Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Transmission costs allocation based on optimal re-dispatch

Congestion management of transmission power systems has achieve high relevance in competitive environments, which require an adequate approach both in technical and economic terms. This paper proposes a new methodology for congestion management and transmission tariff determination in deregulated electricity markets. The congestion management methodology is based on a reformulated optimal power flow, whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the transactions resulting from market operation. The proposed transmission tariffs consider the physical impact caused by each market agents in the transmission network. The final tariff considers existing system costs and also costs due to the initial congestion situation and losses. This paper includes a case study for the 118 bus IEEE test case.

Contexts-management strategy in considering the security in urban computing based on urban design

Urban Computing (UrC) provides users with the situation-proper information by considering context of users, devices, and social and physical environment in urban life. With social network services, UrC makes it possible for people with common interests to organize a virtual-society through exchange of context information among them. In these cases, people and personal devices are vulnerable to fake and misleading context information which is transferred from unauthorized and unauthenticated servers by attackers. So called smart devices which run automatically on some context events are more vulnerable if they are not prepared for attacks. In this paper, we illustrate some UrC service scenarios, and show important context information, possible threats, protection method, and secure context management for people.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Bid definition method for electricity markets based on an adaptive multiagent system

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.

Development of novel ionic liquids based on ampicillin

Novel ionic liquids containing ampicillin as an active pharmaceutical ingredient anion were prepared with good yields by using a new, efficient synthetic procedure based on the neutralization of a moderately basic ammonia solution of ampicillin with different organic cation hydroxides. The relevant physical and thermal properties of these novel ionic liquids based on ampicillin were also evaluated.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature.

Intelligent training in control centres based on an ambient intelligence paradigm

This article describes a new approach in the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Conflicts between employee preferences and ergonomic recommendations in shift scheduling: regulation based on consent is not sufficient

OBJECTIVE: Contribution to the discussion of the role of participation/consent of employees in working hours regulation. METHODS: Exploratory analysis of conflicts between preferences of employees and ergonomic recommendations in shift scheduling by analysing a large number of participative shift scheduling projects. RESULTS: The analysis showed that very often the pursuit of higher income played the major role in the decision making process of employees and employees preferred working hours in conflict with health and safety principles. CONCLUSIONS: First, the consent of employees or the works council alone does not ensure ergonomically sound schedules. Besides consent, risk assessment procedures seem to be a promising but difficult approach. Secondly, more research is necessary to check the applicability of recommendations under various settings, to support the risk assessment processes and to improve regulatory approaches to working hours.

A multidimensional scaling analysis of musical sounds based on pseudo phase plane

This paper studies musical opus from the point of view of three mathematical tools: entropy, pseudo phase plane (PPP), and multidimensional scaling (MDS). The experiments analyze ten sets of different musical styles. First, for each musical composition, the PPP is produced using the time series lags captured by the average mutual information. Second, to unravel hidden relationships between the musical styles the MDS technique is used. The MDS is calculated based on two alternative metrics obtained from the PPP, namely, the average mutual information and the fractal dimension. The results reveal significant differences in the musical styles, demonstrating the feasibility of the proposed strategy and motivating further developments towards a dynamical analysis of musical sounds.

Safety high accuracy Context-Aware Matrix (CAM) making based on X.509 proxy certificate

Human Computer Interaction (HCl) is to interaction between computers and each person. And context-aware (CA) is very important one of HCI composition. In particular, if there are sequential or continuous tasks between users and devices, among users, and among devices etc, it is important to decide the next action using right CA. And to take perfect decision we have to get together all CA into a structure. We define that structure is Context-Aware Matrix (CAM) in this article. However to make exact decision is too hard for some problems like low accuracy, overhead and bad context by attacker etc. Many researcher has been studying to solve these problems. Moreover, still it has weak point HCI using in safety. In this Article, we propose CAM making include best selecting Server in each area. As a result, moving users could be taken the best way.

