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The operational problem of container unloading from the ship is analyzed in this paper. Dynamic “crane-cargo-ship” system was investigated, and a mathematical model was created. In the model, the gap between the container and the ship’s cargo hold, the mass of the cargo, the container’s center of the mass, and the frictional forces that may occur during lifting from the cargo hold were estimated. Numerical analysis of the system was performed. Results of numerical analysis were compared with experimental measurements of containers unloading process in port. Requirement of lifting power was modelled depending on mass of cargo. Additional power needs in case of contact forces between container and wall of the ship’s cargo hold were calculated. Rational lifting conditions could be deduced using a created mathematical model and the reliability of the container and cargo during lifting could be deduced.

Machine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much attention is given to its illumination conditions. The paper examines the adverse factors affecting the quality of image recognition and presents an adaptive method for reducing the influence of changing illumination conditions in the color inspection process of transparent PET preforms. The method is based on predicting measured color intensity correction parameters according to illumination conditions. To test this adaptive method, a hardware and software system for image capture and processing was developed. This system is capable of inspecting large quantities of preforms in real time using a neural network with a modified gradient descent and momentum algorithm. The experiment showed that correction of the measured color intensity value reduced the standard deviation caused by variable and uneven illumination by 61.51 & x0025;, demonstrating that machine vision color intensity evaluation is a robust and adaptive solution under illuminated conditions for detecting abnormalities in machine-based PET inspection procedures. Web of Science 8 83198 83189

Introduction: In the paper, we examine the energy consumption efficiency of specialized container diesel trucks engaged in container transportation at a seaport terminal. Objectives: Using the container terminal at Klaipėda in Lithuania as the background for the research, we produced an improved energy consumption model for measuring the theoretical energy consumption and regeneration of diesel trucks at the terminal and provide a comparative analysis. Methods: We created a mathematical model which describes the instantaneous energy consumption of the diesel trucks, taking into account their dynamic properties and the overall geometry of their routes—“Ship-Truck-Stack-Ship”—using the superposition principle. We investigated other critical parameters relevant to the model and provide a statistical evaluation of the transportation process using data from a case study of Klaipėda port, where we collected measurements of container transportation parameters using georeferenced movement detection and logs from wireless equipment positioned on the diesel-powered container trucks. Results: The modeling results showed that an instantaneous evaluation of energy consumption can reveal areas in the container transportation process which have the highest energy loss and require the introduction of new management and process control initiatives to address the regulations which are designed to decrease harmful industrial emissions and encourage novel technologies and thereby increase the eco-friendliness of existing systems. Conclusion: Based on the research results, the article can provide a reference for the estimation of diesel truck efficiency in seaport terminal operations.

In this work, the method and prototype for managing the quality of wireless communication channel is presented. The proposed methodology, based on multi-criteria information utility assessment process, is designed. The heterogeneous context awareness data collection subsystems integration is enabled by transforming the acquired data to the highly dynamic intelligent transport systems. Each element of this intellectual transport system runs as a separate component having a specific environmental monitoring and control elements. Evaluation of context information utility allowed realizing the methods and algorithms that adaptively reduce the load on the wireless channel, and the transmitted and stored data volume without losing the enriched contextual information quality, which allows developing more complex heterogeneous services. It is believed that the obtained scientific results will have positive effect for the future research and development of context awareness and service adaptation systems.

In this paper, a novel approach is presented to increase the security of shipping containers transportation and storage in container yards. This approach includes wireless sensors networks with programmable modules to increase the effectiveness of the decision support functionality for operators’ onsite. This approach is closely related to the Container Security Initiative and is intended to deepen knowledge in the intelligent transportation research area. This paper examines an urgent challenge - secure of cargo transportation in containers, i.e., how quickly it is possible to detect dangerous goods in shipping containers without changing their tightness and hence rationally implements international security regulations all around the world. This paper contributes to the development of new approaches of shipping containers handling and monitoring in terms of smart cities and smart ports (for the development of the Smart Port initiative) for ports that have higher levels of security violations. This contribution is addressed as an informative measure to the general public working in the Information and Communications Technologies (ICT) research area.

Unmanned aerial vehicle (UAV) tracking fiducial marker is a challenging problem, because of camera system vibration, which causes visible frame-to-frame jitter in the airborne videos and unclear marker vision. Multirotors have very limited weight carrying, controller, and battery power resources. While obtaining and processing motion blurred images, which have no useful information, requires much more image processing subsystem resources. The paper presents blurry image frame elimination based approach of UAV resource saving fiducial marker visual tracking. The proposed approach integrates accelerometer and visual data processing algorithms to predict image blur and skip blurred frames. Experiments have been performed to verify the validity of the proposed approach.

