不同酶制剂对生长猪效果试验

选择１２８头长大二元杂交猪随机分为４组，每组设２个重复。４组分别饲喂含０ｍｇ／ｋｇ、１０００ｍｇ／ｋｇ的红八利酶制剂、万达福酶制剂和溢多利酶制剂的饲粮，试验为期２９天。试验结果表明，猪日粮中添加红八利酶和溢多利酶可促进生长和改善饲料利用率。与对照组相比，红八利酶组平均日增重提高４．９２％（Ｐ＞０．０５），饲料利用率提高１．７２％（Ｐ＞０．０５），经济效益提高５．０４％；溢多利酶组平均日增重提高３．８７％（Ｐ＞０．０５），饲料利用率提高５．５０％（Ｐ＞０．０５），经济效益提高９．３６％；而万达福酶组对猪的生长性能和饲料利用率有负效应     

葡萄糖脱氢酶传感器的研制及应用

本文报道了以甲苯胺兰键合修饰浸蜡石墨电极为基体电极，将葡萄糖脱氢酶（ＧＤＨ），烟酰胺腺嘌呤二核苷酸（ＮＡＤ＾＋）用戊二醛一起固定在蚕丝蛋白膜上，构成葡萄糖脱氢酶传感器。在ｐＨ＝７．４的ＮａＯＨ－ＫＨ２ＰＯ４介质中，该传感器与葡萄糖浓度在２．０×１０＾－５－６．４×１０＾－４ｍｏｌ／Ｌ范围内有良好的线性关系。响应叶间为２０ｓ。本文讨论了影响传感器响应电流的各种因素和测试葡萄糖的最佳条件。用此传感器测     

新闻

闪亮登场英特尔２ＧＨｚ奔腾４处理器 本刊讯英特尔 ８月 ２８日在京正式发布２ＧＨＺ奔腾４处理器国内外２０多家ＰＣ厂商同步推出基于新款奔腾４的ＰＣ系统。新奔４采用０．１８微米工艺制造,封装形式为 Ｓｏｃｋｅｔ ４７８的 ｍＰＧＡ,核心电压１．７５Ｖ,二级缓存２５６ＫＢ． 据称,基于 ２ＧＨＺ奔腾 ４处理器的ＰＣ, 运算速度比１ＧＨＺ奔腾Ⅲ系统快８１％,适合数字娱乐、三维图形和互联网等场合应用,在多任务环境中处理后台任务如病毒检查、文件压缩时表现尤其出众。（罗国昭）８４５芯片组亮相主板厂商跟进 本刊讯英特尔９…     

乳香素对断奶仔猪饲喂效果试验

本试验采取随机区组设计，选用４５日龄断奶的杜长大三元杂种猪３０头，随机分成对照组、试１组、试２组，饲粮分别添加０％、０．０５％、０．１％乳香素，进行对比试验。经３１天试验表明，试验１组、２组与对照组相比，平均日增重提高１４．６％（Ｐ＜０．０５）与１２．９％（Ｐ＜０．０５），平均日采食量提高１０．２％与１０．５％，饲料利用率提高４％与２．３％，头均净增利润１１．４５元与７．８６元。     

P4第二季前进入533MHz外频时代

根据Ｉｎｔｅｌ方面公布的最新发布计划，首批采用５３３ＭＨｚ外频的Ｐ４　Ｎｏｒｔｈｗｏｏｄ将在明年第二季前投放市场，这比原先的计划稍微提前了一些，Ｉｎｔｅｌ在第二季前发布２．４ＧＨｚ的Ｐ４　Ｎｏｒｔｈｗｏｏｄ处理器，分别提供４００ＭＨｚ／５３３ＭＨｚ的型号，同时发布的还有５３３ＭＨｚ　ＦＳＢ的２．２６ＧＨｚ及２．２０ＧＨｚ（均为盒装零售版）；而旧式的０．１８微米Ｗｉｌｌａｍｅｔｔｅ将逐步被Ｎｏｒｔｈｗｏｏｄ所取代，预计在第二季末仅剩１．９ＧＨｚ／１．８ＧＨｚ这两个型号；而根据我们之前得到的ＡＭＤ…     

