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 use of 222 nm far-UVC radiation can be an effective means of disinfecting public buses against viruses, including SARS-CoV-2. However, it can cause degradation of the mechanical and visual properties of interior materials. The purpose of this study is to investigate the effects of 222 nm far-UVC radiation on the color and mechanical degradation of materials used to construct public bus interiors. This research work involves exposure of samples of materials commonly used in bus interiors to various levels of far-UVC radiation and measuring and evaluating changes in color and mechanical properties. The results of the study showed that far-UVC irradiation causes significant color degradation (∆E00 >5) in all the polymeric materials tested, after 290 J/cm2 radiant exposure. In addition, significant changes in mechanical properties were observed when evaluating elasticity modulus, elongation at ultimate strength, elongation at break, and tensile strength. A particularly large decrease in elongation at break (up to 26%) was observed in fiber-reinforced composite materials. The results of this study can be used as a guide for the development of protocols for the use of far-UVC disinfection in public transportation, which can help limit the transmission of infections while preserving the integrity and visual properties of bus interior materials.
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. Santrauka Šio mokslinio tyrimo tikslai yra nukreipti į intektualizuotų įrenginių, skirtų žmonėms su judėjimo negalia ir užtikrinančių jų būklės stebėseną ir darnaus judėjimo valdymo aplinkoje galimybes, kūrimą. Sprendžiami uždaviniai skirti neįgaliųjų diagnozės pagrindinių komponenčių tyrimams, sudarant lanksčius sprendimų priėmimo modelius, integruojamus į specialių įrenginių valdymo mechanizmus, kurie priskiriami biorobotų klasei. Straipsnyje pateikiami metodai, kaip konstruoti tokio tipo biorobotų sistemas, leidžiant skirtingų žinių vaizdavimo priemones integruoti į sistemą, kad būtų sukurta daugelio agentų bendradarbiavimo aplinka, skirta neįgaliųjų emocinės būklės diagnuozei analizuoti. Neįgaliųjų diagnozės procesams formalizuoti reikia kelių metodų, kurie grindžiami skirtingais žinių vaizdavimo formalizmais, skirtingų matų parametrų atpažinimo algoritmais. Sensorinės sistemos fiksuoja pirminius stebėsenos duomenis, tačiau nenormalioms situacijos būklėms atpažinti reikia sudėtingų išvedimo metodų, taikant lanksčias neuroninių tinklų valdymo priemones. Tyrimo rezultatai pateikiami per daugelio lygmenų darbo infrastruktūrą, kuri integruoja miglota logika grindžiamų neuroninių tinklų valdymo būdus, taikant juos neįgaliojo vežimėlio valdymo konstrukcijoms, kurios leidžia valdyti vežimėlio judėjimą automatiškai valdoma trajektorija. Miglota logika grindžiamų Petri tinklų taikymas leido pademonstruoti galimybes atpažinti neįgaliojo psichologinės būsenos pokyčius ir vertinti juos laike stebint pacientus skirtingą laiką.
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%, 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. INDEX TERMS Image processing, machine vision, neural nets, data mining.
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