Many Kinds of Reserved Judgement in No-Data Problem

The reserved judgment can be broadly categorized into three types: Re-Do, Re-Set, and Natural Flowing Case (i.e. step by step in Re-Try). Hori et al. constructed the Bayes-Fuzzy Estimation and demonstrated that system theory can be applied to the possibility of Markov processes, and that decision-making approaches can be applied to sequential Bayes estimation. In this paper, we focus on the Natural Flowing Case within reserved judgment. Here, the possibility of oblique (or principal) factor rotation is considered as a part of the tandem fuzzy system that follows step by step for sequential Bayes estimation. Ultimately, we achieve a significant result whereby the expected utility can be calculated automatically without the need to construct a utility function for reserved judgment. There, this utility in Re-Do can be calculated by the prior utility, and that utility in Re-set does not exist by our research in this paper. Finally, we elucidate the relationship between fuzzy system theory and fuzzy decision theory through an applied example of Bayes-Fuzzy theory. Fuzzy estimation can be applied to only normal making decision, but it is impossible to apply abnormal decision problem. Our Vague, specially Type 2 Vague can be applied to abnormal case, too.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

The Application of Multi-level Fuzzy Judgement in the Appraisement of Green Products

The appraisement of "the green level" for a green product is a problem that has to deal with multi aspects and multi stratification. According to the property of the items that affect "the green level" of the product, they can be divided into aspects, elements and factors. These make appreciable grades. Multi level fuzzy judgement can solve the appraisement problem that considers the influences of design, manufacture, use and recovery in all life cycle or multi life cycle of a product. In the judging process it not only deals with the action of all factors, but also continues to have all messages in grade judgement. The power coefficients stand out as the main items. An example is given to show the judgement process.

The Application of Multi-level Fuzzy Judgement in the Appraisement of Green Products

The appraisement of "the green level" for a green p ro duct is a problem that has to deal with multi-aspect and multi-stratification. According to the property of the items that affect "the green level" of the pro duct, they can be divided into aspects, elements and factors. These make appreci able grades. The multi-level fuzzy judgement can solve the appraisement problem that considers the influences of design, manufacture, use and recovery in all- life cycle or multi-life cycle of product. In the judging process it is not onl y deal with the action of all factors, but also continue to have all messages in grade judgement. The power coefficients stand out as the main items. An example is given to show the judgement process.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

江苏促进民营企业做大做优做强的路径研判

民营经济是推进中国式现代化的生力军,民营企业是民营经济的经营主体或微观载体,因此,凡是能促进民营经济发展壮大的政策举措,都应在民营企业身上添力赋能、见功见效。《中共中央国务院关于促进民营经济发展壮大的意见》明确提出“促进民营经济做大做优做强”,并且竖起了三大“指路牌”。这三个“引导”,契合当下江苏民营企业发展的实际情况,但做大做优做强民营企业是一项十分艰巨复杂的系统工程,江苏政企双方不仅要围绕政策落实、法律执行、合规经营等“相向而行”,更要深入研判推进路径。当前江苏做大做优做强民营企业,首要路径是解放思想弘扬精神,关键之路是坚定方向转型升级,成功之道在于改革开放创新驱动。如此三路并进从而形成强大合力,定能推动民营企业在高质量发展征程上“打头阵、当先锋、树高峰、建新功”。

基于情感和认知协同的道德判断方法

随着大语言模型的迅速发展,大语言模型的安全性逐渐引起了研究者和公众的密切关注.为了防止大语言模型在与人类协作中对人类产生伤害,如何确保大语言模型在日常场景中的判断能与人类道德观念相符成为了一个重要问题.其中一个关键的挑战是,如何确保大语言模型在道德判断方面,能够像人类那样,针对不同的情境,灵活地调整或重新考虑预定的规则,从而使其判断与人类的道德观念保持一致.受心理学和认知科学中关于人类道德判断的情感和认知影响因素研究的启发,结合大语言模型在认知推理和情感分析能力上的优势,设计了一种模仿人类道德判断过程中情感判断和认知判断能力交互的方法,从而提升了大语言模型的道德判断表现.实验结果证明了所提方法在该任务上的有效性.总的来说,不仅为大语言模型的道德判断提供了一种创新的方法,也强调了心理学与认知科学理论在此领域的重要性,为未来的进一步研究奠定基础.

