Design of 1 kbit antifuse one time programmable memory IP using dual program voltage

A 1 kbit antifuse one time programmable(OTP) memory IP,which is one of the non-volatile memory IPs,was designed and used for power management integrated circuits(ICs).A conventional antifuse OTP cell using a single positive program voltage(VPP) has a problem when applying a higher voltage than the breakdown voltage of the thin gate oxides and at the same time,securing the reliability of medium voltage(VM) devices that are thick gate transistors.A new antifuse OTP cell using a dual program voltage was proposed to prevent the possibility for failures in a qualification test or the yield drop.For the newly proposed cell,a stable sensing is secured from the post-program resistances of several ten thousand ohms or below due to the voltage higher than the hard breakdown voltage applied to the terminals of the antifuse.The layout size of the designed 1 kbit antifuse OTP memory IP with Dongbu HiTek's 0.18 μm Bipolar-CMOS-DMOS(BCD) process is 567.9 μm×205.135 μm and the post-program resistance of an antifuse is predicted to be several ten thousand ohms.

A DUAL-RELAX PENALTY FUNCTION APPROACH FOR SOLVING NONLINEAR BILEVEL PROGRAMMING WITH LINEAR LOWER LEVEL PROBLEM

The penalty function method, presented many years ago, is an important nu- merical method for the mathematical programming problems. In this article, we propose a dual-relax penalty function approach, which is significantly different from penalty func- tion approach existing for solving the bilevel programming, to solve the nonlinear bilevel programming with linear lower level problem. Our algorithm will redound to the error analysis for computing an approximate solution to the bilevel programming. The error estimate is obtained among the optimal objective function value of the dual-relax penalty problem and of the original bilevel programming problem. An example is illustrated to show the feasibility of the proposed approach.

Experimental Study of Methods of Scenario Lattice Construction for Stochastic Dual Dynamic Programming

The stochastic dual dynamic programming (SDDP) algorithm is becoming increasingly used. In this paper we present analysis of different methods of lattice construction for SDDP exemplifying a realistic variant of the newsvendor problem, incorporating storage of production. We model several days of work and compare the profits realized using different methods of the lattice construction and the corresponding computer time spent in lattice construction. Our case differs from the known one because we consider not only a multidimensional but also a multistage case with stage dependence. We construct scenario lattice for different Markov processes which play a crucial role in stochastic modeling. The novelty of our work is comparing different methods of scenario lattice construction. We considered a realistic variant of the newsvendor problem. The results presented in this article show that the Voronoi method slightly outperforms others, but the k-means method is much faster overall.

METHOD BASED ON DUAL-QUADRATIC PROGRAMMING FOR FRAME STRUCTURAL OPTIMIZATION WITH LARGE SCALE

The optimality criteria （OC） method and mathematical programming （MP） were combined to found the sectional optimization model of frame structures. Different methods were adopted to deal with the different constraints. The stress constraints as local constraints were approached by zero-order approximation and transformed into movable sectional lower limits with the full stress criterion. The displacement constraints as global constraints were transformed into explicit expressions with the unit virtual load method. Thus an approximate explicit model for the sectional optimization of frame structures was built with stress and displacement constraints. To improve the resolution efficiency, the dual-quadratic programming was adopted to transform the original optimization model into a dual problem according to the dual theory and solved iteratively in its dual space. A method called approximate scaling step was adopted to reduce computations and smooth the iterative process. Negative constraints were deleted to reduce the size of the optimization model. With MSC/Nastran software as structural solver and MSC/Patran software as developing platform, the sectional optimization software of frame structures was accomplished, considering stress and displacement constraints. The examples show that the efficiency and accuracy are improved.

A Primal-Dual Infeasible-Interior-Point Algorithm for Multiple Objective Linear Programming Problems

A primal-dual infeasible interior point algorithm for multiple objective linear programming (MOLP) problems was presented. In contrast to the current MOLP algorithm. moving through the interior of polytope but not confining the iterates within the feasible region in our proposed algorithm result in a solution approach that is quite different and less sensitive to problem size, so providing the potential to dramatically improve the practical computation effectiveness.

A Primal-Dual Simplex Algorithm for Solving Linear Programming Problems with Symmetric Trapezoidal Fuzzy Numbers

Two existing methods for solving a class of fuzzy linear programming (FLP) problems involving symmetric trapezoidal fuzzy numbers without converting them to crisp linear programming problems are the fuzzy primal simplex method proposed by Ganesan and Veeramani [1] and the fuzzy dual simplex method proposed by Ebrahimnejad and Nasseri [2]. The former method is not applicable when a primal basic feasible solution is not easily at hand and the later method needs to an initial dual basic feasible solution. In this paper, we develop a novel approach namely the primal-dual simplex algorithm to overcome mentioned shortcomings. A numerical example is given to illustrate the proposed approach.

