Hierarchical demand response using dantzig-wolfe decomposition

Demand Side Management (DSM) programmes are designed to shift electrical loads from peak times. Demand Response (DR) algorithms automate this process for controllable loads. DR can be implemented explicitly in terms of Peak to Average Ratio Reduction (PARR), in which case the maximum peak load is minimised over a prediction horizon by manipulating the amount of energy given to controllable loads at different times. A hierarchical predictive PARR algorithm is presented here based on Dantzig-Wolfe decomposition. © 2013 IEEE.

Interpreting pedestrian behaviour by visualising and clustering movement data

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.

Hierarchical Demand Response for Peak Reduction using Dantzig‐Wolfe Decomposition

Demand Response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralised agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus it is desirable to use a scalable decentralised algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for Peak Minimisation (PM) based on Dantzig-Wolfe Decomposition (DWD). In addition, a Time Weighted Maximisation option is included in the cost function which improves the Quality of Service for devices seeking to receive their desired energy sooner rather than later. The paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

Latent and Explicit Authority in Hierarchical Organizations

We consider the problem of the exercise of authority within social production organizations, embedding the decision makers into a structure of formal authority relationships. We distinguish two types of behavior. First, we introduce an equilibrium notion implementing latent authority under which subordinates submit themselves to authority even though such authority is not en- forced explicitly. Second, we compare this with a non-cooperative equilibrium concept describing explicit exercise of authority. We show that for low enough enforcement costs both forms of authority will be exercised in equilibrium, but for higher enforcement costs latent authority will be exercised while explicit authority will not.

Hierarchical energy management system for multi-source multi-product microgrids

This paper proposes a hierarchical energy management system for multi-source multi-product (MSMP) microgrids. Traditional energy hub based scheduling method is combined with a hierarchical control structure to incorporate transient characteristics of natural gas flow and dynamics of energy converters in microgrids. The hierarchical EMS includes a supervisory control layer, an optimizing control layer, and an execution control layer. In order to efficiently accommodate the systems multi time-scale characteristics, the optimizing control layer is decomposed into three sub-layers: slow, medium and fast. Thermal, gas and electrical management systems are integrated into the slow, medium, and fast control layer, respectively. Compared with wind energy, solar energy is easier to integrate and more suitable for the microgrid environment, therefore, potential impacts of the hierarchical EMS on MSMP microgrids is investigated based on a building energy system integrating photovoltaic and microturbines. Numerical studies indicate that by using a hierarchical EMS, MSMP microgrids can be economically operated. Also, interactions among thermal, gas, and electrical system can be effectively managed.

In situ preparation of 3D graphene aerogels@hierarchical Fe3O4 nanoclusters as high rate and long cycle anode materials for lithium ion batteries

We describe a novel strategy for in situ fabrication of hierarchical Fe₃O₄ nanoclusters-GAs. Fe₃O₄ NCs-GAs deliver excellent rate capability (the reversible capacities obtained were 1442, 392 and 118 mA h g^-1 at 0.1C, 12C and 35C rates), and a high reversible capacity of 577 mA h g^-1 over 300 cycles at the current density of 5.2 A g^-1 (6C).

Active site labeling of cysteine cathepsins by a straightforward diazomethylketone probe derived from the N-terminus of human cystatin C

We designed a straightforward biotinylated probe using the N-terminal substrate-like region of the inhibitory site of human cystatin C as a scaffold, linked to the thiol-specific reagent diazomethylketone group as a covalent warhead (i.e. Biot-(PEG)2-Ahx-LeuValGly-DMK). The irreversible activity-based probe bound readily to cysteine cathepsins B, L, S and K. Moreover affinity labeling is sensitive since active cathepsins were detected in the nM range using an ExtrAvidin®-peroxidase conjugate for disclosure. Biot-(PEG)2-Ahx-LeuValGly-DMK allowed a slightly more pronounced labeling for cathepsin S with a compelling second-order rate constant for association (kass = 2,320,000 M−1 s−1). Labeling of the active site is dose-dependent as observed using 6-cyclohexylamine-4-piperazinyl-1,3,5-triazine-2-carbonitrile, as competitive inhibitor of cathepsins. Finally we showed that Biot-(PEG)2-Ahx-LeuValGly-DMK may be a simple and convenient tool to label secreted and intracellular active cathepsins using a myelomonocytic cell line (THP-1 cells) as model.

Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction
Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods
Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results
Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion
LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.

Accelerating Large Scale Centroid-based Clustering with Locality Sensitive Hashing

Most traditional data mining algorithms struggle to cope with the sheer scale of data efficiently. In this paper, we propose a general framework to accelerate existing clustering algorithms to cluster large-scale datasets which contain large numbers of attributes, items, and clusters. Our framework makes use of locality sensitive hashing (LSH) to significantly reduce the cluster search space. We also theoretically prove that our framework has a guaranteed error bound in terms of the clustering quality. This framework can be applied to a set of centroid-based clustering algorithms that assign an object to the most similar cluster, and we adopt the popular K-Modes categorical clustering algorithm to present how the framework can be applied. We validated our framework with five synthetic datasets and a real world Yahoo! Answers dataset. The experimental results demonstrate that our framework is able to speed up the existing clustering algorithm between factors of 2 and 6, while maintaining comparable cluster purity.