Efficient Knowledge Base Management in DCSP

DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI (Artificial Intelligence). There are many application problems in distributed AI that can be formalized as DSCPs. With the increasing complexity and problem size of the application problems in AI, the required storage place in searching and the average searching time are increasing too. Thus, to use a limited storage place efficiently in solving DCSP becomes a very important problem, and it can help to reduce searching time as well. This paper provides an efficient knowledge base management approach based on general usage of hyper-resolution-rule in consistence algorithm. The approach minimizes the increasing of the knowledge base by eliminate sufficient constraint and false nogood. These eliminations do not change the completeness of the original knowledge base increased. The proofs are given as well. The example shows that this approach decrease both the new nogoods generated and the knowledge base greatly. Thus it decreases the required storage place and simplify the searching process.Comment: 11 page

A Model for an Intelligent Support Decision System in Aquaculture

The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system.support decision system, diagnosis, multi-agent system, fish diseases

Development clusters for small places and rural development for territorial cohesion?

This article proposes an alternative policy development approach for territories encompassing rural areas with small urban settlements or ‘small places’, which normally suffer from lagging territorial development trends. The proposed ‘Development Clusters for Small Places’ approach draws on the potential of all places to further their development via municipal clustering, based on four analytic dimensions: (i) existing functional areas; (ii) similarities in economic circularity and specialisation; (iii) presence of ongoing territorial and governance cooperation processes; and (iv) spatial physical connectivity and accessibility. Besides a theoretical overview of this policy approach, the article analyses concrete examples of its potential implementation in two case studies: Alentejo in Portugal and Innlandet in Norway. The findings highlight the potential advantages of municipal clustering over current mainstream regional development rationales to implement endogenous rural development in a supra-municipal scale, thus increasing institutional thickness and policy influence towards a more territorial cohesive region.info:eu-repo/semantics/publishedVersio

A learning experience toward the understanding of abstraction-level interactions in parallel applications

In the curriculum of a Computer Engineering program, concepts like parallelism, concurrency, consistency, or atomicity are usually addressed in separate courses due to their thoroughness and extension. Isolating such concepts in courses helps students not only to focus on specific aspects, but also to experience the reality of working with modern computer systems, where those concepts are often detached in different abstraction levels. However, due to such an isolation, it exists a risk of inducing to the students an absence of interactions between these concepts, and, by extension, between the different abstraction levels of a system. This paper proposes a learning experience showcasing the interactions between abstraction levels addressed in laboratory sessions of different courses. The driving example is a parallel ray tracer. In the different courses, students implement and assemble components of this application from the algorithmic level of the tracer to the assembly instructions required to guarantee atomicity. Each lab focuses on a single abstraction level, but shows students the interactions with the rest of the levels. Technical results and student learning outcomes through the analysis of surveys validate the proposed experience and confirm the students learning improvement with a more integrated view of the system

Modeling Complex Object Changes in Satellite Image Time-Series: Approach based on CSP and Spatiotemporal Graph

This paper proposes a method for automatically monitoring and analyzing the evolution of complex geographic objects. The objects are modeled as a spatiotemporal graph, which separates filiation relations, spatial relations, and spatiotemporal relations, and is analyzed by detecting frequent sub-graphs using constraint satisfaction problems (CSP). The process is divided into four steps: first, the identification of complex objects in each satellite image; second, the construction of a spatiotemporal graph to model the spatiotemporal changes of the complex objects; third, the creation of sub-graphs to be detected in the base spatiotemporal graph; and fourth, the analysis of the spatiotemporal graph by detecting the sub-graphs and solving a constraint network to determine relevant sub-graphs. The final step is further broken down into two sub-steps: (i) the modeling of the constraint network with defined variables and constraints, and (ii) the solving of the constraint network to find relevant sub-graphs in the spatiotemporal graph. Experiments were conducted using real-world satellite images representing several cities in Saudi Arabia, and the results demonstrate the effectiveness of the proposed approach

Assessing the compliance of a product with an eco-label: from standards to constraints

The new awareness of the consumers regarding environmental issues should allow companies to gain a competitive advantage by obtaining eco-labels which certify the low impact of a product on the environment. Getting such label requires to analyse a product according to rules expressed in natural language which may be difficult to interpret but also to apply when the product is complex. In order to address this problem, we suggest a method aiming at providing support to the user when checking the compliance of a product with an eco-label. The method is applied on an illustrative example of the literature

Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan

A Model for an Intelligent Support Decision System in Aquaculture

The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system

Advanced control strategies for vehicle to grid systems with electric vehicles as distributed sources

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis focuses on the control and implementation of the vehicle to grid (V2G) system in a smart grid. Important issues like structure, principle, performance, and control of energy storage systems for electrical vehicles and power systems are discussed. In recent decades, due to rapid consumption of the earth’s oil resources, air pollution and global warming (a result of the “greenhouse effect”), the development of electrical vehicles (EVs), hybrid electrical vehicles (HEVs) and plug-in electric vehicles (PEVs) are attracting more and more attentions. In order to provide regulation services and spinning reserves (to meet sudden demands for power), V2G services have a promising prospective future for grid support. It has been proposed that in the future development, such use of V2G could buffer and support effectively the penetration of renewable sources in power systems. This PhD thesis project aims to develop novel and competitive control strategies for V2G services implementation for EVs in smart electrical car parks or Smartparks. Through a comprehensive literature review of the current EV development and energy storage systems used for EVs, several energy storage technologies are compared and a hybrid energy storage system consisting of batteries and supercapacitors is proposed. This system combines effectively the advantages of high energy density of battery banks and high power density of supercapacitor banks. Supercapacitor and battery cells are tested in the laboratory using different charging and discharging procedures. Different supercapacitor and battery models are compared, discussed, and verified using the experimental data. For the energy storage system package, a cell voltage balance circuit is developed for the supercapacitor module. The principle of this circuit is also applicable to the battery module. The proposed balancing method is simple and reliable, and presents good performance for voltage balancing to prolong the lifetime of the energy storage system. The essential technology of V2G is based on the bidirectional power flow control of the charger. Besides charging the EV batteries, it can utilize the stored energy to feed electricity back to the power grid when there is a need. Three-phase AC/DC converters have been extensively used in industrial applications and also the V2G chargers. The power converters used for the V2G services are required to operate more efficiently and effectively to maintain high power quality and dynamic stability. Then the AC/DC converter used for the bidirectional V2G charger is developed and modelled. For the control aspect of AC/DC converter, a new control approach using a model predictive control (MPC) scheme is developed for V2G applications. With the advanced control strategy, the EVs in Smartparks can exchange both active and reactive power with the grid flexibly. The MPC algorithm presents excellent steady-state and dynamic performance. When a very large number of EVs are aggregated in Smartparks, the charging and discharging power should be a significant viable contributor to the power grid. New challenges will be introduced into the power system planning and operation. While discharging, the V2G power brings more potential benefits to enhance the power quality and system reliability. Using V2G services, EVs can provide many grid services, such as regulation and spinning reserve, load levelling, serving as external storage for renewable sources. An effective approach to deal with the negligibly small impact of a single EV is to group a large number of EVs. An aggregator is a new player whose role is to collect the EVs by attracting and retaining them so as to result in a MW capacity that can beneficially impact the grid. From the aggregator’ decision, the EVs are determined by the optimal deployment. The aggregator can act as a very effective resource by helping the operator to supply both capacity and energy services to the grid. By supplying active power and reactive power from EVs, the aggregation may be used for frequency and voltage regulation to control frequency and voltage fluctuations that are caused by supply–demand imbalances. Different case studies of EVs’ support to grid are carried out; the results show that V2G services can stabilize the frequency and voltage variations and have control flexibilities to fulfil system reliability and power quality requirements. The main attractiveness of V2G to consumers is that it can produce income to the vehicle owner to maximize car use. On the other hand, the utility companies can use EVs to stabilize the frequency in the power system and improve the utility operation. It also makes the utility companies more efficient with less loss because the energy is generated locally. From this point of view, V2G is a source of revenue in both electricity and transportation system, and it can help the environment reduce pollution and global warming. Various data of V2G systems have been collected for economic analysis, such as EV battery capacities, charging time, and grid electricity price and load demands. Then for the economic issues related to V2G services, optimal charging based on different objectives is presented. Dumbing charging, maximization of the average state of charge (SOC), maximum revenue and minimum cost are compared. Economic issues are a very special aspect of the V2G technology and how a large profit from V2G services can be produced is the main point of attraction to vehicle owners. Significant conclusions based on the research findings are drawn, and possible future works for further development including commercialisation of the V2G technology are proposed