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Search results for: BOUNDING METHODS
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Bounding approach to parameter estimation without priori knowledge on model structure error.
PublicationArtykuł przedstawia estymację parametrów modelu ARMA (Autoregresive moving average) metodą zbiorów ograniczonych. Założono brak wiedzy na temat ograniczeń na błąd struktury modelu lub, że wiedza ta jest bardzo konserwatywna. W celu redukcji tego konserwatyzmu, zaproponowano koncepcje modelu punktowo-parametrycznego. W podejściu tym zakłada się istnienie zbioru parametrów modelu oraz błędu struktury odpowiadających każdej z trajektorii...
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Bounding approach to parameter estimation without prior knowledge on modeling error and application to quality modeling in drinking water distribution systems
PublicationW artykule rozważana jest estymacja parametrów modelu autoregresji z ruchoma średnią i sygnałem wejściowym (ARMAX) z wykorzystaniem przedziałowego modelu błędu. Zakłada się, że granice błędu struktury modelu są nieznane, bądź znane, ale bardzo konserwatywne. Dla zmniejszenia tego konserwatyzmu proponowane jest idea modeli punktowo-parametrycznych, w której występują zbiory parametrów i błędu modelu odpowiadające wszystkim wejściom....
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AITP - AI Thermal Pedestrians Dataset
PublicationEfficient pedestrian detection is a very important task in ensuring safety within road conditions, especially after sunset. One way to achieve this goal is to use thermal imaging in conjunction with deep learning methods and an annotated dataset for models training. In this work, such a dataset has been created by capturing thermal images of pedestrians in different weather and traffic conditions. All images were manually annotated...
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Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function
PublicationObject detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection to achieve higher performance. However, existing models have some limitations such as overfitting problem and lower efficiency in small object detection. Object detection in remote...
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Segmentation-Based BI-RADS ensemble classification of breast tumours in ultrasound images
PublicationBackground: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using arti- ficial intelligence. However, only two papers have dealt with complex BI-RADS classifications. Purpose: This study addresses the automatic classification of breast lesions into binary classes (benign vs. ma- lignant)...
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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublicationObjective: The purpose of the study was to create an Artificial Neural Network (ANN) based on X-ray images of the pelvis, as an additional tool to automate and improve the diagnosis of coxarthrosis. The research is focused on joint space narrowing, which is a radiological symptom showing the thinning of the articular cartilage layer, which is translucent to X-rays. It is the first and the most important of the radiological signs...
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Quantum entanglement in time
PublicationIn this paper we present a concept of quantum entanglement in time in a context of entangled consistent histories. These considerations are supported by presentation of necessary tools closely related to those acting on a space of spatial multipartite quantum states. We show that in similarity to monogamy of quantum entanglement in space, quantum entanglement in time is also endowed with this property for a particular history....
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Simple gait parameterization and 3D animation for anonymous visual monitoring based on augmented reality
PublicationThe article presents a method for video anonymization and replacing real human silhouettes with virtual 3D figures rendered on a screen. Video stream is processed to detect and to track objects, whereas anonymization stage employs animating avatars accordingly to behavior of detected persons. Location, movement speed, direction, and person height are taken into account during animation and rendering phases. This approach requires...
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Augmented Reality for Privacy-Sensitive Visual Monitoring
PublicationThe paper presents a method for video anonymization and replacing real human silhouettes with virtual 3D figures rendered on the screen. Video stream is processed to detect and to track objects, whereas anonymization stage employs fast blurring method. Substitute 3D figures are animated accordingly to behavior of detected persons. Their location, movement speed, direction, and person height are taken into account during the animation...
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FURTHER REMARKS ON THE SURFACE VIS IMPRESSA CAUSED BY A FLUID-SOLID CONTACT
PublicationIt is well-known that, nano-mechanics should take into account not only physical phenomena occuring within the bulk but, first of all, the physical phenomena appropriate for a surface of two materials contact. The huge volume density of internal surfaces as well countours lines located within the nanomaterial results in our interest in, apart from classical form of mass, momentum and entropy transport, those modes of transportation...
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Engineering Challenges in the Design of Cochlear Implants
PublicationHearing aids such as cochlear implants have been used by both adults and children for a long time. In addition, cochlear implants are used by patients who have severe hearing loss either by birth or after an accident. This paper aims to investigate the engineering challenges bounding the design of cochlear implants and present its possible solution...
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Real-Time Facial Features Detection from Low Resolution Thermal Images with Deep Classification Models
PublicationDeep networks have already shown a spectacular success for object classification and detection for various applications from everyday use cases to advanced medical problems. The main advantage of the classification models over the detection models is less time and effort needed for dataset preparation, because classification networks do not require bounding box annotations, but labels at the image level only. Yet, after passing...
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Preliminary Results from the Removal of Phosphorus Compounds with Selected Sorption Material
PublicationDue to the resources of phosphorous are limited and are exhausted in the next 30 years the management of the resources is become current issue. Most of the phosphorus compounds is lost forever, because it is discharged with sewage into surface waters, causing eutrophication and in this way causing a further problem and challenge. On the other hand, there is a considerable need for phosphorus compounds, primarily in bioavailable...
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The hybrid estimation algorithm for wastewater treatment plant robust model predictive control purposes at medium time scale
PublicationThe paper proposes an approach to designing the hybrid estimation algorithm/module (HEA) with moving measurements window for Wastewater Treatment Plant (WWTP) Robust Model Predictive Control (RMPC) purposes at medium time scale. The RMPC uses a dedicated grey-box model of biological reactor for the system outputs prediction purposes. The grey-box model parameters are dependant on the plant operating point. Hence, these parameters...
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An interval estimator for chlorine monitoring in drinking water distribution systems under uncertain system dynamics, inputs and chlorine concentration measurement errors
PublicationThe design of an interval observer for estimation of unmeasured state variables with application to drinking water distribution systems is described. In particular, the design process of such an observer is considered for estimation of the water quality described by the concentration of free chlorine. The interval observer is derived to produce the robust interval bounds on the estimated water quality state variables. The stability...