光催化剂SrTiO3的制备及其性能的研究

论述用化学液相共沉淀法制备SrTiO_3的最佳条件,及在不同的活化条件下对SrTiO_3光催化性能的影响。此外还研究了掺入过渡金属氧化物对SrTiO_3催化性能的改善。     

聚氨酯防水涂料及其市场发展概况

聚氨酯防水涂料是当今最有效的柔性防水材料之一，介绍了聚氨酯防水涂料的发展状况，并对其市场前景进行了展望。     

基于多步迭代算法的数字预失真技术

为了更好地补偿功率放大器的非线性,提出一种新型多步迭代算法来求取数字预失真器。新算法结合了多步迭代算法与频域削峰技术,使功放在保证工作效率的同时更大程度提高其线性度。实验结果显示,该方法较传统的线性化技术有明显优势,功放输出信号的ACPR可改善20.66 dB。     

透明聚烯烃用成核剂合成的研究

介绍了聚烯烃成核剂合成反应所用助剂选择,反应条件的确立,利用均匀设计实现最佳配比实验,对不同侧基苯甲醛缩合反应进行了研究。     

应用免疫斑点法检测不同工艺灭活的乙肝血源疫苗中的前S_1和S_2蛋白

作者应用前 S_1、前 S_2和 HBsAg/a 单克隆抗体,用免疫斑点法(Immuno-spot)检测同一批的乙肝表面抗原分别经加热灭活和三步化学灭活后的前 S_1和前 S_2蛋白保留情况,比较了两种工艺对前 S_1和前 S_2蛋白的影响。结果表明;加热灭活可保留前 S_1和前 S_2蛋白,三步化学灭活使前 S_1和前 S_2蛋白丢失,从抗原组成上看,加热灭活后的抗原更接近自然抗原。首次报告了含有前 S_1蛋白的乙肝疫苗,并对前 S_1和前 S_2蛋白在乙肝血源疫苗中的可能作用进行了讨论。     

基于XC2287M的工程机械ECU设计

ECU是工程机械电控系统的核心部件,目前国内中高端ECU市场基本被国外厂商所垄断。设计了一款基于XC2287M微控制器的工程机械智能ECU,采用CODESYS软PLC作为编程平台,支持IEC 61131-3定义的5种标准PLC语言进行编程。所设计的ECU拥有丰富的I/O资源和完善的安全保护机制,能够广泛应用于各种工程机械控制系统,打破国外厂商对ECU市场的垄断。     

A saliency-driven robotic head with bio-inspired saccadic behaviors for social robotics

This paper presents a robotic head for social robots to attend to scene saliency with bio-inspired saccadic behaviors. Scene saliency is determined by measuring low-level static scene information, motion, and object prior knowledge. Towards the extracted saliency spots, the designed robotic head is able to turn gazes in a saccadic manner while obeying eye–head coordination laws with the proposed control scheme. The results of the simulation study and actual applications show the effectiveness of the proposed method in discovering of scene saliency and human-like head motion. The proposed techniques could possibly be applied to social robots to improve social sense and user experience in human–robot interaction.     

Toward seed-insensitive solutions to local community detection

Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices’ neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms.     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.