Using Cholesky factorization, the dual face algorithm was described forsolving standard Linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper pre...Using Cholesky factorization, the dual face algorithm was described forsolving standard Linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper presents a variant usingGauss-Jordan elimination for solving bounded-variable LP problems.展开更多
Existing rotary ultrasonic motors operating in extreme environments cannot meet the requirements of good environmental adaptability and compact structure at same time,and existing ultrasonic motors with Langevin trans...Existing rotary ultrasonic motors operating in extreme environments cannot meet the requirements of good environmental adaptability and compact structure at same time,and existing ultrasonic motors with Langevin transducers show better environmental adaptability,but size of these motors are usually big due to the radial arrangement of the Langevin transducers.A novel dual driving face rotary ultrasonic motor is proposed,and its working principle is experimentally verified.The working principle of the novel ultrasonic motor is firstly proposed.The 5th in-plane flexural vibration travelling wave,excited by the Langevin transducers around the stator ring,is used to drive the rotors.Then the finite element method is used in the determination of dimensions of the prototype motor,and the confirmation of its working principle.After that,a laser Doppler vibrometer system is used for measuring the resonance frequency and vibration amplitude of the stator.At last,output characteristics of the prototype motor are measured,environmental adaptability is tested and performance for driving a metal ball is also investigated.At room temperature and 200 V(zero to peak) driving voltage,the motor’s no-load speed is 80 r/min,the stalling torque is 0.35 N·m and the maximum output power is 0.85 W.The response time of this motor is 0.96 ms at the room temperature,and it decreases or increases little in cold environment.A metal ball driven by the motor can rotate at 210 r/min with the driving voltage 300 V(zero to peak).Results indicate that the prototype motor has a large output torque and good environmental adaptability.A rotary ultrasonic motor owning compact structure and good environmental adaptability is proposed,and lays the foundations of ultrasonic motors’ applications in extreme environments.展开更多
Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and pos...Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition(FPPR) model and the dualdictionary sparse representation classification(DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory(ORL) and face recognition technology(FERET) databases, and also reflect comparisons with SRC, linear regression classification(LRC), and two-phase test sample sparse representation(TPTSSR).展开更多
煤矿井下综采工作面视频目标跟踪作为井下视频监控的重要一环,在保障井下作业人员安全、构建智慧煤矿等方面发挥着重要作用。煤矿井下场景复杂多变,现有的目标跟踪算法在煤尘、目标形变和矿灯动态光束等场景下难以取得良好的跟踪效果。...煤矿井下综采工作面视频目标跟踪作为井下视频监控的重要一环,在保障井下作业人员安全、构建智慧煤矿等方面发挥着重要作用。煤矿井下场景复杂多变,现有的目标跟踪算法在煤尘、目标形变和矿灯动态光束等场景下难以取得良好的跟踪效果。因此,在ATOM(Accurate Tracking by Overlap Maximization)算法基础上建立双重调制网络和通道注意力机制,可克服复杂环境的影响。双重调制网络由通用交并比(Intersection over Union,IoU)调制和空间调制网络构成。其中,空间调制网络通过记忆查询的方式提取出第一帧与当前帧特征点间的关联程度,以对目标关键特征信息进行增强。通道注意力模块嵌入主干网络,通过建立残差通道注意力机制,学习表示通道重要程度的权重系数,提升主干网络对关键特征信息的提取能力。算法的有效性在工作面视频目标跟踪数据集上得到验证,成功率提升1.48%,精确率提升3.9%,Pnorm得分提升2.75%。改进后的模型效果提升显著,更加适应于煤矿场景下的目标跟踪。展开更多
现有的人脸反欺诈(face anti-spoofing,FAS)方法虽然在域内测试表现良好,但在跨域场景下性能会大幅度下降.当前基于域对抗对齐的跨域人脸反欺诈方法,因其对齐网络和分类网络彼此独立,无法保证对齐任务直接服务于分类任务.提出了一种基...现有的人脸反欺诈(face anti-spoofing,FAS)方法虽然在域内测试表现良好,但在跨域场景下性能会大幅度下降.当前基于域对抗对齐的跨域人脸反欺诈方法,因其对齐网络和分类网络彼此独立,无法保证对齐任务直接服务于分类任务.提出了一种基于二次解耦与活体特征课程学习渐进式对抗对齐的域自适应人脸反欺诈(domain adaptation for face anti-spoofing based on dual disentanglement and liveness feature curriculum learning progressive adversarial alignment,DDCL)方法,首先将源域特征启发式解耦为域相关特征和域无关特征,之后使用分类器的梯度信息将域无关特征中的活体相关和无关特征进行第2次解耦.在训练过程中为减轻优化难度,通过课程学习的方式对目标域特征与活体相关、无关特征的组合进行渐进式对抗对齐,逐步提高活体相关特征的比重,增强目标域特征与活体检测任务的相关性,从因果角度给出活体对齐域自适应的解释.在CASIA-MFSD,Idiap Replay-Attack,MSU-MFSD与OULU-NPU公开数据集上的实验结果表明,与现有10种方法相比,所提出的方法获得了22.5%的最佳平均HTER值,并在4个测评协议上均达到了当前先进水平,尤其是I-M和O-M测评协议的HTER值分别达到了12.4%和12.8%,能显著降低模型在目标域上的错误率,具有更好的跨域泛化能力.展开更多
基金the National Natural Science Foundation of China(Nos.10871043 and 70971136).
