Browsing by Author "Diao, Kegong"
Now showing 1 - 20 of 51
Results Per Page
Sort Options
Item Open Access Analysing Gas Data using Deep Learning and 2D Gramian Angular Fields(IEEE SENSORS JOURNAL, 2023) Jaleel, Muhammad; Kucukler, Omer; Alsalemi, Abdullah; Amira, Abbes; Malekmohamadi, Hossein; Diao, KegongThe notion of employing a Deep Learning (DL) for gas classification has kindled revolution in the field that has both improved data collection measures and classification performance. Yet, the current literature, with its vast contributions, has potential in enhancing the current state-of-the-art by employing both DL and novel visualization methods to boost classification performance and speed. Therefore, this paper presents a dual classification system for high-performance gas classification: on 1D time series data and on 2D Gramian Angular Field (GAF) data. For the GAF case, 1D data is converted into 2D counterparts by means of normalization, segmentation, averaging, and color-coding. The Gas Sensor Array (GSA) dataset is used for evaluating the implemented AlexNet model for classifying 2D GAF data and an improved version of GasNet for 1D time-based data. Using a cloud-based architecture, the two models are evaluated and benchmarked with the state-of-the-art. Evaluation results of the modified GasNet model on time series data signifies state-of-the-art accuracy of 96.0%, while AlexNet achieved 81.3% test accuracy of GAF classification with near real-time performance on edge computing platforms.Item Open Access Analysing Gas Data using Deep Learning and 2D Gramian Angular Fields(IEEE, 2023-02-10) Jaleel, Muhammad; Kucukler, Omer; Alsalemi, Abdullah; Amira, Abbes; Malekmohamadi, Hossein; Diao, KegongThe notion of employing a Deep Learning (DL) for gas classification has kindled a revolution that has improved both data collection measures and classification performance. Yet, the current literature, with its vast contributions, has potential in enhancing the current state-of-the-art by employing both DL and novel visualisation methods to boost classification performance and speed. Therefore, this paper presents a dual classification system for high-performance gas classification: on 1D time series data and on 2D Gramian Angular Field (GAF) data. For the GAF case study, 1D data is converted into 2D counterparts by means of normalisation, segmentation, averaging, and colour-coding. The Gas Sensor Array (GSA) dataset is used for evaluating the implemented AlexNet model for classifying 2D GAF data and an improved version of GasNet for 1D time-based data. Using a cloud-based architecture, the two models are evaluated and benchmarked with the state-of-the-art. Evaluation results of the modified GasNet model on time series data signifies state-of-the-art accuracy of 96.5%, while AlexNet achieved 81.0% test accuracy of GAF classification with near real-time performance on edge computing platforms.Item Embargo Analysis of Trajectories towards Pareto-Optimal Water Distribution Networks: A Dual Graph Approach(ASCE, 2022-06-05) Sitzenfrei, Robert; Hesarkazzazi, Sina; Hajibabaei, Mohsen; Diao, KegongThe multi-objective design of water distribution networks (WDNs) is a very challenging task and can be addressed with evolutionary algorithms. Especially for large WDNs, such a process is very computationally demanding, and it is difficult to assess if the obtained solutions could be further optimized. In other words, due to the stochastic nature of evolutionary algorithm, it is not straighforward to recognize whether the solutions are optimal or further generations need to be processed. Characteristics of the network graphs could reveal the progress of the optimization process. For optimal WDNs, the primal graph characteristics and network patterns have already been explored. However, literature is missing a dual graph approach to investigate the properties of optimal WDNs in a dual space. Such a dual graph approach is developed in this work. Therewith, the trajectories of the optimization process from random initialization to the (final) optimal generation are described and systematically investigated. With that the answer to the question is successfully addressed: When is an optimal stage at the optimization achieved, and how can that be assessed and predicted?Item Open Access Analyzing Domestic Energy Behavior with a Multi-Dimensional Appliance-Level Dataset(IEEE, 2022-10) Alsalemi, Abdullah; Amira, Abbes; Malekmohamadi, Hossein; Diao, KegongData, in its purest nature, has an authority on the systems it accompanies by feeding an accurate representation of the observed reality. In energy efficiency, the underlying motivation for big data efforts revolves around the intrinsic need to understand end-user electric energy consumption and means