Application of neural networks and machine learning in networkdesign

Communication network design is becoming increasingly complex, involving making networks more usable, affordable, and reliable. To help with this, we have proposed an expert network designer (END) for configuring, modeling, simulating, and evaluating large structured computer networks, employing artificial intelligence, knowledge representation, and network simulation tools. We present a neural network/knowledge acquisition machine-learning approach to improve the END's efficiency in solving the network design problem and to extend its scope to acquire new networking technologies, learn new network design techniques, and update the specifications of existing technologies     

Series equivalence/operation of current-controlled boost-typesingle-phase voltage source converters for bidirectional power flow

Single-phase voltage source power converters (VSCs) under consideration are AC-DC current-controlled boost-type power converters with bidirectional power-handling capability. Equivalence between two series-connected two-level power converters and a single three-level power converter is considered here. Further considered is the series operation of three-level power converters. Simulation results and experimental verification for both are provided. Economical configurations of three-level power converters leading to multilevel waveforms are presented thereafter     

Malignant bone pain: pathophysiology and treatment

2-acetyl aminofluorene (AAF) reacts in acidic conditions with nitrous fume yielding N-nitroso-AAF (N-NO-AAF), as previously described, that exerts more toxic and mutagenic effects than its parental compound. In this study, the effect of sodium nitrite (NaNO2) on the tumorigenicity of AAF in rats fed with AAF and NaNO2 was observed. Wistar rats were divided into five groups: group I served as control; group II were treated with NaNO2 (0.3%); group III was given 0.02% AAF alone; groups IV and V received both AAF and NaNO2 (0.2 and 0.3% respectively) in their diet for 12 weeks. At the end of the experiment, all rats in groups III, IV and V developed early stage phenomena of hepatocellular carcinoma, including hepatomegaly with variable-sized foci and neoplastic nodules. Severe damage was observed in the rats treated with AAF and NaNO2. Feeding of AAF (0.02%) for 3 months elevated the levels of c-Fos, c-Jun and c-Myc proteins in the rat livers. The AAF-induced c-Jun, c-Fos and c-Myc expressions were significantly magnified (P < 0.001) by NaNO2. These data confirmed that the strengthening of AAF-induced hepatocarcinogenesis by NaNO2 should be associated with its enhancing effect on the AAF-induced increases in the expressions of c-Jun, c-Fos and c-Myc.     

Low-truncation-error finite difference equations for photonicssimulation. II. Vertical-cavity surface-emitting lasers

For part I see, ibid., p. 134, 1998. The basic approach outlined in the previous article is applied to the difficult problem of computing the optical modes of a vertical-cavity surface-emitting laser. The formulation utilizes a finite difference equation based upon the lowest order term of an infinite series solution of the scalar Helmholtz equation in a local region. This difference equation becomes exact in the one-dimensional (1-D) limit, and is thus ideally suited for nearly 1-D devices such as vertical-cavity lasers. The performance of the resulting code is tested on both a simple cylindrical cavity with known solutions and an oxide-confined vertical-cavity laser structure, and the results compared against second-order-accurate code based upon Crank-Nicolson differencing     

E-kernel: an embedding kernel on the IBM victor V256 multiprocessorfor program mapping and network reconfiguration

We present the design of E-kernel, an embedding kernel on the Victor V256 message-passing partitionable multiprocessor, developed for the support of program mapping and network reconfiguration. E-kernel supports the embedding of a new network topology onto Victor's 2D mesh and also the embedding of a task graph onto the 2D mesh network or the reconfigured network. In the current implementation, the reconfigured network can be a line or an even-size ring, and the task graphs meshes or tori of a variety of dimensions and shapes or graphs with similar topologies. For application programs having these task graph topologies and that are designed according to the communication model of E-kernel, they can be run without any change on partitions connected by the 2D mesh, line, or ring. Further, E-kernel attempts the communication optimization of these programs on the different networks automatically, thus making both the network topology and the communication optimization attempt completely transparent to the application programs. Many of the embeddings used in E-kernel are optimal or asymptotically optimal (with respect to minimum dilation cost). The implementation of E-kernel translated some of the many theoretical results in graph embeddings into practical tools for program mapping and network reconfiguration in a parallel system. E-kernel is functional on Victor V256. Measurements of E-kernel's performance on V256 are also included     

Approximation of the optimal-time problem for controlled differential inclusions

Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 172–176, May–June, 1994