Osmar Betazzi Dordal

Lessons learned from a simulated environment for trains conduction

This paper consolidates and discuss the results of a software agent development, named SDriver, which is able to drive an intercity freight train in a secure, economic and fast way. The SDriver executes a small set of instructions, named: reducing, increasing or maintaining the acceleration point, and start breaking. Three approaches have been studied to implement the core of SDriver: (i) machine learning (classification methods), (ii) distributed constraint optimization, and (iii) specialized rules (if-then). The SDriver performance was evaluated comparing fuel consumption and actions similarity with a real conduction, using a simulated environment. The validation of the knowledge discovered from the machine learning approach was done quantitatively, calculating a degree of similarity between the simulation and the history of travel. The main results are expressed by their mean values: 32% of fuel consumption reduction and 85% action similarity between the SDriver and the real conductor.

An architecture of BDI agent for autonomous locomotives controller

In this paper we propose an architecture of intelligent agent for automatic locomotives operating. The system agent generates its action policy using a set of resources, such as type of railway, composition, belief perception and reasoning about the actions. The focus of the operator agent is directed to the choice of acceleration points (gear) and preparation of travel plans in a journey guided by goals and objectives. The system is equipped with a module capable to plan the actions to move the vehicle from an initial point P to an end point Q and an executor module that implements the generated plan and modifies the state of the environment. For this purpose, we use the mental model that is based on the triple Belief, Desire and Intention (BDI) to which the perception of the agent is guaranteed by a set of sensors that provide speed information, position and breaks condition. The main focus on this research is the usage of mental model BDI for the resolution of a problem that combines travel naturally conflicting factors, such as safety, time and fuel consumption. Experimental results show that the developed architecture using the mental model BDI increases the efficiency of autonomous vehicles operating.

An intelligent system for driving trains using Case-Based Reasoning

This paper presents a planning approach using Case-Based Reasoning (CBR) to generate plans for driving trains. The main idea of a planning strategy is to generate a sequence of actions for an agent, which can use these actions to change its environment. CBR allows using prior experiences in the situation assessment task. In the proposed approach, each previous experience (if not applicable) is adjusted resulting in cases specializations. Our interest is reducing the number of corrections triggered when a case retrieved is not applicable, based on these specializations. Experiments showed that the plans generated using this proposed method had a significant increase in the number of cases recovered satisfactorily, also reducing the need of adaptations for the cases recovered.

A multi-layer architecture proposal for conducting trains employing CBR

This paper presents a planning approach using Case-Based Reasoning (CBR) modeled as a Subsumption Architecture to generate plans for driving trains. The main idea of a planning strategy is to generate a sequence of actions for an agent, which can use these actions to change its environment. CBR allows using prior experiences for new task assignments. In the proposed ap-proach, each previous experience (if not applicable) is adjusted us-ing one or more adaptation methods like substitutive and genetic algorithm. Our interest is to create a flexible architecture for an agent and apply it to simulate train conductions. We expect that the plans generated by this approach generate better results com-pared to another studies already developed for the area mainly considering fuel consumption and travel time.

Towards an optimal driving trains in single line using crossing loops

This paper describes an intelligent approach based on agents that are able to drive and coordinate trains on stretches of railway line containing a crossing loop. Halts close to or even in crossing loops lead to increased consumption of fossil fuels, longer journey times and exhaustion of track capacity. In this paper the agents make use of a set of resources — railway line characteristics, train characteristics, driving rules and information about other trains — to generate their action policy. The agents perception is guaranteed by a set of sensors that provide data such as speed, position and information about the line. The tasks that the agent performs include carrying out actions such as increasing or reducing the speed of the train. The main objective of this study was to avoid unnecessary halts, which are the main cause of increased fuel consumption and journey time. Our results show that strong reductions can be made in terms of fuel consumption (average reduction of 25.5%), journey time (average reduction of 22.5%) and exhaustion of track capacity. Simulations were performed in which traditional driving techniques, with halts at several points along the stretch of track, were compared with driving performed by the multi-agent system, without any halts.

An Economic Approach for Generation of Train Driving Plans using Continuous Case-based Planning

We present an approach for reusing and sharing train driving plans P using continuous (or without human intervention) Case-Based Planning (CBP). P is formed by a set of actions, which when applied, can move a train in a stretch of railroad. This is a complex task due to the variations in the (i) composition of the train, (ii) environmental conditions, and (iii) stretches travelled. To overcome these difficulties we provide to the driver a support system to help the driver in this complex task. CBP was chosen because it allows directly reuse the human drivers experience as well as from other sources. The main steps of the CBP are distributed among specialized agents with different roles: Planner and Executor. Our approach was evaluated by different metrics: (i) accuracy of the case recovery task, (ii) efficiency of task adaptation and application of such cases in realistic scenarios and (iii) fuel consumption. We show that the inclusion of new experiences reduces the efforts of both the Planner and the Executor, reduces significantly the fuel consumption and allow the reuse of the obtained experiences in similar scenarios with low effort.