to improve it. Hence, developing a rich, detailed, and realistic power consumption dataset entails a deliberate process of preparing the data collection environment, configuring proper Internet of Energy (IoE) sensors and managing the collected data. In this work, a novel power consumption dataset is presented in efforts to improve the state-of-the-art of energy efficiency research in buildings. The dataset is also accompanied by a two-dimensional (2D) counterpart produced using Gramian Angular Fields (GAF) that creates pictorial summaries from one-dimensional (1D) data. Data acquisition is carried out using the ODROIDXU4 edge computing hub, Home Assistant software, and a collection of smart plugs and sensors. A notable use case is presented to signify the merits of the data and its analysis tools to achieve computationally efficient classification.Item Open Access Animating inter-organisational resilience communication: a participatory social network analysis of water governance in the UK(CellPress, 2020-10-03) Ward, Sarah; Meng, Fanlin; Bunney, Sarah; Diao, Kegong; Butler, DavidResilience as a concept and resilience assessment as a practice are being explored across a range of social, ecological and technical systems. In this paper, we propose a new method and visualisation approach for interrogating the communication of resilience within organisational networks, using participatory social network analysis and message passing. Through an examination of the UK water sector organisational network, represented by multiple co-produced network graphs, we identify organisations having a key role in the communication of resilience regulatory and evidence messages, as well as highlighting the potential role of complexity tools in strategy formulation. Animations are presented showing the dynamics of resilience communication, which is discussed. Reflections on the use of participatory social network analysis are explored, as the method opens new doors to potentially examine how network changes could alter communication. Key insights highlight that perceived responsibilities for resilience in the UK water sector rest with a small core of organisations; water customers play a limited role in the two-way communication of resilience and water sector organisations do not communicate widely on resilience with other sectors (such as energy). Additionally, who an organisations’ neighbours are and what catalyses a message to be passed are important in determining how quickly messages spread. Results lead to a recommendation that high level governmental and policy organisations should engage to a greater extent with new resilience knowledge and consider the use of complexity tools in policy making. Policy in relation to resilience is not keeping pace with such knowledge, limiting the communication and learning of organisations who ardently follow policy and regulation. For inter-organisational cooperation to make a difference to water governance, such organisations need to be encouraged to communicate and embed the latest approaches in relation to resilience and complexity thinking and practice.Item Metadata only Assessing Model Structure Uncertainties in Water Distribution Models(ASCE, 2014) Sitzenfrei, R.; Mair, M.; Diao, Kegong; Rauch, W.Awareness of uncertainties is an important issue in every modeling task. In literature, extensive discussion of uncertainties regarding e.g. measurements in general, water consumption, pipe roughness coefficients, etc., can be found. Depending on the modeling aim and the desired outcome (i.e., required accurateness of the results), the question arises what is the impact of not available or poor quality structural data on model simulation results (e.g., if position and properties of pipes and other components are unknown). Therefore, a spanning tree-based algorithm to automatically assemble many possible water distribution models by using free available street network data as input is applied. The obtained uncertainty bandwidth of 180 created models with respect to the pressure head is approximately within ±20m, but can be much lower (±4m) for well performing systems. A qualitative visual comparison of the best performing created system indicates a similar and comparable pressure distribution as in the real system.Item Embargo Automated Creation of District Metered Area Boundaries in Water Distribution Systems(ASCE, 2012-04-03) Diao, Kegong; Zhou, Y.; Rauch, W.Accounting for water in a distribution system can be improved by dividing systems into smaller, metered zones. This paper proposes an approach that could create boundaries for district metered areas (DMA) automatically on the basis of the community structure of water distribution systems. Community structure—the gathering of vertices into communities