Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CI...Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CIM has the advantage of high computing density,non-volatility as well as high energy efficiency.However,previous CIM research has predominantly focused on realizing high energy efficiency and high area efficiency for inference,while little attention has been devoted to addressing the challenges of on-chip programming speed,power consumption,and accuracy.In this paper,a fabri-cated 28 nm 576K RRAM-based CIM macro featuring optimized on-chip programming schemes is proposed to address the issues mentioned above.Different strategies of mapping weights to RRAM arrays are compared,and a novel direct-current ADC design is designed for both programming and inference stages.Utilizing the optimized hybrid programming scheme,4.67×programming speed,0.15×power saving and 4.31×compact weight distribution are realized.Besides,this macro achieves a normalized area efficiency of 2.82 TOPS/mm2 and a normalized energy efficiency of 35.6 TOPS/W.展开更多
Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumptio...Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.展开更多
Matlab has a high performance at engineering calculation.C# is good at interface development.Combining their advantages together,hybrid programming with Matlab and C # will help to improve the reliability analysis sof...Matlab has a high performance at engineering calculation.C# is good at interface development.Combining their advantages together,hybrid programming with Matlab and C # will help to improve the reliability analysis software efficiency and accuracy significantly.Procedures of hybrid programming with Matlab and C# in reliability analysis software are introduced in this paper.Finally a mathematical problem is tested to verify the feasibility of this programming method.展开更多
Male sterility induced by a chemical hybridization agent （CHA） is an important tool for utilizing crop heterosis. Leaves, especially the flag leaves, as CHA initial recipients play a decisive role in inducing male s...Male sterility induced by a chemical hybridization agent （CHA） is an important tool for utilizing crop heterosis. Leaves, especially the flag leaves, as CHA initial recipients play a decisive role in inducing male sterility. To investigate effects of different treatment times of CHA-SQ-1 used, morphological, biochemical and physiological responses of wheat flag leaves were detected in thistudy. CHA induced programmed cell death （PCD） as shown in terminal deoxynucleotidyl transferase-mediated dUTP nick end-labelling （TUNEL） and DNA laddering analysis. In the early phase, CHA-SQ-1 trig- gered organelle changes arid PCD in wheat leaves accompanied by excess production of reactive oxygen species （O2- and H202） and down-regulation of the activities of superoxide dismutase （SOD）, catalase （CAT） and guaiacol peroxidase （POD）. Meanwhile, leaf cell DNAs showed ladder-like patterns on agarose gel, indicating that CHA-SQ-1 led to the activation of the responsible endonuclease. The oxidative stress assays showed that lipid peroxidation was strongly activated and photosynthesis was obviously inhibited in SQ-l-induced leaves. However, CHA contents in wheat leaves gradually reduced along with the time CHA-SQ-1 applied. Young flags returned to an oxidative/antioxidative balance and ultimately developed into mature green leaves. These results provide explanation of the relations between PCD and anther abortion and practical application of CHA for hybrid breeding.展开更多
The purpose of this paper is to develop an implementable strategy of brake energy recovery for a parallel hydraulic hybrid bus. Based on brake process analysis, a dynamic programming algorithm of brake energy recovery...The purpose of this paper is to develop an implementable strategy of brake energy recovery for a parallel hydraulic hybrid bus. Based on brake process analysis, a dynamic programming algorithm of brake energy recovery is established. And then an implementable strategy of brake energy recovery is proposed by the constraint variable trajectories analysis of the dynamic programming algorithm in the typical urban bus cycle. The simulation results indicate the brake energy recovery efficiency of the accumulator can reach 60% in the dynamic programming algorithm. And the hydraulic hybrid system can output braking torque as much as possible.Moreover, the accumulator has almost equal efficiency of brake energy recovery between the implementable strategy and the dynamic programming algorithm. Therefore, the implementable strategy is very effective in improving the efficiency of brake energy recovery.The road tests show the fuel economy of the hydraulic hybrid bus improves by 22.6% compared with the conventional bus.展开更多
The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark a...The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.展开更多