Detection of objects emitting radiation is a classical problem widely analyzed by many authors worldwide; however, new computerized data analysis wireless sensor network and systems are emerging daily and require application of optimal sensors data fusion methods to increase their effectiveness even further. Conventional monitoring and other radiation emission detection systems lack optimal resources allocation in harsh and huge environments with shielding materials preventing in-time easy event detection. That is why many security issues are partially omitted in our daily lives and even in industry, including international cargo transportation operations, due to the need for a larger amount of high precision sensors to be deployed on larger areas with different technological standards and their high procurement and exploitation costs. Such conventional sensor systems are already used in many areas including detection of dirty bombs in containers (US Homeland Security initiative - Container security initiative (CSI)), but only in certain areas with relatively low efficiency. In this paper, a theoretical mathematical model is presented and discussed to lower the radiation emission detection time using DAI-DAO data fusion technique in crucial security systems. A known problem of dirty bomb detection in a container terminal is presented as an example to demonstrate the problem area and the effectiveness of the proposed solution.

Usually, a hydrometeorological information system is faced with great data flows, but the data levels are often excessive, depending on the observed region of the water. The paper presents advanced buoy communication technologies based on multiagent interaction and data exchange between several monitoring system nodes. The proposed management of buoy communication is based on a clustering algorithm, which enables the performance of the hydrometeorological information system to be enhanced. The experiment is based on the design and analysis of the inexpensive but reliable Baltic Sea autonomous monitoring network (buoys), which would be able to continuously monitor and collect temperature, waviness, and other required data. The proposed approach of multiagent based buoy communication enables all the data from the costal-based station to be monitored with limited transition speed by setting different tasks for the agent-based buoy system according to the clustering information.

The paper presents the possibilities of development the hydro meteoro-logical data collection system (HMDCS) involving advanced technologies such as multi agent based interaction and data collection between several monitoring system’s nodes (i.e. buoys) based on self-organizing maps (SOM). The require-ments for such system development are rather complex and are attached to allow-ing the real-time monitoring, control, and prediction of the negative consequences of contamination of surface water recourses and making their evaluation by effec-tiveness in monitoring of Baltic Sea surface water. The experiment is based on the design an inexpensive, but reliable Baltic Sea autonomous monitoring network (buoys), which would be able, continuously monitor and collect temperature, waviness, and other required data. Moreover, it makes ability to monitor all the data from the costal-based station with limited transition speed by setting different tasks for agent based buoy system according to the SOM.

The article discusses analyses and assesses the key proposals how to deal with the situation identification for the heterogeneous service support in vehicular cooperation environment. This is one of the most important topics of the pervasive computing. Without the solution it is impossible to adequately respond to the user's needs and to provide needed services in the right place at the right moment and in the right way. In this work we present our developed real time situation identification model for adaptive service support in vehicular communication networks domain. Our solution is different from the others as it uses additional virtual context information source - information from other vehicles which for our knowledge is not addressed in the past. The simulation results show the promising context exchange rate between vehicles. The other vehicles provided additional context source in our developed model helps to increase situations identification level.

The paper presents a research based on a vision of a multi-agent model working for the ambient comfort measurement and environment control system. Such means are used for developing the Smarter Eco-Social Laboratory (SrESL). The human Ambient Comfort Affect Reward (ACAR) index is proposed for development of the Reinforcement Learning Based Ambient Comfort Controller (RL-ACC) for experiments using equipment of SrESL. The ACAR index is recognized as dependent on human physiological parameters, such as the temperature, the electrocardiogram (ECG) and the electro-dermal activity (EDA). The fuzzy logic is used to approximate the ACAR index function by defining two fuzzy inference systems: the Arousal-Valence System, and the Ambient Comfort Affect Reward (ACAR) System. The goal of the RL-ACC is to find such the environmental state characteristics that create an optimal comfort for people affected by this environment. The Radial Basis Neural Network is used as the main component of the RL-ACC to performing of two roles: the policy structure, known as the Actor, used to select actions, and the estimated value function, known as the Critic that criticizes the actions made by the Actor. The Actor which manages Critic processes was used as a value function approximation of the continuous learning tasks of the RL-ACC and presented in this paper.

