分别采用直接水提法(W)、纤维素酶酶解法(C)和高压均质耦合纤维素酶法(HC)提取椪柑渣可溶性膳食纤维(SDF)。结果表明:W-SDF、C-SDF和HC-SDF对胆酸钠的吸附量分别为313.1,560.0和761.2 mg/g;对胆固醇的吸附量,在胃环境(p H 2)中分别为4.8...分别采用直接水提法(W)、纤维素酶酶解法(C)和高压均质耦合纤维素酶法(HC)提取椪柑渣可溶性膳食纤维(SDF)。结果表明:W-SDF、C-SDF和HC-SDF对胆酸钠的吸附量分别为313.1,560.0和761.2 mg/g;对胆固醇的吸附量,在胃环境(p H 2)中分别为4.80,10.95和9.11 mg/g,在肠道环境(p H 7)中分别为5.43,11.87和10.58 mg/g;对NO2-的清除率分别为23.03%,18.48%和31.12%;对胰脂肪酶活性的抑制率分别为11.96%,31.69%和35.80%;表观黏度分别为0.104,0.188和0.245 Pa·s,HC-SDF的表观黏度明显大于前两者,且随着剪切速率的增长而减小,具有假塑性。展开更多
中国西部地区正在发展集约化和规模化的设施养羊业,通过监测羊舍内的声信号可以判别羊只的行为状态,从而为设施养羊的福利化水平评估提取基础依据。梅尔频率倒谱系数(mel frequency cepstrum coefficient,MFCC)模拟了人耳对语音的处理...中国西部地区正在发展集约化和规模化的设施养羊业,通过监测羊舍内的声信号可以判别羊只的行为状态,从而为设施养羊的福利化水平评估提取基础依据。梅尔频率倒谱系数(mel frequency cepstrum coefficient,MFCC)模拟了人耳对语音的处理特点且抗噪音性强,被广泛用于畜禽发声信号的特征提取,但其没有考虑各个特征分量表征声信号的能力。该研究构建羊舍无线声音数据采集系统,采集20只羊在设施羊舍内的打斗、饥饿、咳嗽、啃咬和寻伴共5种行为下的声信号,并通过Audacity音频处理软件选出720个清晰且不重叠的声音样本数据。根据MFCC各分量对羊舍声信号表征能力,特征参数提取采用一种熵值加权的MFCC参数,再求其一、二阶差分并进行主成分分析降维,得到优化的19维特征参数。通过对羊舍声信号的声谱图分析,设计了支持向量机二叉树识别模型,并对模型内的4个分类器参数进行网格化寻优测试,该识别模型对羊只5种行为下的声信号进行分类识别,用改进的特征参数与传统MFCC和线性预测倒谱系数(linear predictive cepstrum coefficient,LPCC)进行对比分析。结果表明,该特征参数对5种行为的识别率平均可达83.6%,分别高于MFCC和LPCC参数14.1%和26.8%,羊只打斗和咳嗽行为的声信号属于相似的短时爆发类声音,其识别率分别仅为80.6%和79.5%,啃咬声特征显著不易混淆,其查全率可达到为92.5%,改进特征参数更好的表征了羊舍声信号的特征,提高了羊只不同行为的识别率,为羊只健康和福利状况的监测提供理论依据。展开更多
Extracting and analyzing network traffic feature is fundamental in the design and implementation of network behavior anomaly detection methods. The traditional network traffic feature method focuses on the statistical...Extracting and analyzing network traffic feature is fundamental in the design and implementation of network behavior anomaly detection methods. The traditional network traffic feature method focuses on the statistical features of traffic volume. However, this approach is not sufficient to reflect the communication pattern features. A different approach is required to detect anomalous behaviors that do not exhibit traffic volume changes, such as low-intensity anomalous behaviors caused by Denial of Service/Distributed Denial of Service （DoS/DDoS） attacks, Internet worms and scanning, and BotNets. We propose an efficient traffic feature extraction architecture based on our proposed approach, which combines the benefit of traffic volume features and network communication pattern features. This method can detect low-intensity anomalous network behaviors and conventional traffic volume anomalies. We implemented our approach on Spark Streaming and validated our feature set using labelled real-world dataset collected from the Sichuan University campus network. Our results demonstrate that the traffic feature extraction approach is efficient in detecting both traffic variations and communication structure changes. Based on our evaluation of the MIT-DRAPA dataset, the same detection approach utilizes traffic volume features with detection precision of 82.3% and communication pattern features with detection precision of 89.9%. Our proposed feature set improves precision by 94%.展开更多
文摘分别采用直接水提法(W)、纤维素酶酶解法(C)和高压均质耦合纤维素酶法(HC)提取椪柑渣可溶性膳食纤维(SDF)。结果表明:W-SDF、C-SDF和HC-SDF对胆酸钠的吸附量分别为313.1,560.0和761.2 mg/g;对胆固醇的吸附量,在胃环境(p H 2)中分别为4.80,10.95和9.11 mg/g,在肠道环境(p H 7)中分别为5.43,11.87和10.58 mg/g;对NO2-的清除率分别为23.03%,18.48%和31.12%;对胰脂肪酶活性的抑制率分别为11.96%,31.69%和35.80%;表观黏度分别为0.104,0.188和0.245 Pa·s,HC-SDF的表观黏度明显大于前两者,且随着剪切速率的增长而减小,具有假塑性。
基金supported by the National Natural Science Foundation of China (No. 61272447)Sichuan Province Science and Technology Planning (Nos. 2016GZ0042, 16ZHSF0483, and 2017GZ0168)+1 种基金Key Research Project of Sichuan Provincial Department of Education (Nos. 17ZA0238 and 17ZA0200)Scientific Research Staring Foundation for Young Teachers of Sichuan University (No. 2015SCU11079)
文摘Extracting and analyzing network traffic feature is fundamental in the design and implementation of network behavior anomaly detection methods. The traditional network traffic feature method focuses on the statistical features of traffic volume. However, this approach is not sufficient to reflect the communication pattern features. A different approach is required to detect anomalous behaviors that do not exhibit traffic volume changes, such as low-intensity anomalous behaviors caused by Denial of Service/Distributed Denial of Service （DoS/DDoS） attacks, Internet worms and scanning, and BotNets. We propose an efficient traffic feature extraction architecture based on our proposed approach, which combines the benefit of traffic volume features and network communication pattern features. This method can detect low-intensity anomalous network behaviors and conventional traffic volume anomalies. We implemented our approach on Spark Streaming and validated our feature set using labelled real-world dataset collected from the Sichuan University campus network. Our results demonstrate that the traffic feature extraction approach is efficient in detecting both traffic variations and communication structure changes. Based on our evaluation of the MIT-DRAPA dataset, the same detection approach utilizes traffic volume features with detection precision of 82.3% and communication pattern features with detection precision of 89.9%. Our proposed feature set improves precision by 94%.