To implement structural hybrid simulation independent of the control system of any testing equipment in civil engineering, an external command control approach is put forward. Several setup technologies and the corres...To implement structural hybrid simulation independent of the control system of any testing equipment in civil engineering, an external command control approach is put forward. Several setup technologies and the corresponding API approaches are investigated to simultaneously combine numerical simulation with physical testing. Hybrid program technology is put forward and described in detail, using Visual C++ program to effectively and accurately control testing equipment and MATLAB program to implement numerical simulation with easy extension. The control program of testing equipment and numerical simulation program are integrated by calling MATLAB engine in Visual C++. A hybrid simulation about a full-scale six-story masonry structure is carried out. The testing results manifest that the external command control approach has the versatility because of simple hardware connection and control program independent on control software of testing equipment; powerful program function of Visual C++ and flexible program of MATLAB are integrated by hybrid program technology; hybrid simulation system provides a realistic and cost-effective testing platform that enables earthquake engineer researchers to accurately and efficiently capture the seismic performance of large or complex structures without having to carry out physical testing of the entire structure.展开更多
针对P1P3构型混联式混合动力汽车(hybrid electric vehicles,HEVs)的能量管理问题,本文提出一种基于模型预测控制(model predictive control,MPC)的能量管理策略。首先,根据控制算法构建系统预测模型,使用二次规划算法优化求解车辆最小...针对P1P3构型混联式混合动力汽车(hybrid electric vehicles,HEVs)的能量管理问题,本文提出一种基于模型预测控制(model predictive control,MPC)的能量管理策略。首先,根据控制算法构建系统预测模型,使用二次规划算法优化求解车辆最小化油耗的优化问题;然后,利用MATLAB/Simulink仿真平台,在2种标准循环工况下对本文所提出的能量管理控制策略进行仿真验证,并与基于规则的能量管理控制策略进行了对比分析。结果表明,相对于基于规则的控制策略,采用基于MPC的控制策略在2种循环工况下的车辆百公里油耗分别降低了5.6%和5.2%,可有效提升燃油经济性。展开更多
Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generally cannot be easil...Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generally cannot be easily tackled in EMS design. Most existing EMSs act upon fixed parameters and cannot adapt to varying driving conditions. Therefore, they usually fail to fully explore the potential of these advanced vehicles. In this paper, a novel EMS design procedure based on neural dynamic programming (NDP) is proposed. The NDP is a generic online learning algorithm, which combines stochastic dynamic programming (SDP) and the temporal difference (TD) method. Instead of computing the utility function and optimal control actions through Bellman equations, the NDP algorithm uses two neural networks to approximate them. The weights of these neural networks are updated online by the TD method. It avoids the high computational cost that SDP suffers from and is suitable for real-time implementation. The main advantages of NDP EMS is that it does not rely on prior information related to future driving conditions, and can self-tune with a wide variance in operating conditions. The NDP EMS has been applied to “Qianghua-I”, a prototype of a parallel HEV, using a revolving drum test bench for verification. Experiment results illustrate the potential of the proposed EMS in terms of fuel economy and in keeping state of charge (SOC) deviations at a low level. The proposed research ensures the optimality of NDP EMS, as well as real-time applicability.展开更多
This study presents a hybrid algorithm obtained by combining a genetic algorithm (GA) with successive quadratic sequential programming (SQP), namely GA-SQP. GA is the main optimizer, whereas SQP is used to refine the ...This study presents a hybrid algorithm obtained by combining a genetic algorithm (GA) with successive quadratic sequential programming (SQP), namely GA-SQP. GA is the main optimizer, whereas SQP is used to refine the results of GA, further improving the solution quality. The problem formulation is done in the framework named RUNE (fRamework for aUtomated aNalog dEsign), which targets solving nonlinear mono-objective and multi-objective optimization problems for analog circuits design. Two circuits are presented: a transimpedance amplifier (TIA) and an optical driver (Driver), which are both part of an Optical Network-on-Chip (ONoC). Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results obtained with SQP algorithm. The outcome is very encouraging and suggests that the hybrid proposed method is very efficient in solving analog design problems.展开更多
基金supported in part by the National Natural Science Foundation of China (62422405, 62025111,62495100, 92464302)the STI 2030-Major Projects(2021ZD0201200)+1 种基金the Shanghai Municipal Science and Technology Major Projectthe Beijing Advanced Innovation Center for Integrated Circuits
文摘Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CIM has the advantage of high computing density,non-volatility as well as high energy efficiency.However,previous CIM research has predominantly focused on realizing high energy efficiency and high area efficiency for inference,while little attention has been devoted to addressing the challenges of on-chip programming speed,power consumption,and accuracy.In this paper,a fabri-cated 28 nm 576K RRAM-based CIM macro featuring optimized on-chip programming schemes is proposed to address the issues mentioned above.Different strategies of mapping weights to RRAM arrays are compared,and a novel direct-current ADC design is designed for both programming and inference stages.Utilizing the optimized hybrid programming scheme,4.67×programming speed,0.15×power saving and 4.31×compact weight distribution are realized.Besides,this macro achieves a normalized area efficiency of 2.82 TOPS/mm2 and a normalized energy efficiency of 35.6 TOPS/W.