文摘Using Cholesky factorization, the dual face algorithm was described forsolving standard Linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper presents a variant usingGauss-Jordan elimination for solving bounded-variable LP problems.
基金supported by National Natural Science Foundation of China(Grant Nos.5120520351275228+7 种基金5107521291123020)Science and Research FoudotionNanjing University of Aeronautics and Astronautics(Grant Nos.56YAH12015NZ2010002S0896-013)Innovation and Entrepreneurship Program of Jiangsuand Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Existing rotary ultrasonic motors operating in extreme environments cannot meet the requirements of good environmental adaptability and compact structure at same time,and existing ultrasonic motors with Langevin transducers show better environmental adaptability,but size of these motors are usually big due to the radial arrangement of the Langevin transducers.A novel dual driving face rotary ultrasonic motor is proposed,and its working principle is experimentally verified.The working principle of the novel ultrasonic motor is firstly proposed.The 5th in-plane flexural vibration travelling wave,excited by the Langevin transducers around the stator ring,is used to drive the rotors.Then the finite element method is used in the determination of dimensions of the prototype motor,and the confirmation of its working principle.After that,a laser Doppler vibrometer system is used for measuring the resonance frequency and vibration amplitude of the stator.At last,output characteristics of the prototype motor are measured,environmental adaptability is tested and performance for driving a metal ball is also investigated.At room temperature and 200 V(zero to peak) driving voltage,the motor’s no-load speed is 80 r/min,the stalling torque is 0.35 N·m and the maximum output power is 0.85 W.The response time of this motor is 0.96 ms at the room temperature,and it decreases or increases little in cold environment.A metal ball driven by the motor can rotate at 210 r/min with the driving voltage 300 V(zero to peak).Results indicate that the prototype motor has a large output torque and good environmental adaptability.A rotary ultrasonic motor owning compact structure and good environmental adaptability is proposed,and lays the foundations of ultrasonic motors’ applications in extreme environments.
基金supported by the National Natural Science Foundation of China(6137901061772421)
文摘Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition(FPPR) model and the dualdictionary sparse representation classification(DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory(ORL) and face recognition technology(FERET) databases, and also reflect comparisons with SRC, linear regression classification(LRC), and two-phase test sample sparse representation(TPTSSR).
文摘煤矿井下综采工作面视频目标跟踪作为井下视频监控的重要一环,在保障井下作业人员安全、构建智慧煤矿等方面发挥着重要作用。煤矿井下场景复杂多变,现有的目标跟踪算法在煤尘、目标形变和矿灯动态光束等场景下难以取得良好的跟踪效果。因此,在ATOM(Accurate Tracking by Overlap Maximization)算法基础上建立双重调制网络和通道注意力机制,可克服复杂环境的影响。双重调制网络由通用交并比(Intersection over Union,IoU)调制和空间调制网络构成。其中,空间调制网络通过记忆查询的方式提取出第一帧与当前帧特征点间的关联程度,以对目标关键特征信息进行增强。通道注意力模块嵌入主干网络,通过建立残差通道注意力机制,学习表示通道重要程度的权重系数,提升主干网络对关键特征信息的提取能力。算法的有效性在工作面视频目标跟踪数据集上得到验证,成功率提升1.48%,精确率提升3.9%,Pnorm得分提升2.75%。改进后的模型效果提升显著,更加适应于煤矿场景下的目标跟踪。
文摘现有的人脸反欺诈(face anti-spoofing,FAS)方法虽然在域内测试表现良好,但在跨域场景下性能会大幅度下降.当前基于域对抗对齐的跨域人脸反欺诈方法,因其对齐网络和分类网络彼此独立,无法保证对齐任务直接服务于分类任务.提出了一种基于二次解耦与活体特征课程学习渐进式对抗对齐的域自适应人脸反欺诈(domain adaptation for face anti-spoofing based on dual disentanglement and liveness feature curriculum learning progressive adversarial alignment,DDCL)方法,首先将源域特征启发式解耦为域相关特征和域无关特征,之后使用分类器的梯度信息将域无关特征中的活体相关和无关特征进行第2次解耦.在训练过程中为减轻优化难度,通过课程学习的方式对目标域特征与活体相关、无关特征的组合进行渐进式对抗对齐,逐步提高活体相关特征的比重,增强目标域特征与活体检测任务的相关性,从因果角度给出活体对齐域自适应的解释.在CASIA-MFSD,Idiap Replay-Attack,MSU-MFSD与OULU-NPU公开数据集上的实验结果表明,与现有10种方法相比,所提出的方法获得了22.5%的最佳平均HTER值,并在4个测评协议上均达到了当前先进水平,尤其是I-M和O-M测评协议的HTER值分别达到了12.4%和12.8%,能显著降低模型在目标域上的错误率,具有更好的跨域泛化能力.