A Primal-dual Interior Point Method for Nonlinear Programming

In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local maximum, we utilize a merit function to guide the iterates toward a local minimum. Especially, we add the parameter ε to the Newton system when calculating the decrease directions. The global convergence is achieved by the decrease of a merit function. Furthermore, the numerical results confirm that the algorithm can solve this kind of problems in an efficient way.

Sufficiency and Wolfe Type Duality for Nonsmooth Multiobjective Programming Problems

In this paper, a class of nonsmooth multiobjective programming problems is considered. We introduce the new concept of invex of order??type II for nondifferentiable locally Lipschitz functions using the tools of Clarke subdifferential. The new functions are used to derive the sufficient optimality condition for a class of nonsmooth multiobjective programming problems. Utilizing the sufficient optimality conditions, weak and strong duality theorems are established for Wolfe type duality model.

Dual-Object: 面向对象的并行程序设计

面向对象的并行程序设计提供了类似于共享内存模型对通讯和计算的抽象能力，从而非常适合于大型并行软件系统的开发．但是基于远程对象调用的分布式对象的实现效率一直是面向对象方法在分布式／并行程序设计中得到广泛应用的障碍．本文介绍了并行机ＭＡＮＮＡ上所采用的面向对象的并行程序设计模型——Ｄｕａｌ－Ｏｂｊｅｃｔ模型．该模型通过引入从语义角度出发给出的数据一致特性的描述，在一定程度上解决了实现效率低下的问题．其次，文章通过程序设计实例详细地讨论了基于Ｄｕａｌ－Ｏｂｊｅｃｔ模型的扩展Ｃ＋＋并行程序设计，并给出了部分实际测试结果．

基于FPGA内部Dual-RAM的多轴机器人指令存储

为了解决高精度多轴机器人控制系统中FPGA块RAM容量有限的问题,提出了一种保存规划运动指令的方法。利用FPGA块RAM生成Dual-RAM,通过轴选判断器选择出有效运动参数,再按轴号分别存入各Dual-RAM。实验结果表明:采用该方法,直接使用FPGA内部RAM,避免了外部RAM扩展,从而精简了指令存储电路,增强了系统可移植性。该机器人系统目前已成功应用于工件上下料作业,系统运行稳定,点重复精度达0.02 mm,水平联动速度达到5.2 m/s。

考虑灾害不确定性和调度过程非预期性的配电网多阶段韧性提升策略

近年来,台风暴雨成为影响沿海城市配电网安全稳定运行的典型自然灾害。为有效应对该极端灾害的影响,提出一种考虑台风暴雨灾害时空不确定性的配电网多阶段韧性提升策略。首先,根据台风历史数据模拟其灾害场景,计算相应场景下的配电线路故障概率,从而构建考虑台风暴雨灾害攻击的不确定集。在此基础上,结合电力系统调度过程的非预期性特点,建立基于上述不确定集的多阶段鲁棒优化模型。其次,提出一种预拓展鲁棒对偶动态规划算法对其进行求解,获取灾前防御策略和训练好的灾中最恶劣情况未来成本函数,据此求解极端灾害发生过程中各时段的紧急响应策略,从而在离线训练和在线应用有机结合的框架下制定最优的配网韧性提升策略。最后,以不同规模的配网为测试系统,验证了所提模型和方法的有效性。

Research on Dual Control

This paper summarizes recent progress by the authors in developing two solution frameworks for dual control. The first solution framework considers a class of dual control problems where there exists a parameter uncertainty in the observation equation of the LQG problem. An analytical active dual control law is derived by a variance minimization approach. The issue of how to determine an optimal degree of active learning is then addressed, thus achieving an optimality for this class of dual control problems. The second solution framework considers a general class of discrete-time LQG problems with unknown parameters in both state and observation equations. The best possible (partial) closed-loop feedback control law is derived by exploring the future nominal posterior probabilities, thus taking into account the effect of future learning when constructing the optimal nominal dual control.

Global convergent algorithm for the bilevel linear fractional-linear programming based on modified convex simplex method

A global convergent algorithm is proposed to solve bilevel linear fractional-linear programming, which is a special class of bilevel programming. In our algorithm, replacing the lower level problem by its dual gap equaling to zero, the bilevel linear fractional-linear programming is transformed into a traditional sin- gle level programming problem, which can be transformed into a series of linear fractional programming problem. Thus, the modi- fied convex simplex method is used to solve the infinite linear fractional programming to obtain the global convergent solution of the original bilevel linear fractional-linear programming. Finally, an example demonstrates the feasibility of the proposed algorithm.