嫁接方式对番茄生长发育及产量的影响

以野生番茄Ａｃｈｉｌｌｅｓ－Ｍ为砧木又日本番茄品种Ｍｏｍｏｔａｒｏ为接鳎,进行了０真叶法（砧木不留真中）、１真叶法（砧木留１片真叶）和２真叶法（砧木留２片真叶）３种不同部位嫁接方式的研究。结果表明：采用２真叶法嫁接的番茄的株高、叶数、花序数等生长动态指标均显高于其它两种方式,前期产量和总产量比０真叶法分别提高５４．４％和２２．４％。     

国防科技大学研究生院一九九八年硕士生入学考试软件基础试题(下)

二、数据结构部分（３０分）１．（４分）设无向图Ｇ有ｎ个结点、ｍ个连通分支,问按亮度方向周游所得到的生成树林中边的条数是多少？并给出证明。２．（４分）对ｎ个记录进行直接选择排序,什么情况下记录移动次数最少？什么情况下记录移动次数最多？最少和最多的移动次数是多少？并举例说明。３．（４分）设有如下关键码和散列函数值：Ｋｅｙ　　Ａ　Ｂ　Ｃ　Ｄ　Ｅ　Ｆ　Ｇ　Ｈｈ（Ｋｅｙ）４　０　３　４　２　６　７　５基本存储区编址为０～７,用结合的同义词子表法解决碰撞试画出其存储图示,并说明是否发生堆积现象？４．（４分）…     

硬件报价篇(广州,12/21/2001)

ＣＰＵＩｎｔｅｌ　Ｐ４　２．０Ｇ／１．８Ｇ／１．Ｇ４２００／２３８０／１７４５元Ｉｎｔｅｌ　Ｐ４　１．５Ｇ／１．４Ｇ／１．３Ｇ１２９０／１１３５／８９０元Ｉｎｔｅｌ　ＰⅢ１Ｇ／８６６１１８０／１０５０元Ｉｎｔｅｌ　ＣｅｌｅｒｏｎⅡ１．２Ｇ／１０００／９００１０１０／５７０／４９０元ＡＭＤ　Ａｔｈｌｏｎ　ＸＰ　１９００＋／１８００＋２１５０／１７５０元ＡＭＤ　Ａｔｈｌｏｎ　ＸＰ　１７００＋／１６００＋１２３０／１０２５元ＡＭＤ　雷鸟１．２Ｇ／１．１／１Ｇ　７９５／６８５／６７５元ＡＭＤ　Ｄｕｒｏｎ…     

基于平面薄膜型电极的半乳糖传感器的研究

本文采用微电子薄膜技术制作了平面薄膜型基底电极芯片,其中包括一人铂盘工作电极和一个对电极。用化学交联法将半乳糖氧化酶固定在铂盘电极表面,即制成半乳糖传感器。该酶传感器对半乳糖测定的线性范围为０．２～５．０ｍｍｏｌ／Ｌ,相关系数为０．９９８,响应时间为４５ｓ。适宜的ｐＨ值为７．０－８．０。该酶电极在两个星期内连续使用,响应电流无明显变化。保存一个月后测试仍有较好的电流响应。     

不同形态锌对断奶仔猪补锌效果的研究

采用同窝分组法将１０头“长二”断奶仔猪按性别和体重均分为两组，分别喂含富锌真菌（有机组）和氧化锌（无机组）添加剂的日粮，补锌量均为５７．４ｍｇ／ｋｇ日粮，经１４天试验，结果表明，在日增重和料重比，有机组分别为４５０ｇ和１．５３，比无机组的４２０ｇ和１．７０分别提高７．１４％和１０．００％，但差异不显著（Ｐ＞０．０５）；有机组的血清ＡＫＰ含量为１６０．５６ｍｉｕ／ｍｌ，高于无机组的１１７．１４ｍｉｕ／ｍｌ，差异显著（Ｐ＜０．０５）；有机组和无机组的血清ＩｇＧ含量分别为２．８２ｍｇ／１００ｍｌ和２．６６ｍｇ／ｍｌ，差异不显著（Ｐ＞０．０５）；肾、脾、肝、跖骨、血清、血细胞等组织器官中锌含量差异不显著（Ｐ＞０．０５）。     