基金the Specialized Research Fund for Doctoral Program of Higher Education of China(20060003087)
文摘Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.
文摘Matlab has a high performance at engineering calculation.C# is good at interface development.Combining their advantages together,hybrid programming with Matlab and C # will help to improve the reliability analysis software efficiency and accuracy significantly.Procedures of hybrid programming with Matlab and C# in reliability analysis software are introduced in this paper.Finally a mathematical problem is tested to verify the feasibility of this programming method.
基金supported by the National High Technology Research and Development Program of China (2011AA10A106)the National Natural Science Foundation of China (31171611, 31371697)+1 种基金the Technological Innovation and Over Planning Projects of Shaanxi Province, China (2014KTZB02-01-02, 2011KTZB02-01-01)the Projects Opening Up New Function of Precision Instrument of Northwest A&F University, China (dysb130210)
文摘Male sterility induced by a chemical hybridization agent （CHA） is an important tool for utilizing crop heterosis. Leaves, especially the flag leaves, as CHA initial recipients play a decisive role in inducing male sterility. To investigate effects of different treatment times of CHA-SQ-1 used, morphological, biochemical and physiological responses of wheat flag leaves were detected in thistudy. CHA induced programmed cell death （PCD） as shown in terminal deoxynucleotidyl transferase-mediated dUTP nick end-labelling （TUNEL） and DNA laddering analysis. In the early phase, CHA-SQ-1 trig- gered organelle changes arid PCD in wheat leaves accompanied by excess production of reactive oxygen species （O2- and H202） and down-regulation of the activities of superoxide dismutase （SOD）, catalase （CAT） and guaiacol peroxidase （POD）. Meanwhile, leaf cell DNAs showed ladder-like patterns on agarose gel, indicating that CHA-SQ-1 led to the activation of the responsible endonuclease. The oxidative stress assays showed that lipid peroxidation was strongly activated and photosynthesis was obviously inhibited in SQ-l-induced leaves. However, CHA contents in wheat leaves gradually reduced along with the time CHA-SQ-1 applied. Young flags returned to an oxidative/antioxidative balance and ultimately developed into mature green leaves. These results provide explanation of the relations between PCD and anther abortion and practical application of CHA for hybrid breeding.
基金supported by Shanghai Science and Technology Committee(No.0904H155100)
文摘The purpose of this paper is to develop an implementable strategy of brake energy recovery for a parallel hydraulic hybrid bus. Based on brake process analysis, a dynamic programming algorithm of brake energy recovery is established. And then an implementable strategy of brake energy recovery is proposed by the constraint variable trajectories analysis of the dynamic programming algorithm in the typical urban bus cycle. The simulation results indicate the brake energy recovery efficiency of the accumulator can reach 60% in the dynamic programming algorithm. And the hydraulic hybrid system can output braking torque as much as possible.Moreover, the accumulator has almost equal efficiency of brake energy recovery between the implementable strategy and the dynamic programming algorithm. Therefore, the implementable strategy is very effective in improving the efficiency of brake energy recovery.The road tests show the fuel economy of the hydraulic hybrid bus improves by 22.6% compared with the conventional bus.