Development of novel ionic liquids based on valproate anion

Valproic acid (2-propyl pentanoic acid) is a pharmaceutical drug used for treatment of epileptic seizures absence, tonic-clonic (grand mal), complex partial seizures, and mania in bipolar disorder [1]. Valproic acid is a slightly soluble in water and therefore as active pharmaceutical ingredient it is most commonly applied in form of sodium or magnesium valproate salt [1].However the list of adverse effects of these compounds is large and includes among others: tiredness, tremor, sedation and gastrointestinal disturbances [2]. Ionic liquids (ILs) are promising compounds as Active Pharmaceutical Ingredients (APIs)[3]. In this context, the combinations of the valproate anion with appropriate cation when ILs and salts are formed can significantly alter valproate physical, chemical and thermal properties.[4] This methodology can be used for drug modification (alteration of drug solubility in water, lipids, bioavailability, etc)[2] and therefore can eliminate some adverse effect of the drugs related to drug toxicity due for example to its solubility in water and lipids (interaction with intestines). Herein, we will discuss the development of ILs based on valproate anion (Figure 1) prepared according a recent optimized and sustainable acid-base neutralization method [4]. The organic cations such as cetylpyridinium, choline and imidazolium structures were selected based on their biocompatibility and recent applications in pharmacy [3]. All novel API-ILs based on valproate have been studied in terms of their physical, chemical (viscosity, density, solubility) and thermal (calorimetric studies) properties as well as their biological activity.

Development and biological evaluation of API-ILs based on anti-bacterial and anti-fungal drugs

In recent years Ionic Liquids (ILs) are being applied in life sciences. ILs are being produce with active pharmaceutical drugs (API) as they can reduce polymorphism and drug solubility problems [1] Also ILs are being applied as a drug delivery device in innovative therapies What is appealing in ILs is the ILs building up platform, the counter-ion can be carefully chosen in order to avoid undesirable side effects or to give innovative therapies in which two active ions are paired. This work shows ILs based on ampicillin (an anti-bacterial agent) and ILs based on Amphotericin B. Also we show studies that indicate that ILs based on Ampicillin could reverse resistance in some bacteria. The ILs produced in this work were synthetized by the neutralization method described in Ferraz et. al. [2] Ampicillin anion was combined with the following organic cations 1-ethyl-3-methylimidazolium, [EMIM]; 1-hydroxy-ethyl-3-methylimidazolium, [C2OHMIM]; choline, [cholin]; tetraethylammonium, [TEA]; cetylpyridinium, [C16pyr] and trihexyltetradecylphosphonium, [P6,6,6,14]. Amphotericin B was combined with [C16pyr], [cholin] and 1-metohyethyl-3-methylimidazolium, [C3OMIM]. The ILs-APIs based on ampicillin[2] were tested against sensitive Gram-negative bacteria Escherichia coli ATCC 25922 and Klebsiella pneumonia (clinical isolated), as well as on Gram positive Staphylococcus Aureus ATCC 25923, Staphylococcus epidermidis and Enterococcus faecalis. The arising resistance developed by bacteria to antibiotics is a serious public health threat and needs new and urgent measures. We study the bacterial activity of these compounds against a panel of resistant bacteria (clinical isolated strains): E. coli CTX M9, E. coli TEM CTX M9, E. coli TEM1, E. coli CTX M2, E. coli AmpC Mox2. In this work we demonstrate that is possible to produce ILs from anti-bacterial and anti-fungal compounds. We show here that the new ILs can reverse the bacteria resistance. With the careful choice of the organic cation, it is possible to create important biological and physic-chemical properties. This work also shows that the ion-pair is fundamental in ampicillin mechanism of action.

A model updating technique based on FRFs for damped structures

Model updating methods often neglect that in fact all physical structures are damped. Such simplification relies on the structural modelling approach, although it compromises the accuracy of the predictions of the structural dynamic behaviour. In the present work, the authors address the problem of finite element (FE) model updating based on measured frequency response functions (FRFs), considering damping. The proposed procedure is based upon the complex experimental data, which contains information related to the damped FE model parameters and presents the advantage of requiring no prior knowledge about the damping matrix structure or its content, only demanding the definition of the damping type. Numerical simulations are performed in order to establish the applicability of the proposed damped FE model updating technique and its results are discussed in terms of the correlation between the simulated experimental complex FRFs and the ones obtained from the updated FE model.