一种面积与功耗优化的卷积器设计

提出一种基于随机存取存储器(RAM)的卷积器结构。将2组阶数不同的卷积器进行VLSI实现,每组包含1个基于RAM结构的卷积器和1个基本型FIR结构的卷积器。DC及Prime Power分析结果表明,当阶数为63时,基于RAM结构的卷积器面积和功耗相比基本型FIR结构的卷积器分别减少10.1%和8.4%;当阶数为255时,该优化百分比分别为14.9%和15.2%,并且卷积器阶数越高,优化效果越明显。63阶卷积器成功流片后,芯片实测结果显示,与经典结构相比,基于RAM的卷积器功耗减少了7.9%。     

基于皮肤纹理特征的高分辨率人脸图像识别

提出一种基于皮肤纹理特征的高分辨人脸图像识别算法。通过人脸轮廓提取人脸的皮肤纹理特征点,利用Gabor滤波器提取人脸关键区域的皮肤纹理特征,根据纹理区域相关性进行特征匹配。在FRGC v2.0数据库上的实验结果表明,当认假率为0.1%时,该算法的认证率可达97.8%,且使用高分辨率人脸图像可提高人脸识别的性能。     

基于词典属性特征的粗粒度词义消歧

本文依据《现代汉语语法信息词典》中对词语多义的属性特征描述,对《人民日报》语料中155 个词语共 4 996 个同形实例进行了粗粒度词义自动消歧实验,同时用贝叶斯算法进行了比较测试。基于词典属性特征的消歧方法在同形层面上准确率达到 90%, 但召回率偏低。其优点在于两个方面: 1) 不受词义标注语料库规模的影响;2) 对特定词语意义的消歧准确率可达到100%。本文也讨论了适用于不同词类的消歧特征。     

权马尔可夫链在降水量预测中的应用

选取1976—2017年曲靖市年降水量资料,采用均值-标准差分级法对其进行分级,以规范化的各阶自相关系数为权重,应用权马尔可夫链模型并结合模糊集理论中的级别特征值,分析预测2016—2018年的年降水量,并对模型进行验证。研究结果表明:预测值与实测值的相对误差为3.54%～8.38%,符合《水文情报预报规范》规定的误差值要求,将该方法应用于降水预测是可行、有效的。     

Score level fusion of voting strategy of geometric hashing and SURF for an efficient palmprint-based identification

This paper proposes an efficient indexing scheme for palmprint-based identification system. The proposed system uses geometric hashing of SURF key-points to index the palmprint into hash table and makes score level fusion of voting strategy based on geometric hashing and SURF score to identify the live palmprint. All ordered pairs of SURF key-points of the palmprint are scaled and mapped to a predefined coordinate system and all other points are similarity transformed. The new location after transformation serves as the index of the hash table. During identification, all ordered pairs of key-points of live palmprint are scaled and mapped to the coordinate system while remaining points are similarity transformed. A vote is casted to all images in the corresponding bins. Images having votes more than certain threshold are considered as candidate images of the live palmprint. SURF features of the live palmprint and the candidate images are compared for matching. Matching scores which are based on SURF key-points and vote of the corresponding candidate image are fused using weighted sum rule. The candidate image with the highest fused score is considered as the best match. The system is tested on IITK, CASIA and PolyU datasets. It has been observed that penetration rate of the proposed system is less than 30% for 0% bin miss rate (BMR) and has the identification accuracy of more than 97% for all three datasets. Further, the system is evaluated for robustness on downscaled and rotated. It has been found that the identification accuracy of the system for top best match is more than 90% for images downscaled up to 49% and accuracy is more than 85% when images are rotated at any angle.     

