Diagnosing Analogue Linear Systems Using Dynamic Topological Reconfiguration
Authors: Alexander Feldman, Gregory Provan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the theoretical predictions through extensive experimentation on a benchmark of circuits. |
| Researcher Affiliation | Collaboration | Alexander Feldman General Diagnostics Burgwal 47 2611GG, Delft, The Netherlands email: alex@general-diagnostics.com Gregory Provan University College Cork College Road, Cork, Ireland email: g.provan@cs.ucc.ie |
| Pseudocode | Yes | Algorithm 1: DIAGNOSISSEARCH(M,α), Algorithm 2: SIMULATESYSTEM(L), Algorithm 3: OPTIMIZEGRAPH(G) |
| Open Source Code | No | Algorithms 1 3 are implemented as a part of the (deleted for anonymity) diagnostic framework. |
| Open Datasets | No | No benchmark for analogue linear circuits exists, to the best of our knowledge... Consequently, we have generated the circuits that we need. |
| Dataset Splits | No | The paper describes generating circuits and performing simulations, but it does not specify how these generated circuits or any other data were explicitly split into training, validation, and test sets for model development or evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions that algorithms are implemented as part of a 'diagnostic framework' and refers to methods like SPICE and Modified Nodal Analysis, but it does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or simulation tools used for implementation). |
| Experiment Setup | No | The paper describes the characteristics of the generated benchmark circuits and the theoretical analysis of the algorithm, but it does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings typically found in experimental sections. |