基于解耦方法的可配置MIPS微处理器设计

传统固定功能微处理器的设计是为了满足通用应用的需求,因此可能无法充分适应特定领域或特定任务的要求,缺乏灵活性,所以在某些特定任务下,提供的不是最优的性能。针对该问题,本文设计了一种可配置的微处理器,通过模块解耦的方法选择是否添加CP0相关模块的微处理器架构,使该微处理器在是否可以实现异常指令、特权态指令两种架构中进行切换,并且可以优化资源利用、降低功耗和减少成本。同时,可以广泛地应用在使用轻量级微处理器的场景中。

一种基于MIPS架构的数据处理模块设计

LS2K1000是一种基于MIPS架构的双核处理器,该芯片具有尺寸小、功耗低、扩展性好等特点。数据处理模块基于LS2K1000处理器,通过主处理器LS2K1000的局部并行总线(简称Local Bus)访问FLASH与NvRAM。模块通过配置CPLD与DDR3等硬件资源保证处理器能够正常启动,并在天脉1嵌入式操作系统下进行测试,经测试模块CPU、DDR、Flash、看门狗等功能均正常工作。同时在模块中设计以太网、串口、USB与CAN等接口的周边配置电路,确保模块能够与外设有效交联。该模块具有处理能力强、外设接口丰富与功耗低等特点,具有较高应用价值。目前已应用到多种嵌入式电子设备中。

智库视角下的智能决策研究——基于MIPS逻辑层次法

[目的/意义]决策范式逐渐从信息化向智能化升级转型。为使智能决策更好地赋能智库问题研究,本文通过探索智能决策与智库问题研究的耦合机理、影响状况、相关政策,提出具体对策建议,以期助力我国智库高质量发展。[方法/过程]本文基于MIPS(Mechanism analysis-Impact analysis-Policy analysis-Solution)逻辑层次法,在机理分析层,引入智能决策技术和思路,从智库问题研究领域、研究方法和实践工具入手,剖析智能决策与智库研究的内在耦合机理;在影响分析层,从学术研究、学科发展和机构赋能3个角度分析智能决策对智库领域发展带来的影响;在政策分析层,梳理和对比分析国内外智能决策相关的代表性政策。[结果/结论]综合上述研究结果,本文从要重视新技术发展、关注科技伦理和知识产权问题、促进智能决策相关方向的智库人才培养和加强制度政策保障等4个层面提出思考建议。

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

固定床渣油加氢与MIP或DCC组合工艺对比研究

利用催化裂化油浆和重循环油(HCO)质量比为3∶1的混合原料A,在体积空速为0.30h^(-1)、氢分压为3.0MPa、氢油体积比为100、温度为360℃的条件下,开展固定床加氢试验,得到收率为99.78%的加氢重馏分油;利用中东渣油(MM)和混合原料B(A加氢重馏分油与MM的质量比为7.98∶100)、保持MM空速不变,分别开展4项不同深度的固定床加氢试验,得到4种加氢常压渣油;进而利用4种加氢常压渣油分别开展MIP和DCC试验。结果表明:以100%MM进料计,采用渣油加氢回炼油浆与MIP组合工艺试验时,加氢较浅和加氢较深产物的轻质油品(汽油+柴油)产率分别较非组合工艺试验增加2.20百分点和2.52百分点;采用渣油加氢回炼油浆与DCC组合工艺试验时,较深加氢产物丙烯产率较非组合工艺降低1.22百分点。

