Let A be a finite dimensional k-algebra and T be a supportτ-tilting right A-module.In this note,we give lower and upper bounds for the global dimension of the endomorphism algebra End_(A)(T)under some mild conditions...Let A be a finite dimensional k-algebra and T be a supportτ-tilting right A-module.In this note,we give lower and upper bounds for the global dimension of the endomorphism algebra End_(A)(T)under some mild conditions.Finally,we give some examples to illustrate that both the upper and lower bounds can be reached.展开更多
Motivated by T-tilting theory developed by T. Adachi, O. Iyama, I. Reiten, for a finite-dimensional algebra A with action by a finite group G, we introduce the notion of G-stable support τ-tilting modules. Then we es...Motivated by T-tilting theory developed by T. Adachi, O. Iyama, I. Reiten, for a finite-dimensional algebra A with action by a finite group G, we introduce the notion of G-stable support τ-tilting modules. Then we establish bijections among G-stable support τ-tilting modules over ∧, G-stable two-term silting complexes in the homotopy category of bounded complexes of finitely generated projective ∧-modules, and G-stable functorially finite torsion classes in the category of finitely generated left ∧-modules. In the case when ∧ is the endomorphism of a G-stable cluster-tilting object T over a Horn-finite 2-Calabi- Yau triangulated category L with a G-action, these are also in bijection with G-stable cluster-tilting objects in L. Moreover, we investigate the relationship between stable support τ-tilitng modules over ∧ and the skew group algebra ∧G.展开更多
To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPT...To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.展开更多
The design and developmental steps for an auxiliary machining module utilizing a database framework are discussed in this work to contribute to an improvement in workshop operations. The underlining objective is for t...The design and developmental steps for an auxiliary machining module utilizing a database framework are discussed in this work to contribute to an improvement in workshop operations. The underlining objective is for the provision of easily accessible and applicable machining operations data to enable and improve job accuracy and conformity to industrial standards. The design of the database for the decision support system is based on a relational frame with Microsoft Access Application package and Microsoft Structured Query Language Server, which serves as the back end of the module. A user interface designed on .Net Framework 3.5 and the windows installer 3.1 running on windows XP operating system serve as the software front end. The developed module is to serve as a decision support system for machine tool operations.展开更多
光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量...光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量机(support vector machine,SVM)、粒子群优化支持向量机(particle swarm optimized support vector machine,PSO-SVM)、遗传优化支持向量机(genetic optimized support vector machine,GA-SVM)、麻雀优化支持向量机(sparrow optimized support vector machine,SSA-SVM)、灰狼优化支持向量机(gray wolf optimized support vector machine,GWO-SVM)和鲸鱼优化支持向量机(whale optimized support vector machine,WOA-SVM)算法。首先,六种SVM混合算法都克服了SVM诊断结果易受参数初始值影响的缺点,识别精度相较传统SVM算法都有所提升,但是识别时间都增加。其次,7种算法中SCSO-SVM识别效果最好,克服了SVM易受参数初始值的影响,相较SVM识别精度提高了约9.4594%;是因为更能有效找到SVM惩罚因子和核函数参数。然后,对于同一种算法而言,算法的识别精度是随输入特征减少而降低的,是因为输入特征越少,越不能有效表征光伏组件在不同故障类型下的输出属性。但算法的识别时间却不是随输入特征减少而减短。所以选取合适的输入特征才能兼顾算法的故障识别准确率和效率。最后,发现七种算法的识别效果依赖于数据集的影响。原因可能是各个算法参数选择过多导致泛化性有差异,且依赖参数初始值选择。展开更多
基金supported by the National Natural Science Foundation of China(11971255,11901567,12071120)supported by the Hunan Provincial Natural Science Foundation of China(2023JJ30008)the National Natural Science Foundation of China(12371034).
文摘Let A be a finite dimensional k-algebra and T be a supportτ-tilting right A-module.In this note,we give lower and upper bounds for the global dimension of the endomorphism algebra End_(A)(T)under some mild conditions.Finally,we give some examples to illustrate that both the upper and lower bounds can be reached.
基金The authors would like to thank Dong Yang and Yuefei Zheng for their helpful discussion. This work was partially supported by the National Natural Science Foundation of China （Grant No. 11571164） and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Motivated by T-tilting theory developed by T. Adachi, O. Iyama, I. Reiten, for a finite-dimensional algebra A with action by a finite group G, we introduce the notion of G-stable support τ-tilting modules. Then we establish bijections among G-stable support τ-tilting modules over ∧, G-stable two-term silting complexes in the homotopy category of bounded complexes of finitely generated projective ∧-modules, and G-stable functorially finite torsion classes in the category of finitely generated left ∧-modules. In the case when ∧ is the endomorphism of a G-stable cluster-tilting object T over a Horn-finite 2-Calabi- Yau triangulated category L with a G-action, these are also in bijection with G-stable cluster-tilting objects in L. Moreover, we investigate the relationship between stable support τ-tilitng modules over ∧ and the skew group algebra ∧G.
文摘To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.
文摘The design and developmental steps for an auxiliary machining module utilizing a database framework are discussed in this work to contribute to an improvement in workshop operations. The underlining objective is for the provision of easily accessible and applicable machining operations data to enable and improve job accuracy and conformity to industrial standards. The design of the database for the decision support system is based on a relational frame with Microsoft Access Application package and Microsoft Structured Query Language Server, which serves as the back end of the module. A user interface designed on .Net Framework 3.5 and the windows installer 3.1 running on windows XP operating system serve as the software front end. The developed module is to serve as a decision support system for machine tool operations.
文摘光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量机(support vector machine,SVM)、粒子群优化支持向量机(particle swarm optimized support vector machine,PSO-SVM)、遗传优化支持向量机(genetic optimized support vector machine,GA-SVM)、麻雀优化支持向量机(sparrow optimized support vector machine,SSA-SVM)、灰狼优化支持向量机(gray wolf optimized support vector machine,GWO-SVM)和鲸鱼优化支持向量机(whale optimized support vector machine,WOA-SVM)算法。首先,六种SVM混合算法都克服了SVM诊断结果易受参数初始值影响的缺点,识别精度相较传统SVM算法都有所提升,但是识别时间都增加。其次,7种算法中SCSO-SVM识别效果最好,克服了SVM易受参数初始值的影响,相较SVM识别精度提高了约9.4594%;是因为更能有效找到SVM惩罚因子和核函数参数。然后,对于同一种算法而言,算法的识别精度是随输入特征减少而降低的,是因为输入特征越少,越不能有效表征光伏组件在不同故障类型下的输出属性。但算法的识别时间却不是随输入特征减少而减短。所以选取合适的输入特征才能兼顾算法的故障识别准确率和效率。最后,发现七种算法的识别效果依赖于数据集的影响。原因可能是各个算法参数选择过多导致泛化性有差异,且依赖参数初始值选择。