such that there is a higher density of edges within communities than between them—is a common property of many complex systems. For verification, the method was tested on a real-world distribution system, and the result was compared with a manually designed DMA layout. Although further improvements are necessary, because the achieved community structure is in excellent agreement with the zoning plan in reality, this approach is a new addition to the number of automated methods aimed at complementing and eventually substituting the empirical trial-and-error approach.Item Metadata only Automated Pipe-sizing of Storm Sewer or Combined Sewer Systems Based on Hydrodynamic Modelling(Proceedings of the 9th International Conference on Urban Drainage Modelling, 2012) Diao, Kegong; Mair, M.; Möderl, M.; Kleidorfer, M.; Sitzenfrei, R.; Urich, C.; Rauch, W.This paper introduces a method for automated pipe-sizing of storm sewer or combined sewer systems based on hydrodynamic modelling. The methodology includes three steps. Initially, Graph theoretical description of network topology (e.g. “Sewer branch order”) is utilized for classification of the studied sewer network’s topology. Then, the network is decomposed hierarchically into a number of subsystems based on the network topology. Finally, the pipe sizing is carried out subsystem by subsystem with no flooding in the whole system as the objective. To verify the results of the method, the algorithm is tested on a real world sewer network, and then the solution is compared with the global optimal solution. As proved by the case study, the author-designed method could guarantee a near-optimal solution that is very close to the global optimal solution, while requires dramatically less computational effort than global optimization method. Compared with evolutionary methods, the method has its own advantages, since it does not require any parameter for configuration and execution control, and could produce unique solutions as long as the design principles are fixed.Item Open Access Battle of Postdisaster Response and Restoration(ASCE, 2020-06-10) Paez, Diego; Filion, Yves; Quintiliani, Claudia; Santopietro, Simone; Sweetapple, Chris; Meng, Fanlin; Farmani, Raziyeh; Fu, Guangtao; Butler, David; Zhang, Qingzhou; Zheng, Feifei; Diao, Kegong; Ulanicki, Bogumil; Huang, Yuan; Deuerlein, Jochen; Gilbert, Denis; Abraham, Edo; Piller, Olivier; Bałut, Alicja; Brodziak, Rafał; Bylka, Jędrzej; Zakrzewski, Przemysław; Li, Yuanzhe; Gao, Jinliang; Jian, Cai; Ou, Chenhao; Hu, Shiyuan; Sophocleous, Sophocles; Nikoloudi, Eirini; Mahmoud, Herman; Woodward, Kevin; Romano, Michele; Santonastaso, Giovanni Francesco; Creaco, Enrico; Di Nardo, Armando; Di Natale, Michele; Bibok, Attila; Salcedo, Camilo; Aguilar, Andrés; Cuero, Paula; González, Sebastián; Muñoz, Sergio; Pérez, Jorge; Posada, Alejandra; Robles, Juliana; Vargas, Kevin; Franchini, Marco; Galelli, Stefano; Kim, Joong Hoon; Iglesias-Rey, Pedro; Kapelan, Zoran; Saldarriaga, Juan; Savic, Dragan; Walski, ThomasThe paper presents the results of the Battle of Post-Disaster Response and Restoration (BPDRR), presented in a special session at the 1st International WDSA/CCWI Joint Conference, held in Kingston, Ontario, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/non visibility of damages. Each team/approach was evaluated against six performance criteria that included: 1) Time without supply for hospital/firefighting, 2) Rapidity of recovery, 3) Resilience loss, 4) Average time of no user service, 5) Number of users without service for 8 consecutive hours, and 6) Water loss. Three main types of approaches were identified from the submissions: 1) General purpose metaheuristic algorithms, 2) Greedy algorithms, and 3) Ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that, for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably between solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event.Item Unknown Battle of the Water Calibration Networks(ASCE, 2011) Ostfeld, A.; Salomons, E.; Ormsbee, L.; Uber, J.; Bros, C.; Kalungi, P.; Burd, R.; Zazula-Coetzee, B.; Belrain, T.; Kang, D.; Lansey, K.; Shen, H.; McBean, E.; Yi Wu, Z.; Walski, T.; Alvisi, S.; Franchini, M.; Johnson, J.; Ghimire, S.; Barkdoll, B.; Koppel, T.; Vassiljev, A.; Kim, J.; Chung, G.; Yoo, D.; Diao, Kegong; Zhou, Y.; Li, J.; Liu, Z.; Chang, K.; Gao, J.; Qu, S.; Yuan, Y.; Prasad, T.; Laucelli, D.; Vamvakeridou Lyroudia, L.; Kapelan, Z.; Savic, D.; Berardi, L.; Barbaro, G.; Giustolisi, O.; Asadzadeh, M.; Tolson, B.; McKillop, R.Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler’s experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of the Water Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems.Item Unknown Battle of the Water Calibration Networks (BWCN): A Component Status Changes Oriented Calibration Method for Zonal Management Water Distribution Networks(ASCE, 2010) Diao, Kegong; Zhou, Y.; Li, J.; Liu, Z.Calibration is the prerequisite for a hydraulic model of water distribution system to be utilized in practice. Given the case-specific feature of model calibration, this paper introduces a method that is oriented by component status changes in zonal management water distribution systems. This method is specified for zonal systems in which the operation of every zone could be framed to a stable periodical performance based on control routines, which would not be affected substantially by condition variation in other zones or the effects could be deduced. The principle of this methodology is to identify scenarios with status changes of several crucial components in water distribution networks which give rise to significant effects on hydraulic performance at least in a certain region of the water distribution system. After, those scenarios could be used for calibration of both pipe roughness and demand pattern multipliers with measured data (e.g. SCADA data) as constraints. For verification, this methodology is applied to the case study of "The Battle of the Water Calibration Networks (BWCN)" based on understanding of the system features and behaviors according to available data and corresponding statistical analysis. The final outcomes demonstrate that this approach could reach acceptable results more efficiently if characteristics of the studied system could be well identified.Item Unknown Building resilience in urban water systems for cities of the future(International Water Association, 2014) Mugume, S.; Diao, Kegong; Astaraie-Imani, M.; Fu, G.; Farmani, R.; Butler, D.Item Unknown Case Study of Urban Water Distribution Networks Districting Management Based on Water Leakage Control(ASCE, 2009) Wu, S.; Li, Xiaohong; Tang, S.; Zhou, Y.; Diao, KegongGlobally, water demand is rising and resources are diminishing. Most of the world's water systems have been highly successful in delivering high-quality water to large populations. However, most of these systems also incur a notable amount of loss in their operations. Water loss from the water supply system has long been a feature of operations management, even in the countries with a well-developed infrastructure and good operating practices. There is no doubt that the sustainable management of water supply system is a challenge for the whole world. Water leakage cannot be completely avoided, but they can be managed so that they remain within economic limits. Some new models of water loss management have been developed recently. As the technical and economic levels varied in different cities, the best practice using in their water supply systems leakage control should be taken suited to local conditions. In this case study a water loss management strategy for a large city in north China was suggested and the technological means for the optimized its managing were provided. The basic principles for structuring district meter areas in this water supply system according its features were developed. The GIS as the tool was used during the study. Based on the field test, the water balance calculations of three DMAs were carried out, and using the fuzzy appraisal method to evaluated their degree of water losses.Item Unknown Clustering analysis of water distribution systems: identifying critical components and community impacts(International Water Association, 2014-12) Diao, Kegong; Farmani, R.; Fu, G.; Astaraie-Imani, M.; Ward, Sarah; Butler, D.Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.Item Unknown Controllability analysis as a pre-selection method for sensor placement in water distribution systems(International Water Association, 2013-07-31) Diao, Kegong; Rauch, W.Detection of contamination events in water distribution systems is a crucial task for maintaining water security. Online monitoring is considered as the most cost-effective technology to protect against the impacts of contaminant intrusions. Optimization methods for sensor placement enable automated sensor layout design based on hydraulic and water quality simulation. However, this approach results in an excessive computational burden. In this paper we outline the application of controllability analysis as preprocessing method for sensor placement. Based on case studies we demonstrate that the method decreases the number of decision variables for subsequent optimization dramatically to app. 30 to 40 percent.Item Unknown Creating 3D Gramian Angular Field Representations for Higher Performance Energy Data Classification(IEEE, 2022-10-18) Alsalemi, Abdullah; Amira, Abbes; Malekmohamadi, Hossein; Diao, KegongThe industrial revolution has elevated science and engineering