In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select...In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.展开更多
The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of exp...The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.展开更多
响应需求的末端配送方案可显著提升顾客满意度,识别并提取末端配送快递三轮车配送停留点特征是分析配送时空分布和动态需求的基础。因此,本文提出结合兴趣点(POI)与停留时长规则的停留点识别方法。首先,利用POI信息和瞬时速度初步筛选...响应需求的末端配送方案可显著提升顾客满意度,识别并提取末端配送快递三轮车配送停留点特征是分析配送时空分布和动态需求的基础。因此,本文提出结合兴趣点(POI)与停留时长规则的停留点识别方法。首先,利用POI信息和瞬时速度初步筛选快递三轮车轨迹数据;然后,引入停留时长阈值作为二次筛选条件;最后,合并临近的聚集点,形成完整的停留点集。采用人工校验识别结果的准确性,并借助熵率法计算停留链的熵率,量化评估不同识别方法的精确度。以苏州市顺丰速运快递网点的快递三轮车配送轨迹数据为实证对象,将所提出的方法与货运卡车停留点识别中常用的基于密度的聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)进行对比分析。结果表明,DBSCAN算法易将交通信号灯等待误判为配送停留点,而本文所提出的方法则有效规避了该问题,实现高达98%的精确率和召回率;同时,熵率法的应用进一步验证了所提方法在准确率上的有效性。在此基础上,扩大研究范围并识别配送停留点后,分析快递三轮车的出行链与配送时空分布特征。结果表明,8:00左右的高峰期配送车辆数显著多于16:00左右的高峰期;住宅区为配送热点,车辆数最多,且出行距离和工作时长最长;酒店类配送呈现停留时长较短的特点;此外,停留点空间分布亦揭示了部分配送距离偏远的情况。展开更多
基金The Spring Plan of Ministry of Education,China(No.Z2012017)
文摘In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.
文摘The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.
文摘响应需求的末端配送方案可显著提升顾客满意度,识别并提取末端配送快递三轮车配送停留点特征是分析配送时空分布和动态需求的基础。因此,本文提出结合兴趣点(POI)与停留时长规则的停留点识别方法。首先,利用POI信息和瞬时速度初步筛选快递三轮车轨迹数据;然后,引入停留时长阈值作为二次筛选条件;最后,合并临近的聚集点,形成完整的停留点集。采用人工校验识别结果的准确性,并借助熵率法计算停留链的熵率,量化评估不同识别方法的精确度。以苏州市顺丰速运快递网点的快递三轮车配送轨迹数据为实证对象,将所提出的方法与货运卡车停留点识别中常用的基于密度的聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)进行对比分析。结果表明,DBSCAN算法易将交通信号灯等待误判为配送停留点,而本文所提出的方法则有效规避了该问题,实现高达98%的精确率和召回率;同时,熵率法的应用进一步验证了所提方法在准确率上的有效性。在此基础上,扩大研究范围并识别配送停留点后,分析快递三轮车的出行链与配送时空分布特征。结果表明,8:00左右的高峰期配送车辆数显著多于16:00左右的高峰期;住宅区为配送热点,车辆数最多,且出行距离和工作时长最长;酒店类配送呈现停留时长较短的特点;此外,停留点空间分布亦揭示了部分配送距离偏远的情况。