There are enlarged capabilities of the Ambient Comfort Affect Reward Based Laboratory Climate Controller (ACAR-Controller) by developing and integrating of the Heating/Ventilation/Air Conditioning (HVAC) and the Red-Green-Blue-Yellow (RGBY) Light Emitting Diode (LED) lighting sub-models of one room laboratory in the ACAR-Controller model. The model was validated by implementation and testing of the following elements of the laboratory prototype of ACAR-Controller: a) the sustainable electric power distribution subsystem; b) the intelligent RGBY LED lighting subsystem; c) the non-invasive measuring subsystem of human reaction to comfort conditions in the laboratory; d) the ATMEGA128RFA1-ZU transceivers based wireless communication subsystem; e) the software for the ACAR-Controller. DOI: http://dx.doi.org/10.5755/j01.eee.19.8.5399

The paper presents the human Affect Reward Based Automation Ambient Comfort Controller (ACARBC) as the interconnected cloud computing intelligent services that provide intelligent calculus for any instrumented interconnected environment sense and control system. The ACARBC has been modelled and, as the experimental results show, that an environmental state characteristics that create an optimum ambient comfort can be obtained by ACAR index. The ACAR index is dependent on human physiological parameters: the temperature, the ECG- electrocardiogram and the EDA-electro-dermal activity. The fuzzy logic is used to approximate the ACAR index function by defining two fuzzy inference systems: the Arousal-Valence System, and the Ambient Comfort Affect Reward (ACAR) System. The Radial Basis Neural Network is used as the main component of the ACARBC to performing of two roles - the policy structure, known as the Actor, used to select actions, and the estimated value function, known as the Critic that criticizes the actions made by the Actor. The Critic in this paper was used as a value function approximation of the continuous learning tasks of the ACARBC. DOI: http://dx.doi.org/10.5755/j01.eee.18.10.3060

The paper presents a vision of sustainable eco-social laboratory, the ESLab which might be used to speed up the process of development of the recently proposed by authors of the Smart Eco-Social Apartment. It is presented the multi-agent model of the ambient comfort measurement and environment control system to be used for the development of the ESLab . The human Ambient Lighting Affect Reward index, the ALAR index is proposed at the first time used for development of the Reinforcement Learning Based Ambient Comfort Controller, the RLBACC for the ESLab. The ALAR index is dependent on human physiological parameters: the temperature, the ECG - electrocardiogram and the EDA -electro-dermal activity. The fuzzy logic is used to approximate the ALAR index function by defining two fuzzy inference systems: the Arousal-Valence System , and the Ambient Lighting Affect Reward (ALAR) System. The goal of the RLBACC is to find such the environmental state characteristics that create an optimal comfort for people affected by this environment. The Radial Basis Neural Network is used as the main component of the RLBACC to performing of two roles - the policy structure, known as the Actor , used to select actions, and the estimated value function, known as the Critic that criticizes the actions made by the Actor . The Critic in this paper was used as a value function approximation of the continuous learning tasks of the RLBACC . Ill. 9, bibl. 7 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.121.5.1656

This paper presents further development of intelligent multi-agent based e-health care system for people with movement disabilities. The research results present further development of multi-layered model of this system with integration of fuzzy neural control of speed of two wheelchair type robots working in real time by providing movement support for disabled individuals. An approach of filtering of skin conductance (SC) signals using Nadaraya-Watson kernel regression smoothing for emotion recognition of disabled individuals is described and implemented in the system by R software tool. The unsupervised clustering by self organizing maps (SOM) of data sample of physiological parameters extracted from SC signals was proposed in order to reduce teacher noise as well as to increase of speed and accuracy of learning process of multi-layer perceptron (MLP) training.

Large-scale of multidimensional recognitions of emotional diagnosis of disabled persons often generate large amount of multidimensional data with complex recognition mechanisms. The problem is to reveal main components of diagnosis and to construct flexible decision making support system. Sensors can easily record primary data, however the recognition of abnormal situations, clusterization of emotional stages and resolution for certain type of diagnosis is oncoming issue for bio-robot constructors. This paper analyses the possibilities of integration of different knowledge representation techniques, especially data mining methods, for development of the reinforcement framework with multiple cooperative agents for recognition of the prediction criteria of diagnosis of emotional situation of disabled persons. The research results present further development of model of framework with integration of the evaluation of data mining methods for wheelchair type robots working in real time by providing movement support for disabled individuals.