Efficient approach for reusing and sharing train driving plans using case-based reasoning

This paper presents an efficient collaboration approach for reusing and sharing freight train driving plans P using case-based reasoning (CBR). P is formed by a set of actions that can move a train from one end to the other in a railroad. Collaboration is established by sharing different train driving experiences in different stretches. Three agents are positioned at each end: Planner, Executor, and Memory. Planner is responsible for generating P. Executor tests/adjusts (if necessary)/executes the actions of P. Until the train reaches the end, P may undergo δ adjustments depending on environmental conditions. The modified plan P + δ is returned to the origin to be integrated into the local experience base, maintained by the Memory. The approach was evaluated according the fuel consumption, accuracy of the case recovery task, and efficiency of task adaptation and application of such cases. The expansion of the experiences reduced the efforts of both the Planner and the Executor. In addition, our approach allowed the reuse, with low effort, of the obtained experiences in similar scenarios.

An Intelligent System for train overtaking using distributed coordination

This paper presents an Intelligent System, based on a dynamic time table definition, which coordinates the overtaking process of trains traveling in the same section of a railroad through a crossing loop. Each train involved on the overtaking process is represented by an intelligent agent capable of taken his actions based on his relative position on the railroad and his scheduling. The main goal of these agents is to react during the driving to avoid that more than one train stays on the same section of track at the same time (resource concurrency), and also to avoid unnecessary halts. The agents actions are previously defined, based on a simulation of the journey to the next crossing loop. For each stretch the agents selects another agent to be his coordinator, based on specific criteria. Then, each agent creates its action policy based on his local view and the data of the coordinator. The coordinator receives the actions and validates them generating a time table containing the actions for the next stretch, called dynamic time table. The definition of the action policy occurs dynamically, as each agent simulates the next step of the journey virtually and takes the decisions at runtime. The communication between the agents is done through the environment, where each agent updates his relative location and time. The main goal of the Intelligent System is the coordination of the trains focusing on reducing fuel consumption and also reducing the travel time. This is chased avoiding unnecessary halts, collisions of the trains and driving the trains with a Cruising Speed. The best simulations results achieved a 33.72% reduction in fuel consumption and a 33.30% reduction on travel time.

Strong reduction in fuel consumption driving trains in bi-directional single line using crossing loops

This paper presents an intelligent approach based on software agents capable of conducting and coordination trains in stretches of single railway track, aiming to reduce the utilization of railway and environment impacts. In the Brazilian rail modal, due to the low duplication of tracks, trains that journey on single railways should accomplish required halts, in order to wait for other trains to use the crossing loop safely. The technological evolution resulted on the appearance of new railway traffic system control. However, systems that rely on software agents are not well explored yet. Therefore, this paper elaborated a Multi-Agent System capable of simulating railway environment using agent drivers and agents with a highest level in managing the railway tracks. The behaviour of agents was based on specialized rules of conduction. The coordination between them occurs through message exchanges, always aiming to avoid halts during the journey. Results have shown an strong average reduction of 22.5% in journey time and 25.5% in fuel consumption when compared to journeys using the traditional method of conduction. The reduction, not only on the journey time, but also on the fuel consumption, entails on the decrease of CO2 emission.

Genome Compression: An Image-Based Approach

With the advent of Next Generation Sequencing Technologies, it has been possible to reduce the cost and time of genome sequencing. Thus, there was a significant increase in demand for genomes that were assembled daily. This demand requires more efficient techniques for storing and transmitting genomic data. In this research, we discussed the horizontal compression of lossless genomic sequences, using two image formats, WEBP, and FLIF. For this, the genomic sequence is transformed into a matrix of colored pixels, where an RGB color is assigned to each symbol of the A, T, C, G alphabet at a position x-y. The WEBP format showed the best data-rate saving (76.15%, SD = 0.84) when compared to FLIF. In addition, we compared the data-rate savings of two specialized DELIMINATE and MPCompress genomic data compression tools with WEBP. The results obtained show that the WEBP is close to DELIMINATE (76.03%, SD = 2.54%) and MFCompress (76.97%). SD = 1.36%). Finally, we suggest using WEBP for genomic data compression.