A NEURAL-BASED NONLINEAR L_1-NORM OPTIMIZATION ALGORITHM FOR DIAGNOSIS OF NETWORKS*

Based on exact penalty function, a new neural network for solving the L1-norm optimization problem is proposed. In comparison with Kennedy and Chua’s network(1988), it has better properties.Based on Bandler’s fault location method(1982), a new nonlinearly constrained L1-norm problem is developed. It can be solved with less computing time through only one optimization processing. The proposed neural network can be used to solve the analog diagnosis L1 problem. The validity of the proposed neural networks and the fault location L1 method are illustrated by extensive computer simulations.

Freezing imaginarity of quantum states based onℓ_(1)-norm

We discuss freezing of quantum imaginarity based onℓ_(1)-norm.Several properties about a quantity of imaginarity based onℓ_(1)-norm are revealed.For a qubit(2-dimensional)system,we characterize the structure of real quantum operations that allow for freezing the quantity of imaginarity of any state.Furthermore,we characterize the structure of local real operations which can freeze the quantity of imaginarity of a class of N-qubit quantum states.

EQUIVALENCE BETWEEN NONNEGATIVE SOLUTIONS TO PARTIAL SPARSE AND WEIGHTED l_1-NORM MINIMIZATIONS

Based on the range space property （RSP）, the equivalent conditions between nonnegative solutions to the partial sparse and the corresponding weighted l1-norm minimization problem are studied in this paper. Different from other conditions based on the spark property, the mutual coherence, the null space property （NSP） and the restricted isometry property （RIP）, the RSP- based conditions are easier to be verified. Moreover, the proposed conditions guarantee not only the strong equivalence, but also the equivalence between the two problems. First, according to the foundation of the strict complemenrarity theorem of linear programming, a sufficient and necessary condition, satisfying the RSP of the sensing matrix and the full column rank property of the corresponding sub-matrix, is presented for the unique nonnegative solution to the weighted l1-norm minimization problem. Then, based on this condition, the equivalence conditions between the two problems are proposed. Finally, this paper shows that the matrix with the RSP of order k can guarantee the strong equivalence of the two problems.

An automated approach to calculating the maximum diameters of multiple cutters and their paths for sectional milling of centrifugal impellers on a 4_(1/2)-axis CNC machine

Power generators and chemical engineering compressors include heavy and large centrifugal impellers. To produce these impellers in high-speed machining, a 4?-axis milling machine(or a 4-axis machine plus an indexing table) is often used in the industry, which is more rigid than a5-axis milling machine. Since impeller blades are designed with complex B-spline surfaces and impeller channels spaces vary significantly, it is more efficient to use multiple cutters as large as possible to cut a channel in sections and a blade surface in patches, instead of only using a small cutter to machine a whole blade and a channel. Unfortunately, no approach has been established to automatically calculate the largest diameters of cutters and their paths, which include the indexing table angles. To address this problem, an automated and optimization approach is proposed. Based on the structure of a 4?-axis machine, a geometric model for a cutter gouging/interfering the impeller is formulated, and an optimization model of the cutter diameter in terms of the indexing table angle is established at a cutter contact(CC) point on a blade surface. Then, the diameters of the tools,their orientations, and the indexing table angles are optimized, and each tool's paths are generated for machining its corresponding impeller section. As a test, an impeller is efficiently machined with these tools section by section; thus, this approach is valid. It can be directly used in the industry to improve efficiency of machining centrifugal impellers.

THE RATES OF CONVERGENCE OF M-ESTIMATORS FOR PARTLY LINEAR MODELS IN DEPENDENT CASES

Consider the partly linear model K = X1& + go(Ti) + ei, where {(Ti, Xi)}T is a strictlystationary Sequence of random variable8, the ei’8 are i.i.d. random errorsl the K’s are realvalued responsest fo is a &vector of parameters, X is a &vector of explanatory variables,Ti is another explanatory variable ranging over a nondegenerate compact interval. Bnd ona segmnt of observations (T1, Xi 1 Y1 ),’’’ f (Tn, X;, Yn), this article investigates the rates ofconvrgence of the M-estimators for Po and go obtained from the minimisation problemwhere H is a space of B-spline functions of order m + 1 and p(-) is a function chosen suitablyUnder some regularity conditions, it is shown that the estimator of go achieves the optimalglobal rate of convergence of estimators for nonparametric regression, and the estdriator offo is asymptotically normal. The M-estimators here include regression quantile estimators,Li-estimators, Lp-norm estimators, Huber’s type M-estimators and usual least squares estimators. Applications of the asymptotic theory to testing the hypothesis H0: A’β0 =β are alsodiscussed, where β is a given vector and A is a known d × do matrix with rank d0.