A Dual Approach for Solving Nonlinear Infinity-Norm Minimization Problems with Applications in Separable Cases

In this paper,we consider nonlinear infinity-norm minimization problems.We device a reliable Lagrangian dual approach for solving this kind of problems and based on this method we propose an algorithm for the mixed linear and nonlinear infinity- norm minimization problems.Numerical results are presented.

Application of Dual Model to Animal Feed Formulation Optimizing System

This study introduced a dual model on an original linear programming to obtain those shadow prices of resources that take part in optimizing. Of feed formulation, the shadow prices of nutrient resources show their influencing degree on a diet last cost when increasing or decreasing expected diet nutrient values. The higher the shadow price of one nutrient resource, the more obvious its influencing action on a diet last cost. When the shadow price of a kind of resource equals 'zero', it means that reaching of this nutrient value does not have influence on a special diet last cost within a particular value range. At the same time, this paper discussed the future development direction of feed formulation optimizing techniques in China.

AN ALGORITHM FOR L-SHAPED CONVEX PROGRAMMING

In this paper we study L-shaped convex programming. An algorithm for it is given. The result of computation shows that the algorithm is effective. The algorithm can be applied to two stage problem of stochastic convex programming.

OASIS4.0—a new version of the program OASIS for phasing protein diffraction data

The program OASIS4.0 has been released. Apart from the improved single-wavelength anomalous diffraction （SAD） phasing algorithm described in a separate paper, an important new feature in this version is the automation of the iterative phasing and model-building process in solving protein structures. A new graphical user＇s interface （GUI） is provided for controlling and real-time monitoring the dual-space iterative process. The GUI is discussed in detail in the present paper.

结合数据驱动与物理模型的主动配电网双时间尺度电压协调优化控制

高比例电动汽车、分布式风电、光伏接入配电网,导致电压频繁地剧烈波动。传统调压设备与逆变器动作速度差异巨大,如何协调是难点问题。该文结合数据驱动与物理建模方法,提出一种配电网双时间尺度电压协调优化控制策略。针对短时间尺度(min级)电压波动,以静止无功补偿器、分布式电源无功功率为决策变量,以电压二次方偏差最小为目标函数,针对平衡与不平衡配电网,基于支路潮流方程,计及物理约束构建了二次规划模型。针对长时间尺度(h级)电压波动,以电压调节器匝比、可投切电容电抗器挡位、储能系统充放电功率为动作,当前时段配电网节点功率为状态,节点电压二次方偏差为代价,构建了马尔可夫决策过程。为克服连续-离散动作空间维数灾,提出了一种基于松弛-预报-校正的深度确定性策略梯度强化学习求解算法。最后,采用IEEE 33节点平衡与123节点不平衡配电网验证了所提出方法的有效性。

双试点政策对数字创新的影响--来自创新型产业集群和“宽带中国”双试点政策的经验证据

当前我国加快推进制造业数字化转型,而数字化转型离不开数字创新的支撑。基于2010—2022年292个地级市面板数据,运用渐进双重差分模型评估创新型产业集群和“宽带中国”双试点政策对数字创新的影响,研究发现:创新型产业集群和“宽带中国”双试点政策具有明显的数字创新促进效应,且与单试点政策相比,双试点政策的协同效应更明显;机制分析表明,创新型产业集群和“宽带中国”双试点政策能通过提高创新能力和促进人均消费支出增长对数字创新综合评价指数提升产生积极影响;异质性分析表明,创新型产业集群和“宽带中国”双试点政策对东部地区、数字基础设施建设水平较高的城市、双创水平较高的城市的数字创新促进作用更为明显。该研究对于地方政府统筹释放双试点政策的联动效应,加快实现数字创新综合评价指数不断提升具有启示意义。

Multiobjective Nonlinear Symmetric Duality Involving Generalized Pseudoconvexity

The purpose of this paper is to introduce second order (K, F)-pseudoconvex and second order strongly (K, F)- pseudoconvex functions which are a generalization of cone-pseudoconvex and strongly cone-pseudoconvex functions. A pair of second order symmetric dual multiobjective nonlinear programs is formulated by using the considered functions. Furthermore, the weak, strong and converse duality theorems for this pair are established. Finally, a self duality theorem is given.