The aims of this research are focused on the construction of intellectualized equipments for people with moving disabilities to help them in sustainable integration into environment. The problem is to reveal main components of diagnosis of disabled persons, as well as to develop decision making models which are integrated into the control mechanisms of the special equipments, that are assigned to the class of bio‐robots. This paper analyses the approach of the construction of such type of bio‐robots with possibilities to integrate different knowledge representation techniques for the development of the reinforcement framework with multiple cooperative agents for the recognition of the diagnosis of emotional situation of disabled persons. Large‐scale of multidimensional recognitions of emotional diagnosis of disabled persons often generate a large amount of multi‐dimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. Sensors can easily record primary data; however, the recognition of abnormal situations, cauterisation of emotional stages and resolution for certain type of diagnosis is an oncoming issue for bio‐robot constructors. The research results present the development of multi‐layered model of this framework with the integration of the evaluation of fuzzy neural control of speed of two wheelchair type robots working in real time by providing moving support for disabled individuals. An approach for representation of reasoning processes, using fuzzy logical Petri nets for evaluation of physiological state of individuals is presented. The reasoning is based on recognition of emotions of persons during their activities. Daugiaagentis biorobotų valdymas taikant intelektualizuotus asmenis su judėjimo negalia diagnozės atpažinimo metodus Santrauka.Šio mokslinio tyrimo tikslai yra nukreipti į intektualizuotų įrenginių, skirtų žmonėms su judėjimonegalia ir užtikrinančių jų būklės stebėseną ir darnaus judėjimo valdymo aplinkoje galimybes, kūrimą.Sprendžiami uždaviniai skirti neįgaliųjų diagnozės pagrindinių komponenčių tyrimams, sudarantlanksčius sprendimų priėmimo modelius, integruojamus į specialių įrenginių valdymo mechanizmus,kurie priskiriami biorobotų klasei. Straipsnyje pateikiami metodai, kaip konstruoti tokio tipo biorobotųsistemas, leidžiant skirtingų žinių vaizdavimo priemones integruoti į sistemą, kad būtų sukurta daugelioagentų bendradarbiavimo aplinka, skirta neįgaliųjų emocinės būklės diagnuozei analizuoti. Neįgaliųjųdiagnozės procesams formalizuoti reikia kelių metodų, kurie grindžiami skirtingais žinių vaizdavimoformalizmais, skirtingų matų parametrų atpažinimo algoritmais. Sensorinės sistemos fiksuoja pirminiusstebėsenos duomenis, tačiau nenormalioms situacijos būklėms atpažinti reikia sudėtingų išvedimo metodų,taikant lanksčias neuroninių tinklų valdymo priemones. Tyrimo rezultatai pateikiami per daugeliolygmenų darbo infrastruktūrą, kuri integruoja miglota logika grindžiamų neuroninių tinklų valdymobūdus, taikant juos neįgaliojo vežimėlio valdymo konstrukcijoms, kurios leidžia valdyti vežimėlio judėjimąautomatiškai valdoma trajektorija. Miglota logika grindžiamų Petri tinklų taikymas leido pademonstruotigalimybes atpažinti neįgaliojo psichologinės būsenos pokyčius ir vertinti juos laike stebint pacientusskirtingą laiką. Reikšminiai žodžiai: daugiaagentis sistemos valdymas, biorobotai, išskirstytosios informacinės sistemos,žinių vaizdavimo priemonės, miglota logika, neuroniniai tinklai, Petri tinklai. First published online:21 Oct 2010

This paper analyses the possibilities of integrating different technological and knowledge representation techniques for the development of a framework for the remote control of multiple agents such as wheelchair-type robots. Large-scale multi-dimensional recognitions of emotional diagnoses of disabled persons often generate a large amount of multi-dimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. The problem is to reveal the main components of a diagnosis as well as to construct flexible decision making models. Sensors can help record primary data for monitoring objects. However the recognition of abnormal situations, clustering of emotional stages and resolutions for certain types of diagnoses is an oncoming issue for bio-robot constructors. The prediction criteria of the diagnosis of the emotional situations of disabled persons are described using knowledge based model of Petri nets. The research results present the development of multi-layered framework architecture with the integration of artificial agents for diagnosis recognition and control of further actions. The method of extension of Petri nets is introduced in the reasoning modules of robots that work in real time. The framework provides movement support for disabled individuals. The fuzzy reasoning is described by using fuzzy logical Petri nets in order to define the physiological state of disabled individuals through recognizing their emotions during their different activities.

An approach is proposed in creating of an intelligent e-health care environment by modelling of an adaptive multi-agent-based ehealth and e-social care system for people with movement disabilities. Human’s Arousal Recognition Module (HARM) described based on online recognition of human’s ECG, EDA and body temperature signals by using embedded Atmega32 type microcontrollers. Multiagent based online motion control of multiple wheelchair-type robots is realized based on integration of an adaptive Fuzzy Neural Network Control algorithm into ATmega32 microcontroller. Human Computer Interaction in the system is realized in providing of necessary e-health care support actions for users with some movement disabilities by using Java–based JACK agent oriented environment. The dynamic multi-agent system is proposed to permanently realizing e-social care support actions for disabled by gathering data such as current position of robot and user‘s state information; finding decisions for given situation; sending signals to performing appropriate actions of the objects in the system.