Evolutionary Synthesis of HVAC System Configurations: Algorithm Development.

This paper describes the development of an optimization procedure for the synthesis of novel heating, ventilating, and air-conditioning (HVAC) system configurations. Novel HVAC system designs can be synthesized using model-based optimization methods. The optimization problem can be considered as having three sub-optimization problems; the choice of a component set; the design of the topological connections between the components; and the design of a system operating strategy. In an attempt to limit the computational effort required to obtain a design solution, the approach adopted in this research is to solve all three sub-problems simultaneously. Further, the computational effort has been limited by implementing simplified component models and including the system performance evaluation as part of the optimization problem (there being no need in this respect to simulation the system performance). The optimization problem has been solved using a Genetic Algorithm (GA), with data structures and search operators that are specifically developed for the solution of HVAC system optimization problems (in some instances, certain of the novel operators may also be used in other topological optimization problems. The performance of the algorithm, and various search operators has been examined for a two-zone optimization problem (the objective of the optimization being to find a system design that minimizes the system energy use). In particular, the performance of the algorithm in finding feasible system designs has been examined. It was concluded that the search was unreliable when the component set was optimized, but if the component set was fixed as a boundary condition on the search, then the algorithm had an 81% probability of finding a feasible system design. The optimality of the solutions is not examined in this paper, but is described in an associated publication. It was concluded that, given a candidate set of system components, the algorithm described here provides an effective tool for exploring the novel design of HVAC systems. (c) HVAC & R journa

Fully online clustering of evolving data streams into arbitrarily shaped clusters

In recent times there has been an increase in data availability in continuous data streams and clustering of this data has many advantages in data analysis. It is often the case that these data streams are not stationary, but evolve over time, and also that the clusters are not regular shapes but form arbitrary shapes in the data space. Previous techniques for clustering such data streams are either hybrid online / offline methods, windowed offline methods, or find only hyper-elliptical clusters. In this paper we present a fully online technique for clustering evolving data streams into arbitrary shaped clusters. It is a two stage technique that is accurate, robust to noise, computationally and memory efficient, with a low time penalty as the number of data dimensions increases. The first stage of the technique produces micro-clusters and the second stage combines these micro- clusters into macro-clusters. Dimensional stability and high speed is achieved through keeping the calculations both simple and minimal using hyper-spherical micro-clusters. By maintaining a graph structure, where the micro-clusters are the nodes and the edges are its pairs with intersecting micro-clusters, we minimise the calculations required for macro-cluster maintenance. The micro- clusters themselves are described in such a way that there is no calculation required for the core and shell regions and no separate definition of outer micro-clusters necessary. We demonstrate the ability of the proposed technique to join and separate macro-clusters as they evolve in a fully online manner. There are no other fully online techniques that the authors are aware of and so we compare the tech- nique with popular online / offline hybrid alternatives for accuracy, purity and speed. The technique is then applied to real atmospheric science data streams and used to discover short term, long term and seasonal drift and the effects on anomaly detection. As well as having favourable computational characteristics, the technique can add analytic value over hyper-elliptical methods by character- ising the cluster hyper-shape using Euclidean or fractal shape factors. Because the technique records macro-clusters as graphs, further analytic value accrues from characterising the order, degree, and completeness of the cluster-graphs as they evolve over time

A novel algorithm for the modelling of complex processes

In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples

Co-ordinated Airborne Studies in the Tropics (CAST)