HICH患者血清VEGF、MIP-1α、CD40水平变化及与预后的关系

目的 探究高血压脑出血(HICH)患者血清血管内皮生长因子(VEGF)、巨噬细胞炎症蛋白-1α(MIP-1α)、CD40水平变化及与预后的关系。方法 选取2020年1月至2023年1月期间于达州市中西医结合医院/成都医学院附属达州市中西医结合医院神经外科就诊的208例HICH患者及同期就诊的208例非HICH的脑血管疾病患者临床资料,分别纳入HICH组和对照组,比较两组患者入院时血清VEGF、MIP-1α、CD40水平差异,经受试者工作特征曲线(ROC)分析入院时血清VEGF、MIP-1α、CD40水平对HICH患者的诊断效能。记录HICH组患者格拉斯哥预后量表(GOS)评分结果,并以其作为预后评估标准将其分为预后不良组(GOS≤3分,n=89)和预后良好组(GOS>3分,n=119)两亚组,比较两亚组患者入院时血清VEGF、MIP-1α、CD40水平和GOS评分的关系;经Pearson相关系数分析HICH患者入院时血清VEGF、MIP-1α、CD40水平与GOS评分的相关性。结果 HICH组入院时的血清VEGF、MIP-1α、CD40水平显著高于对照组,差异有统计学意义(P<0.05)。经受试者工作特征曲线(ROC)分析,血清VEGF、MIP-1α、CD40水平三者单一和联合指标检测的ROC曲线下面积(AUC)值分别是0.828、0.874、0.843、0.930;截断值分别是356.73 pg/mL、350.81 ng/mL、420.60 pg/mL;敏感度分别是66.35%、66.83%、60.01%、86.06%;特异度分别是88.94%、93.27%、91.35%、85.58%(P<0.05)。根据预后评估标准,HICH组中89例患者为预后不良,119例患者预后良好。其中,预后不良组入院时的血清VEGF、MIP-1α、CD40水平显著高于预后良好组,差异有统计学意义(P<0.05)。Pearson相关系数分析显示,HICH患者入院时血清VEGF、MIP-1α、CD40水平与GOS评分呈负相关(P<0.05)。结论 血清VEGF、MIP-1α、CD40等因子在HICH患者中呈现高表达,且影响患者的预后情况,可作为早期临床上的辅助指标,为HICH患者的治疗和康复提供更精准的指导。

In vivo astrocyte reprogramming following spinal cord injury

Harmful and helpful roles of astrocytes in spinal cord injury(SCI):SCI induce gradable sensory,motor and autonomic impairments that correlate with the lesion severity and the rostro-caudal location of the injury site.The absence of spontaneous axonal regeneration after injury results from neuron-intrinsic and neuron-extrinsic parameters.Indeed,not only adult neurons display limited capability to regrow axons but also the injury environment contains inhibitors to axonal regeneration and a lack of growth-promoting factors.Amongst other cell populations that respond to the lesion,reactive astrocytes were first considered as only detrimental to spontaneous axonal regeneration.Indeed,astrocytes.

Transcriptional reprogramming during human osteoclast differentiation identifies regulators of osteoclast activity

Enhanced osteoclastogenesis and osteoclast activity contribute to the development of osteoporosis,which is characterized by increased bone resorption and inadequate bone formation.As novel antiosteoporotic therapeutics are needed,understanding the genetic regulation of human osteoclastogenesis could help identify potential treatment targets.This study aimed to provide an overview of transcriptional reprogramming during human osteoclast differentiation.Osteoclasts were differentiated from CD14+monocytes from eight female donors.RNA sequencing during differentiation revealed 8980 differentially expressed genes grouped into eight temporal patterns conserved across donors.These patterns revealed distinct molecular functions associated with postmenopausal osteoporosis susceptibility genes based on RNA from iliac crest biopsies and bone mineral density SNPs.Network analyses revealed mutual dependencies between temporal expression patterns and provided insight into subtype-specific transcriptional networks.The donor-specific expression patterns revealed genes at the monocyte stage,such as filamin B(FLNB)and oxidized low-density lipoprotein receptor 1(OLR1,encoding LOX-1),that are predictive of the resorptive activity of mature osteoclasts.The expression of differentially expressed G-protein coupled receptors was strong during osteoclast differentiation,and these receptors are associated with bone mineral density SNPs,suggesting that they play a pivotal role in osteoclast differentiation and activity.The regulatory effects of three differentially expressed G-protein coupled receptors were exemplified by in vitro pharmacological modulation of complement 5 A receptor 1(C5AR1),somatostatin receptor 2(SSTR2),and free fatty acid receptor 4(FFAR4/GPR120).Activating C5AR1 enhanced osteoclast formation,while activating SSTR2 decreased the resorptive activity of mature osteoclasts,and activating FFAR4 decreased both the number and resorptive activity of mature osteoclasts.In conclusion,we report the occurrence of transcriptional reprogramming during human osteoclast differentiation and identified SSTR2 and FFAR4 as antiresorptive G-protein coupled receptors and FLNB and LOX-1 as potential molecular markers of osteoclast activity.These data can help future investigations identify molecular regulators of osteoclast differentiation and activity and provide the basis for novel antiosteoporotic targets.

