optimal thermal conductivity and fracture toughness were obtained to be 66 W (m K)^-1 and 12.0 MPa m^1/2, respectively. As a comparison, the nitridation rate and β/(α+β) phase rate in a static pressure nitriding system, i.e., 97% (MS10), 97% (MS15), 97% (MS20) and 8.3% (MS10), 8.3% (MS15), 8.9% (MS20), respectively, have obvious advantages over those in a flowing nitriding system, i.e., 91% (MS10), 91% (MS15), 93% (MS20) and 3.1% (MS10), 3.3% (MS15), 3.3% (MS20), respectively. Moreover, high lattice integrity of the β-Si₃N₄ phase was observed, which can effectively confine O atoms into the β-Si₃N₄ lattice using MgO as a sintering additive. This result indicates that one-step sintering can provide a new route to prepare Si₃N₄ ceramics with a good combination of thermal conductivity and mechanical properties.     

Optimizing the stability and electrical transport properties of CeNbO4+δ-based oxide ceramics by regulating oxygen ion content

CeNbO_4+δ ceramics have attracted extensive research interest because of their unique mixed ion-electron transport characteristics and interesting structure-functional characteristics caused by the difference in oxygen ion content. Although the change of oxygen ion content brings rich redox properties, it also causes serious crystal transformation and abnormal electrical transport properties. In order to obtain stable structure and excellent electrical transport properties, the directional regulation of the oxygen ion content has been realized through introducing Al₂O₃ and high temperature aging. After 600 h of aging at 1073 K, the prepared composite ceramics not only obtain a stable structure without crystal transformation, but also show good negative temperature coefficient (NTC) thermistor characteristics in the temperature range of 473 K–1273 K, in which the linear fitting maximum Pearson's r of the relationship between lnρ and 1000/T can reach 99.97%. The proposed method provides a new thought for the design and application of high-temperature electronic ceramics.     

卡尔费休法测定液态氯乙烯中水含量的研究

采用卡尔费休法测定了液态氯乙烯单体中的水含量。试验确定了采样工具、样品的预处理方式及选用溶剂的最优方案,并进行了精密度与准确度的验证。结果表明卡尔费休法测定液态氯乙烯单体中的水含量准确、快速,可以用来准确、及时地指导生产。     

氮含量对HPD-1双相不锈钢组织及力学性能的影响

通过显微组织观察与力学性能测试研究了氮含量(0.08%~0.22％,质量分数)对HPD-1双相不锈钢硬度、拉伸性能、低温冲击性能及疲劳性能的影响。结果表明,氮含量变化可显著影响试验钢γ/α相比例,当氮含量由0.08％升高到0.22％,γ相含量由38.1％提高至56.5％。α相的硬度由273 HV10提高到343 HV10,γ相的硬度由238 HV10提高到299 HV10,试验钢强度明显提升。氮元素对两相比例和奥氏体相韧性的双重影响导致试验钢低温冲击性能呈先上升后下降的趋势;更高的氮含量抑制疲劳裂纹萌生与拓展,是影响HPD-1双相不锈钢室温疲劳性能的主要因素。撕裂棱是疲劳断口的显著特征。