to foster the development of Image Processing and Artificial Intelligence (AI) and put the visualization of information on an even higher pedestal. Yet, the demands of the industrial age have contributed to an ever-growing wildfire of climate change, sparking a revolution in energy efficiency research. With the aim to advance energy efficiency research from an AI standpoint, a novel transformation of raw-formatted data repositories, known as data lakes, into multi-dimensional visualizations data coupled with computationally lightweight, edge-based AI implementations are proposed as means to understand the energy consumption patterns in buildings. As a novel method of understanding energy data visually, current results comprise a Multi-Dimensional Gramian Angular Field (GAF) representation of energy data as both 2D and 3D interactive forms. Moreover, a case study on deep learning classification employed on ODROID-XU4 yields ~90% accuracy and a classification rate of 17.5 msec/image.Item Metadata only Development of Integrated Information Management System for Water Distribution and Drainage Systems: A Case Study of Jinnan District, Tianjin(Proceedings of the 9th International Conference Hydroinformatics, 2010) Diao, Kegong; Zhou,Y. W.; Li, J.Item Open Access Dual graph characteristics of water distribution networks – how optimal are design solutions?(Springer, 2022-06-23) Sitzenfrei, Robert; Hajibabaei, Mohsen; Hesarkazzazi, Sina; Diao, KegongUrban water infrastructures are an essential part of urban areas. For their construction and maintenance, major investments are required to ensure an efficient and reliable function. Vital parts of the urban water infrastructures are water distribution networks (WDNs), which transport water from the production (sources) to the spatially distributed consumers (sinks). To minimize the costs and at the same time maximize the resilience of such a system, multi-objective optimization procedures (e.g., meta-heuristic searches) are performed. Assessing the hydraulic behavior of WDNs in such an optimization procedure is no trivial task and is computationally demanding. Further, deciding how far from optimal design solutions are, is difficult to assess and often results in unnecessary extent of experiment. To tackle these challenges, an answer to the questions is sought: when is an optimization stage achieved from which no further improvements can be expected, and how can that be assessed? It was found that graph characteristics based on complex network theory (number of dual graph elements) converge towards a certain threshold with increasing number of generations. Furthermore, a novel method based on network topology and the demand distribution in WDNs, specifically based on changes in ‘demand edge betweenness centrality’, for identifying that threshold is developed and successfully tested. With the proposed novel approach, it is feasible, prior to the optimization, to determine characteristics which optimal design solutions should fulfill, and thereafter, test them during the optimization process. Therewith, numerous simulation runs of meta-heuristic search engines can be avoided.Item Open Access Edge Deep Learning for Smart Energy Applications(CRC Press, 2022) Alsalemi, Abdullah; Amira, Abbes; Malekmohamadi, Hossein; Diao, Kegong; Bensaali, FaycalThe Internet of Energy (IoE) paradigm is an advancing area of research concerning the fusion of smart technology and energy efficiency [1], combing data collection, processing, and visualization. Smart energy monitoring witnesses technological advancements such as smart metering and IoE networking, allowing the expansion of smart energy networks in a smart house. In this research, we aim to understand energy behavior through big data collection and classification and improve energy efficiency using behavioral economics, deep learning-based recommender systems, and intuitive data visualizations. In specific, a specialized case study is reported on the ODROID XU4 platform [3], and a setup developed at De Montfort University (DMU) at the Energy Lab and AI Lab, it is aimed to build a novel appliance level dataset with contextual ambient environmental data. As a novel advancement in the field, the ODROID performs edge deep learning computations on the collected data, to clean it, summarize it, anonymize it, and classification, it transmits it to a cloud server for further deep processing and storage. Concluding, the proposed work provides aids in exploiting energy-efficiency technologies for improving energy efficiency via an innovative, automated energy efficiency deep learning engine.Item Open Access Editorial: Cutting-edge innovations in drinking water management(IWA Publishing, 2025-02-01) Diao, Kegong; Ulanicki, Bogumil
- «
- 1 (current)
- 2
- 3
- »