Glia-to-neuron reprogramming to the rescue?

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

基于MIP系统的放射治疗设备运行管理系统的模块介绍及应用效果

为规范医院放射治疗中心的设备调配、人员安排工作,实现医院放射治疗业务的全流程信息化管理,某大型肿瘤专科医院根据以往放射治疗设备运行管理方面的难点和痛点,结合医院实际情况,配合医科达(上海)医疗器械有限公司建立了一套基于ELEKTA放射治疗整合平台MOSAIQIntegrationPlatform2.0(MIP系统)的放射治疗设备运行管理系统。该系统实现了MIP系统与医院信息系统(HIS)数据的互联互通及放射治疗定位、治疗及质控设备的联网,同时具有可视化的设备预约管理模块、放射治疗全流程无纸化流转模块、自动排队叫号模块、短信通知模块、实时确费收费管理模块及数据记录分析处理模块,经实际应用证实该系统可实现放射治疗一站式前台服务、可视化预约、环节审核规范流转、一键式信息高效沟通及全流程精确记录,有效提高了服务质量和水平,解决了各大放射治疗中心面临的诸多具有共性的难点和痛点。

Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

Optimization of Product Distribution for MIP Units Using Data Mining

Based on data from a petrochemical company’s MIP unit over the past three years,19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation coefficient among 155 variables,which included properties of feedstock oil and spent catalyst,operational variables,and material flows.The distillation range variables were reduced using factor analysis,and the feedstock oils were clustered into three types using the K-means++algorithm.Each feedstock oil type was then used as an input variable for modeling.An XGBoost model and a back propagation(BP)neural network model with a structure of 20-15-15-2 were developed to predict the combined yield of gasoline and propylene,as well as the coke yield.In the test set,the BP neural network model demonstrated better fitting and generalization abilities with a mean absolute percentage error and determination coefficient of 1.48%and 0.738,respectively,compared to the XGBoost model.It was therefore chosen for further optimization work.The genetic algorithm was utilized to optimize operational variables in order to increase the combined yield of gasoline and propylene while controlling the growth of coke yield.Seven commercial test results in the MIP unit showed an average increase of 1.39 percentage points for the combined yield of gasoline and propylene and an average decrease of 0.11 percentage points for coke yield.These results indicate that the model effectively improves the combined yield of gasoline and propylene while controlling the increase in coke yield.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

降低干气和焦炭产率的MIP-DCR技术

中石化石油化工科学研究院有限公司在MIP及MIP-CGP工艺技术的基础上开发了降低干气和焦炭产率的催化裂化技术MIP-DCR。该技术以重质油为原料,通过降低再生催化剂与原料油的接触温差,实现降低干气和焦炭产率以提高产品总液体收率、达到从石油资源中获取更多高价值产品的目的。

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

A Dimensional Reduction Approach Based on Essential Constraints in Linear Programming

This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.

MIP-CGP工艺加工俄罗斯原油重油总结

中国石油辽阳石化分公司2.2 Mt/a俄罗斯原油重油催化裂化装置采用MIP-CGP技术(增产丙烯、多产异构烷烃的清洁汽油生产技术)进行改造,使用专用催化剂CGP-LY。标定结果表明:在100%生产负荷条件下,与改造前的催化裂化技术相比,采用MIP-CGP技术改造后,液化石油气产率基本不变;丙烯产率提高了0.3百分点;汽油产率提高了5.47百分点,研究法辛烷值为92.23,提高了1.18单位,抗爆性提高,烯烃质量分数降至30%以下,降低了16.78百分点,有效减少了油品对大气环境的污染;焦炭产率提高了3.73百分点;原料油转化率提高了8.71百分点。上述结果说明MIP-CGP技术具有良好的降低汽油烯烃含量的能力,在改善汽油性质的同时,能显著提高产品产率,满足生产清洁型汽油、降低汽油烯烃含量、提高汽油辛烷值、提高丙烯产率的市场需求。

Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.