This is the author accepted manuscript. The final version is available from the American Meteorological Society via http://dx.doi.org/10.1175/BAMS-D-14-00290.1The Co-ordinated Airborne Studies in the Tropics (CAST) project is studying the chemical composition of the atmosphere in the Tropical Warm Pool region to improve understanding of trace gas transport in convection. The main field activities of the CAST (Co-ordinated Airborne Studies in the Tropics) campaign took place in the West Pacific in January/February 2014. The field campaign was based in Guam (13.5°N, 144.8°E) using the UK FAAM BAe-146 atmospheric research aircraft and was coordinated with the ATTREX project with the unmanned Global Hawk and the CONTRAST campaign with the Gulfstream V aircraft. Together, the three aircraft were able to make detailed measurements of atmospheric structure and composition from the ocean surface to 20 km. These measurements are providing new information about the processes influencing halogen and ozone levels in the tropical West Pacific as well as the importance of trace gas transport in convection for the upper troposphere and stratosphere. The FAAM aircraft made a total of 25 flights between 1°S-14°N and 130°-155°E. It was used to sample at altitudes below 8 km with much of the time spent in the marine boundary layer. It measured a range of chemical species, and sampled extensively within the region of main inflow into the strong West Pacific convection. The CAST team also made ground-based measurements of a number of species (including daily ozonesondes) at the Atmospheric Radiation Measurement program site on Manus Island, Papua New Guinea (2.1°S, 147.4°E). This article presents an overview of the CAST project focussing on the design and operation of the West Pacific experiment. It additionally discusses some new developments in CAST, including flights of new instruments on the Global Hawk in February/March 2015.CAST is funded by NERC and STFC, with grant NE/ I030054/1 (lead award), NE/J006262/1, NE/J006238/1, NE/J006181/1, NE/J006211/1, NE/J006061/1, NE/J006157/1, NE/J006203/1, NE/J00619X/1, and NE/J006173/1. N. R. P. Harris was supported by a NERC Advanced Research Fellowship (NE/G014655/1). P. I. Palmer acknowledges his Royal Society Wolfson Research Merit Award. The BAe-146-301 Atmospheric Research Aircraft is flown by Directflight Ltd and managed by the Facility for Airborne Atmospheric Measurements, which is a joint entity of the Natural Environment Research Council and the Met Office. The authors thank the staff at FAAM, Directflight and Avalon Aero who worked so hard toward the success of the aircraft deployment in Guam, especially for their untiring efforts when spending an unforeseen 9 days in Chuuk. We thank the local staff at Chuuk and Palau, as well as the authorities in the Federated States of Micronesia for their help in facilitating our research flights. Special thanks go to the personnel associated with the ARM facility at Manus, Papua New Guinea without whose help the ground-based measurements would not have been possible. Thanks to the British Atmospheric Data Centre (BADC) for hosting our data and the NCAS Atmospheric Measurement Facility for providing the radiosonde and ground-based ozone equipment. Chlorophyll-a data used in Figure 1 were extracted using the Giovanni online data system, maintained by the NASA GES DISC. We also acknowledge the MODIS mission scientists and associated NASA personnel for the production of this data set. Finally we thank many individual associated with the ATTREX and CONTRAST campaigns for their help in the logistical planning, and we would like to single out Jim Bresch for his excellent and freely provided meteorological advice

Synthesis of Some Novel 11b-Substituted Pyrimido[6,1-a]-isoquinoline Derivatives

A series of novel 11b-substituted 1,6,7,11b-tetrahydropyrimido[6,1-a]- isoquinoline-2,4-diones and 4-thioxo-1,3,4,6,7,11b-hexahydropyrimido[6,1-a]isoquinolin-2- ones were synthesized, utilizing two alternative strategies for ring closure of tetrahydroisoquinoline intermediates obtained from N-phenethyl enaminone

A centre-of-gravity-based recombination operator for genetic algorithms

A new recombination operator is introduced and treated in the paper. It performs specific crossover (breeding) between the two fittest parental chromosomes producing a new child chromosome, which is based on the center of gravity (CoG) paradigm. This new child chromosome is one of the members of the new population. The rest of the chromosomes are produced by the conventional procedures. The new operator could be used both in a binary as well as a real-coded GA.. The insight of the proposed mechanism as well as the test results indicate that it leads to better results in most cases or at least the same results as without its use. With almost no increasing computational expenses the speed of convergence as well as the final result in tests surpass the conventional approach. This new approach has been tested with a practical problem of scheduling of the supply air temperature and flow rate to a ventilated slab thermal storage system as well as with a number of numerical test functions. All results demonstrate its superiority compared with the case when CoG is not used

