Why is it important to verify/validate model transformations？ The motivation is to improve the quality of the trans- formations, and therefore the quality of the generated software artifacts. Verified/validated model...Why is it important to verify/validate model transformations？ The motivation is to improve the quality of the trans- formations, and therefore the quality of the generated software artifacts. Verified/validated model transformations make it possible to ensure certain properties of the generated software artifacts. In this way, verification/validation methods can guarantee different requirements stated by the actual domain against the generated/modified/optimized software products. For example, a verified/ validated model transformation can ensure the preservation of certain properties during the model-to-model transformation. This paper emphasizes the necessity of methods that make model transformation verified/validated, discusses the different scenarios of model transformation verification and validation, and introduces the principles of a novel test-driven method for verifying/ validating model transformations. We provide a solution that makes it possible to automatically generate test input models for model transformations. Furthermore, we collect and discuss the actual open issues in the field of verification/validation of model transformations.展开更多
退役锂离子电池的梯次利用具有可观的经济效益,但较长的检测时间限制了其大规模应用。针对退役锂电池健康状态(State of Health,SOH)检测的问题,引入超声测量手段,提出了一种基于“电压-超声差分”健康指标和Informer-GRU模型的退役电池...退役锂离子电池的梯次利用具有可观的经济效益,但较长的检测时间限制了其大规模应用。针对退役锂电池健康状态(State of Health,SOH)检测的问题,引入超声测量手段,提出了一种基于“电压-超声差分”健康指标和Informer-GRU模型的退役电池SOH快速估计方法。利用超声波穿透电池,检测其内部电化学物质变化,并在短电压窗内采集与电池SOH强相关的超声特征,构建“电压-超声差分”健康指标。基于此,构建Informer-GRU模型,刻画健康指标与电池SOH间的关系映射,实现退役锂电池SOH估计。在实验室环境下对软包锂电池进行实际测试,结果表明,仅需在3.64~3.68 V的40 mV电压窗内进行超声检测,即可实现平均绝对误差0.016%、均方根误差0.021%的电池SOH准确估计,提高了梯次利用检测效率。展开更多
R-DSP(Radar Digital Signal Processor)芯片中BSU(Branch Shift Unit)运算部件具有较大的设计规模和复杂度,传统Verilog验证平台难以满足其验证需求问题。针对该问题,文中采用UVM(Universal Verification Methodology)方法对BSU运算部...R-DSP(Radar Digital Signal Processor)芯片中BSU(Branch Shift Unit)运算部件具有较大的设计规模和复杂度,传统Verilog验证平台难以满足其验证需求问题。针对该问题,文中采用UVM(Universal Verification Methodology)方法对BSU运算部件进行功能验证。搭建基于SystemVerilog语言实现的UVM验证平台,使用定向测试和带约束的随机测试进行验证,并采用覆盖率驱动的方法指导测试用例的生成,以充分覆盖BSU运算部件的各个功能和代码路径。经过多轮测试激励验证,代码覆盖率接近100%,完成了对BSU运算部件的功能验证。所提方法为R-DSP芯片中的ALU(Arithmetic Logic Unit)、AGU(Address Generation Unit)、MU(Multiplication Unit)等运算部件的验证工作提供了参考和借鉴。展开更多
基金Project partially supported by the European Union and the European Social Fund(No.TAMOP-4.2.2.C-11/1/KONV-2012-0013)
文摘Why is it important to verify/validate model transformations？ The motivation is to improve the quality of the trans- formations, and therefore the quality of the generated software artifacts. Verified/validated model transformations make it possible to ensure certain properties of the generated software artifacts. In this way, verification/validation methods can guarantee different requirements stated by the actual domain against the generated/modified/optimized software products. For example, a verified/ validated model transformation can ensure the preservation of certain properties during the model-to-model transformation. This paper emphasizes the necessity of methods that make model transformation verified/validated, discusses the different scenarios of model transformation verification and validation, and introduces the principles of a novel test-driven method for verifying/ validating model transformations. We provide a solution that makes it possible to automatically generate test input models for model transformations. Furthermore, we collect and discuss the actual open issues in the field of verification/validation of model transformations.
文摘退役锂离子电池的梯次利用具有可观的经济效益,但较长的检测时间限制了其大规模应用。针对退役锂电池健康状态(State of Health,SOH)检测的问题,引入超声测量手段,提出了一种基于“电压-超声差分”健康指标和Informer-GRU模型的退役电池SOH快速估计方法。利用超声波穿透电池,检测其内部电化学物质变化,并在短电压窗内采集与电池SOH强相关的超声特征,构建“电压-超声差分”健康指标。基于此,构建Informer-GRU模型,刻画健康指标与电池SOH间的关系映射,实现退役锂电池SOH估计。在实验室环境下对软包锂电池进行实际测试,结果表明,仅需在3.64~3.68 V的40 mV电压窗内进行超声检测,即可实现平均绝对误差0.016%、均方根误差0.021%的电池SOH准确估计,提高了梯次利用检测效率。