融合Levy飞行和精英反向学习的WOA-SVM多分类算法

元启发算法-SVM是多分类评价模型的典型架构,在多分类综合决策判定中具有重要的理论与实践意义,为此提出了一种融合Lévy飞行和精英反向学习的鲸鱼优化算法(Lévy flight and elite opposition-based whale optimization algorithm,LFEO-BWOA)-SVM多分类评价算法.利用Lévy飞行策略替代螺旋轨迹策略更新位置信息,有效克服了鲸鱼优化算法易陷入局部寻优的不足;引入精英反向学习机制增加种群多样性,提高了鲸鱼优化算法全局寻优的能力.实验仿真结果表明,LFEO-BWOA-SVM算法在分类准确率上比传统SVM、BP神经网络分别提高17.84％和4.51％,准确率为98.73％,在训练时间上比标准WOA-SVM和PSO-SVM分别缩短了9.34％和84.94％.实验结果证明,LFEO-BWOA-SVM算法的寻优能力和收敛速度均有明显提升,准确率和快速性良好.     

基于标记的规则统计模型与未登录词识别算法

该文针对小型词库,提出了基于规则统计模型的消歧方法和识别未登录词的词加权算法。通过大量语料库学习获取歧义高频字,作为歧义标记,利用规则统计模型对标记的上下文信息分类处理,剩下的部分进行正向或逆向动态最大匹配,对连续单字串使用词加权算法来判断其是否为未登录多字词。经过实验测试,该系统的准确率为98.88%,召回率为98.32%。     

一种基于改进支持向量机的入侵检测方法研究

提出基于粒子群优化（Particle Swarm Optimization,PSO）算法和支持向量机（Support Vector Machines,SVM）的入侵检测方法,为优化SVM性能,使用PSO的全局搜索特性寻找SVM的最优参数[C]和[σ];为避免PSO算法陷入局部最优,引入变异操作,找到最优参数组合后进行基于PSO_SVM入侵检测算法的训练和检测,解决了入侵检测系统准确度难题。仿真实验表明该方法的检测率为92.8%,误报率为6.911 9%,漏报率为9.708 7%,对KDDCUP竞赛的最佳结果有一定程度的提高,实验结果验证了该算法的有效性和可行性。     

Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines.      

A Unique Discrete Wavelet & Deterministic Walk-Based Glaucoma Classification Approach Using Image-Specific Enhanced Retinal Images

Glaucoma is a group of ocular atrophy diseases that cause progressive vision loss by affecting the optic nerve. Because of its asymptomatic nature, glaucoma has become the leading cause of human blindness worldwide. In this paper, a novel computer-aided diagnosis (CAD) approach for glaucomatous retinal image classification has been introduced. It extracts graph-based texture features from structurally improved fundus images using discrete wavelet-transformation (DWT) and deterministic tree-walk (DTW) procedures. Retinal images are considered from both public repositories and eye hospitals. Images are enhanced with image-specific luminance and gradient transitions for both contrast and texture improvement. The enhanced images are mapped into undirected graphs using DTW trajectories formed by the image’s wavelet coefficients. Graph-based features are extracted from these graphs to capture image texture patterns. Machine learning (ML) classifiers use these features to label retinal images. This approach has attained an accuracy range of 93.5% to 100%, 82.1% to 99.3%, 95.4% to 100%, 83.3% to 96.6%, 77.7% to 88.8%, and 91.4% to 100% on the ACRIMA, ORIGA, RIM-ONE, Drishti, HRF, and HOSPITAL datasets, respectively. The major strength of this approach is texture pattern identification using various topological graphs. It has achieved optimal performance with SVM and RF classifiers using biorthogonal DWT combinations on both public and patients’ fundus datasets. The classification performance of the DWT-DTW approach is on par with the contemporary state-of-the-art methods, which can be helpful for ophthalmologists in glaucoma screening.