On-line Evolving Clustering of Web Documents

In this paper an approach that is using evolving, incremental (on-line) clustering to automatically group relevant Web-based documents is proposed. It is centred on a recently introduced evolving fuzzy rule-based clustering approach and borrows heavily from the Nature in the sense that it is evolution-inspired. That is, the structure of the clusters and their number is not predefined, but it self-develops, they grow and shrink when new web documents are accessed. Existing Web-based search engine technology returns long lists of web pages that contain the user's search term query but are not presented in order of contextual similarity. For example, search terms that have more than one meaning such as "cold" are presented to the user in a list containing documents relating to the Cold War and the common cold. If these results could be clustered “on the fly” then this improved presentation of results would allow the end user to find relevant documents more easily by requiring the inspection of one cluster of contextually similar documents rather than entire list of documents containing information pertaining to irrelevant contexts. An issue that is paid a special attention to is the similarity measure between the textual documents. Euclidean, Levenstein, and Cosine similarity measures have been used with cosine dissimilarity/distance performing best and addressing the problem of different number of features in each document. The proposed evolving classifier has also learning capability – it improves the result on-line with any new document that has been accessed. Finally, the proposed approach is characterized by low complexity. This paper reports the results of research that is going on for more than two years at Lancaster University on development of a novel clustering method that is suitable for real-time implementations. It is based on evolution principles and tries to address the limitations of existing clustering algorithms which cannot cope in an online mode with high dimensional datasets. This evolution-inspired and Nature-inspired approach introduces the new concept of potential values which describes the fitness of a new sample (web document) to be the prototype of a new cluster without the need to store each previously encountered documents but taking into account the contextual similarity density between all previous documents in a recursive and thus computationally efficient way (thereby reducing memory requirements and improving speed compared with existing approaches). This paper examines the clustering of documents by contextual similarity using extracted keywords represented in a vector space model

Optimal design synthesis of component-based systems using intelligent techniques

In this chapter we have considered a special case of application of intelligent techniques, namely evolutionary algorithms and fuzzy optimization for au- tomatic synthesis of the design of a component-based system. We have also considered the problem of adaptation of the solutions found and the ways it can be done ’smartly’, maximizing the reuse of the previous knowledge and search history. Some useful hints and tips for practical use of these techniques in their application to different problems has been made. The approach we have presented for automatic synthesis of optimal design of component-based systems is novel and original. It can be applied to similar problems in automatic design of complex engineering systems, like petrochem- ical pipelines, electronic circuits design, etc. The complex problem of design synthesis, which involves human decisions at its early stages, is treated as a soft constraints satisfaction problem. EA and problem-specific operators are used to numerically solve this fuzzy optimization problem. Their use is mo- tivated by the complex nature of the problem, which results in the use of different types of variables (real and integer) that represent both the physical and the topological properties of the system. The ultimate objective of this fuzzy optimization problem is to design a feasible and efficient system. The important problem of the design adaptation to the changed speci- fications (constraints) has also been considered in detail. This adds to the flexibility of the approach and its portability. The approach which is pre- sented in this chapter has been tested with real systems and is realized in Java. It fully automates the design process, although interactive supervision by a human-designer is possible using a specialized GUI. (c) Springe

Automatic design generation of component-based systems using GA and fuzzy optimisation

The problem of the automatic synthesis of design of complex component-based systems is treated using fuzzy constraints and genetic algorithm (GA). The approach is demonstrated with a heating, ventilating and air-conditioning (HVAC) systems design, but it can easily be extended to other component-based systems such as VLSI. The complex problem of system design that involves human decisions, especially at its early stages, is treated as a fuzzy constraints satisfaction problem. GA and problem-specific operators are used to solve numerically this fuzzy optimization problem. The use of GA is motivated by the complex nature of the design problem, which results in the use of different types of variables (real and integer) that represent both the physical and the topological properties of the system. The ultimate objective of this fuzzy optimization problem is to design a feasible and efficient system. A software realization is in Java and it fully automates the design process. An interactive supervision by a human-designer is also possible using a specialized GUI. An example of automatic design of HVAC system is presented which does not limit the range of possible applications in a variety of other types of component-based systems such as VLSI

An Approach to Real-Time Color-based Object Tracking

Object tracking is of great interest in different areas of industry, security and defense. Tracking moving objects based on color information is more robust than systems utilizing motion cues. In order to maintain the lock on the object as the surrounding conditions vary, the color model needs to be adapted in real-time. In this paper an on-line learning method for the color model is implemented using fuzzy adaptive resonance theory (ART). Fuzzy ART is a type of neural network that is trained based on competitive learning principle. The color model of the target region is regularly updated based on the vigilance criteria (which is a threshold) applied to the pixel color information. The target location in the next frame is predicted using evolving extended Takagi-Sugeno (exTS) model to improve the tracking performance. The results of applying exTS for prediction of the position of the moving target were compared with the usually used solution based on Kalman filter. The experiments with real footage demonstrate over a variety of scenarios the superiority of the exTS as a predictor comparing to the Kalman filter. Further investigation concentrates on using evolving clustering for realizing computationally efficient simultaneous tracking of different segments in the object (c) IEEE Pres