民事枉法裁判罪认定难点刍议

民事枉法裁判行为严重损害司法权威和司法公信力,伤害人民群众对司法公平正义的信任,是刑法严厉打击的对象。实务中在具体认定时需要解决三个方面的问题,一是本罪的主体包括未直接参与民事案件审理但对枉法结果的发生起到决定性作用的实质裁判者;二是判断行为人违背事实和法律作出枉法裁判的主观内容,可以从客观行为、裁判结果、认知水平三个维度进行考察综合判定;三是民事枉法裁判的发生往往始于“徇私情、徇私利”的主观动机,遵循这一引线考察行为人是否存在徇私情节—故意违背事实和法律—作出枉法裁判的行为逻辑,可以有效排除行为人关于自由裁量权的辩解,准确认定相关犯罪。

案件溢出事实对法院裁判形成过程的影响

准确把握与合理运用案件溢出事实,对于案件定量裁判的公正性具有重要的实践价值和意义。以民事纠纷中的彩礼案为例,我国已形成案件溢出事实运用的规范主义的三种进路。然而,这些规范主义进路存在难以对溢出事实作出穷尽规定,难以量化把握溢出事实的运用,以及已有的典型案例和司法解释尚有矛盾或欠严谨等主要问题。因而在案件的定量裁判中,裁判者对溢出事实的判断和运用仍需遵循实践主义逻辑,要基于个案溢出事实与主要事实的内在联系,从情理角度对案件的溢出事实加以衡量,并以可靠性作为判断和运用案件溢出事实的前提。运用案件溢出事实作出的定量裁判结果的正当性证成,需以当事人对裁判结果存在最低限度的可接受性为前提。

基于改进自适应IMM算法的高速列车组合定位

针对列车高精度定位问题,该文提出基于改进自适应交互多模型(IMM)的高速列车高精度组合定位方法。首先,根据列车定位需求和各传感器特点,设计了卫星接收器、轮轴测速传感器、测速雷达以及单轴陀螺仪4种传感器的组合定位方案。然后,针对IMM融合滤波算法因先验信息不准导致固定参数设置不当的问题,引入Sage-Husa自适应滤波和转移概率矩阵(TPM)自适应更新集成为自适应IMM算法。针对多模型切换的滞后问题,利用子模型似然函数值能快速反映模型变化趋势的特点,将似然函数值设为判定标志,并引入判定窗对TPM矩阵元素进行修正,有效提升了模型的切换速度。最后,基于改进自适应IMM算法对4种传感器定位信息进行融合滤波,实现高速列车的高精度组合定位。仿真结果表明:改进后的算法相比其他自适应IMM算法提升定位精度1.6%~14.7%,并且能通过提高模型间切换速度来有效降低位置误差峰值,同时具备较好的抗噪性能。

面向超分辨率重建的层次间局部特征增强网络

基于卷积神经网络的超分辨率重建模型以单项传播为主,层次越靠后感知信息的能力越微弱,导致层次间局部特征部分丢失,难以实质提升网络的特征表达能力。针对此问题,提出层次间局部特征增强网络。该方法由级联残差模块、层次间特征增强块和特征感知注意力机制组成。级联残差模块通过有效残差连接增加对残差分支信息的利用;层次间特征增强块提取不同深度特征的依赖关系,自适应调整中间层特征权值增强捕获关键信息的能力;特征感知注意力机制采用方向感知和位置判断的方式准确定位和识别感兴趣对象。多项标准数据集的实验结果表明,该方法能改善超分辨率的视觉重建效果,整体性能优于现有方法。

测量通信信号调制识别技术研究

测量通信信号的调制识别是对多种信号交互并伴有噪声的环境下辨识特定信号的方法进行研究,也为详细理解信号信息做出相应分析;通过从原理基础到方案设计以及验证结果,层层递进地揭示了信号调制识别技术的原理、发展现状以及当前的先进识别技术,采用了自己设计的一套识别方案对不同的模拟调制信号进行制式识别并分类;具有较高的可靠性、便捷性、可操作性强,基于Matlab的软件平台对基本的模拟调制信号进行仿真测试,确定判别门限,经实验验证成功识别了AM、DSB、SSB和FM四种信号;实验得出在SNR>12 dB的时候能达到90%的制式识别正确率,证明设计的识别方案的可行性。