文摘The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.
基金Funded by National Natural Science Foundation of China under the Grant No.90715036Open Project of Jiangsu Key Laboratory of Structural Engineering (Grant No.ZD1004)Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘To implement structural hybrid simulation independent of the control system of any testing equipment in civil engineering, an external command control approach is put forward. Several setup technologies and the corresponding API approaches are investigated to simultaneously combine numerical simulation with physical testing. Hybrid program technology is put forward and described in detail, using Visual C++ program to effectively and accurately control testing equipment and MATLAB program to implement numerical simulation with easy extension. The control program of testing equipment and numerical simulation program are integrated by calling MATLAB engine in Visual C++. A hybrid simulation about a full-scale six-story masonry structure is carried out. The testing results manifest that the external command control approach has the versatility because of simple hardware connection and control program independent on control software of testing equipment; powerful program function of Visual C++ and flexible program of MATLAB are integrated by hybrid program technology; hybrid simulation system provides a realistic and cost-effective testing platform that enables earthquake engineer researchers to accurately and efficiently capture the seismic performance of large or complex structures without having to carry out physical testing of the entire structure.
文摘针对P1P3构型混联式混合动力汽车(hybrid electric vehicles,HEVs)的能量管理问题,本文提出一种基于模型预测控制(model predictive control,MPC)的能量管理策略。首先,根据控制算法构建系统预测模型,使用二次规划算法优化求解车辆最小化油耗的优化问题;然后,利用MATLAB/Simulink仿真平台,在2种标准循环工况下对本文所提出的能量管理控制策略进行仿真验证,并与基于规则的能量管理控制策略进行了对比分析。结果表明,相对于基于规则的控制策略,采用基于MPC的控制策略在2种循环工况下的车辆百公里油耗分别降低了5.6%和5.2%,可有效提升燃油经济性。
基金supported by Innovation Technology Fund of the Hong Kong Special Administrative Region of China (Grant No. GHP/011/05)
文摘Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generally cannot be easily tackled in EMS design. Most existing EMSs act upon fixed parameters and cannot adapt to varying driving conditions. Therefore, they usually fail to fully explore the potential of these advanced vehicles. In this paper, a novel EMS design procedure based on neural dynamic programming (NDP) is proposed. The NDP is a generic online learning algorithm, which combines stochastic dynamic programming (SDP) and the temporal difference (TD) method. Instead of computing the utility function and optimal control actions through Bellman equations, the NDP algorithm uses two neural networks to approximate them. The weights of these neural networks are updated online by the TD method. It avoids the high computational cost that SDP suffers from and is suitable for real-time implementation. The main advantages of NDP EMS is that it does not rely on prior information related to future driving conditions, and can self-tune with a wide variance in operating conditions. The NDP EMS has been applied to “Qianghua-I”, a prototype of a parallel HEV, using a revolving drum test bench for verification. Experiment results illustrate the potential of the proposed EMS in terms of fuel economy and in keeping state of charge (SOC) deviations at a low level. The proposed research ensures the optimality of NDP EMS, as well as real-time applicability.
文摘This study presents a hybrid algorithm obtained by combining a genetic algorithm (GA) with successive quadratic sequential programming (SQP), namely GA-SQP. GA is the main optimizer, whereas SQP is used to refine the results of GA, further improving the solution quality. The problem formulation is done in the framework named RUNE (fRamework for aUtomated aNalog dEsign), which targets solving nonlinear mono-objective and multi-objective optimization problems for analog circuits design. Two circuits are presented: a transimpedance amplifier (TIA) and an optical driver (Driver), which are both part of an Optical Network-on-Chip (ONoC). Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results obtained with SQP algorithm. The outcome is very encouraging and suggests that the hybrid proposed method is very efficient in solving analog design problems.
