Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.     

Spatio‐temporal trends of nutrients and physico‐chemical parameters on lake ecosystem and fisheries prior to onset of cage farming and re‐opening of the Mbita passage in the Nyanza Gulf of Lake Victoria

Many large lake ecosystems are experiencing increasing eutrophication and persistent cyanobacteria‐dominated algal blooms affecting their water quality and ecosystem productivity because of widespread non‐point and point nutrient sources. Accordingly, the present study utilized data of July 2003 and January–February 2004, as well as previous measurements of nutrients and physico‐chemical variables (electrical conductivity, dissolved oxygen, temperature, pH, turbidity and chlorophyll‐a), to characterize the spatial and temporal trends, as a means of better understanding the factors influencing lake environmental conditions, as support tools for long‐term ecosystem management and for better understanding the long‐term trends and effects. Inshore gulf areas were found to represent zones of maximum nutrient concentrations, compared to the deep main lake zones, with significant inter‐parameter correlations. Phosphorus, silicon and chlorophyll‐a concentrations were significantly correlated. Water electrical conductivity was also significantly and positively correlated with soluble reactive silicon (SRSi), alkalinity hardness DO, while exhibiting a negative association with water transparency. Water turbidity and transparency, electrical conductivity, and SRSi concentrations clearly describe a gradient from the gulf into the main lake. For such a shallow gulf, these findings suggest primary productivity is influenced mainly by the availability of nutrients, light transparency and the extent of availability of resuspended nutrients. The increasing eutrophic state of Lake Victoria is a serious concern since it contributes to an increased potential of more frequent occurrences of cyanobacterial blooms, a potential public health risk to both humans and wildlife. Improved understanding of influences from previous fish species introductions and concomitant changes in indigenous fish species, increased lake basin population and anthropogenic activities, water hyacinth resurgences, sustainability of biodiversity, and current interests in cage farming, are among the major concerns and challenges facing the contemporary Lake Victoria. The trends regarding nutrients and physico‐chemical characteristics are intended to support better monitoring efforts and data to promote the lake's ecosystem services and the sustainable management of the lake ecosystem.     

Achieving greater anonymity in network latency

Each lnternet communication leaves trails that can be followed back to the user. Notably, anonymous communication schemes are purposed to hide users＇ identity as to personal, source, destination location and content information. Notably, no network capability is in existence to completely negate anonymity leakage in network latency[~l, thus, the minimization of anonymity leakage in network latency becomes critically salient. The purpose of this paper is to investigate network latency anonymity leaks, and propose practical techniques for their reduction. In this direction, the author investigate the following technical question： What implementation techniques can be configured to truly reduce anonymity leaks using deployable systems and exploiting the popular Tor security strategies.     

Magnetic quadrupole doublet focusing system for high energy ions

A high energy focused ion beam microprobe using a doublet arrangement of short magnetic quadrupole lenses was used to focus 1-3 MeV protons to spot sizes of 1x1 microm2 and 1-4.5 MeV carbon and silicon ion beams to spot sizes of 1.5x1.5 microm2. The results presented clearly demonstrate that this simple doublet configuration can provide high energy microbeams for microanalysis and microfabrication applications.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

The Forgiving Graph: a distributed data structure for low stretch under adversarial attack

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds to each such change by quick “repairs,” which consist of adding or deleting a small number of edges. These repairs essentially preserve closeness of nodes after adversarial deletions, without increasing node degrees by too much, in the following sense. At any point in the algorithm, nodes v and w whose distance would have been ? in the graph formed by considering only the adversarial insertions (not the adversarial deletions), will be at distance at most ? log n in the actual graph, where n is the total number of vertices seen so far. Similarly, at any point, a node v whose degree would have been d in the graph with adversarial insertions only, will have degree at most 3d in the actual graph. Our distributed data structure, which we call the Forgiving Graph, has low latency and bandwidth requirements. The Forgiving Graph improves on the Forgiving Tree distributed data structure from Hayes et?al. (2008) in the following ways: 1) it ensures low stretch over all pairs of nodes, while the Forgiving Tree only ensures low diameter increase; 2) it handles both node insertions and deletions, while the Forgiving Tree only handles deletions; 3) it requires only a very simple and minimal initialization phase, while the Forgiving Tree initially requires construction of a spanning tree of the network.     

Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.     

Impaired Higher Order Implicit Sequence Learning on the Verbal Version of the Serial Reaction Time Task in Patients With Parkinson's Disease.

Although neuroimaging studies have strongly implicated basal ganglia involvement in implicit sequence learning, serial reaction time (SRT) studies with Parkinson's disease (PD) patients have yielded mixed results. The present research sought to examine the ability of people with PD to implicitly learn sequences with different sequential structures and to objectively assess explicit knowledge. A version of the SRT task that reduces motor demands was used to compare 19 patients with PD but not dementia and 37 matched controls. PD patients showed less implicit sequence-specific learning for both sequences and reduced response time improvement over sequential trials for the more complex sequence. A closer examination revealed that the deficit involved higher order sequential associations as well as the learning of pairwise information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)