Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

Macroservers: An Execution Model for DRAM Processor-In-Memory Arrays

The emergence of semiconductor fabrication technology allowing a tight coupling between high-density DRAM and CMOS logic on the same chip has led to the important new class of Processor-In-Memory (PIM) architectures. Newer developments provide powerful parallel processing capabilities on the chip, exploiting the facility to load wide words in single memory accesses and supporting complex address manipulations in the memory. Furthermore, large arrays of PIMs can be arranged into a massively parallel architecture. In this report, we describe an object-based programming model based on the notion of a macroserver. Macroservers encapsulate a set of variables and methods; threads, spawned by the activation of methods, operate asynchronously on the variables' state space. Data distributions provide a mechanism for mapping large data structures across the memory region of a macroserver, while work distributions allow explicit control of bindings between threads and data. Both data and work distributuions are first-class objects of the model, supporting the dynamic management of data and threads in memory. This offers the flexibility required for fully exploiting the processing power and memory bandwidth of a PIM array, in particular for irregular and adaptive applications. Thread synchronization is based on atomic methods, condition variables, and futures. A special type of lightweight macroserver allows the formulation of flexible scheduling strategies for the access to resources, using a monitor-like mechanism

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

The frequent occurrence of major public emergencies in China has caused significant human and economic losses. To carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. Using earthquakes as an example of a public emergency, this paper combines the Deep Belief Network (DBN) and Case-Based Reasoning (CBR) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. The validity of the model is then verified by an example. The results of this study can be applied to maximize the efficiency of emergency rescue decisions

V2V Routing in VANET Based on Heuristic Q-Learning

Designing efficient routing algorithms in vehicular ad hoc networks (VANETs) plays an important role in the emerging intelligent transportation systems. In this paper, a routing algorithm based on the improved Q-learning is proposed for vehicle-to-vehicle (V2V) communications in VANETs. Firstly, a link maintenance time model is established, and the maintenance time is taken as an important parameter in the design of routing algorithm to ensure the reliability of each hop link. Aiming at the low efficiency and slow convergence of Q-learning, heuristic function and evaluation function are introduced to accelerate the update of Q-value of current optimal action, reduce unnecessary exploration, accelerate the convergence speed of Q-learning process and improve learning efficiency. The learning task is dispersed in each vehicle node in the new routing algorithm and it maintains the reliable routing path by periodically exchanging beacon information with surrounding nodes, guides the node’s forwarding action by combining the delay information between nodes to improve the efficiency of data forwarding. The performance of the algorithm is evaluated by NS2 simulator. The results show that the algorithm has a good effect on the package delivery rate and end-to-end delay

Production planning of energy systems: Cost and risk assessment for district heating

This dissertation is a collection of research articles that assess economic andoperational risk in production planning of district heating. District heatingsystems are typically coupled to the electricity system through cogenerationand power-to-heat technologies, and production planners must account foruncertainty stemming from changing weather, demands and prices. Years ofhigh-resolution data from the district heating system in Aarhus, Denmark havebeen used throughout the project to model the system and estimate uncertainties.Risk management tools have been developed to aid district heating operatorsand investment decision makers in short-, medium- and long-term productionplanning.Short-term production planning involves commitment of production unitsand trading on the electricity markets and relies on forecasts of the heat load.Weather predictions are a significant source of uncertainty for heat load forecasts,because the heat load is highly weather-dependent. I introduce the method ofensemble weather predictions from meteorology to heat load forecasting andcreate a probabilistic load forecast to estimate the weather-based uncertainty.Better estimates of the weather-based uncertainty can be applied to optimizesupply temperature control and reduce heat losses without compromising securityof supply in heat distribution systems.Consumer behavior is another substantial, but difficult to capture, source ofuncertainty in short-term heat load forecasts. I include local holiday data instate-of-the-art load forecasts to improve accuracy and capture how load patternschange depending on the behavior of the consumers. A small overall improvementin forecast accuracy is observed. The improvement is more significant on holidaysand special occasions that are difficult to forecast accurately.In medium-term production planning, there can be substantial economicpotential in performing summer shutdown of certain production units. Theshutdown decision carries significant risk, due to changing seasonal weatherpatterns. Based on 38 years of weather data, the uncertainty on the timing ofthe optimal decision is estimated. This information is used to develop practicaldecision rules that are robust to rare weather events and capable of realizingmore than 90% of the potential savings from summer shutdown.Long-term production planning decisions regarding investments in futuredistrict heating production systems are affected by uncertainty from changingelectricity prices, fuel prices and investment cost for technology. The effects ofthese uncertainties on a cost-optimal heat production system are explored, usingwell-established production and storage technologies and extensive multivariatesensitivity analysis. The optimal technology choices are highly stable and,taxes aside, large heat pumps and heat storages dominate the cost-optimal heatproduction systems. However, the uncertainty on the exact capacity allocationis substantial. Excluding heat production based on fossil fuels increases theuncertainty on the system cost, but drastically reduces the uncertainty on theoptimal capacity allocation

Dynamic Network Notation: A Graphical Modeling Language to Support the Visualization and Management of Network Effects in Service Platforms

Service platforms have moved into the center of interest in both academic research and the IT industry due to their economic and technical impact. These multitenant platforms provide own or third party software as metered, on-demand services. Corresponding service offers exhibit network effects. The present work introduces a graphical modeling language to support service platform design with focus on the exploitation of these network effects

A microsimulation approach for modelling the growth of small urban areas

Tese de mestrado. Projecto e Planeamento do Ambiente Urbano. Faculdade de Engenharia. Universidade do Porto, Universidade de Coimbra. Faculdade de Ciências e Tecnologia. 200

V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning

To ensure the transmission quality of real-time communications on the road, the research of routing protocol is crucial to improve effectiveness of data transmission in Vehicular Ad Hoc Networks (VANETs). The existing work Q-Learning based routing algorithm, QLAODV, is studied and its problems, including slow convergence speed and low accuracy, are found. Hence, we propose a new routing algorithm FLHQRP by considering the characteristics of real-time communication in VANETs in the paper. The virtual grid is introduced to divide the vehicle network into clusters. The node’s centrality and mobility, and bandwidth efficiency are processed by the Fuzzy Logic system to select the most suitable cluster head (CH) with the stable communication links in the cluster. A new heuristic function is also proposed in FLHQRP algorithm. It takes cluster as the environment state of heuristic Q-learning, by considering the delay to guide the forwarding process of the CH. This can speed up the learning convergence, and reduce the impact of node density on the convergence speed and accuracy of Q-learning. The problem of QLAODV is solved in the proposed algorithm since the experimental results show that FLHQRP has many advantages on delivery rate, end-to-end delay, and average hops in different network scenarios

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse