Darko Stipaničev

Networked embedded greenhouse monitoring and control

Networked embedded systems have become quite important nowadays, especially for monitoring and control of distance and dislocated objects. Small greenhouses are typical examples. First, they are usually located far away from the owners house, and second, the plant growth is an example of the process which needs constant 24 hours monitoring. In this paper networked embedded greenhouse monitoring and control based on simple embedded web servers and 1-wire protocol for connecting sensors and actuators is described. Hardware and software architecture of embedded web servers are described and the experimental results of monitoring and control of laboratory greenhouse model are presented.

Agent based data collecting in a forest fire monitoring system

In forest fire protection systems 24 hour surveillance is of most importance. Real time data must be acquired to react fast enough to suppress fire occurrence or to minimize damage made by forest fires. In forest fire monitoring systems, usually a large area must be controlled. Real time data has to be collected and processed in time. When the amount of data reaches critical volume, modern software techniques have to be implemented in order to accomplish system goals. In this work we have implemented agent technology on data retrieval and processing. A multi-agent system for real-time data collection and processing is described. This work is a part of a more complex integral project of forest fire protection in Split-Dalmatia County. The integral forest fire protection system will be based on the information system for integration of all activities connected with early fire detection by 24 hours video and meteorological monitoring, management of forest-fire fighting and post-fire recuperation of burned landscape

Visual spatial-context based wildfire smoke sensor

Sensors for early fire detection based on visual analysis have been under constant development and improvement, especially during the last decade. However, there is still a lot of room for advancement to increase the accuracy and reliability of such sensors. In this paper, a novel method for wildfire smoke detection based on spatial context analysis as well as motion detection, chromatic, texture and shape analysis is introduced. Several measures for evaluating quality of smoke detection are used, both on image and pixel scale. Smoke is a semi-transparent and amorphous phenomenon whose boundaries are hard to determine precisely; therefore, fuzzy measures are introduced for assessing the detection error. The proposed method is evaluated using the proposed measures and compared with two existing methods. The results show that the wildfire sensor based on proposed method is capable of detecting fire-smoke accurately and reliably, and in most detection aspects it outperforms the existing methods.

A Recursive TF-ISF Based Sentence Retrieval Method with Local Context

Sentence retrieval consists of retrieving relevant sentences from a document base in response to a query. Question answering, novelty detection, summarization, opinion mining and information provenance make use of sentence retrieval. Most of the sentence retrieval methods are trivial adaptations of document retrieval methods. However some newer sentence retrieval methods based on the language modeling framework successfully use some kind of context of sentences. Unlike that there is no successful improvement of the TF-ISF method that takes into account the context of sentences. In this paper we propose a recursive TF-ISF based method that takes into account the local context of a sentence. The context is considered the previous and next sentence of current sentence. We compared the new method to the TF-ISF baseline and to an earlier unsuccessful method that also incorporates a similar context into TF-ISF. We got statistically significant improvements of the results in comparison to both of the methods. Additional benefit of our method is the clear explicit model of the context that will allow us to automatically generate a document representation with context suitable for sentence retrieval which is important for our future work.

Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index

Standard wildfire smoke detection systems detect fires using remote cameras located at observation posts. Images from the cameras are analyzed using standard computer vision techniques, and human intervention is required only in situations in which the system raises an alarm. The number of alarms depends largely on manually set detection sensitivity parameters. One of the primary drawbacks of this approach is the false alarm rate, which impairs the usability of the system. In this paper, we present a novel approach using GIS and augmented reality to include the spatial and fire risk data of the observed scene. This information is used to improve the reliability of the existing systems through automatic parameter adjustment. For evaluation, three smoke detection methods were improved using this approach and compared to the standard versions. The results demonstrated significant improvement in different smoke detection aspects, including detection range, rate of correct detections and decrease in the false alarm rate.

Vision based wildfire and natural risk observers

Wildfires are natural risk phenomena that cause significant economic and environmental damage. In wildfire fighting strategy it is important to detect the wildfire in its initial stage and to apply, as soon as possible, the most appropriate fire fighting action. In both cases wildfire monitoring and surveillance systems are of great importance, so in the last decade the interest for various wildfire monitoring and surveillance systems has increased, both on the research and the implementation level. This paper describes one such system named iForestFire. It is an example of advanced terrestrial vision based wildfire monitoring and surveillance system, today widely used in various Croatian National and Nature Parks and regions, but it is also a system in constant development and improvement, both on theoretical and practical level. This paper describes its last improvements in video detection part that are based on notation of observer, cogent confabulation theory and mechanism of thought. Inclusion of cogent confabulation theory allows us to expend the use of existing wildfire observers to more general natural risk observers.

Engineering of holonic multi agent intelligent forest fire monitoring system

In this paper we describe holonic multi agent architecture of iForestFire Intelligent Forest Fire Monitoring System through phases and activities of the holonic software engineering process. iForestFire is a web-based information system used in all aspects of forest fire management. Its central part is an artificial perception system, observer network, whose aim is early detection of forest fires. Although the agents that the system consists of are inseparably connected, they can be clustered in several independent systems that can be used separately. Independent holons are built from agents that are part of the observer network, as well as other agents with auxiliary functionalities.

Multi-Agent systems in distributed computation

The paper investigates the possibility of multi-agent systems application in distributed computation illustrated by an example of generic distributed computation multi-agent system. The system is defined upon finite state machine architecture with agent internal and external behavior. For testing purpose, the model was implemented in Fuzzy Cognitive Map (FCM). FCM is qualitative modeling and simulation technique that is based on discrete distributed computation. Our proposed multi-agent system has shown expected characteristics so the conclusion is that the proposed generic model could be used as a foundation for building distributed computation multi-agent systems.

Bayesian proprioceptor for forest fire observer network

The paper describes design and implementation of Bayesian proprioceptor for forest fire observer network. The proprioceptor, sometimes also referred to as network observer has task of syintactical and semantical sensor and data validation in advanced sensor network Multi agent Bayesian network is used for cooperative data analysis and data understanding having false alarm reduction as final goal. A multi agent system for data sampling and data analysis is described. The proprioceptor is deployed as a part of intelligent forest fire monitoring system (iForestFire).

Self-Tuning Self-Organizing Fuzzy Robot Control

Abstract The paper describes the idea of self-tuning self-organising fuzzy control. Simple fuzzy controller, introduced at the beginning of seventies as a rule-based controller, had two main disadvantages: difficulty with definition of good control rules and problems with tuning of controller parameters. Self-organising controller was developed to overcome the first problem. In this paper we propose a self-tuning procedure to overcome the second problem. That procedure is based on expert knowledge about the influence of the tuning parameters on the system response. Theoretical results are illustrated and tested by simulation a two link robot manipulator.