基于业务过程模型的刑事案件裁判文书过程信息自动梳理技术

在信息时代,大量过程信息被隐藏在由自然语言写成的文档中,从中自动抽取过程模型并可视化将有助于对过程信息的查看和管理。在中国刑事案件裁判文书中,存在着以下两个挑战:①事件在文本中的描述未严格按照时间顺序;②存在大量噪声信息。尽管该特征广泛存在于不同领域的文本中,相关的研究却很少。为此,提出一种具有可移植性的文本过程挖掘算法,通过结合领域知识和机器学习建立名为“事件框架”的新型数据结构,从而解决上述难点并最终自动构建业务过程模型。通过人工构建的大量过程模型与自动生成的过程模型在结构、文本相似度上的实验对比,证明该算法能有效地解决上述挑战。

程序出罪的内涵重塑与判断标准

随着犯罪治理多元化进程的加快,程序出罪的重要性日益凸显。如何在实体入罪的同时通过程序出罪实现犯罪治理多元化成为理论研究的重点课题。既有研究未能妥善解决程序出罪的概念共识问题,导致程序出罪的理论基础不清、适用范围不明、体系协调性不足。对此,应通过逐级分析“不入罪”与“出罪”,以及“实体出罪”与“程序出罪”的理念差异,凸显程序出罪的独立性。程序出罪的本质属性表现为因裁量性程序终止行为使行为人在法律意义上无罪。因存在犯罪阻却事由、法益恢复事由、证据不足、欠缺诉讼条件导致的法定性程序终止因不具有个案利益衡量特性,与程序出罪存在本质差异。在此基础上,应进一步明确程序出罪的判断标准,处理好不同属性利益之间的衡量问题,即制定不同出罪情节的叠加适用标准,以及出罪与入罪情节之间的冲突衡量标准。

基于制造加工因素诱导的涡轮泵叶片故障与可靠性分析

[目的]对制造加工因素可能诱导的液体火箭发动机涡轮泵转子系统的涡轮叶片故障及其可靠性进行分析.[方法]首先总结叶片加工方法中可能会形成的加工缺陷并提取特征,然后选取具有代表性的缺陷因素构建缺陷-力学模型,通过有限元分析软件得到仿真结果,再采用模糊聚类分析的方法拟定评价指标,预测叶片寿命并计算由加工缺陷诱导的可靠性程度.[结果]制造加工因素会较大概率引起涡轮叶片的故障与可靠性问题.[结论]未来应在涡轮泵等重要核心零部件的可靠性分析中加入对制造加工因素诱导的故障分析,论文提出的研究方法对发动机涡轮叶片的可靠性预测及故障分析具有一定的理论指导意义.

基于实测数据的机动目标探测性能评估研究

基于TTP准则的静目标鉴别性能理论模型评估方法具有环境适应性强、性能预测准确、系统参数优化便利等优点,但此方法对动目标探测性能评估的准确性还需展开进一步研究。为了准确评估动目标的探测性能,文中搭建了靶标运动系统、红外目标生成系统和目标采集及储存系统;采用人眼阈值判断实验方法实现不同成像作用距离、不同运动速度、不同靶标尺寸下的目标探测性能评估,获得了目标探测概率性能曲线;结合NVThermIP现场性能评估理论模型,在系统性能参数输入一致时计算目标探测性能。实验结果表明:NVThermIP现场性能预测模型对于动目标探测性能的预测存在一定偏差,在实验测量的目标运动速率范围内,随着目标运动速率的提升,目标更易被探测。实验结果为动目标性能评估理论模型的修正提供了数据支持。

精英式孵育理念的价值判断与作用路径

孵育理念转变是创业经济高质量发展的重要驱动要素,落地的关键在于孵育机制创新。基于2010—2020年江苏省孵化器与在孵企业年报及调研数据,从严格入孵机制创新视角检验精英式孵育理念的可行性和操作性。结果表明:严格入孵在提升创业企业孵育绩效的同时,与孵化器孵育绩效存在非线性关系,且上述因果关系及长效机制通过PSM-DID方法得以验证;进一步研究发现,孵化器组织特质与严选导向适配性对严格入孵的促进效应具有差异化影响。研究结论不仅能够弥补孵育机制在入孵环节的不足,也能够为我国孵化产业改革与高质量转型提供理论依据。