A Marginal Contribution Theory-Based Energy Efficiency Contribution Analysis for Integrated Energy System

The energy efficiency analysis is a prerequisite for the construction of the integrated energy system (IES). In this study, a novel energy efficiency analysis method is proposed considering different energy subsystems in the IES. First, the energy efficiency index of the subsystems and conversion devices is formed for elaborating their influence on the IES. The IES is composed of four energy subsystems, i.e., power/gas/heat/cooling subsystems, and six energy conversion devices. Next, the energy efficiency contribution models of energy subsystems and conversion devices are proposed based on their energy efficiency index, respectively. Then, in order to calculate the energy flow in the IES, an equivalent topological model of the IES weighted directed graph is proposed to calculate the energy efficiency contribution. Finally, an actual park is employed to illustrate the validity of the proposed analysis method.

New graph algorithms via polyhedral techniques

This article gives a short overview of my dissertation, where new algorithms are given for two fundamental graph problems. We develop novel ways of using linear programming formulations, even exponential-sized ones, to extract structure from problem instances and to guide algorithms in making progress. The first part of the dissertation addresses a benchmark problem in combinatorial optimization: the asymmetric traveling salesman problem (ATSP). It consists in finding the shortest tour that visits all vertices of a given edge-weighted directed graph. A ρ-approximation algorithm for ATSP is one that runs in polynomial time and always produces a tour at most ρ times longer than the shortest tour. Finding such an algorithm with constant ρ had been a long-standing open problem. Here we give such an algorithm. The second part of the dissertation addresses the perfect matching problem. We have known since the 1980s that it has efficient parallel algorithms if the use of randomness is allowed. However, we do not know if randomness is necessary – that is, whether the matching problem is in the class NC. We show that it is in the class quasi-NC. That is, we give a deterministic parallel algorithm that runs in poly-logarithmic time on quasi-polynomially many processors.

Efficient fastest-path computations for road maps

In the age of real-time online traffic information and GPS-enabled devices, fastest-path computations between two points in a road network modeled as a directed graph, where each directed edge is weighted by a “travel time” value, are becoming a standard feature of many navigation-related applications. To support this, very efficient computation of these paths in very large road networks is critical. Fastest paths may be computed as minimal-cost paths in a weighted directed graph, but traditional minimal-cost path algorithms based on variants of the classical Dijkstra algorithm do not scale well, as in the worst case they may traverse the entire graph. A common improvement, which can dramatically reduce the number of graph vertices traversed, is the A* algorithm, which requires a good heuristic lower bound on the minimal cost. We introduce a simple, but very effective, heuristic function based on a small number of values assigned to each graph vertex. The values are based on graph separators and are computed efficiently in a preprocessing stage. We present experimental results demonstrating that our heuristic provides estimates of the minimal cost superior to those of other heuristics. Our experiments show that when used in the A* algorithm, this heuristic can reduce the number of vertices traversed by an order of magnitude compared to other heuristics.

Spectral Ranking of Causal Influence in Complex Systems

Similar to natural complex systems, such as the Earth’s climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders of magnitude in space and time. Thousands of sensors measure relevant process variables at appropriate sampling rates, to provide time series as primary sources for system diagnostics. However, high-dimensionality, non-linearity and non-stationarity of the data are major challenges to efficiently, yet accurately, diagnose rare or new system issues by merely using model-based approaches. To reliably narrow down the causal search space, we validate a ranking algorithm that applies transfer entropy for bivariate interaction analysis of a system’s multivariate time series to obtain a weighted directed graph, and graph eigenvector centrality to identify the system’s most important sources of original information or causal influence. The results suggest that this approach robustly identifies the true drivers or causes of a complex system’s deviant behavior, even when its reconstructed information transfer network includes redundant edges.

Structure and influence in a global capital–ownership network

Relationships between legal entities can be represented as a large weighted directed graph. In this work, we model the global capital ownership network across a hundred of millions of such entities with the goal of establishing a methodology for extracting and analysing meaningful patterns of capitalistic influence from the graph structure. To do so, we adapt and employ metrics from graph analytics and algorithms from the area of influence maximization. We characterize the relationships extracted and show that our analysis aligns with information from macro-economic studies; for example it recovers the presence of known tax heavens, which appear in dense subgraphs of countries. We also identify and quantify cases where capital is principally owned by others, corresponding to global influence. Beyond confirming known patterns and justifying our novel application of influence maximization methodology in this area, the outcome also offers new insight and metrics in this domain, by highlighting the existence of strong communities of capitalistic property. We leverage influence maximization methods as a means to evaluate the impact of entities in these contexts. Finally we formulate the results of our study into recommendations for future analyses of this kind.

不定自然変換理論に基づく比喩理解モデルの計算論的実装の試み

Machine learning techniques have realized some principal cognitive functionalities such as nonlinear generalization and causal model construction, as far as huge amount of data are available. A next frontier for cognitive modelling would be the ability of humans to transfer past knowledge to novel, ongoing experience, making analogies from the known to the unknown. Novel metaphor comprehension may be considered as an example of such transfer learning and analogical reasoning that can be empirically tested in a relatively straightforward way. Based on some concepts inherent in category theory, we implement a model of metaphor comprehension called the theory of indeterminate natural transformation (TINT), and test its descriptive validity of humans' metaphor comprehension. We simulate metaphor comprehension with two models: one being structure-ignoring, and the other being structure-respecting. The former is a sub-TINT model, while the latter is the minimal-TINT model. As the required input to the TINT models, we gathered the association data from human participants to construct the ``latent category'' for TINT, which is a complete weighted directed graph. To test the validity of metaphor comprehension by the TINT models, we conducted an experiment that examines how humans comprehend a metaphor. While the sub-TINT does not show any significant correlation, the minimal-TINT shows significant correlations with the human data. It suggests that we can capture metaphor comprehension processes in a quite bottom-up manner realized by TINT.

Dynamic Graph based Method for Mining Text Data

An improved graph based association rules mining (ARM) approach to extract association rules fromtext databases is proposed in this paper. The text document in the proposed technique is read only once to lookfor the terms whose occurrences are greater than some threshold value, these terms are stored in a file with theirfrequencies, then they are represented as nodes in a weighted directed graph where edges represent relationsbetween these terms, the edges will denote the associations between terms while the edges' weights denote thestrength or confidence of these rules. The proposed method is called Dynamic Graph based Rule Mining fromText (DGRMT) because the graph is built level by level according the length of a sentence (number of frequentterms). Weighted subgraph mining is used to ensure the efficiency and throughput of the proposed technique;only the most frequent subgraphs are extracted. The proposed technique is validated and evaluated using realworld textual data sets and compared with one of the best graph based rule mining technique, which is algorithmfor Generating Association Rules based on Weighting scheme(GARW). The results determine that the proposed approach is better than GARW on almost all textual datasets.